EPA Report Number
May 1, 1990
ESTIMATION OF SEASONAL AND ANNUAL ACIDIC DEPOSITION
THROUGH AGGREGATION OF THREE-DAY EPISODIC PERIODS
by
Perry J. Samson, Jeffrey R. Brook and Sanford Sillman
Department of Atmospheric, Oceanic and Space Sciences
University of Michigan
Ann Arbor, Michigan 48109-2143
EPA Cooperative Agreement CR-814854-01
I
Project Officer
John F. Clarke
Atmospheric Sciences Modeling Division
Atmospheric Research and Exposure Assessment Lab
Research Triangle Park, North Carolina 27709
EJBD
ARCHIVE ATMOSPHERIC RESEARCH AND EXPOSURE ASSESSMENT LAB
EPA OFFICE OF RESEARCH AND DEVELOPMENT
6 ° ° - U.S. ENVIRONMENTAL PROTECTION AGENCY
2~ _ RESEARCH TRIANGLE PARK, NORTH CAROLINA 27709
059
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DISCLAIMER
The information in this document has been funded wholly or in part by the U.S. Environmental
Protection Agency under Cooperative Agreement CR-814854-01 to the Department of
Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, Michigan. It has
been subjected to Agency peer and administrative review, and has been approved for publication
as an EPA document.
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qo- US
OS?
RepostoryMjrt
Permanent Collection
ABSTRACT
The Regional Acid Deposition Model (RADM) simulates the complex physical and chemical
processes involved in the formation of acidic deposition. However, because of the complexity of
RADM it has only been applied to selected episodes of a few days duration. While the detail
provided by RADM is highly desirable to understand the interactions between emissions and
deposition on a storm-by-storm basis there is also a need for understanding the probable long-
term relationship between changing emissions patterns and deposition. The cost of simulating
and interpreting seasonal or annual deposition patterns using RADM directly would be high.
A method for aggregating episodic deposition estimates has been developed and used to identify
which meteorological situations merit simulation by RADM based on their likelihood of
producing sulfate (SO4=) wet deposition at multiple locations across eastern North America,
their frequency of occurrence, and their seasonally. The aggregation approach is based on four
years (1982-1985) of meteorological and precipitation chemistry data.
!«o The aggregation approach is based on the. stratification of three-day periods into categories, of
1; similar 850 mb wind flow across eastern North America and subsequent selection of 30 RADM
^' simulation periods from this stratification which represent the range pf storm patterns present
5 over eastern North America. The program has also provided the RADM project with scaling
factors for use in weighing episodic simulation results to seasonal and annual deposition. The
4- approach is shown to improve upon the use of a random selection process in reproducing
*- seasonal and annual wet deposition patterns.
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CONTENTS
SECTION PAGE
DISCLAIMER ii
ABSTRACT iii
UST OF FIGURES v i i i
USTOFTABLES xv
EXECUTIVE SUMMARY xvii
1. INTRODUCTION 1
1.1 Goals and Purposes 1
1.2 History 2
1.3 Conceptual Approach _ 3
2. METHODS : .'...: 6
2.1 Aggregation Approach 6
2.1.1 Meteorological Stratification 6
2.1.2 Selection of Stratification Parameters 8
2.1.3 Categorization for Other Years 1 0
2.1.4 Aggregation Procedures 1 0
2.1.5 Selection Process 1 3
2.1.6 Source/Receptor Relationships 1 5
2.1.6.1 Trajectory Calculations 15
2.1.6.2 Quantitative Transport Analysis 16
2.1.6.3 The Potential Source Contribution
Function 18
2.2 Description of Data Base 1 9
2.2.1 Meteorological Data 1 9
2.2.2 Precipitation Chemistry Data 20
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SECTION
2.3 Testing 24
2.3.1 Aggregation Versus Random Selection 2 4
2.3.2 Statistical Regression 2 5
2.3.3 Source-Receptor Relationships 2 7
3. RESULTS 29
3.1 Meteorological Categories 2 9
3.1.1 Choice of Stratification Parameters 29
3.1.2 Description of Categories 30
3.1.2.1 Wind Flow Patterns 31
3.1.2.2 Deposition Patterns 36
3.1.3 Statistical correlations between category and
observed deposition 51
3.1.3.1 Three alternative statistical
approaches 5 1
3.1.3.2 Time-of-Year Term 52
3.1.3.3 Results 5 3
3.1.4 Interannual variability : 6 0
3.1.5 Evaluation of the category-based aggregation
method 67
3.2 Selection of Cases 6 9
3.3 Comparison of Aggregate Estimate with Long-Term
Deposition 70
3.3.1 Annual Wet Deposition 70
3.3.1.1 Sulfate Wet Deposition 7 1
3.3.1.2 Nitrate Wet Deposition 74
3.3.1.3 Acidity Wet Deposition 77
3.3.2 Seasonal Wet Deposition 7 7
3.3.2.1 Sulfate Wet Deposition 77
3.3.2.2 Nitrate Wet Deposition 77
3.3.2.3 Acidity Wet Deposition 88
3,3.3 Uncertainties in the Aggregate Estimates 8 8
3.4 Source-Receptor Relationships 9 9
3.4.1 Climatological Wind Flow Patterns during
Precipitation 99
3,4.2 Wind Flow Associated with Sulfate Wet Deposition 100
3.4.3 Potential Source Contribution Functions 107
VI
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SECTION PAGE
3.5 The Variation in Source-Receptor Relationships between
Categories 1 1 2
3.6 Selecting episodes to estimate source-receptor
relationships 1 25
3.7 Source-receptor relationships with the 30 episodes 128
4. SUMMARY 138
4.1 Accomplishments 1 38
4.2 Recommendations for Future Work 1 39
4.2.1 Evaluation of the 30 Episodes and the Meteorological
Categories 1 39
4.2.1.1 Source-Receptor Relationships 140
4.2.1.2 Chemical and Meteorological Behavior
of the Categories 1 40
4.2.2 Application of aggregation to other atmospheric
phenomena 1 4 1
4.2.2.1 Dry Deposition 1 4 1
4.2.2.2 Visibility 1 42
4.2.3 Quantification of Aggregation Sensitivity 143
5. REFERENCES 1 44
APPENDIX A: MEAN 850 MB WIND FLOW PATTERNS FOR THE 19
CATEGORIES A-1
APPENDIX B: 85TH PERCENTILE WET SULFATE DEPOSITION PLOTS
FOR THE 19 CATEGORIES B -1
APPENDIX C: SITE SPECIFIC TABLES OF TOTAL AND PERCENT OF
TOTAL WET SULFATE DEPOSITION C-1
APPENDIX D: SITE SPECIFIC TABLES OF TOTAL AND PERCENT OF
TOTAL PRECIPITATION D -1
VII
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LIST OF FIGURES
NUMBER
1 Illustration of the aggregation concept [[[ 5
2 NMC grid and subset of 48 grid points used to describe similar
meteorological patterns [[[ 7
3 Location of the precipitation chemistry sites in the United States
and Canada used for analysis of transport patterns ................................. 2 1
4 Beale and Calinsky criteria (F and C statistics) versus the number
of categories (step number), based on 850 mb wind field clusters
and observed SO4° deposition for 1983 [[[ 3 1
5 Reduction in variance in SO4= wet deposition resulting from
meteorological stratification [[[ 32
6 Comparison of frequency of occurrence of the 1 9 meteorological
categories defined in this study between 1 979-1 98T and 1982-
1985 [[[ 35
7a-f Monthly mean number of occurrences of meteorological categories
during 1982 to 1985 [[[ 3 8
8a-d Variation in amount of SO4= deposition between categories at four
locations [[[ 4 1
9 The ratio of the percent of SO4= deposition to the percent of
precipitation for each meteorological category ...................................... 43
1 0 The percentage of total SO4" wet deposition associated with each
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NUMBER PACE
1 3 Ts statistic for meteorological category 7, indicating deposition-to-
precipitation ratios significantly above or below the norm at each
site 58
14 Ts statistic for meteorological category 11, indicating deposition-
to-precipitation ratios significantly above or below the norm at
each site 59
1 5 Year-by-year variation in sulfate wet deposition, nitrate
deposition, sulfate deposition-to-precipitation ratio, and
precipitation at Turners Falls, Massachusetts 62
1 6 Year-by-year variation in sulfate wet deposition, nitrate
deposition, sulfate deposition-to-precipitation ratio, and
precipitation at Zanesville, Ohio 63
1 7 Year-by-year variation in sulfate wet deposition, nitrate
deposition, sulfate deposition-to-precipitation ratio, and
precipitation at Giles County, Tennessee 64
1 8 Year-by-year frequency of occurrence of events of meteorological
categories 6, 7 and 11, expressed as a fraction of all events 65.
1 9 Comparison between annual sulfate deposition observed during
1986 and 1987 at 13 northeastern and midwestern sites with
projected deposition based on 1982-1985 observations and 1986-
1987 meteorology 66
2 0 Comparison of selection and aggregation techniques for S04= wet
deposition in northeastern North America.using nineteen three-day
episodes 68
21 a The mean annual wet sulfate deposition pattern across eastern
North America for 1982-1985. (Units=mgm"2) 72
21 b The aggregate estimate of the mean annual wet sulfate deposition
pattern across eastern North America for 1982-1985 73
22a The mean annual wet nitrate deposition pattern across eastern
North America for 1982-1985 75
22b The aggregate estimate of the mean annual wet nitrate deposition
pattern across eastern North America for 1982-1985 76
i x
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NUMBER
23a The mean annual acidic wet deposition pattern across eastern North
America for 1982-1985 7 8
23b The aggregate estimate of the mean annual acidic wet deposition
pattern across eastern North America for 1982-1985 79
24 a The mean summer wet sulfate deposition pattern across eastern
North America for 1982-1985 80
24b The aggregate estimate of the mean summer wet sulfate deposition
pattern across eastern North America for 1982-1985 81
24c The mean winter wet sulfate deposition pattern across eastern
North America for 1982-1985 82
24d The aggregate estimate of the mean winter wet sulfate deposition
pattern across eastern North America for 1982-1985 83
25a The mean summer wet nitrate deposition pattern across eastern
North America for 1982-1985 84
25b The aggregate estimate of the mean summer wet nitrate deposition
pattern across eastern North America for 1982-1985 •. 85
25c The mean winter wet nitrate deposition pattern across eastern
North America for 1982-1985 86
25d The aggregate estimate of the mean winter wet nitrate deposition
pattern across eastern North America for 1982-1985 87
26a The mean summer wet acid (H*) deposition pattern across eastern
North America for 1982-1985 89
26b The aggregate estimate of the mean summer wet acid (H*)
deposition pattern across eastern North America for 1982-1985 90
26c The mean winter wet acid (H+) deposition pattern across eastern
North America for 1982-1985 91
26d The aggregate estimate of the mean winter wet acid (H*) deposition
pattern across eastern North America for 1982-1985 92
2 7 The spatial and temporal variation in the percent of annual wet
sulfate deposition due to the categories selected 93
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NUMBER
2 8 The year to year variability in the annual wet sulfate deposition and
in the total deposition due to the categories selected at two
Midwestern sites 94
2 9 The year to year variability in the annual wet sulfate deposition and
in the total deposition due to the categories selected at two
northeastern sites 95
3 0 Uncertainty ranges due to the aggregate estimates of wet sulfate
deposition 97
31 Uncertainty ranges due to the aggregate estimates of wet nitrate
deposition , 98
3 2 Uncertainty ranges due to the aggregate estimates of wet acidity
deposition 98
3 3 The mean probability for transport on precipitation days to Big
Moose, NY, during the period 1982-1985 101
3 4 The mean probability for transport on. precipitation days to
Gaylord, Ml, during the-period 1982-1985.... 102
3 5 The mean probability for transport on precipitation days to
Raleigh, NC, during the period 1982-1985 103
3 6 The sulfur deposition weighted probability for transport to Big
Moose, NY during the period 1982-1985 104
37 The sulfur deposition weighted probability for transport to
Gaylord, Ml, during the period 1982-1985 105
38 The sulfur deposition weighted probability for transport to
Raleigh, NC, during the period 1982-1985 106
3 9 The bias for transport associated with large S04= wet deposition at
Big Moose, NY, during the period 1982-1985. 108
4 0 The bias for transport associated with large water deposition at Big
Moose, NY, during the period 1982-1985 1 09
4 1 The bias for transport associated with large SO4= wet deposition at
Gaylord, Ml, during the period 1982-1985 110
4 2 The bias for transport associated with large water deposition at
Gaylord, Ml, during the period 1982-1985 111
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NUMBER PAGE
4 3 The bias for transport associated with large SO4= wet deposition at
Raleigh, NC, during the period 1982-1985 11 2
4 4 The bias for transport associated with large water deposition at
Raleigh, NC, during the period 1982-1985 1 1 3
4 5 The ensemble bias for transport associated with large SO4= wet
deposition at Big Moose, NY; Gaylord, Ml; and Raleigh, NC, during
the period 1982-1985 1 14
46 Difference between climatological Q field (1982-1985) and the Q
field for category 20 (unbiased set of missing days) 11 5
47 Difference between climatological Q field (1982-1985) and the Q
field for category 1 1 1 6
48 Difference between climatological Q field (1982-1985) and the Q
field for category 5 1 1 7
49 Difference between climatological Q field (1982-1985) and the Q
field for category 6 1 1 8
50 .Difference between climatological Q field (1982-1985) and the Q
field for category 7 1 1 9
51 Difference between climatological Q field (1982-1985) and the Q
field for category 8 1 20
52 Difference between climatological Q field (1982-1985) and the Q
field for category 9 1 21
53 Difference between climatological Q field (1982-1985) and the Q
field for category 10 1 22
54 Difference between climatological Q field (1982-1985) and the Q
field for category 11 1 23
5 5 Percent RMS errors, ranked from smallest to largest, for the
random and category-based ensembles using trajectories from all
days in 30 episode sample 1 27
56 Percent RMS errors, ranked from smallest to largest, for the
random and category-based ensembles using trajectories from
precipitating days only in 30 episode sample 1 27
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NUMBER PAGE
5 7 The Q field for Big Moose, NY derived from both precipitating and
non-precipitating days within the 30 episodes 129
5 8 The Q field for Gaylord, Ml derived from both precipitating and
non-precipitating days within the 30 episodes.....; 130
5 9 The Q field for Raleigh, NC derived from both precipitating and
non-precipitating days within the 30 episodes 131
60 Bias in source receptor analysis resulting from use of 30-event
sample instead of 4-year sample at Big Moose, NY. Bias is
expressed as a percent of predicted Q-field from the 4-year
analysis. Both precipitating and non-precipitating days during the
30 episodes are included in the analysis 1 32
6 1 Bias in source receptor analysis resulting from use of 30-event
sample instead of 4-year sample at Gaylord, Ml. Bias is expressed
as a percent of predicted Q-field from the 4-year analysis. Both
precipitating and non-precipitating days during the 30 episodes are
included in the analysis .• .• 133
6 2 Bias in source receptor analysis resulting from use of 30-event
sample instead of 4-year sample at Raleigh, NC. Bias is expressed
as a percent of predicted Q-field from the 4-year analysis. Both
precipitating and non-precipitating days during the 30 episodes are
included in the analysis 1 34
63 Bias in source receptor analysis resulting from use of 30-event
sample instead of 4-year sample at Big Moose, NY. Bias is
expressed as a percent of predicted Q-field from the 4-year
analysis. Only days with precipitation reported at Big Moose are
included in the analysis 1 35
64 Bias in source receptor analysis resulting from use of 30-event
sample instead of 4-year sample at Gaylord, Ml. Bias is expressed
as a percent of predicted Q-field from the 4-year analysis. Only
days with precipitation reported at Big Moose are included in the
analysis 1 36
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NUMBER
65 Bias in source receptor analysis resulting from use of 30-event
sample instead of 4-year sample at Raleigh, NC. Bias is expressed
as a percent of predicted Q-field from the 4-year analysis. Only
days with precipitation reported at Big Moose are included in the
analysis 1 37
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LIST OF TABLES
NUMBER PAGE
1 Sample aggregation 1 2
2 Name and location of the daily (event) precipitation chemistry
sites 22
3 Meteorological factors used in initial cluster analysis 29
4 Synoptic description of the nineteen 850 mb wind flow categories 33
5 Matrix of percent of total deposition by site and category 45
6 Percent of total sulfate wet deposition by meteorological category 5 0
7 Results of statistical regression 55
8 Initial selection of 30 days : : 71
9 Root-mean-square deviations between estimated and observed
summer, winter and annual wet deposition at 13 eastern sites 74
1 0 Description of source-receptor relationships for selected
meteorological categories 1 24
xv
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EXECUTIVE SUMMARY
This report presents methods for developing estimates for long-term (climatological) patterns
of atmospheric wet deposition based on in-depth knowledge of a finite number of samples. These
methods are designed to complement predictive models for physical processes in the
atmosphere, which characteristically provide detailed analysis for a small number of days but
are unable to represent physical processes on a multi-year time scale due to computational
constraints. Predicted future effects are to be derived based on statistical methods of
aggregation developed here, in combination with results obtained from physical models. The
method has been applied to the specific problem of estimating future rates of wet deposition in
the eastern United States in combination with the Regional Acid Deposition Model (RADM).
The method is based upon the stratification of three-day periods into categories of similar wind
flow at 850 mb (roughly 1500 meters above mean sea level). The stratification has been used
to identify which meteorological situations merit simulation by RADM based on their likelihood
of producing sulfate deposition at multiple locations across eastern North America, their
frequency of occurrence, and their seasonality. A specific ensemble of 3-day events has been
recommended for future analysis by RADM, as part of ongoing efforts by the Environmental
Protection Agency (EPA) to develop predictive methods for air quality. In addition to even;
selection, the meteorological stratification has been used as the basis of a statistical technique to
derive aggregate estimates for long-term average wet deposition. The aggregation technique has
been simulated using four years of precipitation chemistry data to demonstrate that
uncertainties in estimates of seasonal and annual sulfur deposition using the aggregation
technique is significantly less than those obtained from random (non-stratified) sampling of the
data.
The following specific tasks have been completed as part of this project:
(1 ) An extensive data base with daily precipitation and wet deposition of sulfate, nitrate
and total H+was developed. The data base covered the years 1S82-1S65 and
included 23 sites in the northeastern, southern and midwestern U.S. and southern
Canada. Meteorological information to be used in subsequent analysis was also
collected.
xvi i
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( 2) Techniques of cluster analysis were used to stratify 3-day events over the 4-year
period into categories based on the meteorological data described above. Four
independent stratification schemes were investigated: stratification by 850 mb
wind field, stratification by 850 mb geopotential and mean surface pressure,
stratification by 850 mb temperature advection and 500 mb vorticity, and
stratification by 500 mb vorticity advection and 850 mb relative humidity. These
four stratification schemes were evaluated to find the combination of meteorological
parameters which best explained variations in sulfur wet deposition.
( 3) A set of 19 meteorological categories was compiled based on similarity in wind flow
at 850 mb. These 19 categories were each subdivided into "wet" and "dry" cases,
making 38 categories. These 38 categories were used as the basis for selection of
individual days and derivation of aggregated estimates for annual deposition.
(4) A statistical correlation has been developed between observed sulfate and nitrate
wet deposition during 1982 through 1987 and wind flow category. Results show
that wet deposition rates over eastern North America show significant differences
for events with different 850 mb circulation.
( 5 ) Accuracy and validity of the 38-category stratification was evaluated based on the
•ability to predict annual sulfate deposition at eastern and midwestern sites from a
firjite sample of 3-day events. We compared estimates for annual deposition"
derived from a random sample of 3-day events with estimates from a cluster-based
sample of events. The standard deviation between estimated and actual deposition
was lower by 25% with cluster-based selection.
{ 6 ) A preliminary selection of 30 individual 3-day events for in-depth study with
RADM was made. The selected events included representatives of a wide variety cf
circulation patterns, including meteorological categories that account for the bulk
of wet deposition at sites throughout the eastern U.S. A selection of relatively rare
winter circulation patterns and events without rainfall for evaluation of dry
deposition are also included. Aggregate estimates for sulfate and nitrate wet
deposition have been derived based on the 30-event sample. Deviations between the
aggregate estimate and observed wet deposition during 1982-1985 are of the same
magnitude as the natural variation in wet deposition from one year to the next.
( 7 ) A simplified model for identifying emitted sources of wet deposition has been
applied to the selected 30-event sample and also to the entire 1982-1985 period
for selected sites in eastern North America. Differences between predicted source-
receptor relationships based on the 30-event sample and based on the entire 4-
year period are assessed.
xvi 11
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1 . INTRODUCTION
1.1. GOALS AND PURPOSES
The development of policies to address problems arising from acid deposition requires a
thorough understanding of processes that affect acid deposition. To this end the United States
Environmental Protection Agency (EPA) has supported investigations into the physics, chem-
istry and meteorology of acid deposition. These efforts have culminated in the development of
the Regional Acid Deposition Model (RADM) (Chang et al., 1987), which simulates the
processes involved in the formation of acid deposition and other pollutants.
In order to complete the evaluation of acid deposition processes it is necessary to establish
a relationship between individual deposition events and the long-term climatology of acid
deposition. The relationship between individual events and climatology is difficult because
deposition varies greatly from one event to the next in terms of £>oth the amount of deposition
and the relation between deposition and emission sources. Establishment, of long-term deposi-
tion patterns and source-receptor relationships requires analysis over an extended time period,
preferably including several years to account for meteorological variability. Such a lengthy
analysis is beyond the capacity of the resources available to the National Acid Precipitation
Assessment Program.
The goal of this study has been to provide methods for representing aggregate long-term
patterns of acid deposition from a finite number of samples. These methods involve both the
selection of individual deposition events which are representative of recurring climatic
patterns and the calculation of climatic averages based on a finite sample. The aggregation
methods have been applied to the specific problem of representing the climatology of acid depo-
sition based on in-depth analysis by RADM for a finite sample of events. An array of 30 three-
day events has been identified which includes the major climatic patterns that contribute to acid
deposition over eastern North America. RADM is currently being used to analyze physical
processes during these 30 events. Assessment of the impact of future changes in precursor
emissions will be performed by (1) using RADM to predict impacts for the thirty sample
events, and (2) estimating long-term impacts by aggregating from the 30 sample events, based
on the techniques described in this report. While designed specifically in support of RADM the
methods described here may have broad applicability to problems that involve the characteri-
zation of long-term climatic impacts of human activities.
Identification of representative deposition events and methods of aggregation are based on
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meteorological and statistical analyses of deposition events that occurred over a 4-year period
(1982-1985). Events during this period were divided into categories with similar three-day
patterns of 850 mb wind flow across eastern North America. The three-day time period and
850 mb wind fields are broadly representative of the evolution of distinctive storm patterns
that are likely to affect atmospheric transport and deposition rates. Events have been selected
for analysis by RADM from among the meteorological strata based on their likelihood of
producing sulfate (804*) wet deposition at multiple locations across eastern North America,
their frequency of occurrence, and their seasonality. - -
The method developed here represents a significant improvement over the alternative of
selecting events for RADM analysis purely at random. Analysis of observed wet deposition
during the 1982-1985 time period shows that deposition rates are statistically correlated with
meteorological category with greater than 95% confidence at most sites in eastern North
America. The aggregation technique has been used to obtain estimates for annual deposition at
21 sites in eastern North America based solely on observed deposition during the selected 30-
day ensemble. The resulting estimates differ from the observed 4-year average wet deposition
by amounts comparable to normal year-to-year variations. Aggregated estimates based on
meteorological strata are also significantly more accurate than estimates from randomly
selected events.
The aggregation project has served two purposes:
1. to provide the RADM project with a list of simulation periods which represent the
range of storm patterns present over eastern North America. The selection of
storm types was based on their likelihood of producing SO4= wet deposition at
multiple locations across eastern North America, their frequency of occurrence,
and their seasonality; and
2. to provide the RADM project with scaling factors for use in weighing episodic
simulation results to seasonal and annual deposition.
1.2. HISTORY
The use of synoptic climatological methods (meteorological stratification) to identify
homogeneous weather types is a popular tool for use in studying climate-environment rela-
tionships. Kalkstein and Corrigan (1986) presented a methodology to characterize air masses
at a given locale using principal component analysis to reduce the dimensionality of surface
weather variables followed by cluster analysis on the most important components. The means cf
the original variables and other parameters were determined for each category and a physical
explanation was attached to the results. Kalkstein, et at. (1987) refined the method and
demonstrated it for two other sites. Samson and Moody (1986, see also Moody and Samson,
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1989) used cluster analysis to stratify deposition events at a single location based upon back-
trajectories. The concentrations of S04°, NO3", NH3* and I-T in precipitation were found to be
statistically different between strata but there was also a considerable amount of within strata
variation and overlap between strata. Moody and Galloway (1988) studied the influence of
atmospheric flow patterns (isobaric back-trajectories) on the precipitation chemistry at
Bermuda. They identified the important wind flow categories for both warm and cold seasons and
concluded that transport was an important but not sole factor influencing the composition of
precipitation.
Initially this project attempted to cluster patterns of precipitation across eastern North
America. The stochastic nature of daily precipitation patterns lead to the production of cate-
gories which, in essence, represented the days when a particular station dominated the precip-
itation of the region. This course was abandoned in favor of a categorization scheme based on
wind flow patterns.
Fernau (1988) categorized three-day periods of the vectors of trajectories at precipi-
tation chemistry locations in the United States and Canada. These categories were shown to
resemble meteorological patterns common to eastern North America. Eighteen categories of
three-day wind flow patterns were found using cluster analysis. These categories represented
periods whose mean three-day atmospheric transport patterns were statistically different from
one another. Fernau (1988) also showed that the behavior of SOA= wet deposition varied
between these categories and that the differences held statistical significance. Nonetheless,
there was concern that the results may have been influenced by the lack of rawinsonde observa-
tions over the Atlantic Ocean, It was therefore decided that the analysis should switch, to use of
the National Meteorological Center (NMC) global meteorological data base which included
analyzed meteorological information at selected pressure surfaces over all of the Northern
Hemisphere.
1.3. CONCEPTUAL APPROACH
The aggregation technique is based on the hypothesis that there are recurring weather
patterns which can be characterized and described in terms of observable meteorological
parameters (wind fields, precipitation, etc.). It is further hypothesized that distinct weather
patterns will be associated with characteristic patterns of wet deposition, both in terms of
spatial extent and deposition amounts. Given those assumptions, long-term wet deposition
processes may be analyzed based on a finite number of individual deposition events, each of
which is "representative" of a major meteorological category. The goal of the aggregation
project has been to identify the degree to which weather patterns may be separated into identi-
fiable categories and to assess how this information would improve our ability to extrapolate
case study results to longer-term deposition estimates.
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The initial stratification of events into meteorological categories is used both in the
selection of individual events for in-depth analysis with RADM and in the calculation of aggre-
gated estimates for total wet deposition from 30-event samples. Event selection has been
designed to include representatives from all major meteorological categories and to insure that
the selected ensemble includes events with significant wet deposition at sites throughout eastern
North America. Estimates for long-term deposition are derived from known deposition rates
(either from observations or from RADM analyses) for the 30-event sample. The aggregation
concept, illustrated in Figure 1, represents long-term deposition as the sum of deposition from
sample events weighted by the frequency of occurrence of each meteorological category. The
aggregation technique has subsequently been modified to make use of statistical correlations
between wet deposition, category and other meteorological parameters (precipitation, season,
etc.) in deriving estimates for long-term deposition.
Testing the aggregation technique poses a special problem because the most important
issue, response of the method to changes in precursor emission rates, cannot be tested empiri-
cally. A variety of techniques have been used to explore aggregation accuracy. These include the
establishment of statistical correlations between observed wet deposition, wind flow pattern and
other meteorological variables and application of the aggregation technique to arrays cf
randomly selected events. Discrepancies between aggregate estimates and observed deposition
are compared to the natural uncertainty arising from year-to-year variation in wet deposition.
Lastly, an abbreviated model for source-receptor relationships is used to identify similarities
and differences between randomly selected 30-day ensembles and long-term averages. These
combined tests should establish both the level of accuracy and limitations of the aggregation
technique.
The presentation is divided into a discussion of methods (Section 2) and presentation cf
results (Section 3). The discussion of methods includes a description of the meteorological-
stratification technique, procedures for deriving aggregated estimates based on finite samples,
selection of sample events based on the meteorological stratification, and methods used to test
the aggregation procedure. A description of the simplified model for source-receptor rela-
tionships is also included. Results of the analysis are presented in Section 3. These include
meteorological descriptions of individual wind flow categories, results of statistical correla-
tions between wind flow and observed wet deposition, selection and identification of thirty 3-day
events for RADM analysis, and aggregated estimates for wet deposition and source receptor
relationships based on the chosen sample.
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Group 1 Group 2
Group 3
I
Group n
r
D,(x,y)
D,(x,y)
Dn(x,y)
N
Wt(x,y)=If1Q(x,y) ±e(x,y)
n=1
Figure 1. Illustration of the aggregation concept. Each meteorological category occurs with
some mean frequency, /„, and has associated with it a mean deposition pattern
Dn(x,y). These are aggregated through a weighting scheme (though not
necessarily the simple frequency weighting shown) to achieve an estimate of the
spatial pattern of deposition, Wt(x,y) with an uncertainty e(x,y) which includes
variability in frequency and deposition patterns from year-to-year. The goal is
to estimate WJx.y) accurately and keep e(x,y) less than the observed magnitude
of interannual variation in deposition.
-------
2. METHODS
2.1. AGGREGATION APPROACH
2.1.1. Meteorological Stratification
The method for selection of representative 3-day deposition events is based on a
stratification scheme that classifies all events into categories based on meteorology. The
purpose of the meteorological stratification is to systematically identify the different types of
storm patterns that contribute to acid deposition in the eastern United States. Category-based
selection will then insure that all major storm types are included in the ensemble of
representative events. Stratification schemes were attempted based on a variety of
meteorological parameters, including sea level pressure, temperature, 850 millibar
geopotential height, vorticity advection and wind fields.
To portray the "life-cycle" of a typical eastern North American "storm" the duration of
RADM simulations has generally been on the order of three days (not counting initialization
days). To allow compatibility with RADM the categories have been based on three-day periods.
This necessitated re-organizing the meteorological data into running three-day periods (record
one = days 1, 2, 3; record two = days 2. 3, 4; etc.) prior to stratification.
For purposes of categorization each three-day event is evaluated based on the values
assumed by 2 independent meteorological parameters over the three-day period on a 48-point
horizontal grid covering eastern North America. Each three-day event is characterized by arv
array of 268 parameters, which consist of the values of the 2 independent parameters on 3
successive days at 48 grid points. Location of grid points is shown in Figure 2. The 288
parameters represent the maximum number of parameters that could be included in the
meteorological stratification, given computational constraints. The choice of 48 grid points and
two independent parameters were dictated by this constraint.
Classifying all of the running three-day periods into similar meteorological categories
required computer assistance. The statistical method of cluster analysis was selected for this
purpose. Ideally the data from the entire time period should have been clustered together,
however, due to the computer memory constraints of the statistical software used this was not
possible. To circumvent this limitation, consecutive periods (record 1 - days 1,2,3; record 2
« days 4,5.6; etc.) from independent years were selected. It was anticipated that this would
-------
10
65
Figure 2. NMC grid and subset of 48 grid points used to describe similar meteorological
patterns.
-------
minimize the effect of missing data and capture a broader cross-section of situations.
Additionally, the limitation on computer memory allowed only the use of three years of data.
Due to this limitation the cluster analysis was used to generate meteorological categories based
on the years 1979. 1981 and 1983 only.
Ward's method of cluster analysis (Ward, 1963) was selected to stratify the reduced set
of cases. The goal of Ward's method, as with other cluster analysis techniques, is to divide a data
set into categories consisting of similar cases that are as distinct from the other categories as
possible. Evaluation of similarity between events is based on the euclidean distance between
events for a specified array of parameters, defined as
- Pa .1 ( 1 )
where XAB is the euclidean distance between points A and B and PAj and P8j. represent the value
of the jtn parameter for events A and B respectively. Euclidean distances are used to compare
both individual events and category means in Ward's method.
Identification of individual categories follows a hierarchical sequence beginning with a
case in which each individual event is regarded as a separate category. Successive categorization
schemes are then generated, with the total number of categories reduced by one at each stage.
Category selection is performed in a manner to minimize the cluster variance , or within-group
sum of squares (£). The cluster variance is the sum of squared Euclidean distances from each
data point to the centroid (mean) for its assigned category, with distance defined in Equation
(1). Cluster variance provides a measure of the degree of similarity within each category in
terms of the selected meteorological parameters. The minimal-variance criterion insures that
events within the same category are most similar to each other.
2.1.2. Selection of Stratification Parameters
Stratification based on Ward's method generates optimal categorization schemes for the
specified base of meteorological parameters and for the specified number of categories.
Completion of the stratification process requires selection of the appropriate number cf
parameters and number of categories. A large number of individual categories permits finer
distinctions between meteorological events, but a large number of categories becomes unwieldy
and lacks statistical robustness.
As discussed by Anderberg (1973, p. 194 and 212) evaluation of clustering may be based
on its effectiveness in distinguishing an external variable, rather than the parameters that
formed the basis of the initial stratification. In this instance, generation of meteorological
strata is based on the meteorological parameters discussed in the preceding section. However
the evaluation of the proper number of categories is based on the ability of the strata to
distinguish between events with high and low S04* wet deposition.
8
-------
Observed SO4" deposition is available from 21 sites (See Section 2.2 below). Deposition-
based differences between events are established by determining Euclidean distances (Equation 1
above) with S04° deposition at the individual sites as the independent parameter. Cluster
variance, defined above, may also be calculated based on differences in SO4" deposition and
represents similarity (in terms of SO4* deposition) of events within the same category. Note
that the cluster variance based on SO4= deposition is different from the variance based on
meteorological parameters which are used to define the meteorological categories.
The separation of categories in terms of SO4* deposition was determined by comparing the
total within-groups error sum of squares (E). If a category of n, units is merged with a
category of n2 units, the within-group sum of squares for the merged categories equals the sum
of the within-group sum of squares of the two categories merged plus n^n2l(n^ + n2) times the
square of the Euclidean distance between the means of the two categories. A merger thus
increases the within-groups sum of squares by a quantity proportional to the (distance)2
between the merged categories. So if Eg^ is the total within-groups error sum of squares when
there are g+1 categories and Eg is the same quantity when two categories are merged,
ft-fii. , . , %
(2)
where na, nb, are sizes of the categories and Z~a and Z~b are the column vectors of their means.
Initially, when each of N data records is a separate category there is no within-group
error sum of squares; hence EN = 0. When multivariate analysis of variance is carried out for g
categories the total sum of squares of the within-groups matrix is Eg and the sum of squares of
the between-groups matrix Bg (used below) is defined as
The relative advantage gained by increasing the number of categories may be evaluated based on
the reduction in within-group sum of squares (Eg ) with increasing categorization. Two other
measures of the marginal effectiveness of increasing numbers of categories are Eeale's
criterion (Beale, 1969), computed at each step as:
• N
and the criterion of Calinsky and Harabasz (1974), computed as
The Beale (F ) and Calinsky (C) criteria both provide measures for the decrease in within-
-------
cluster variance and the increase in variance between clusters as the number of steps increases.
If Fis large, g categories are better than g-1. The "best" number of categories was obtained by
plotting F against g and choosing the value of g corresponding to large F. Similarly, the value of
g which maximized C was the best value according to the C criterion. A monotonically decreasing
C indicated a hierarchical structure. However, C, rising to a maximum at g, suggests the pres-
ence of g categories.
2.1.3. Categorization for Other Years
As described above, meteorological categorization is developed based on data from three
individual years, 1979, 1981 and 1983. Once the categorization is established it is necessary
to identify appropriate categories for running three-day periods for other years. To classify a
particular case into a given category, the 288 meteorological parameters that define each
category are compared to parameter values for the individual 3-day event. This was done by
computing the Euclidean distance (Equation 1) between the event and the category mean. The
events are assigned to the category producing the smallest Euclidean distance.
2.1.4. Aggregation Procedures
The goal of the aggregation procedure is to estimate annual deposition of sulfate and other
species based on a finite number of episodes. Ideally, an aggregation procedure would be derived
based on observations that correlate changed deposition during individual events with changes in
long-term deposition patterns. Data of this sort is unfortunately not available from trie
historical record. However a reasonable approximation to the desired situation may be obtained
by developing procedures for estimated annual deposition based on finite samples of randcrr.iy
selected events. We postulate that future changes in deposition during an individual event rr.ay
be correlated with changes in long-term deposition in the same way that observed deposition for
a random ensemble may be correlated with observed long-term deposition.
The aggregation approach makes use of the varying frequency of occurrence and expected
S04* and NO3" deposition within each category. As will be demonstrated below (Sections 2.3.2
and 3.1.5) selection of days by category provided a more accurate estimate of annual deposition
than is provided by purely random selection.
An initial estimate for annual deposition may be derived from an array of samples by
projecting the average rate of deposition over a 365-day period. When an ensemble of days is
selected based on subjective criteria (such as from extreme events), projection over a 365-
day period is more likely to result in a biased sample, since the selected categories ensure that
deposition on the sample days will differ from average deposition. The sample deposition must
be adjusted by scaling factors in order to arrive at an unbiased estimate.
In addition tojhe scaling factor, a weighting factor is applied in the summation of depo-
10
-------
sition from the individual days. The weighting factor (wt) reflects the frequency of occurrence
of categories, so that days which represent frequently occurring categories are weighted more
heavily than days which represent unusual meteorology. The weighting factors are equal to the
frequency of occurrence, /j, of each category divided by the number of days from the category,
HJ, included in the representative sample of days:
(6)
The aggregated estimate for annual deposition has the following form:
DEPOSITION, Jfrnnu*' T 7 v^Di ( 7 )
where D\ is the observed deposition during the ith event of the sample to be used in the aggrega-
tion; W| is a weighting factor that reflects the relative frequency of occurrence of the meteor-
ological category associated with the Ith event; Dannuai is the average annual deposition over the
period 1982-1985 at the site; and 5, represents a statistical estimate for deposition during
the ilh event, based on available information prior to selection of the individual event.
The statistical estimate for deposition (b-t) varies depending on the nature of the sample to
be used in the aggregation and on the specific purpose of the aggregation. When sample events
are selected at random, b-t is equal to the average deposition per event, and the ratio of Danrvjaj to
^ w-tbt simply projects the sample deposition over a 365-day period. When sample events
are selected based on meteorological categories, 5; represents the average deposition for events
within the category associated with the itn event. The accuracy of the aggregate estimate can
further be improved by using statistical correlations with other factors in estimating Dj. For
example, it will be shown below that S04= and NO3' wet deposition for individual events can be
statistically correlated with precipitation, and that an estimate for deposition may be developed
by linear regression:
where Bcat(j) represents the least-squares estimate (i.e. average deposition-to-precipita:icn
ratio) over all events within the meteorological category containing event /', and P, is the
precipitation amount for the event.
1 1
-------
TABLE 1. A SAMPLE AGGREGATION OF ANNUAL SO4" WET DEPOSITION CALCULATED FOR BIG
MOOSE, N.Y., BASED ON A 30-EVENT SAMPLE. THE AGGREGATION PROCEDURE FOLLOWS
EQUATION (7) WITH 6, EQUAL TO THE AVERAGE DEPOSITION ASSOCIATED WITH THE
METEOROLOGICAL CATEGORY. CATEGORIES ARE DESCRIBED IN SECTION 3.1.
EVENT
4/16/82
1 1/3/85
5/30/83
3/18/84
7/13/85
9/10/83
10/12/85
1 1/2/83
8/22/84
6/14/84
4/3/85
9/1 0/84
9/15/83
9/25/82
8/5/83
1 1/1 7/85
9/27/85
7/16/84
12/16/82
4/30/82
9/14/85
7/23/85
12/24/84
1/2/84
1 1/6/83
2/2/82
12/10/83
1 1/5/85
3/30/85
1 1/14/83
CATEGORY
1W
3W
5W
5W
6W
6W
6W
7W
7W
7W
8W
9W
9W
9W
9W
10W
11W
11W
11W
7D
8D
8D
10D
18D
19D
2W
4W
12W
14W
17W
ZN A
1-Wi&i
y^.WiDj.
^annual =
estimated
WEIGHTING
FACTOR (Wj)
0.0397
0.0380
0.0463
0.0463
0.0419
0.0419
0.0419
0.0397
0.0397
0.0397
0.0545
0.0331
0.0331
0.0331
0.0331
0.0397
0.0391
0.0391
0.0391
0.0231
0.0231
0.0231
0.0314
0.0397
0.0397
0.0149
0.0066
0.0116
0.0182
0.0099
•B
=
deposition «
DEPOSITION FOR
CATEGORY (6 ,)
55.5
15.2
49.0
49.0-
35.8
35.8
35.8
56.1
56.1
56.1
33.0
35.5
35.5
35.5
35.5
48.6
42.5
42.5
42.5
1.0
2.6
2.6
8.1
3.8
1.4
8.4
18.1
19.9
10.1
17.6
33.6
31.4
3107 mgm'2
2910 mgm"2
DEPOSITION FOR
EVENT (Dj)
174.9
0.0
82.3
0.9
7.9
44.8
68.8
53.8
108.4
40.9
0.0
65.0
0.0
8.0
0.0
19.2
18.1
35.8
27.4
0.0
0.6
9.0
6.5
4.2
2.2
10.2
5.3
6.9
0.0
0.0
12
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The procedure for deriving estimates for annual deposition may also be modified to derive
an estimate for seasonal deposition (i.e. summer half-year, winter half-year, etc.). Seasonal
estimates may be derived from a given ensemble of events by application of equations 6 and 7
along with the following changes:
(1) Average annual deposition for 1982-1985 (Dannual) is replaced by the average
deposition for the season in question (Dsummer, Dwinter).
( 2 ) The weighting factor (w-() is set equal to zero for all events outside the specified
season, so that summer deposition is estimated based on summer events only, etc.
( 3 ) The estimated deposition ($ is derived based the 1982-1985 average for events
from the specified season, rather than from all events.
An example of the aggregation procedure is shown in Table 1. Aggregated annual deposi-
tion is calculated for Big Moose, N.Y., based on a 30-event sample. The aggregation procedure
follows equation (7) with 6| equal to the average deposition for events associated with each
category1. Weighted sums are ^ .w,fi>, and
2.1.5. Selection Process
Selection of individual events for detailed analysis by the RADM simulation meets, two
broad criteria. First, the selection process must provide an ensemble of events that are broadly
representative of the range of meteorological conditions that contribute to wet and dry deposi-
tion in eastern North America. Note in particular that the ensemble must include representa-
tive deposition events at sites throughout the region, rather than at a few sites only. Second, the
selection process is based on rigidly defined objective criteria, rather than on subjective
judgement.
The selection process involved generation of groups of individual events from the 1 982-
1985 period, each of which reflected desired characteristics for the final event ensemble. The
grouping of events in the selection process was based partly on the meteorological categories
(Section 2.1.1) and partly on criteria discussed below. Once the groupings were determined, a
series of event ensembles were chosen as candidates for final selection. The candidate ensembles
were each composed of a pre-determined number of events from each category, with the indi-
The reader may note that the estimated deposition reported in Table 1 differs from the estimate
for Big Moose shown in Figure 21 b below. The final estimates shown in Figures 21-25 make
use of observed correlations between deposition and precipitation ( Equation 8) to calculate
Dj . The simpler calculation illustrated in Table 1 ignores precipitation and sets 5j.equal to
the average deposition for the meteorological category.
13
-------
vidual events selected randomly from within each group. These candidate ensembles were then
put through a two-part evaluation procedure to establish the final selection. This evaluation
was based on calculation of aggregated estimates for annual SO4= deposition and total annual
precipitation.
The grouping of events for selection was based primarily on meteorological categories
generated by the wind-based cluster analysis described above. The meteorological categories
identified by the cluster analysis- were assumed to represent characteristic atmospheric
circulation patterns over eastern North America; and selection of events from these categories
assures that an appropriate range of circulation patterns are represented in the final ensemble.
In addition, the initial cluster-based categories were further divided into subcategories that
reflect desired characteristics for the ensemble. Since our primary concern is to represent wet
deposition processes, it was decided to select the majority of events from groupings that include
substantial wet deposition. To this end the initial categories were divided into "wet" and "dry"
subcategories based on SO4" wet deposition received at 13 eastern sites (sites are described
below). The "wet" subcategories consist of events with greater than SO4" wet deposition; the
"dry" subcategories consist of events with less than median SO4" wet deposition.
Candidate events were selected primarily from "wet" subcategories, although a number of
extreme "dry" events (with little wet deposition or precipitation at the eastern sites) were
prescribed to permit evaluation of dry deposition processes. The ensembles of candidate events
(candidate ensembles) generated based on these criteria are presumed to represent the desired
range of atmospheric circulation patterns as represented by the cluster analysis. However the
candidate ensembles do not necessarily represent typical patterns of wet deposition or precip-
itation, as neither deposition nor precipitation have been accounted for by the meteorological
categories. In particular, candidate ensembles may include abnormally high precipitation and
deposition at certain sites and abnormally low deposition at others.
Evaluation of deposition and precipitation in the candidate ensembles is accomplished by
aggregating estimates for annual and seasonal wet deposition and precipitation from candidate
ensembles. The aggregation procedure essentially represents a projection of annual deposition
based on (a) long-term average deposition (or average deposition-to-precipitation ratios) for
the meteorological categories from which events were selected; and (b) deposition received
during the individual events of the candidate ensemble. When deposition and precipitation for
candidate events are broadly representative of average conditions for the associated meteoro-
logical category, the aggregation procedure yields a reasonably accurate estimate for annual
deposition. When the candidate ensemble includes extreme or unrepresentative events, the
aggregation procedure yields large errors.
The candidate ensembles are evaluated based on three separate aggregation procedures.
These are:
14
-------
(a) Aggregated wet deposition, calculated based on the difference between ensemble wet
deposition and average wet deposition within each meteorological subcategory;
(b) Aggregated wet deposition, calculated based on the difference between ensemble and
average wet deposition-to-precipitation ratios; and
(c) Aggregated annual precipitation, based on the difference between ensemble precip-
itation and average precipitation within each subcategory
The three aggregation evaluations tested the extent to which the candidate ensembles are char-
acterized by: (a) wet deposition rates, (b) wet deposition-jo-precipitation ratios, and (c)
precipitation rates that represent average values for the chosen meteorological subcategories.
Accuracy of the aggregated estimates for each candidate ensemble is evaluated based on the
root-mean-square difference (RMSD) between estimated and observed deposition over 13
eastern sites, calculated by
,1 PJ - o,r o)
where b-. .is the aggregated estimate for annual deposition at the /n site from Equation (7) and
Dj.is the observed annual deposition (1982-1985). In the final selection process 20 candidate'
ensembles were generated and the candidate with the lowest RMSD for in the three aggregation
tests was selected for use by RADM.
2.1.6. gource/ReceDtor Relationships
2.1.6.1. Trajectory Calculations—
The transport of air to measurement locations was estimated using the NOAA ARL-ATAD
trajectory model (Heffter, 1980). This model calculates the mean upwind track of the air
within the "mixed-layer" of the atmosphere (a layer of variable height extending from the
surface to roughly 1500 m above the surface in the summer and 800 m above the surface in the
winter). Rawinsonde measurements of wind, temperature and humidity in the vertical were
used to calculate the trajectories. The upper air observing network recording these data over
North America has a spatial resolution of about 500 km and a temporal resolution of 12 hours
with measurements taken at OOZ and 12Z (Z=GMT). The trajectory model interpolates the
upper data in time and space (1/r2), utilizing all information within 400 km of the receptor or
previous trajectory endpoint, to determine the backward motion of the air. The model outputs a
new position (latitude and longitude) for the air parcel after each three-hour period out to 72
hours (24 three-hour segments). Four trajectories, arriving at the receptor at OOZ, 06Z,
12Z and 18Z, are calculated for each day.
15
-------
Determining the mean wind velocity along each trajectory segment required a value for
the height of the mixed-layer at that location and time. This value was estimated for OOZ and
122 by scanning the vertical temperature profile for the height of the first non-surface based
inversion. When no such inversion was found the top of the layer was set at 3000 meters above
the elevation of the nearest rawinsonde station. The OOZ and 12Z estimates are linearly
interpolated in time to yield a mixing height for the trajectory segment in question. The wind
velocity measurements taken below this height were then used to calculate the mean motion of
the mixed-layer over the next three-hour period. This procedure is repeated up to 24 times
(72 hours back in time) or until there is no data within 400 Km. As with all methods for
calculating trajectories those employed here contain uncertainties which arise due to the fact
that the data are limited (cf. Kahl and Samson, 1988) and because it is not possible to model in
detail all of the complex motions within the atmosphere. ARL-ATAD trajectories were compared
to data from the Cross-Appalachian Tracer Experiment (CAPTEX). The results showed lateral
displacement uncertainties on the order of 100 to 200 Km at 24 h upwind and 100 to 400 Km
at 72 h upwind (Kahl and Samson, 1986; Draxler, 1987). Relative to southwest trajectories
arriving at Big Moose, NY these errors amount to an an area about the size of the state of Ohio.
These uncertainties are not significantly different than those resulting from other methods of
calculating trajectories. Given the availability of the mixed-layer trajectories, they were
selected for the assessment of source-receptorrelationships within the context of this study. !n
using them it was assumed that they represent the most probable path of any air parcel advected
with the mean motion of the mixed-layer.
2.1.6.2. Quantitative Transport Analysis—
The transport potential for a given sampling time includes the mean transport computed
using the trajectory model plus the horizontal spread imposed by atmospheric dispersion and
uncertainty in transport estimates (Samson, 1980). The probability of a reactive, depositing
tracer arriving at a point x at a time t, Arfx.t), can be expressed as
t + 00 +00
Af(x,{) - J J | T(x.t\x',n dx' dr (10)
t-T -oo -oo
where 7"(jr,fljr',f), the potential mass transfer function in two dimensions, is defined by
Tfx.tix-.n - Q(x,t\x:n Ra\n Dfx-.n Afx-.n n 1)
and O(x,flx',0 is the transition probability density function of an air parcel located at x' at
time t1 arriving at a receptor x at time t, R(t\f) is the probability of the tracer not being
reacted to another species from time t' to time t, D(x',tr) is the probability that the tracer was
not dry deposited at (x',0 and A(x',0
-------
The transition probability density function, Q(x,t\x',tl), is most important in under-
standing source-receptor relationships since it describes the motion of the air transporting the
pollutants. If a particular source is located at x' at time t' then the probability that it will
impact upon the receptor at x at time t, is given by ,Q(x,i\x',t*). The last three probability
functions on the right side of (7) are not well defined for soluble elements. In this work they
are assumed to equal one so that the effects of dry and wet deposition and chemical reactions are
neglected. The main drawback to these simplifications is that they eliminate the possibility of
determining the relative importance of local versus distant sources.
The transition probability density function can be approximated from the upwind
trajectories. The axis of the computed trajectory is assumed to represent the highest proba-
bility at any time upwind for contributing to the trace element composition at the monitor. The
spatial distribution of the transition probability function away from the axis of the trajectory
depends upon the degree of vertical mixing, coupled with the magnitude of the wind velocity
shear. As a first approximation, it has been assumed that the "puff" of transition probability is
normally distributed about the trajectory with a standard deviation which increases linearly
with time upwind (Samson, 1980; Draxler, 1982). Thus Q(x,t\x',t") is assumed to be
expressed as:
• 2 ,,«2
2a/
where x" = X - x'(f) and y" = Y - y'(t") with (X,Y) being the coordinates of the grid and x'(0
and y'(0 being the coordinates of the centerline of the trajectory. If x" or y" is greater than
3ax or 3ay which are defined below, then segment / is not counted in grid box (X, Y). It is
assumed that cx and ay can be approximated by
ax(f) = oy(t') = a t1 (13)
with a dispersion speed, a, equal to 5.4 km hr"1 (Samson, 1980). To determine the overall
probability field (Q-field), Q(x,^x',t") is integrated over time and space as in (10). This
field shows where over time period, T, the air was most and least likely to have originated; i.e.
the probability of the air over any point moving over the receptor during the time period, -. In
practice this is carried out by summing the values of Q(x,t\x',t') in each grid box over all
trajectory segments and dividing by the total number of segments.
Various weighting schemes can be applied in the determination of the Q-field to derive an
"implied" transport bias (Keeler and Samson, 1989). For example, trajectories associated
with large amounts of pollutant deposition or precipitation can be given more weight so that the
resulting probability fields are skewed towards those regions more often associated with these
types of events. The weighted Q-field, Ow , is determined by
17
-------
nseg
Wt r / x"2 v"2 N~l
(14)
where x", /", ax and oy are defined as in (12) and (13). The total number of three-hour
trajectory endpoints (segments), nseg, depends upon the number of trajectories considered.
Each one can contribute up to 24 segments (72-hour back-trajectories) depending on the
availability of meteorological information. The weighting factor applied is given by,
Dj is the value of the factor used in the weighting associated with trajectory, j, (eg. wet S04=
deposition) and Dtota) is the total value of that factor over all trajectories. The weighting factor
is also scaled by nseg, the total number of non-missing three-hour segments, and by mseg, the
number of non-missing segments associated with trajectory j. Trajectories arriving at the
receptor that are associated with large weights (i.e.. during high deposition events) are thus
counted more heavily than those associated with smaller weights. For an individual receptor the
Ow field produced when weighted by S04" wet deposition indicates the probable pathway associ-
ated with high and low deposition amounts.
2.1.6.3. The Potential Source Contribution Function —
The potential source contribution function (PSCF) is described by Zeng et al. (1989}. It
basically consists of a ratio between weighted and non-weighted Q-fields. The main differences
between the approach of Zeng era/. (1989) and the approach followed here are that in this study
uncertainties in the trajectories, which are accounted for by Gaussian "puff-like" spreading
(equation 12), are assumed to exist. Zeng et al. simply counted the number of trajectory
endpoints in each grid box when generating the probability fields. In addition, all trajectories
are included and weighted by deposition in this study, while their weighted transition proba-
bilities were determined by only including trajectories associated with events exceeding a pre-
set concentration (equivalent to the average concentration at the site in question). The PSCF as
employed in this study is determined by:
ocrc Q(M*'.OweiQhted
PSCFm _ (16)
U(X,f|X -Onon-weighted
Values greater than one imply an enhanced probability of the air residing over that region prior
to high wet SO4* deposition events.
18
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2.2. DESCRIPTION OF DATA BASE
2.2.1. Meteorological Data
Meteorological data were obtained from the National Meteorological Center (NMC) global
analyses, available at the National Center for Atmospheric Research (NCAR). This data set was
selected because it includes information over the oceans. An alternative data set based on
rawinsonde observations was used in a preliminary study (Fernau, 1988), but was rejected for
this effort because it failed to represent circulation over the western Atlantic Ocean, crucial for
the development of storms affecting eastern North America. In addition, the NMC data offered
better spatial resolution over the entire region of interest. The NMC data also included infor-
mation from a larger variety of sources and had been subject to more extensive quality control
procedures to insure accuracy and consistency (Trenberth and Olson, 1988).
Synoptic and asynoptic meteorological data were collected via NMC's Global Data
Assimilation System (GDAS) twice daily from numerous sources. Included among these were:
surface observations, rawinsondes, aircraft, ships, satellites and buoys. In compiling the final
analysis NMC permitted a data collection cut-off time of up to ten hours'beyond the actual
analysis time. The irregularly spaced data (time and space) were interpolated to a regular 2.5°
grid across the entire globe for both OOZ and 12Z. To calculate the data values at the grid points
an optimal interpolation scheme was used and the first guess fields for the interpolation were
generated from six or occasionally twelve hour global model predictions.
The Global Data Assimilation System at NMC, has been evolving since September 1978.
Changes were largely implemented to improve the accuracy, coverage and/or detail of the
analysis. Other modifications were necessary to resolve operational difficulties as they
occurred or were discovered. The details of the changes that occurred, and their effect on the
analysis, are discussed in Trenberth and Olsen (1988). During the 1978-1987 period the
majority of the changes had no effect on the information used in this study. However, from
March 1984 to March 1986 there were difficulties that resulted in up to a 5 m-sec"1 error in
the 850 mb level winds. This problem was attributed to small scale noise in the 1000 mb
temperature field and it is believed that the resulting errors were not systematic.
NCAR houses NMC data from July 1976 to nearly the present for temperature, relative
humidity, geopotential height, zonal and meridional wind components and pressure at up to 14
different levels. During this period, however, NMC failed to archive portions of the data and
thus there are periods of missing information in the data set. The amount and type of missing
data were also summarized by Trenberth and Olsen (1988). In the years 1978-1986 an
average of 7.8% of the data were missing. The minimum amount lost was 2.5% in 1986 and the
maximum was 13.6%-in both 1982 and 1984.
19
-------
For this study we obtained the information from NCAR for the years 1979-1985. The
14-layer, 2.5° global data were reduced to 5° and 5 layers for OOZ covering eastern North
America. The choice of 5° intervals is discussed in Section 2.1.1. The extent of the horizontal
grid is shown Figure 2. The OOZ data were then arranged into overlapping three-day records in
order to correspond to the three day duration of RADM simulation periods. The first record of
the year contained information from days 1, 2 and 3; the second record contained days 2, 3, and
4 etc.. This process increased the amount of missing cases since a three-day record was not
considered complete unless all three days were available. One isolated missing day from the
NMC data would become three missing cases because it would be included in three separate cases.
2.2.2. Precipitation Chemistry Data
Precipitation chemistry data were obtained from event precipitation chemistry moni-
toring locations. The data in the United States were obtained from the Utility Acid Precipitation
Study Program (UAPSP) which provided a six-year (1982-1987), 19-site precipitation
chemistry data set covering the eastern United States (Mueller and Allan, 1987). The UAPSP
data were augmented by precipitation chemistry measurements from two Canadian sites. Figure
3 shows the location of sampling stations chosen for this study. Table 2 lists the number and
location of the sites. Since the focus of this study is on eastern sites with high deposition the
precision of the aggregation technique is tested for sites located in the northeast and midwest,
including the southern border and Ontario sites but excluding sites from the deep south and
Great Plains. The sites commonly included in northeast/midwest analyses are identified in
Figure 3 and Table 2.
The UAPSP data provided extensive information including: the start day and hour of the
first precipitation event, the end day and hour of the last precipitation event (occasionally the
timing of multiple events were specified), the precipitation amount, the pH, and a thorough
chemical analysis that included the determination of the major ionic species in solution. The
only meteorological information included in the data set were in the form of sample codes which
distinguished events as either (1) snow and ice events, (2) mixed (liquid and solid sample)
events or hail events, (3) rain (uncoded events), and (4) events with thunder.
All chemistry records were examined for incorrect data. Some errors in the dates and
times of precipitation begin/end and sample removal date/time had to be corrected or set to
missing. Midpoint times and durations were calculated from the precipitation begin/end
information. Some chemical measurements were discarded, including phosphate, total acidity,
aluminum, sulfite. nitrite and total organic carbon. Hydronium ion concentration was
calculated using the lab pH or, if missing, the field pH according to the formula
20
-------
Figure 3. Location of the precipitation chemistry sites in the United States and Canada used
for analysis of transport patterns. These sites represent locations of daily
precipitation monitoring locations. Sites indicated by solid circles were used to
test precision of aggregation technique.
-------
TABLE 2. NAME AND LOCATION OF DAILY (EVENT) PRECIPITATION CHEMISTRY SITES.
INCLUDES SELECTED SITES FROM THE UTILITY ACID PRECIPITATION STUDY PROGRAM (UAPSP)
AND FROM TWO CANADIAN NETWORKS. ASTERISKS (*) DENOTE NORTHEASTERN AND
MIDWESTERN SITES USED TO TEST THE PRECISION OF THE AGGREGATION TECHNIQUE.
SITE
* 1
* 2
' 4
•5
* 6
• 7
* 8
*1 0
* 1 1
1 2
•1 3
1 4
15
1 6
1 7
1 8
1 9
*20
*21
24
25
•27
*28
LOCATION
UAPSP
Turners Falls, Massachusetts (> 8/80)
Tunkhannock, Pennsylvania
Zanesville, Ohio
Rockport, Indiana
Giles County, Tennessee
Fort Wayne, Indiana
Raleigh, North Carolina
Gaylord, Michigan
Clearfield, Kentucky
Alamo, Tennessee
Winterport, Maine
Uvalda, Georgia
Selma, Alabama
Clinton, Mississippi
Marshall, Texas
Lancaster, Kansas
Brooking s, South Dakota
Underhill, Vermont
Big Moose, New York
Shawano. Wisconsin
Round Lake, Wisconsin
CANADIAN
Dorset, Ontario
Longwoods, Ontario
LATITUDE
42°59'
41°35'
39°59'
37°53'
35°17'
41°03'
35°44'
44°57'
38°08'
35°AQ-
44°37'
32°03'
32°28'
32°21'
32°40'
39°34'
44°20f
44°32'
43°49I
44°42'
46°13'
45°13'
42°49'
LONGITUDE
72°55'
76°00'
82°01'
87°08'
86°54'
85°19'
78°4V
84°39'
83°27'
89°08'
ea'ss1
82°29'
87°05'
90°17'
94°25'
95°18'
96°50l
72°52'
74°54'
88°37'
91°56'
78°56'
81°24'
22
-------
H* (nmoles I"1) = 106'pH. (17)
Quality flags were also included in the data set. They were processed after the method of
Endlich et al. (1986) and the major steps in this process are described as below. All
measurements were set to missing if the sampler was inoperative, water was in the dry bucket,
or agricultural activity, dust or smoke were reported in the vjcinity of the sampler. If lab and
field pH differed by more than 0.7 below pH 5 or 1.0 above pH 5 both measurements were'set to
missing. Events with sample precipitation volume differing from gauge precipitation volume by
more than 30% (a measure of collection efficiency) were flagged. Ion balance was checked
using the following formula:
I Total Anion - Total Cation |
(Total Anion + Total Cation)/2 ~ °'6 (1 8)
Events failing (value s 0.6) were coded as missing. Anions and cations used in the test
were nitrate, sulfate, chloride, hydronium ion, ammonium, sodium, potassium, calcium and
magnesium. Measurements below detection level were flagged and negative values were set to
zero. After application of the percent test, as described below, multiple events on the same day
were totalled and events captured by co-located samplers were combined using arithmetic
averages of the measurements. If one of the two co-located samples was considered to be of
better quality either overall or for a given analyte then only the less suspect record or
measurement was kept.
In an attempt to detect outliers, selected data were subjected to the "percent test"
(Endlich, et al., 1986). The test was only applied to events which were suspect due to leakage,
contamination, pH disagreement, ion balance failure or collection efficiency failure. In
performing the percent test the data for all sites are grouped together by analyte. For each
analyte the 90th, 95th, 99th and 99.9th percentile are found using the pooled data. Then the
analyte values at each site are compared to these global percentiles. For a given precipitation
event, if one analyte exceeds its 99.9th percentile, two analytes exceed their 99th percentiles,
three analytes exceed their 95th percentiles, or four analytes exceed their 90th percentiies the
event is considered to have failed the percent test and is flagged as such. Flagged events were
eventually treated as missing.
The UAPSP data only cover the United States, however, southern Ontario also experiences
large annual SO4* wet deposition (Finkelstein and Seilkop, 1986). Thus, for completeness, two
Canadian sites were added to the data base (Figure 3, Table 2). The sites are from the Acid
Precipitation 1n Ontario Study (APIOS) network and are also daily/event samples. These data
were subjected to the same procedures utilized for the UAPSP data, allowing for minor differ-
ences in content, and were placed in the same data format as the UAPSP measurements.
23
-------
2.3. TESTING
Testing of the aggregation and selection process was accomplished by a variety of proce-
dures which investigate the accuracy of successive stages of the analysis. The following steps
were included:
(1 ) The procedure for selecting sample events and generating aggregated estimates for
annual deposition was tested by comparing estimates for annual deposition derived
from meteorologically stratified samples both against observed deposition patterns
and against estimated deposition from samples selected at random. The discrepancy
between observations and aggregated estimates will also be compared to the natural
year-to-year variability in observed wet deposition rates.
( 2 ) The statistical significance of the observed relations between deposition and
meteorological categories will be tested using the techniques of statistical
regression. The regression model also allows testing relationships between
deposition and other meteorological variables (i.e. precipitation) that are used in
the aggregation process.
(3 ) A simplified model identifying source-receptor relationships for observed wet
deposition was developed and applied based on (a) the final ensemble of events
selected for future in-depth analysis, and (b) all events over the 1982-19S5
period. A comparison between the two estimates illustrates both the accuracy and
the potential limitations involved in predicting source-receptor relationships from
a finite sample.
2.3.1. Aggregation Versus Random Selection
Techniques for deriving aggregated estimates for annual deposition based on a finite
sample of events have been described above (Section 2.1.3) . These aggregation techniques may
be applied to ensembles generated entirely at random or to ensembles generated based en
meteorological stratification. In addition, the aggregation procedure itself can include
knowledge gained from meteorological strata, or the procedure can be constrained to specifically
exclude such knowledge. Referring to Equation (7), the prior estimate for deposition per event
( 6-, ) can be derived either with or without knowledge of the meteorological stratum associated
with each event. The prior estimate can also make use of other information (e.g. time of year,
precipitation) during each event.
A preliminary estimate of the usefulness of meteorological stratification in selection cf
events can be obtained by comparing aggregated estimates for random ensembles derived with
24
-------
and without knowledge of meteorological strata. The accuracy of the aggregated estimates is
estimated by the root-mean-square difference over 13 eastern sites (RMSD, Equation 9). The
advantage gained by the use of strata, both in the selection of individual events and in the
aggregation estimate, will be demonstrated by an improvement in the aggregated estimates
derived with knowledge of meteorological strata.
In an associated test, the RMSD for aggregate estimates is compared to the natural year-
to-year variability in observed wet deposition. Year-to-year variability provides a measure of
inherent uncertainty in estimates for annual deposition. If the RMSD for the aggregate estimate
is no larger than the RMSD between annual and multi-year average wet deposition, then the
aggregated estimate may be judged equivalent to an estimate derived from data for a complete
year.
2.3.2. Statistical Regression
The significance of differences in the observed pattern of wet deposition for events from
different meteorological strata have been tested by means of multivariate linear and logarithmic
statistical regression models. The specific advantage of regression models (e.g. Johnson and
Wichern, 1982) is that it permits a persistent correlation between variables to be distin-
guished from a correlation that may be due to random chance. Regression analysis is a neces-
sary step in demonstrating that the relation between deposition and meteorological strata which
form the basis of the aggregation procedure and selection process is based on long-term trends
in deposition rather than on arbitrary correlations.
Because wet deposition must depend on precipitation, a common starting point is to
assume a relationship between deposition and precipitation amounts. Wet deposition and
precipitation have previously been correlated through both linear (Wilson et al., 1SS2;
Buishand et al., 1988; Serge, 1988; Hooper and Peter, 1988) and log-linear (Stein and Niu ,
1988) regression models. A linear regression model for wet deposition with precipitation that
also includes variation by meteorological category has the following form:
V Bcttd)*, <19>
where £>j is the estimated deposition for event /, fl^) represents the least square estimate (i.e.
average deposition-to-precipitation ratio) over ail events within the meteorological category
containing event /, and P-t is the precipitation amount for the event. The regression parameters
have been determined independently at each site for which observed wet deposition and precip-
itation are available.
The importance of meteorological category in explaining deposition can be verified by
demonstrating that the deposition-to-precipitation ratio (Rcat(i)) varies significantly for
events from different meteorological categories. Variation in 6^) can be evaluated with the
25
-------
following test statistic (heterogeneity of slopes, in Freund et al., 1986):
1(0, - B0Pj)2-(Dj- f3cat(i)Pi)2]/(N-1)
' s n v *•u i
£(0i- Gcat(i)^i)2/("'-1)
where Dj and P-t represent deposition and precipitation during the ith three-day event at an
individual site, B0 represents the overall deposition to precipitation ratio for the site, N is the
number of categories and n' is the number of events with non-zero deposition and precipitation.
Note that events with zero precipitation (and therefore zero wet deposition) are excluded from
the analysis to avoid introducing bias. The test statistic F, has an F-distribution with (A/,/?'-i)
degrees of freedom. When this statistic assumes a value greater than a pre-determined critical
level (1.66 for most cases examined here), the null hypothesis that that observed variations in
wet deposition by meteorological category (i.e. variations in Bcat(i)) are due to chance may be
rejected with 95% confidence.
Further insight into the the variation of deposition among meteorological categories may
be gained by comparing slopes (3cat(j)) for individual meteorological categories with the
average slope (80) for all wet deposition events at a site. The difference between B^,, arc 3,
for an individual category are tested by the following statistic
Ts = n- n-
i 2 // _ « n \ \* in 0 \ 2
:0i" Scatd) P,)
-------
where £)average is average deposition per event at the site. The coefficient of determination
represents the fraction of observed variation in deposition that is explained by the regression
estimate (6j)
The techniques that have been demonstrated here in a linear regression model may also be
used in a logarithmic model with the form
log 5j - Y + (J log Pj (23)
or in a linear model that relates wet deposition to the precipitation quantity raised to a power
other than one:
&!- Pcat(i) PjY (24)
The advantages and disadvantages of these models for use in estimating wet deposition will be
explored below.
2.3.3. Source-Receptor Relationships
Variation between categories is important because if they tend to favor different source-
receptor relationships a sample of episodes from a number of categories should contain a better
overall mix of wind flow patterns. This would insure that the resulting aggregate estimates of
wet deposition are not biased towards a small number of potential source regions.
The wet differential probabilities, AOwel, were generated as:
AOwet - Qj - "Qj (25)
to demonstrate that some of the categories exhibited bias towards transport from various
regions in eastern North America. The wet differential probability is the difference between the
probability, of}, for contribution from grid cell /,/ due tojhe transport associated with category
c on days with precipitation, and the mean probability, O^, for contribution from from cell /,/
for all days with precipitation.
The source-receptor relationships estimated using a small number of episodes (19-30)
were evaluated by comparing Q-fields. RMS errors (RMSE) between the actual Q-fieid and its
estimates were calculated according to:
27
-------
where Oj • is the 19 or 30 day aggregate estimate of transition probability and Oy is the
climatological transition probability value in grid cell / for site ;'. S is the number of sites
considered and N corresponds to the number of grid cells over which the differences are
calculated. To convert to percents the RMS error is divided by the mean transition probability
over NX S grid points.
28
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3. RESULTS
3.1. METEOROLOGICAL CATEGORIES
3.1.1. Choice of Stratification Parameters
Attempts at the stratification of the three-day periods into meteorological categories were
made using the four different combinations of meteorological data listed in Table 3. These
factors were thought to be most likely related to precipitation and/or S04° deposition based on
dynamical considerations. These variables were extracted or derived from the NMC data set.
These included those related to wind patterns: 850 mb wind velocity; 850 mb geopotential
height; sea level pressure; and those related to vertical motion and/or precipitation potential:
1000 mb relative humidity; 850 mb temperature advection, 850 mb vorticity advection and
500 mb vorticity advection. The four combinations were generated at each grid point for 1983.
TABLE 3. METEOROLOGICAL FACTORS USED IN INITIAL CLUSTER ANALYSIS
1. Sea level pressure and 850 mb geopotential height (TR),
2. 850 mb u and v components (UV),
3. 850 mb temperature and 500 mb vorticity advection (AD), and
4. 1000 mb relative humidity and 850 mb vonicity advection (RV)
The AD and RV factors were not as well suited to the goals of this study because, while they
consider vertical motions and/or moisture, they do not include representation of transport
patterns directly. The categories resulting from the clustering of RV and AD factors were also
quite 'unbalanced in size with a couple large categories and many small "outlier" categories.
The TR and UV factors are similar in physical meaning in that both are related to wind
flow. The UV factors are based solely on three-day progressions of 850 mb wind velocity (i.e.
one level at approximately 1500 meters above MSL). The TR factors also account for wind
velocities and, in addition, consider the strength and development of cyclones. Consequently
statistical analysis of the UV and TR categories yielded similar results. It was found that the UV
components were more sensitive to changes in the number of categories than the TR factors.
This provides a better means of determining the optimum number of categories. Therefore the
29
-------
UV components were selected as the parameters of choice for this project.
Due to the computer memory constraints of the statistical software used in this project it
was not possible to place all the cases during 1979-1985 into a single cluster analysis. Only
one year's worth of running cases (365 cases) at 48 grid points could be clustered at one time.
Therefore, in order to minimize the effect of missing u-v data and to capture a broader cross-
section of cases, consecutive three-day periods (record 1 - days 1,2,3; record 2 - days 4,5,6;
etc.) for 1979, 1981 and 1983 were pooled for cluster analysis.
Selection of the number of meteorological categories was based on the ability of the
categorization to separate events with observable differences in SO4" wet deposition. The value
of increases in step number (i.e. increasing number of categories) was evaluated by calculating
the Beale (P> and Calinsky (C) criteria for within-group variance of S04* deposition (defined
in Section 2.1.2). High values of Fand C indicate that significant improvements in the ability to
stratify SO4* deposition occur as a result of increased step number. Results are shown in
Figure 4. As expected, the highest values of F and C occur when the step number is low, with
diminishing returns as the number of categories is increased. Figure 5 shows total within-
group variance of SO4" deposition (defined in Sections 2.1.1 and 2.1.2) as a function of number
of categories. Within-group variance decreases as the step number is increased, reflecting
improved ability to stratify SO4*. deposition with increasing number of categories. The pattern
of diminishing returns with increasing step number is.also apparent in Figure 5.
Choice of the proper number of categories necessitates a trade-off between the desire for
precision in distinguishing between different meteorological patterns (facilitated by a large
number of categories) and the need for robustness in statistical estimates and ease of computa-
tion (facilitated by a small number of categories). Appropriate number of categories can be
identified in Figures 4 and 5 by identifying step numbers that show relative peaks in F and C
and significant reductions in cluster variance. Critical points (identified by changes in slope;
appear to occur at steps 8, 14 and 19. Based on this guidance we initially proposed a choice cf
19 categories.
3.1.2. Description of Categories
As described above (Section 3.1.1) it was decided to base the meteorological stratification
on the 850 mb wind fields (U and V components of wind vectors as the two independent mete-
orological parameters) and to base the stratification on 19 independent categories. This section
presents a description of the categories in terms of meteorology and in terms of deposition
(Section 3.1.2.1). We also describe the choice to divide each category into subcategories
representing "wet" and "dry" events, i.e. events with greater than average or less than average
total sulfate wet deposition at the 13 eastern sites (Section 3.1.2.2). The 38 subcategories,
representing "wet" and "dry" halves of the original 19 categories determined by wind field,
were used as the basis for event selection and aggregation.
30
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F-Statistic -• C-Statistic
10 15 20 25 30 35
NUMBER OF STEPS
40 45
50
Figure 4.
Beale and Caiinsky criteria (Fand C statistics) versus the number of-categories
(step number), based on 850 mb wind field clusters and observed SO4~
deposition for 1983. High values for Fand C indicate significant reductions of
within-group variance of SO4= wet deposition with increasing step number.
The mean 850 mb wind flow patterns on each of the three days were plotted to ensure that
the 19 categories made sense meteorologically (i.e. all major storm patterns were repre-
sented). The plots depicting the flow patterns for the 19 categories are included as an Appendix.
Each category demonstrates some unique mixes of cyclonic and anticyclonic circulations. Table
4 is a summary of the major features associated with each of the 19 categories.
3.1.2.1 .
Wind Flow Patterns—
The 19 meteorological categories represent clusters of 3-day events and can be charac-
terized by the evolution of the meteorology from day #1 to day #3, averaged for all events
contained within the category. The mean 850 mb wind flow patterns on each of the three days
have been plotted and examined to ensure that the 19 categories made sense meteorologically
(i.e. all major storm patterns were represented). The plots depicting the flow patterns for the
19 categories.are included as an Appendix. Each category demonstrates some unique mixes of
-------
cyclonic and anticyclonic circulations. Table 4 is a summary of the major features associated
with each of the 19 categories.
*
i
I
T
I
r
I
a
UU 7»
once, .
QA*.
•«*«
%..
10 15 20 25 30 35
Number of Categories
40
45
50
Figure 5 Cluster variance (within-group sum of squares) for SO4" wet deposition versus
number of meteorological categories included in the stratification, based on 850
mb wind field clusters and observed S04= deposition for 1983.
32
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TABLE 4. SYNOPTIC DESCRIPTION OF THE NINETEEN 850 MB WIND FLOW CATEGORIES.
N CORRESPONDS TO THE NUMBER OF OCCURRENCES; SYR-1979,1981 AND 1983
(CONSECUTIVE 3-DAY PERIODS); 7YR - 1979-1985 (RUNNING 3-DAY PERIODS); AND 4YR -
/Jw., 1982-1985 (RUNNING)
Category N N N Description
(3yr) (7yr) (4yr)
1 13 76 41 Trof from James Bay to KS with a mdt SW flow to E. Hi well off
FL coast. Trof rotates and moves slowly Ewd to Wrn Que thru TN
with W to NW fLow behind and W-SW ahead. Hi moves Ewd.
2 12 56 38 Sharp rdg from Lk Sup SEwd. Mdt Sly winds between the rdg a.-.d
a wk trof in the Dakotas. Trof strengthens and moves across Ml
ending up N to S thru OH. Good cyclonic circulation around the
trof.
3 6 49 28 Well developed Lo moves from lA to Lk Ont. Strong, closed
cyclonic circulation around the Lo.
4 8 42 21 Stationary front W to E thru Lk Erie. Sly winds S of front and
wavy Wly flow to the N.
5 25 135 72 WkLo over Nrn MN with front curling S and W into TX. Lo
strengthens and moves E to Ont-Que boarder with front thru E TN.
W-SW winds ahead of system and mdt NWly winds behind.
6 29 249 157 Stg Hi over NC drifts very slowly E as a wk trof moves SEwd into
Lk Sup. Mdt SW flow over the MS valley spreads Ewd to Atlantic
coast during the period.
7 32 201 112 Continental Hi over SErn US weakens as Nly winds spread Swc
over the Grt Lks and New England. A rdg re-develops from the Hi
across Lk Mi as Nly winds move E.
8 18 159 92 A cold front along the NE Atlantic coast moves slowly offshore.
An extensive area of Hi pressure moves over the Ern US and a mat
Sly flow develops W of the MS river.
9 33 263 139 Bermuda Hi remains nearly stationary over TN/GA. A very wk
trof moves quickly across the Grt Lks. A Igt anticyclonic circula-
tion covers the Ern US throughout the period.
10 8 108 76 Trof thru Lk Sup and SErn KS moves E and rapidly weakens. Met
to stg Wly winds persist W of the MS river and S of the Grt Lks.
ABBREVIATIONS KEY
trof - trough of low pressure
rdg - ridge of high pressure
Lo - low pressure system
Hi - high pressure system
stg - strong
mdt - moderate
wk - weak
thru - through
Lk - lake
Grt Lks - Great Lakes
N, S, E. W - north, south, east, west
NE, SW, SE - northeast, southwest, southeast etc.
Nly - northerly (same format with other directions)
Nrn - northern (same format with other directions)
Nwd - northward (same format with other directions)
All standard State abbreviations are applicable
33
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TABLE 4 (CONTINUED) (N CORRESPONDS TO THE NUMBER OF OCCURRENCES; 3YR -
1979, 1981 AND 1983 (CONSECUTIVE 3-DAY PERIODS); 7YR - 1979-1985
(RUNNING 3-DAY PERIODS); AND 4YR - 1982-1985 (RUNNING) )
I
Category
N
(3yr)
N
(7yr)
N
(4yr)
Description
11
12
13
14
15
1 6
17 -
18
19
27 223 124
15
8
13
13
10
90
50
27
34
23
68
96
78
48
16
19
16
1 4
37
30
48
A cold front N-S thru OH weakens and moves just off E coast. Stg
NW winds behind front also weaken and shift to the W as a Hi
develops over TX.
Well developed Lo NE of Georgian Bay with a trof extending Swd
moves just E of ME. NW winds cover entire Ern US behind system
Nw flow to the W of the Grt Lks moves across Ern US and
strengthens over the Atlantic. A Hi develops over TX and builds
across OH valley.
A sharp rdg from Lk Sup thru GA with stg NW winds to E and stg S
winds to the W moves offshore. A Lo with a stg cyclonic circula-
tion moves over NE IA from CO.
Stg Lo over New Brunswick drifts E. A weak Hi over the Gulf is
replaced by a weak Lo over GA. Entire Ern US ends up in a wea*
cyclonic flow around the Lo.
A well developed Lo over Georgian Bay moves quickly NE and
another stg Lo moves into MO. Stg S flow develops aheac of
second Lo and W of a wk rdg separating the two Lows.
Strengthening trof over MN moves to Wrn Ml and extends Swd to
LA. SW flow increases E of trof as does the NW flow W of re
trof.
A Hi over the Gulf which extends to IA weakens allowing ccns.s-
tent NW winds to cover most of the Ern US.
Well developed coastal Lo near DE moves NE as a rdg aicng MS
river moves over Ern states. Mdt Siy flew W cf rcg anc aneac c'
wk trof NE-SW thru MN.
x,
ABBREVIATIONS KEY
trof - trougn of low pressure
rdg - ridge of high pressure
Lo - low pressure system
Hi • high pressure system
stg - strong
mdt - moderate
wk - weak
thru - through
Lk - lake
Grt Lks - Great Lakes
N, S. E, W - north, south, east, west
NE, SW, SE - northeast, southwest, southeast etc.
Nly - northerly (same format with other directions)
Nrn - northern (same format with other directions)
Nwd • northward (same format with other directions)
All standard State abbreviations are applicable
34
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79-81
82-85
0.12
0.00
1 2 3 4 5 6 7 8 910111213141516171819
Meteorological Category
Figure 6. Comparison of frequency of occurrence of the 19 meteorological categories
defined in this study between 1973-1981 and 1982-1S85.
During the seven year period there were marked differences in the overall frequency and
seasonal frequency of each category. Some types of events were rare while others occurred
quite often. The number of occurrences of each category over seven years are listed in Table 4.
Categories 6, 7, 8, 9 and 11 were the most frequent and categories 4, 13, 14, 15, and 16 were
relatively infrequent during 1979-1985. The change in category frequency between the
1979-81 period and the 1982-85 is shown in Figure 6. The overall importance of each
category did not change significantly.
35
-------
Initially it was thought that it may be necessary to impose seasonal requirements on
category selection due to the seasonal variations known to exist for SO4* deposition. This would
ensure that storms at all times of the year were represented. Figures 7a-g, however, demon-
strate that seasonality is already inherent in the categories. For example, categories 1, 2 and 3
were basically cold season events. Figure 7b shows that category 4 occurred in all seasons with
a tendency towards Fall. Categories 5 and 6 also occurred year round, but they are more
dominant in the Spring and Fall. Categories 7, 8, and 9 (Figure 7c) were predominantly warm
season situations while 10 and 12 (Figure 7d) were cold season type events. Category 11
occurred fairly often in all months but were most frequent between December and June. Cases
in categories 13-16 were all most frequent during the cold season as were the cases in cate-
gories 17, 18 and 19. However, cases in 17 and 18 seemed to" occur more in winter and cate-
gory 19 cases in spring. Missing events, which resulted whenever the 850 mb data for any of
the three days were missing were recorded in every month, however, there were more in
August and less in May and June. On average there were approximately 5 events missing per
month during the four-year period.
3.1.2.2. Deposition Patterns—
While the underlying reason for using the wind flow to stratify deposition episodes was to
insure representation of all major air flow patterns, it was hoped that the metecrclogical
stratification .would also produce distinct S04" deposition patterns associated with each category.
It was found that this was partially true. The stratification separated relatively "wet" periods
from relatively dry periods and thus separated those periods of enhanced pollutant deposition as
well.
The ability to explain the variability in wet deposition by meteorological stratification
was tested in a number of ways. First, the spatial patterns of the 85th percentiles of S04= wet
deposition associated with each category, were studied. All available data (up to 7 years) from
each separate location that satisfied the quality assurance measures discussed earlier were used
to calculate the percentiles. The spatial patterns changed between categories but the differences
were not always great. For certain categories, the magnitudes and locations of the relative
maxima and minima were significantly different compared to the other categories and some
categories appeared to be rather unique at certain sites and not at others.
The 85th percentiles of SO4" deposition for each meteorological category were compared at
four locations in Figures 8 a-d. Deposition appears to vary between categories and at each of the
sites explored there were different categories or groups of categories that resulted in high 85th
percentile depositions. This suggests that the 850 mb flow patterns that were important for
S04* wet deposition at some sites were not necessarily important for other sites. Nonetheless,
there appeared to be some consistency in rank of categories between sites. Category 5 consis-
tently produced relatively large amounts of SO4* wet deposition at all four sites while categories
12 and 13 produced small amounts.
36
-------
The utility of stratification in estimating long term averages relied on the assumption that
the variance within the strata were less than the overall variance. Figure 5 above showed that
there was less variance within each meteorological strata as compared to the variance over all
the data. The average variance within the 19 categories was approximately 85% of the total
variance. Thus, the 850 mb wind categories did reduce the variance of the aggregate estimates.
Statistical significance of the relation between wet deposition and meteorological category will
be discussed in detail in Section 3.1.3 below. Although there were statistically significant
differences in wet deposition among categories, it is obvious that factors other than wind flow
(i.e. precipitation amount and location) explains most of the variance in wet deposition.
One factor that influenced the relative importance of a'category was its frequency of
occurrence. As a first guess, one would expect that the most commonly occurring categories
would be relatively important. However, this was not always be the case. High pressure
systems occurred fairly frequently but they generally produced significantly less precipitation.
Therefore, in order to better identify which categories were the most important, the total
amount of SO4" deposition associated with each category during 1982-1985 was compared.
Percent of total deposition due to each category were calculated for every site. There were a
group of categories that consistently contributed a large percentage of the deposition at each one.
Storms associated with categories 11, 9, 6, 5 and 7 appear to be the most important for the wet
deposition of SO4=. Some categories were important because they were frequently associated
with precipitation. Others were important becaus'e the average deposition per event was large.
In Figure 9 the ratio of the percent of SO4° deposition to the percent of precipitation has been
graphed for each category. In general, one can assume that changes in precipitation should
result in proportional changes in deposition. Therefore, ratios above one in Figure 9 indicate
relatively "dirty" categories. While categories 5, 6, 7, and 9 had ratios greater than one (as
did category 8, which was also fairly important), category 11, was not among the "dirty", its
importance was apparently more related to the fact that it frequently resulted in precipitation
at most of the sites. In fact, the category 11 storms were responsible for the largest percentage
of precipitation at many sites in eastern North America.
37
-------
M
M J J A
MONTH
O N
D2
•
Figure 7a Monthly mean number of occurrences of meteorological categories 1, 2, and 3
during 1982 to 1985 (Note scale is 0-25).
O 50
M
M J J A S
MONTH
O N D
D5
|4
Figure 7b Monthly mean number of occurrences of meteorological categories 4, 5, and 6
during 1982 to 1985 (Note scale is 0-50).
38
-------
Figure 7c Monthly mean number of occurrences of meteorological categories 7, 8, and 9
during 1982 to 1985 (Note scale is 0-75).
Figure 7d Monthly mean number of occurrences of meteorological categories 10, 11, and
12 during 1982 to 1985 (Note scale is 0-50).
39
-------
M
M J J A
MONTH
N
• 15
n 14
Figure 7e Monthly mean number of occurrences of meteorological categories 13, 14. and
15 during 1982 to 1985 (Note scale is 0-25).
c
c
c
u
F
R
E
N
c
E
S
25
20 -
15 -
10 -•
5 .
D17
• 16
M A M J J AS
MONTH
Figure 7f Monthly mean number of occurrences of meteorological categories 16, 17, 18,
and 19 during 1982 to 1985 (Note scale is 0-25).
40
-------
1 00
12345
6 7 8 91011121314151617181920
Meteorological Category
Figure 8a. Variation in amount of SO4" deposition between categories at Big Moose, New
York.
12345
6 7 8 9 1011121314151617181920
Meteorological Category
Figure 8b. Variation in amount of SO4* deposition between categories at Dorset, Ontario
-------
12345
6 7 8 9 1011121314151617181920
Meteorological Category
Figure 8c
Variation in amount of S04" deposition between categories at Raleigh, North
Carolina.
120
n
1 2 3 4 5 6 7 8 9 1011121314151617181920
Meteorological Category
Figure 8d. Variation in amount of S04" deposition between categories at Zanesville, Ohio.
42
-------
2.00
0.00
12345
6 7- 8 9-10111213141516171819
Meteorological Category
Figure 9.
The ratio of the percent of SO4= deposition to the percent of precipitation for each
meteorological category. Values greater than one imply more deposition than
expected due to variations in precipitation amount.
To gain a better understanding of the actual weather associated with each category the sea
level analysis from the NCAA Weekly Summaries were examined. It was hoped that definite
similarities would appear between the cases within categories and that we would then be able to
select a set of "most representative" cases for each category. 'These cases would then be used in
the aggregation. Unfortunately, there was still too much variability within categories and it
was next to impossible to identify typical cases. This was because there were many meteoro-
logical and deposition related parameters still varying within each category.
For the purpose of event selection, it is desirable to establish a pool of events with above
average SO4= wet deposition in eastern North America. The ultimate goal of the categorization
process is to represent meteorological events that contribute significantly to deposition in
eastern North America, using a minimal number of sample events. This aim would be thwarted
by a selection process that includes large numbers of events with minimal total wet deposition.
43
-------
In order to improve the selection process the original 19 wind flow categories were divided into
"wet" and "dry" subcategories. The split into "wet" and "dry" halves was based on actual SO4"
wet deposition received at the 13 northeastern sites during the years 1982-1985, with the
"wet" subcategories consisting of events with total SQ^~ wet deposition above the median. This
resulted in 38 new categories consisting of the "wet" and "dry" halves of the original 19 wind
flow categories.
An examination of 850 mb wind flow for the 38 subcategories revealed little difference
between equivalent "wet" and "dry" halves. The major difference between wet and dry subcat-
egories concerns total precipitation, which is related to meteorological parameters (e.g. abso-
lute and relative humidity, atmospheric stability indices) other-- than 850 mb wind flow. The
split into wet and dry subcategories based on SO4* wet deposition corresponded closely to a split
based on total precipitation. We have used the 38 wet and dry subcategories as the basis for
event selection and aggregation, while analyses for statistical significance {Section 3.1.3
below) were based on the 19 wind flow categories with precipitation as an independent
parameter.
The percent of total deposition due to each of the 38 subcategories were calculated as for
the original 19 categories. Table 5 is a matrix of percent of total deposition by site and category
('CT'). The same information is portrayed graphically in Figure 10. The "wet" sub-categories
are numbered 21-40 with 21'corresponding to 1, 22 to 2, 23 to 3 etc. The categories that
consistently accounted for a relatively large percentage of the deposition at all of the sites were
25, 26. 27, 29, and 31. These were all from the "wet" subset and corresponded tc tne
important categories selected earlier from the undivided set of 19. These categories alcr.e
accounted for 30 to 60% of the 1982-1985 SO4* and N03" wet deposition at the sites for which
data was available.
A crucial step in the development of the aggregation scheme was the selection of an array
of categories from which to select events. It was decided that the categories selected sncuid
account for close to 75% of the total SO4" wet deposition. A significant number of events would
be selected from the major categories (25,26,27,29 and 31). However selection from
additional categories were necessary to bring the SO4 wet deposition for included categories up
to 75% of total deposition at most sites. The relative importance of the remaining categories
varied significantly from one site to the next, as some wind flow patterns contributed
significant wet deposition at some sites but little deposition at others.
44
-------
TABLE 5: MATRIX OF PERCENT OF TOTAL DEPOSITION BY SITE AND CATEGORY
CT
1
2
3
4
5
6
7
8
9
1 0
1 1
1 2
13
1 4
1 5
1 6
1 7
1 8
1 9
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Tot
SITE NUMBER
1
1.3
0.6
0.5
0.3
0.4
1.2
0.9
2.1
2.5
1
2.4
1.8
0.1
0.3
0.1
0.3
0.5
0.2
1.5
4
1 .1
4
0.8
8.9
8.4
8.9
6.3
1 4
2.1
1 4
3.7
0
1.3
0.9
1.3
1.2
0
1.2
100
2
0.3
0.2
0.1
0.2
0.7
1.1
0.9
1.6
2.4
0.2
1
0.7
0
0.2
0.2
0
0.1
0.1
0.3
4.1
0.7
3.3
1.3
9.4
7.3
20
7.8
1 4
2.4
1 1
3.7
0
1.2
0.4
1.1
1.1
0.4
0
100
4
0.4
0.8
0.2
0.2
0.5
1.4
1 .4
1
1 .4
0.8
0.8
0.6
0.3
0.1
0.4
0.2
0.4
0.2
0.5
4.2
1 .1
4.3
2.6
1 1
1 5
1 3
6.5
1 3
3.3
8.6
1.3
0.2
2.4
0.1
0.4
1.3
0.4
0.7
1 00
5
0.33
1.71
0.01
0.87
0.61
2.86
1.78
0.7
2.1
1.69
0.67
0.22
0.34
0.39
0.65
0.62
0.8
0.28
.1.58
5.26
2.14
3.33
1.28
10.7
12.5
11.5
3.02
9.78
4.9
6.25
0.81
0.27
5.37
0.75
1.23
1.52
0.07
1.18
100
7
0.45
0.66
0.06
0.07
0.57
2.8
1.56
1.06
0.78
1.34
0.61
0.52
0.21
0.2
0.27
0.12
0.68
0.45
0.38
6.79
0.77
3.28
1.51
8.61
15.3
15.4
3.03
1 2
3.63
7.65
1.01
0
3.23
0.57
1.24
2.22
0.58
0.48
100
8
1.34
2.83
0.31
0.92
1.08
0.75
1.2
6.26
3.69
2.44
3.59
0.93
0.77
0.58
0.99
0.15
0.81
0.29
2.31
2!16
1.9
5.94
1.93
8.15
1.39
9.75
4.83
9.42
0.59
12.9
2.66
0
1.91
0.41
0.11
2.14
1.1
1.42
100
1 0
0.15
1
0.24
0.2
1.9
6.33
1.52
0.61
2.99
0.75
1.6
0.28
0.11
0.15
0.06
0.05
0.58
0.39
1.29
3.5
1.67
4.01
0.89
1 4
17.6
8.87
1.94
11.7
3.02
7.21
0.65
0
2.06
0
0.66
1.4
0
0.59
100
1 1
.88
0.9
0.25
0.37
0.85
1.3
' 1.81
1.93
1.07
1.89
2.04
1.31
0.34
0.17
0.91
0.1
0.92
0.42
0.53
2.4
0.32
3.75
1.05
9.37
6.06
1 1 .8
6.91
16.8
3.19
11 .9
2.06
0.23
1.46
0.5
0.9
1.74
0.84
0.71
1 00
1 2
1.86
1.72
0.35
0.97
1.11
8.1
0.15
1 .23
7.66
3.73
1.59
0
0.75
0.51
0.6
2.28
0.87
. 0.41
0.8.4
6,31
1.75
4.66
0.99
8.73
8.15
2.8
0.97
1 2
2.36
7.87
1.19
0
3.12
0.67
1.66
1.79
0
0.25
100
1 3
1.17
0.35
0.02
0.17
0.38
1.56
2
4.09
1.38
1.78
3.04
3.39
0.09
0
0
0.64
0.64
0.46
'3.12
4.7
0.53
2.93
0.6
10.9
10.6
6.41
3.26
8.22
1.84
1 6
3.79
0
0.51
1.03
1 .98
1 .49
0.15
0.8
1 00
45
-------
TABLE 5 (CONTINUED)
CT
1
2
3
4
5
6
7
8
9
10
1 1
1 2
13
1 4
1 5
1 6
1 7
1 8
1 9
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Tot
1 4
1.76
2.99
1.24
0.45
2.69
3.45
3.66
11 .4
8.68
1.32
3.89
1.32
2.86
0.73
3.18
0.29
1.9
0.19
4.15
3.3
1.12
4.17
0.7
1.89
1.66
11.9
2.55
4.41
0.58
7.44
0.83
0
1.43
0.26
0.27
0.73
0
0.56
100
1 5
2.47
3.94
0.18
0.91
1.79
3.93
2.07
4.93
8.5
3.03
5.84
0.71
1.61
0.95
1.58
0.81
1.63
0.69
1.1-3
3.62
1.75
4.75
2.23
6.66
3
4.27
2.12
7.03
1.42
9.35
0.48
0.17
1.86
0.8
1.49
2.1 1
0.03
0.15
100
1 6
4.64
2.73
0.98
2.08
1.48
3.66
7.05
3.67
5.68
3.82
4.31
0.96
1 .4
0.09
1.95
1.51
1.28
0.98
3.01
3.07
1.59
2.69
2.7
5.73
3.96
7.54
2.24
5.42
1.32
6.56
0.84
0
2.21
0.41
1.1
1.1 1
0.08
0.12
100
1 7
2.05
1.58
0.46
2.24
1.5
9.01
1.78
4.25
3.71
4.09
2.22
0.21
1.21
0.62
1.6
1.23
1.86
1.01
3.4
3.18
2.2
1.55
2.77
5.42
8.S2
8.75
2.36
6.17
2.87
6.1
0.54
0
2.54
0.72
1.16
0.42
0.16
0.18
100
SITE
1 8
0.4
1.16
0.01
1.05
0.76
7.8
2.91
6.18
5.06
1.34
0.33
0.07
0.74
1.26
0.69
1.38
0.61
0.25
5.65
1.93
0.48
1.66
1.67
6.65
8.82
13.4
3.36
12.9
1.56
2.42
0.46
0.08
2.41
0.64
0.78
0.7
0.92
NUMBER
1 9
0.3
1.1
0
1.8
1
9.7
2.4
8.4
8.1
0.5
0.4
0.0
0.1
0.0
0.1
0.3
0.5
0
4.1
0.8
0
0.8
0.1
6.0
1 8
12
2.2
1 5
0.8
1.5
0.3
0
1.6
0
0.4
0.2
0.2
1.47 1.8
100
100
20
0.3
0.5
0.1
0.2
0.2
1.7
1.6
1.7
0.7
0.9
1.6
1.6
0.2
0.0
0.4
0.5
0.5
0.3
2.0
3.9
0.5
1.2
1.0
8.0
1 3
1 5
5.8
1 1
4.0
15
2.6
0.1
0.5
0.1
0.9
1.2
0.1
1.1
100
21
1.1
0.5
0.1
0.4
0.4
1.7
"1.1
1.1
1.7
2
1.6
1.1
0.1
0.0
0.2
0.4
0.5 .
0.3.
•0.7
- 5.8
0.5
1.4
3.2
1 1
1 1
1 5
4.6
13
3.4
1 2
1.3
0
0.7
0.1
1.3
0.8
0.3
0.3
100
27
0.3
0.9
0
0.4
1.3
3.7
1.2
1.4
2.1
1.4
1.4
0.8
0.3
0.3
0.2
0.1
0.3 .
0.9
0.1
5.8
1.3
2.5
1.5
1 3
1 7
5.8
1.9
13
4.8
1 3
0.5
0
0.4
0.1
0.4
1.7
0
0.3
100
28
0.5
1.1
0.1
0.8
1.3
2.9
1.1
0.6
1.5
1.4
1.0
0.2
0.5
0.1
0.2
0.4
0.5
0.7
•o'.i
5.1
1.3
2.0
2.9
1 3
1 0
1 2
2.3
1 7
3.9
8.7
1.2
0
2.2
0
0.3
3
0.1
0.2
100
46
-------
6
7
8
9
10
12-
35-
36-
:'
• r
-
"-".! ^v*-1
•-
.
V" '; -;
"^
>
"DRY" CATEGORIES "(1-19)
i
"WET" CATEGORIES (21-39)
2 4 5 1 8 10 11 12 13 14 15 16 17 18 19 20 21 2~! 2
SITE NUMBER
Figure 10. The percentage of total SO/ wet deposition associated with each meteoroicc
category by site.
47
-------
A total of 9 to 15 categories (from the 38) were needed to explain 75% of the wet SO4"
deposition at each site during 1982-1985. Rather than try to produce an aggregate estimate
which would be best for the entire domain shown in Figure 1 (which would require up to 15
categories) it was decided to focus on a smaller northeastern region encompassing 13 sites.
(The eastern sites have been identified in Figure 1 and Table 2). With the domain restricted to
the eastern sites a total of 9 categories were needed to approach the 75% goal. These 9 cate-
gories represent a minority of the 850 mb wind flow patterns, it is likely that a modest number
of sample events will represent the meteorological processes that affect SO4" wet deposition
across eastern North America. The identified categories also make sense in terms of the phys-
ical processes (i.e. storm tracks) represented.
Table 6 shows that the 9 categories with greatest SO4* deposition (21, 23, 25, 26, 27,
28, 29. 30 and 31) accounted for about 72% of the total SO4" deposition within the 13 site
region. The curve in Figure 10 indicates the total amount of deposition accounted for as the
number of categories selected increased. To explain more and more of the deposition one needed
more categories at an increasing rate. The cost-to-benefit ratio changed drastically with more
stringent requirements. As a first approximation the nine categories mentioned above were
selected to be represented in the aggregation scheme.
While the 9 major categories account for the bulk of SO4* wet deposition, concern has
arisen that some important physical processes may be excluded by the 9-category selection
criterion. Many of the important categories occurred primarily in the warm season. Cold-
weather processes (which may be highly nonlinear in terms of source-receptor relationships)
might be neglected by a selection process limited to the major categories. In addition, it was
decided to include a number of 3-day events on which very little precipitation fell in the final
ensemble. Inclusion of "dry" events enable the final analysis to include dry deposition
processes.
It was decided that due to external constraints (RADM modeling budget) no more than 30
cases could be included in the final ensemble. It was also decided that from the nine important
categories up to 20 cases would be selected to reproduce the annual SO4* deposition pattern and
that the other ten cases would be selected to account for other, less prevalent conditions (winter
precipitation, unusual meteorology and dry deposition). The final mix of episodes included (1)
an array of episodes chosen from categories that account for the bulk of the annual SO4* wet
deposition, and (2) an array of episodes chosen to represent potentially neglected processes:
cold season events and events with little rainfall that may contribute significant dry deposition.
48
-------
C
U F
M R
U E
L Q
A U
T E
I N
V C
E Y
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
£? ?4f '•'•'V;i
.•
'»i^?^**?~~~~ ~
0
6 9 12 15 18 21 24 27 30
NUMBER OF CATEGORIES
36
Figure 11. Cumulative explanation of total SO4' deposition using categories ranked from
greatest to least deposition.
To determine how the cases should be partitioned among the 9 important categories the
percent of total wet SO4' deposition figures shown in Table 6 were considered. Relative t
percent contribution, a proportionate number of samples were selected from each of the cate-
gories. The frequency of occurrence was also taken into consideration. Constrained
numbers, the following breakdown of category sampling was subjectively determined: four cases
were to be selected from category 29, three from 27, 26 and 31, two from 25 and one fi
23, 28 and 30. Initial tests of the aggregation procedure were then based upon <
selected at random but according to the predetermined sampling criteria.
49
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TABLE 6. PERCENT OF TOTAL SULFATE WET DEPOSITION BY METEOROLOGICAL CATEGORY
Category
1
2
3
4
5
6
7
8
9
10
1 1
12
13
14
15
16
17
18
1 9
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Total Dep.
1,927
2,459
474
1,042
2,112
5,638
3,683
4,189
4,570
3,857
4,474
2,854
796
517
970
789
1,582
1,060
2.459
11,953
3.347
8.683
4.381
27.497
28.978
32.625
11,725
33.151
9,108
29.049
5.134
254
5,252
1,046
2.467
4.212
917
2,072
% of Tot.
0.72
0.92
0.18
0.39
0.79
2.11
1.38
1.57
i.n
1.44
1.67
1.07
0.3
0.19
0.36
0.3
0.59
0.4
0.92
4.47
1.25
3.25
1.64
10.29
10.84
12.21
4.39
12.4
3.41
10.87
1 .92
0.09
1.96
0.39
0.92
1.58
0.34
0.78
50
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Eleven additional cases were selected to represent winter situations, which were
hypothesized to have a higher proportion of non-linear effects, and dry deposition events. The
basic criteria for sampling these days were as follows: five relatively wet winter cases and six
dry cases (the sum of the 13 site S04" deposition was less than 50 mgm"2) from previously
unsampled categories. Given the number of categories from which these cases could be selected
it was impossible to sample proportionately, therefore, other criteria were developed. The
winter cases were selected from the "wet" (21-39) categories that displayed a relatively high
frequency in winter. This reduced the number of possible categories to 22, 24, 32, 34 and 37.
From this set the five cases were selected randomly, one from each. For the dry situation the
first step in determining the set of categories from which to sample was to consider only the
"dry" half (categories 1-19) . The second step was to isolate the more frequently occurring
categories which also had a low mean deposition. Categories 6, 7, 8, 9, 11, and 19 met these
requirements. From this subset, six cases, each with a total 13-site wet SO4" deposition of less
than 50 mgm"2 were randomly selected, one from each category.
3.1.3. Statistical correlations between category and observed deposition
As described above, wet deposition amounts received at various sites show considerable
variation for events among different categories. In this section results of statistical tests of the
correlation between deposition, precipitation and meteorological category wilt be presented.
3.1.3.1. Three alternative statistical approaches—
The statistical analysis will be based on the techniques of regression discussed above
which use correlations between wet deposition and (a) observed precipitation, (b) time of year,
and (c) meteorological category. Of the various factors which may be correlated with deposition
precipitation is apparently the most important, and results of regression analyses may depend
critically on the treatment of the relationship between deposition and precipitation. Three
alternative correlations between wet deposition and precipitation were investigated. These
were:
• Linear correlation between wet deposition and precipitation (Equation 19);
Logarithmic correlation between wet deposition and precipitation (Equation 23);
and
A hybrid model correlating deposition with precipitation raised to an exponential
power (Equation 24).
Each of the three approaches has advantages. A straight linear regression has the advan-
tage of simplicity; and the regression parameters ( 8cat(j) in Equation 19) simply correspond
to the summed deposition-to-precipitation ratio observed during events associated with each
meteorological category. The logarithmic model, by contrast, has the advantage of accounting for
51
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the observed tendency for SO4" and N03" concentrations to decrease during high-precipitation
events (Stein and Niu, 1988). This tendency is especially important at coastal sites. However
the logarithmic model has two serious disadvantages. First, evaluations of the accuracy of the
logarithmic regression are based on the estimate for log Dj rather than for Dj itself.
Consequently the logarithmic regression gives undue weight to events with very small amounts
of deposition, which have negligible impact on total wet deposition received at a site. On low-
deposition days, the recorded discrepancy between observed deposition and deposition estimated
from a linear regression model (Dj • 6j ) will be small, but the discrepancy recorded with a
logarithmic model ( log Dj - tog 6j ) can be very large. For this reason we evaluated the
accuracy of logarithmic models based on the linear coefficient of variation (i.e. ability to
predict D\ , Equation 22) rather than based on its ability to predict log Dj.
A second disadvantage of the logarithmic model is that it requires twice as many regres-
sion variables as does a simple linear regression. Referring to Equation 23, if both regression
variables ( |J and 7 ) are allowed to be category-dependent, then the regression will contain 40
independent variables. Accounting for other factors (i.e. time of year) increases the number of
independent variables still further. It becomes increasingly difficult to demonstrate statistical
significance when a model contains such a large number of independent variables.
The combined model represents an attempt to preserve the advantages of linear as opposed
. to logarithmic models while at the same time allowing for the tendency for SO4* and NO3"
concentrations to decrease during events with heavier precipitation. The combined'model is
based on a linear regression of wet deposition with P\ ^ as the independent variable. The
exponent Y is derived from logarithmic regression (Equation 23). The exponent is allowed tc
vary for different sites and for different species (i.e. S04* and N03") but is not allowed to vary
with time of year or meteorological category. Category-based variations in deposition are
represented by allowing the linear regression parameter (Pcat(i) 'n Equation 24) to vary with
category.
3.1.3.2. Time-of-Year Term —
Wet deposition rates for both S04" and NO3" both show strong seasonal dependence, with
SO4* wet deposition rates typically much larger in summer than in winter and NO3" showing the
opposite trend. It has been suggested by Stein (1989) that the observed correlation between
wet deposition and meteorological category might result from the seasonal nature of wet depo-
sition rather than from intrinsic differences in circulation pattern among the categories. In
order to avoid this error we have introduced time of year as an additional independent parameter
in the regression analysis. Inclusion of the time-of-year term serves two purposes: (a) it
improves the overall performance of the regression model by including season-based variations
in wet deposition within the model; and (b) it insures that correlations between observed wet
deposition and meteorological category are not just a reflection of seasonal variations.
Season-based variability is represented in the combined regression model by adding a
52
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time-of-year term Yj with a value based on the day of the year:
Yj = Sin [2ji(ND-81)/365] (26)
where NQ is the Julian day. ranging from 1 (January 1) to 365 (December 31). The time-of-
year term is incorporated in the linear and log-linear regression models by replacing the
regression equation (24) with the following:
fy - P
where 8 is a regression parameter which is regarded as independent of meteorological category.
The combined regression model (27) was developed in stages, with P0 (without category-
dependence) and 5 initially determined from a linear regression independent of meteorological
category. After 5 was determined, a second linear regression was performed to identify the
category specific parameters Pcat(i)- Tne second linear regression used the following equation
&i - PC.KI, d+*Y,)P,* (28)
with the category-specific 3^) parameters determined by regression of D{ against the linear
variable (I+SY,)?^ (with 7 and 8 fixed). This indirect method was chosen to minimize the
number of independent regression parameters and to insure that the the parameters represent
physically meaningful quantities. In the completed model (Equation 28) there were 22 inde-
pendent variables, including 20 category-specific 3^ parameters plus y and 8.
3.1 .3.3. Results —
The linear, logarithmic and combined models were evaluated as predictors of SO4= and
N03" wet deposition based on linear coefficient of determination (R2) (Equation 22). Note that
R2 associated with the logarithmic model is the linear R2 and tests model ability to predict
deposition, rather than log D. Results (Table 7) show that the logarithmic model achieves
significantly higher R2 values than the linear model for some sites, especially coastal sites (e.g.
Turners Falls, MA) where the correlation between deposition and precipitation was weak.
However the linear model achieves higher R2 than the logarithmic model at other locations (e.g.
Longwoods, ON).
The combined model appears to represent a significant improvement over both the linear
and logarithmic models. The R2 coefficients were higher for the combined model than for the
logarithmic model at all sites, despite the fact that the combined model had fewer independent
parameters (21 as opposed to 40 in the logarithmic model). The combined model also had
higher R2 than the linear model at most sites. However there were some sites where the linear
model achieved an R2 equal to or greater than the combined model.
Incorporating the time-of-year parameter in the combined model resulted in a significant
improvement at many sites, with reductions in the residual uncertainty (1-R2) of up to 25%.
53
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At other sites incorporation of the time-of-year term had negligible effect. The time-of-year
term had greatest impact at locations with severe winters (e.g. Underbill, VT) and at coastal
sites, and little impact at southern or western sites.
Incorporation of category-dependent P parameters (as opposed to a single po independent
of meteorological category) caused an improvement in R2 in all models. Inclusion of category
dependence caused an improvement even when the seasonal dependence of wet deposition had
already been included in the regression. Residual uncertainty was reduced by -10% when
category-dependent P's were included (e.g. R2 increases from 0.55 to 0.59 at Tunkhannock,
PA).
Statistical significance of category-dependent parameters Pcat(i) was evaluated by the
heterogeneity-of-slopes statistic Fs (Equation 20). As described earlier the null hypothesis
that meteorological category had no impact on deposition (31 - {J2 " •••• P] for all categories)
was rejected with 95% confidence whenever f| assumed a value of 1.66 or greater.
Also shown in Table 7A. the F, statistic demonstrated that category-based variations in
SO4" wet deposition were significant at the 95% confidence interval or higher at all sites
included in this study. For NO3" (Table 7B) wet deposition category-based variations were
significant at 18 of the 20 sites. The remaining two sites, Selma, AL and Brookings, SD, were
both at the geographic edge of the region represented by the meteorological categorization
scheme.
Coefficients of determination (R2) in the combined model were greater than 0.5 at most
sites, indicating that the regression was able to account for 50% or more of the variation in
observed wet deposition. The R2 coefficient was significantly lower at coastal sites (Turners
Fails, MA and Winterport. ME) and at sites that may be influenced by a nearby emission source
(i.e. Fort Wayne, IN., downwind of Chicago). At these sites significant variations in wet depo-
sition rates could have been caused by small changes in local wind direction.
54
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TABLE 7 A. LINEAR R2 COEFFICIENTS ARE SHOWN FOR THE LINEAR REGRESSION MODEL FOR
SO4" WET DEPOSITION (USING EQUATION 19), LOGARITHMIC MODEL (EQUATION 23),
COMBINED MODEL (EQUATION 24) AND COMBINED MODEL WITH TIME-OF-YEAR TERM
INCLUDED (EQUATION 29). ALL MODELS HAVE p PARAMETERS VARYING WITH
METEOROLOGICAL CATEGORY. THE HETEROGENEITY-OF-SLOPES STATISTIC FS (EQUATION 20)
IS SHOWN FOR THE COMBINED MODEL (TIME OF YEAR TERM INCLUDED).
R2 (Sulfate
SFTE
Turners Falls, MA
Tunkhannock, PA
Zanesville, OH
Rockport, IN
Giles County, TN
Fort Wayne, IN
Raleigh, NC
Gaylord, Ml
Alamo, TN
Winterpdft, ME
Uvalda, GE
Selma, AL
Clinton, MS
Marshall, TX
Lancaster, KN
Brookings, SD
Underhill ,VT
Big Moose, NY
Dorset, ON
Longwoods, ON
LINEAR
.144
.357
.731
.631
.575
.305
.656
.609
.406
.347
.266
.379
.365
.591
.727
.726
.436
.418
.576
.780
103
.236
.438
.682
.599
.574
.313
.619
.547
.373
.384.
.214
.430
.403
.651
.707
.731
.396
.358
.516
.678
Wet Deposition)
COMBINED
.273
.448
.730
.641
.590
.373
.658
.605
.463
.422
.276
.467
.432
.666
.731
.744
.455
.428
.573
.746
COMBINED +
TIME-OF-YEAR
.373
.591
.790
.719
.653
.433
.683
.662
.542
.481
.355
.510
.471
.682
.735
.745
.603
.617
.639
.822
FS
2.33
3.29
7.62
4.96
1.91
1.78
2.95
5.08
3.32
5.79
1.97-
4.42
2.77
1.81
3.80
1.81
8.18
5.63
3.79
4.16
55
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TABLE 78. UNEAR R2 COEFFICIENTS ARE SHOWN FOR THE LINEAR REGRESSION MODEL FOR
NOa'WET DEPOSITION (USING EQUATION 19), LOGARITHMIC MODEL (EQUATION 23),
COMBINED MODEL (EQUATION 24) AND COMBINED MODEL WITH TIME-OF-YEAR TERM
INCLUDED (EQUATION 29). ALL MODELS HAVE p PARAMETERS VARYING WITH
METEOROLOGICAL CATEGORY. THE HETEROGENE1TY-OF-SLOPES STATISTIC FS (EQUATION 20)
IS SHOWN FOR THE COMBINED MODEL (TIME OF YEAR TERM INCLUDED).
R2 (Nitrate
SITE
Turners Falls, MA
Tunkhannock, PA
Zanesville, OH
Rockport. IN
Giles County, TN
Fort Wayne, IN
Raleigh, NC
Gaylord, Ml
Alamo, TN
Winterport, ME
Uvalda,GE
Selma, AL
Clinton. MS
Marshall. TX
Lancaster, KN
Brookings, SD
Underhill ,VT
Big Moose, NY
Dorset, ON
Longwoods,ON
LINEAR
.064
.282
.623
.498
.480
.283
.496
.591
.340
.024
.360
.366
.444
.524
.752
.709
.401
.329
.426
.666
IDG
.184
.479
.613
.516
.555
.349
.496
.535
.462
.170
.336
.478
.507
.577
.737
.716
.439
.384
.419
.568
Wet Deposition)
COMBINED
.223
.490
.667
.552
.577
.404
.532
.599
.524
.226
.389
.507
.538
.597
.755
.740
.472
.425
.458
.629
COMBINED +
TIME-OF-YEAR
.284
.547
.695
.644
.589
.488
.532
.631
.612
.286
.504
.647
.667
.682
.780
.751
.498
.433
.464
.670
FS
1.60
2.37
5.36
4.89
2.34
1 .92
2.32
4.26
2. S3
6.62
1 .98
1 .04
1 .74
3.68
3.05
1.30
7.05
5.24
1.96
6.99
56
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s
T
A
T
I
S
T
I
C
6 T
4 --
-4 •-
-6 -L
sulfate D nitrate
Mass.
Vt. N.Y. Ohio Ind.
RANKED SIMULATION NUMBER
Term.
Figure 12. T8 statistic for meteorological category 6, indicating deposition-to-precipitation
ratios significantly above or below the norm at each site. Values of greater than
1.66 or less than -1.66 are statistically significant at the 95% confidence level
Although much of the remaining variations in S04" and NO3" wet deposition may be due to
random fluctuations, there was evidence that at least some of the remaining variations may be
related to meteorological factors other than the ones represented here. Evidence of further
meteorological influence was based on the high correlation (correlation coefficients >0.8)
between residual errors in S04* wet deposition at a given site and residual errors in NO3" at a
given site. In other words, when the amount of SO4" wet deposition observed during a specific
event exceeded the amount predicted from the statistical correlation, there was a high proba-
bility that the amount of NO3' also exceeded the predicted value. Residual errors in S04= wet
deposition at each site were also positively correlated with residual errors at neighboring sites
(especially in the Northeast), though there was little correlation with errors at distant sites.
These two results suggest that the residual errors were associated with aspects of atmospheric
circulation that were not adequately represented in the categorization.
57
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Mass.
VL , N.Y. Ohio Ind.
RANKED SIMULATION NUMBER
Tenn.
Figure 13. T, statistic for meteorological category 7, indicating deposition-to-precipitation
ratios significantly above or below the norm at each site. Values of greater than
1.66 or less than -1.66 are statistically significant at the 95% confidence level.
Additional insights regarding deposition rates for various categories was obtained by
comparing the statistical significance of deposition associated with individual meteorological
categories with the norm at individual sites. As described earlier, the statistic Ts (Equation
21) indicated when deposition associated with individual categories was significantly greater or
less than the norm. Some values for the Ts statistic are shown in Figures 12 through 14, based
on the initial linear model (Equation 19).
58
-------
Mass.
Vt. N.Y. Ohio Ind.
RANKED SIMULATION NUMBER
Tenn.
Figure 14. T8 statistic for meteorological category 11, indicating deposition-to-precipita-
tion ratios significantly above or below the norm at each site. Values of greater
than 1.66 or less than -1.66 are statistically significant at the 95% confidence
level.
Results showed that some categories (most notably the stagnant "waffling high", category
7) had significantly greater than average deposition at sites across the eastern and midwestern
U.S., while categories marked by more vigorous circulation (e.g. category 11) have deposition
below average. An interesting result was obtained for category 6, where deposition was signifi-
cantly greater than average at northeastern sites and significantly below average at sites in the
Ohio valley. Category 6 was characterized by a cold front in the eastern U.S. with a weak
southwesterly flow ahead of the front and more vigorous winds behind it.
Figures 12 through 14 also illustrate the similarity in patterns of wet deposition of S04=
and of NO3". Greater than average wet deposition of N03" was consistently associated with above
59
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average wet deposition of SO4", despite the fact that SO4* and NO3' show different seasonal
variations.
3.1.4. Interannual Variability
The use of either analytical or statistical techniques to predict future wet deposition rates
raises questions concerning year-to-year variations in both deposition and meteorology that
might affect the accuracy of predictions. Wet deposition can vary from year to year due to
meteorological fluctuations, including changes in precipitation and changes in atmospheric
circulation patterns; and some of this variance may be the result of long-term climatic changes.
Although the meteorological categorization and aggregation methods presented here have been
based on observations made during the period from 1982 to 1985, we believe the same tech-
niques may be applied to future years.
This section will present a summary of interannual variations in S04" and N03" wet
deposition observed during the period 1982-1987, and to possible correlation with meteor-
ology. The extent of interannual variations will be used to establish the level of uncertainty in
aggregated estimates for annual deposition. This uncertainty may be reduced somewhat if year-
to-year variations are correlated with observable (and possibly predictable) changes in
meteorology. Special attention will be given to results from the years 1986 and 1987, which
were "not included in the development of the meteorological categorization and aggregation
techniques.
Figures 15 through 17 show annual fluctuations in wet SO4* wet deposition, precipitation
and SO4* wet deposition-to-precipitation ratios at three sites: Turners Falls, MA, Zanesville,
OH and Giles County. TN; respectively. Two features of these fluctuations are worth comment.
First, the year-by-year pattern shows significant differences among the three sites. Annual
fluctuations in both wet deposition and precipitation are much greater at the Massachusetts site
than in the two inland locations, possibly reflecting the importance of local meteorology at -a
location closer to the ocean. Year-to-year fluctuations in wet deposition average -25% at the
Massachusetts site, while at the Ohio site an annual variation of ~12% was more common.
These differences are significant at a 95% confidence level. Second, the year 1987 stands out as
a year of unusually low SO4* wet deposition, although this may be partly due to errors in the
data.2 At the Massachusetts and Tennessee sites observed S04* wet deposition rates are 30%-
40% lower during 1987 than for the 1982-1985 period. Precipitation at these sites during
1987 is only slightly below normal, and the observed deposition-to-precipitation ratios are
much tower than in the preceding years. This pattern of lower SO4* wet deposition during 1987
was repeated at most other sites included in this study, with S04" wet deposition in 1987 aver-
2The sulfate wet deposition data for the second half of 1987 has recently been identified as in
error due to problems in data analysis.
60
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aging 24% lower than the 1982-1985 rates.
Nitrate deposition during 1987 is much closer to the 1982-1985 norm, although
reduced NO3" was observed at the Massachusetts and Tennessee sites. At the Ohio site observed
SO4" wet deposition during 1987 close to the 1982-1985 average, and NO3" wet deposition is
actually higher than average. The S04" wet deposition-to-precipitation ratio is also above
average at the Ohio site. Statistical correlations (described below and in Section 3.1.3) show
that nitrate deposition during 1987 was significantly below the 1982-1985 average at 5 of the
11 sites in the northeastern and midwestern United States,3 and significantly above the 1982-
1985 average at just one site.
The extent of year-to-year variations can be estimated by calculating the root-mean-
square difference (RMSD) between wet deposition during individual years at each site and the
6-year average deposition at the site, i.e.
- V f
i l°H ' DJO'l2 (29)
where DJJ is the observed deposition at site j for the iln year and DJQ is the observed 1982-
1985 average deposition at site j. This interannual RMSD is equivalent to the RMSD used to
evaluate aggregate estimates (Equation 9) and will be used both as a measure of inherent
uncertainty and as a standard for evaluating aggregate estimates. The interannual RMSD for
sulfate wet deposition at the 13 northeastern sites, expressed as a percentage of average annual
deposition, is 0.13. For nitrate the interannual RMSD is 0.14.
Reasons for the observed year-to-year variations in wet deposition are not immediately
apparent. There is some evidence that year-to-year variations may be due to local meteoro-
logical fluctuations, since there is a strong correlation between variations in sulfate deposition
and variations in nitrate at each site. The tendency for sulfate and nitrate to exhibit similar
variations is apparent in Figures 15-17. There is little correlation between sulfate and nitrate
deposition at different locations, as would be expected if year-to-year variations were due to
changed emission rates. However there is also little correlation between observed annual
deposition and meteorological parameters (i.e. precipitation) -or circulation patterns. Although
there is some correlation between wet deposition to precipitation, the ratio of annual sulfate wet
deposition to annual precipitation, also shown in Figures 15-17, varies significantly from one
year to the next.
Figure 2 identifies 13 northeastern and midwestern sites. The multi-site analysis in this
section is based on 11 sites rather than 13, since data for 1986 and 1987 were missing for
the two Canadian sites.
61
-------
3000 -•
2500 -•
2000 ••
•
1500 -
1000 -.
500 ••
0--
SULFATE (mg/m2)
1982
1983
1984
1985
1986
1987
NITRATE (mg/m2)
1982
1983
• 984
-955
1986
1987
25 j
20 -:
15 -.
10 -•
5-
0--
SO4 Wet Deposition/Precipitation Ratio
1983
1984
1985
1986
1987
PRECIPITATION (cm)
'9E2
1983
1984
•953
1986
1987
Figure 15. Year-by-year variation in SO4" wet deposition, NO3" wet deposition, SO4" wet
deposition-to-precipitation ratio, and precipitation at Turners Falls,
Massachusetts. Dashed lines represent predicted annual deposition based on
meteorology and 1982-1985 statistical correlation.
62
-------
2000 -•
1500 -•
1000 -•
500 -•
0
SULFATE (mg/m2)
1982
1983
1984
1985
1986
1987
1982
1983
1984
1985
1986
1987
35-j-
30
25 -•
20 -•
15 ••
10 ••
5-
1982
150 j
120 ••
SO4 Wet Deposition/Precipitation Ratio
1983
1984
1985
1986
1987
PRECIPITATION (cm)
Figure 16.
1982 1983 1984 1985 1986 1987
Year-by-year variation in S04* wet deposition, NO3' wet deposition, S04= wet
deposition-to-precipitation ratio, and precipitation at Zanesville, Ohio. Dashed
lines represent predicted annual deposition based on meteorology and 1982-
1985 statistical correlation.
63
-------
1982
1983
1984
1985
1986
1987
2000
1500
1000 ••
500 ••
NITRATE (mg/m2)
1982
1983
1984
1985
1986
1987
35 T
30
25 ••
20 ••
15 -•
10 -
5--
SO4 Wet Deposition/Precipitation Ratio
1982
1983
1984
1985
1986
1987
150
120
PRECIPITATION (cm)
1983
1984
1985
1986
1987
Figure 17. Year-by-year variation in S04* wet deposition, NO3" wet deposition, SO4= wet
deposition-to-precipitation ratio, and precipitation at Giles County, Tennessee.
Dashed lines represent predicted annual deposition based on meteorology and
1982-1985 statistical correlation.
64
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Year-to-year changes in atmospheric circulation can be represented by the frequency of
occurrence of events corresponding to specific meteorological categories. Changes in frequency
of occurrence have been plotted in Figure 18 for three of the most frequently occurring cate-
gories ("weak trof-southerly flow", category 6; "waffling high", category 7; and "northeast
cold front", category 11). Variations in category frequency are approximately the same order
of magnitude as year-to-year variations in precipitation or wet deposition (-10%). JBince
these categories can be associated with high or low wet deposition (see Figures 7 and 12-14), it
is possible that these variations may explain some of the interannual variability.
0.2 y
0.15--
0.1 •:.
0.05--
CATEGORY 6
CATEGORY7
CATEGORY 11
1982
1 983
• 1 984
1985
1986
1987
Figure 18. Year-by-year frequency of occurrence of events of meteorological categories 6,
7 and 11, expressed as a fraction of all events.
The impact of precipitation and meteorology on wet deposition for specific years can be
estimated by applying the statistical model for event-specific correlations (Section 3.1.3) to
the observed annual meteorological patterns. In the model deposition per event is correlated
with precipitation during the event, meteorological category and time of year, derived from
observations during 1982-1985. An estimate for deposition for a specific year can be obtained
by applying the statistical correlation (Equation 28) to the meteorological record (day-to-day
precipitation, time of year and meteorological category) for the year. The resulting estimate
provides an indication for the extent of year-to-year variations that may be induced by mete-
orology. The annual estimate also provides a test of the ability to predict deposition for future
years.
Projected deposition values from this statistical correlation are also shown for 1982
through 1987 at the Massachusetts, Ohio and Tennessee sites in Figures 15 through 17 (dashed
65
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lines). Results of the statistical model project year-to-year variations in wet deposition that
are nearly as large as the observed fluctuations, suggesting that changes in meteorology may be
a plausible explanation for the observed changes. However the predicted deposition values do not
always correspond with the observed annual fluctuations. For 1987 the statistical model
predicts lower than average deposition at the Massachusetts site but not for Tennessee. Tests for
statistical significance at the 95% confidence level (Ts, Equation 21) indicate that sulfate
deposition during 1987 was significantly lower than in previous years, even accounting for
changes in precipitation and meteorological category, at 13 of the 18 sites for which 1987 data
is currently available. Nitrate deposition is significantly lower at 6 of the 18 sites and
significantly higher at one site. There is room for considerable improvement in models to
predict changes in future deposition associated with meteorology:
Figure 19 shows a comparison between observed SO4" wet deposition during 1986 and
1987 and statistically projected deposition (based on 1982-1985 observations) for all sites
included in this study. Results show that observed deposition tends to be lower than projected
values, largely due to the anomalous year 1987. Projected deposition falls within 20% of the
observed values at most locations. Tests for statistical significance show that the use of
meteorological categories in the statistical projections result in an improved ability to predict
variations in deposition that is significant at 95% confidence.
4000
500 1000 1500 2000 2500 3000 3500
OBSERVED SO4 DEPOSITION (rng/m2)
4000
Figure 19. Comparison between annual SO4* wet deposition observed during 1986 and 1987
at 13 northeastern and midwestern sites with projected deposition based on
1982-1985 observations and 1986-1987 meteorology.
66
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3.1.5. Evaluation of the category-based aggregation method
Evaluation of the aggregation technique focused on comparing nineteen-day aggregate
estimates generated using five different event selection and weighting techniques. Using each
technique one-hundred independent ensembles, each containing nineteen three-day periods,
were selected randomly from 1982-1985 observations. In the first scenario events.were
taken from the entire pool of observations and no additional knowledge was used in estimating
annual depositions. In each of the other cases more information was employed. For the second
and third scenarios events were again selected with no guidance, but aggregation was performed
with prior knowledge of the amount of precipitation that fell lit'each site during each event
(second scenario), and with prior knowledge of both precipitation and the season of each event
(third scenario). In the fourth case events were selected (randomly) with a prescribed
distribution among the seasons, and aggregated using the precipitation and seasonal information.
For the fifth case the events were selected (randomly) from prescribed categories and annual
depositions were estimated with the aid of category-based scaling (as outlined above).
Aggregation was based on equations (7) and (8) with the prior estimate for deposition (D,)
reflecting the extent of prior knowledge available for each case.
The results of the various scenarios are compared in Figure 20. Evaluation was based on
the root mean-square difference (RMSD, Equation 9) between estimated mean annuaj deposition
and actual annual deposition for the 13 sites in the northeast and midwest. The RMSD was
calculated for each ensemble. Figure 20 shows that the deviations between estimated and
observed deposition are smallest for the ensembles chosen by meteorological category and that
the inclusion of more and more information sequentially improved upon the ensembles selected
at random. Table 8 clarifies the meanings of the labels shown in the legend of Figure 20. For
the category-based selection the average deviation is 18% of the observed deposition (2380
mg m-2 over the 13 sites), while the smallest deviation for a 19-day ensemble is 11%. Note
that the 11% deviation is lower than the RMSD for year-to-year variations in wet deposition
(Equation 27). Deviation for ensembles selected at random are significantly higher than for
ensembles selected by category. Use of observed precipitation in deriving aggregate estimates
results in greatly improved accuracy. Aggregation based on random selection, even accounting
for precipitation and season, results in an average deviation of 22% (2850 mg nr2 over the 13
sites). Category-based selection reduces this deviation by approximately 25%.
67
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— R — R-P — R-P.S
S-P.S — C-P.C
100%
N
40% --
N 30% --
20% --
10%
0%
10 20 30 40 50 60 70 80 90 100
ENSEMBLE NUMBER
Figure 20 Comparison of selection and aggregation techniques for S04 wet deposition in
northeastern North America.using nineteen three-day episodes. The selection
techniques include ensembles of events selected purely at random (R), selected
with prescribed seasonal distribution (S) and selection with prescribed
distribution among meteorological categories (C). Aggregation techniques are
based on no prior knowledge of meteorology (R); prior knowledge of precipita-
tion for each event (-P); prior knowledge of both event precipitation and season
(-P.S); and prior knowledge of event precipitation and meteorological category
(-P.C).
68
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3.2. SELECTION OF CASES
A total of 30 three-day events have been chosen for initial use in the analysis of annual
deposition. Selection was based on methods outlined in Section 2.1.5 with selection from
meteorological categories described in Section 3.1.2. Events were selected from prescribed
meteorological categories (including the split into 'wet* and 'dry' subcategories), with the
categories chosen (1) to represent meteorological processes that account for the majority of
total wet deposition in eastern North America, and (2) to include important meteorological
patterns (i.e. cold-season events and events with little precipitation for evaluation of dry
deposition) that might be overlooked in the first criteria. Selection of categories was based on
the following specifications:
(a) 19 3-day events were selected from the 9 'wet1 categories which accounted for the
greatest deposition on an annual basis. We estimate that these 9 categories account
for about 75% of total annual S04" wet deposition at all northeastern and
midwestern sites (see Section 3.1.2).
(b) 6 additional 3-day events were selected to represent dry deposition processes.
These days were selected according to the criteria that the sum of 3-day SO4~ wet
deposition "for the 13 northeastern and midwestern sites total less than 50 mgrrf .
(c) 5 additional 3-day events were selected from among winter days (November-
March). These days were selected from categories that were not included in trie
original 19 and which may offer unusual conditions.
Choice of the individual events was designed to meet two additional criteria: random
selection to avoid the introduction of bias, and accurate representation of deposition at the 13
northeastern and midwestern sites. Evaluation of the accuracy of individual ensembles was
based on the aggregated estimates for annual deposition at each site derived from observed
deposition on the sample days. Selection was made according to the following procedure:
(1 ) The distribution of sample 3-day events among the 38 categories was prescribed
based on deposition attributable to each category and frequency of occurrence (see
Section 3.1.2).
(2) Once the distribution among categories was selected, ensembles of specific 3-day
events were generated. Each ensemble consists of a series of 3-day events with the
pre-determined distribution among the 38 categories. The individual 3-day events
representing each category were selected at random.
69
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(3) For each ensemble of specific events, estimated annual deposition was determined
for the 13 northeastern and midwestem sites, based on observed deposition during
the ensemble events and the category-based scaling factors. The standard deviation
between estimated and actual annual deposition for the 13 sites was determined for
the ensemble.
(4) Ensembles were ranked based on standard deviation, and the best ensemble (with
the lowest standard deviation among 20 randomly generated ensembles) was
selected as the representative ensemble.
The selection was performed in two stages. Initially, the ensemble of 19 3-day events
representing categories with the highest deposition was selected. The additional 11 events
representing dry and winter deposition were selected after the initial ensemble had been
selected. In the selection of the additional 11 events, estimated annual deposition was based on
an ensemble of 30 events, with the prior 19 added to the randomly selected 11 events. The 11
additional events were chosen to provide the most accurate 30-event ensemble. The selected 3-
day periods are shown in Table 8.
3.3. COMPARISON OF AGGREGATE ESTIMATE WITH LONG-TERM DEPOSITION
»
This section explores the accuracy of aggregate estimates derived based on the selected
30-event ensemble (Table 8). Aggregate estimates have been derived for sulfate and nitrate
wet deposition and for total acidity, including estimates for annual and seasonal totals. The
estimates are based on the observed wet deposition for the selected events, and on observed
correlations between wet deposition, precipitation and meteorological category fcr the 1S82-
1S85 period (Equations 7 and 8). Aggregate estimates are compared with observed wet depo-
sition during 1982-1985.
3.3.1. Annual Wet Deposition
The following section examines the ability of the aggregation technique to reproduce
annual deposition patterns for SO4", NO3* and H* wet deposition. The observed deposition values
for the 30 episodes recommended for RADM simulation were used to estimate deposition using
the proposed aggregation technique. From these results, plots of deposition across eastern
North America were drawn. Estimates of annual and seasonal SO4=, NO3" and H* depositions
were determined for all 22 sites. The spatial patterns were analyzed for agreement with the
actual mean deposition patterns for 1982-1985.
70
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TABLE 8. DATES OF THREE-DAY EPISODES (MIDDLE DAY) SELECTED FOR AGGREGATION.
CATEGORY NUMBERS CORRESPOND TO DESCRIPTIONS GIVEN IN TABLE 4, WITH W AND 'D'
REFERRING TO THE SPLIT INTO -WET" AND "DRY" SUBCATEGORIES BASED ON SULFATE
DEPOSITION (SECTION 3.1.2)
Date
Category
Date
Category
(Original 19 Periods)
April 15-17, 1982 1W
November 2-4, 1985 3W
May 29-31, 1983 5W
March 17-19, 1984 5W
July 12-14, 1985 6W
September 9-11, 1983 6W
October 11-13, 1985 6W
November 1-3, 1983 7W
August 21-23, 1984 7W
June 12-14, 1984 7W
(6 dry periods)
April 28-30. 1985 19D
June 10-12, 1983 8D
July 17-19, 1985 9D
(5 winter periods)
February 1-3, 1982 2W
December 9-11, 1983 4W
November 4-6, 1985 12W
May 2-4, 1985,
September 9-11,
September 14-16,
September 25-27,
August 4-6, 1983
November 16-18,
September 26-28,
July 16-18, 1984
December 15-17,
December 16-18, 1985
May 14-16, 1982
November 6-8, 1982
February 9-11, 1985
November 13-15, 1983
1984
1983
1982
1985
1985
I
1982
8W
9W
9W
9W
9W
10W
11W
11W
11W
11D
7D
6D
14W
17W
3.3.1.1. Sulfate Wet Deposition-
Figures 21 a and b show the observed annual SO4* deposition over the four-year period
(1982-1985) and the estimated annual deposition based on the 30-day ensemble. In general,
the estimated depositions and spatial patterns agreed well with observed values within the thir-
teen site domain. The annual and seasonal results are summarized Table 9. The mean RMS
errors shown were determined across all sites and are expressed as a percent of the average
deposition across all of these sites. Annual aggregate estimates were within 18% of the actual
deposition at the thirteen northeastern sites and within 10% at eight of those sites. The mean
RMS error as a percent of the average 22 site SO4* wet deposition was 13%.
71
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Figure 2la
The mean annual SO4" wet deposition pattern across eastern North
America during 1982-1985. (Units-mgm'2). Contours correspond to
1000, 2000 and 3000 mgm"2.
72
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Figure 21b
The aggregate estimate of the mean annual SO4= wet deposition pattern
across eastern North America for 1982-1985. {Units=mgm~2).
Contours correspond to 1000, 2000 and 3000 mgm .
73
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TABLE 9. ROOT-MEAN-SQUARE DEVIATIONS BETWEEN ESTIMATED AND OBSERVED SUMMER,
WINTER AND ANNUAL WET DEPOSITION AT 13 EASTERN SITES.
DEVIATIONS ARE EXPRESSED AS A PERCENTAGE OF OBSERVED DEPOSITION
Species
Sulfate
Nitrate
Hydrogen
Annual
13 %
20 %
17 %
Summer
20 %
27 %
24 %
Winter
20 %
35 %
21 %
It can be seen that throughout eastern North America there was no clear tendency for
under or over estimation of SO4* deposition. However there was a tendency to overpredict or
underpredict deposition in certain regions. Overprediction occurred at sites in the southern
Midwest ( Kentucky, Ohio, Indiana). Underprediction tended to occur at northerly sites
(Wisconsin, Michigan, Ontario, New York, Vermont, Maine and Massachusetts). Underesti-
mation also occurred North Carolina, Tennessee and Georgia, while predictions at the
Mississippi and Alabama sites tended to be high. Summer and winter depositions showed similar
tendencies towards over- and underprediction in specific geographic regions. This apparent
regional consistency in over- and underestimation was not considered unrealistic. On the
regionaJ scale .the spatial variation in precipitation amounts due to a typical weather system
varies significantly. Since only a small number of episodes are used in the aggregation, the
spatial variation associated with them is carried over to the long-term pattern.
3.3.1.2. Nitrate Wet Deposition—
On an annual basis the actual and estimated plots of NO3" wet deposition are shown in
Figures 22a and b. The mean RMS error as a percent of the average 22 site annual deposition
was 20%. The RMS error is greater for nitrate wet deposition than for sulfate. possibly
reflecting the greater importance of local meteorology in controlling the rate of nitrate depo-
sition. Also in contrast to sulfate. the RMS error for nitrate was significantly larger than the
year-to-year RMS deviation (13%, from Section 3.1.4)
Qualitatively the spatial variations of sulfate and nitrate wet deposition were quite
similar. The area of greatest deposition extended from the Midwest to the Northeast. While
depositions were least in the northwest and south. As with S04", whether or not the NO3' wet
deposition at a given location was over and underestimated depended its geographical location. In
the lower Midwest the estimates were somewhat overpredicted in northern Indiana and Ohio.
Similar to SO4", the deposition estimates were generally low from southern Ontario and
northern Pennsylvania to the Northeast coast. The largest deviation was at the Tunkhannock. PA
receptor. As with SO4", overall the was no bias towards under- or overprediction. The slight
changes in regional biases between N03" and SO4* wet deposition were likely due to the differ-
ences in the emission patterns and in the differences in their chemistry and rain-out charac-
teristics.
74
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1470
I/'
1390 1620
Figure 22a The mean annual NO3' wet deposition pattern across eastern North America
during 1982-1985. (Units=mgm'2). Contours correspond to 500, 1000,
1500 and 2000 mgm .
75
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r m
859
Nitrate
Figure 22b The aggregate estimate ot the mean annual NO3" wet deposition pattern across
eastern North America for 1982-1985. (Units=mgm~2). Contours correspond
to 500, 1000. 1500 and 2000 mgm'2.
76
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3.3.1.3. Acidity Wet Deposition—
As expected the pattern of annual H* deposition reflected the behavior of N03' and S04=
deposition with maximum values from the lower Midwest across the eastern Great Lakes to the
Northeast United States (Figures 23a and b). The mean RMS error of 17% is between those of
N03' and SO4=. The pattern of significant underestimation in the Northeast, in particular in
northeastern Pennsylvania and upstate New York, was again apparent. Some of the episodes
selected for aggregation were either associated with small amounts or precipitation or low
pollutant concentrations (or both). It is likely that only one or two of the episodes were biasing
the results. With only 30 events, of which only 21 had precipitation at Big Moose for example,
the characteristics of each one will significantly influence the- deposition approximations. It
would be extremely difficult to identify an ensemble of 30 events that did not result in some
bias somewhere in eastern North America.
3.3.2. Seasonal Wet Deposition
3.3.2.1. Sulfate Wet Deposition-
Figures 24 a-d show estimated and observed S04" deposition for the summer and winter
halves of the year. The mean seasonal results were also summarized in Table 9. The errors
resulting from the estimation of wet deposition using the aggregation method were less than
20% for eleven of the sites during summer and nine in winter. •
3.3.2.2. Nitrate Wet Deposition—
The actual and aggregate patterns for wet NO3' deposition in~ the summer and winter are
included in Figures 25 a-d. In the summer the highest values were reported in Ontario. Using
the aggregation routine, the episodes selected predicted large wet deposition values to occur in
that area. The deposition values were somewhat over-predicted in the Midwest excluding
Rockport, IN and were again under-predicted in the Northeast. At the New York, Pennsylvania
and Massachusetts sites the errors were fairly significant but well within 50%. Throughout
the northeastern region the average error was 19%. In winter the errors were worse. The
average over the same region was 25% but at the Michigan, Vermont and Pennsylvania sites it
was close to 50%. In upstate New York and northern Ontario the errors were around 40%. One
reason the estimates were worse in the winter (for S04* and NO3") is that the actual depositions
were generally less. Therefore, a relatively small departure, that made little difference on an
annual basis, appeared significant.
77
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Figure 23a The mean annual wet acidity (H*) deposition pattern across eastern North
America during 1982-1S85. (Units=mgm"2). Contours correspond to 15, 30,
45 and 60 mgm"2.
78
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Figure 23b The aggregate estimate of the mean annual wet acidity (HT) deposition pattern
across eastern North America for 1982-1985. (Units-mgm'*). Contours
correspond to 15, 30, 45 and 60 mgm" .
79
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UA
V*\
u
\
si
^
^•-ft Na«:
^h
cT
k_ /
Figure 24a The mean summer SO4* wet deposition pattern across eastern North America for
1982-1985. Summer corresponds to the period from April through October.
(Units=mgm~2). Contours correspond to 1000 and 2000 mgm' .
80
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X /
/*
y*~-^wi! •"' \ \
\ Jr
v|
s
Sulfate
Figure 24b The aggregate estimate of the mean summer S04* wet deposition pattern across
eastern North America for 1982-1985. (Units=mgrrf2). Contours correspond
to 1000 and 2000 mgrrf2.
51
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Figure 24c The mean winter S04" wet deposition pattern across eastern North America for
1982-1985. Winter corresponds to the period from November through March.
(Units-mgm"2). Contours correspond to 500 and 1000 mgm'2.
82
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Figure 24d The aggregate estimate of the mean winter S04' wet deposition pattern across
eastern North America for 1982-1985. (Units-mgnV''). Contours corresp<
to 500 and 1000 mgm"2.
83
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Figure 25a The mean summer NO3' wet deposition pattern across eastern North America for
1982-1985. Summer corresponds to the period from April through October.
(Units=mgm~2). Contours correspond to 500 and 1000 mgm"2.
84
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\ L
Nitrate
/•s
Figure 25b The aggregate estimate of the mean summer NO3" wet deposition pattern across
eastern North America for 1982-1985. (Units=mgm~2). Contours correspond
to 500 and 1000 mgm'2.
85
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Figure 25c The mean winter NO3" wet deposition pattern across eastern North America for
1982-1985. Winter corresponds to the period from November through March.
(Units-mgm"2). Contours correspond to 500 and 1000 mgm" .
86
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Fiaure 25d The aggregate estimale of the mean winter NO3' wet deposition pattern across
eastern North America for 1982-1985. (Units=mgnY2). Contours correspond
to 500 and 1000 mgm" .
87
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3.3.2.3. Acidity Wet Deposition—
The seasonal estimates of H* wet deposition behaved similarly to those for SO4" wet
deposition. Maps of the actual and aggregate estimates are included in Figures 26 a and b,
respectively. In summer, the errors ranged from 3% to 34% with the average over the
northeastern region being 16%. For the winter estimates the results were similar, ranging
from 0% to 45%. The average was also 16%. For all three analytes the tendencies for over and
underestimation were not as consistent for the seasonal depositions. As well there were a
number of sites where the bias changed direction between seasons. This further highlights the
influence a few slightly atypical episodes had on the aggregate estimates of deposition. In reality
these episodes were not atypical since the long-term patterns that were observed and that the
aggregate estimates were measured against were a compilation of a large set of highly variable
episodes.
3.3.3. Uncertainties In the Aggregate Estimates-
The estimates of seasonal and annual wet deposition were based upon the categories iden-
tified for event selection and their mean frequency of occurrence and deposition to precipitation
ratio. How well the specific 30 events represented the categories they were selected from
ultimately governed the accuracy of the aggregate estimates. Events that had uncharacteristic
pollutant concentrations, relative to their categories, caused the long-term estimates to deviate
from the actual value. This can be inferred from the aggregation equation (see equation 7}. The
actual deposition (^nnual) is scaled by the ratio of the total amount contributed by the events
selected, weighted by category frequency, to the total amount expected due to the categories
chosen, weighted by their frequencies, and assuming the same precipitation amount as the
events selected. Mis-representation of the categories that occurred more frequently and'or had
large mean deposition to precipitation ratios had the greatest effect on the accuracy of the
estimates. Thus, the real goal of the events selected was to match the mean behavior of the
collection of categories chosen to represent the long-term deposition.
The certainty of the estimates basically rests with the relationship between the actual
long-term depositions and the categories represented. Since events were selected from those
categories containing the meteorological situations contributing the largest percentage of the
annual deposition, the relationship should be relatively strong (i.e. Changes in the total annual
deposition should be similar to changes in the total amount deposited by the sample of cate-
gories). Roughly 75 percent of the mean annual 13-site deposition during 1982-1985
occurred with events in the meteorological categories used. On a site by site basis and from year
to year, however, this percentage varied. Values below 50 would imply that the relationship
between the actual long-term depositions and the categories represented was weak. In this case
the certainty of the annual deposition estimates would be diminished. During 1982-1985 this
situation did not arise, the mean percents accounted for varied from 60 to over 80. For three of
the sites (Big Moose, Gaylord and Zanesville) Figure 27 shows the amount of variation in the
88
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Figure 26a The mean summer wet acid (H*) deposition pattern across eastern North America
for 1982-1985. Summer corresponds to the period from April through
October. (Units=mgm"2). Contours correspond to 10, 20, 30 and 40 mgm"2.
89
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Figure 26b The aggregate estimate of the mean summer wet acid (H*) deposition pattern
across eastern North America for 1982-1S85. (Units=mgm~2). Contours
correspond to 10, 20 and 30 mgm"2.
90
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Figure 26c The mean winter wet acid (H*) deposition pattern across eastern North America
for 1982-1985. Winter corresponds to the period from November through
March. (Units=mgm'2). Contours correspond to 10, 20 and 30 mgm' .
91
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Figure 26d The aggregate estimate of the mean winter wet acid (H*) deposition pattern
across eastern North America for 1982-1985. (Units=mgm ) Contours
correspond to 10 and 20 mgm"2.
92
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percentages from year to year. In general, the meteorological situations represented did not
contribute as much during the earlier years as they did in the later years. The reason for the
increase is not certain, however, one possible cause is that during the earlier years there was
more meteorological data missing (850 mb u-v). More of the deposition events were therefore
classified as missing at the expense of the amount of deposition falling within the categories.
Across all sites and all years, however, the percent accounted for rarely dropped below 50_and
consistently stayed above 60.
In Figures 28 and 29 the total amount of wet S04* deposition by year is compared to the
total amount in each year associated with the set of categories represented. Similar temporal
variations between the two would suggest that deposition from the categories is related to the
annual total. Comparisons were made at two Midwestern sites and at two northeastern sites. At
Zanesville, OH the annual amount and the category totals were closely related. At Rockport, IN
the relationship is masked by the greater frequency of missing categories during the earlier
years but the amounts do vary similarly. In the northeast, there is also some agreement with
increases tracking similarly but again the larger number of missing categories during the
earlier period caused some discrepancies. The effect of the missing category frequency has not
been examined in terms of these results to conclusively say it was causing the problem. This
will be studied in the future. The deposition amounts due only to the collection of categories was
correlated with the total annual amounts. Thus, when the specific events (30) properly
portrayed the behavior of the categories they were selected from, the aggregation technique
produced estimates of long-term deposition with a relatively high level of certainty.
82 83 84 85 86 | 82 83 84 85 86 | 82 83 84 85 86
Zanesville, OH
Gaylord, Ml
YEAR
Bigmoose, NY
Figure 27. The spatial and temporal variation in the percent of annual S04* deposition
accounted for by the meteorological categories selected for representation.
93
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c
s
m
D
S?
~
e
OH
S-IN
OH-C B3 S-IN-C
1982
1983
1984
1985
1986
Figure 28. Comparison between the year to year variation in annual SO4" deposition and the
year to year variation in the total amount of deposition accounted for by the
categories selected for representation. S-IN « annual total at Rockport and S-
IN-C - category total at Rockport. OH = annual total at Zanesville and OH-C -
category total at Zanesville.
The aggregate estimates of deposition were sensitive to the frequency of occurrence of the
categories, the mean deposition to precipitation ratio of the categories and to the concentrations
associated with each event. As mentioned above in reference to equation 7, the estimates were
most sensitive to events from the frequently occurring, "wet" categories. To insure that these
situations were represented properly more than one event was chosen from these categories.
This measure reduced the possibility of one non-representative event being selected and dras-
tically altering the estimates. As the number of events selected from each category is increased
the estimates will approach the actual deposition. Using all four years of data to determine the
category frequencies and the deposition to precipitation ratios insured that the most reiia^ie
values were used. Since these factors and the 30 episodes were constant, the values of the
aggregate estimates at each site did not vary. Given that the long-term depositions were
correlated with the amount of deposition resulting from events within these categories, the
94
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uncertainty ranges associated with the estimated values were expected to be relatively small.
The deviation between the estimated and observed depositions supplied an indication of the level
of certainty to which long-term deposition could be reproduced.
1982
1983
1984
1985
1986
Figure 29. Comparison between the year to year variation in annual S04* deposition and the
year to year variation in the total amount of deposition accounted for by the
categories selected for representation. NY = annual total at Big Moose and NY-C =
category total at Big Moose. VT = annual total at Underhill and VT-C = category _
total at Underhill.
The actual and aggregate maps of annual and seasonal deposition were discussed in a
previous section. The 30 events reproduced the depositions well enough at each site to yield
spatial patterns similar to the observed depositions. The magnitude of the differences
(uncertainty) between estimated and actual were calculated as a percent of the actual deposition
for all of the sites. These were summarized above in Table 9. Expressed as a percent of the
mean deposition across all sites, the RMS errors were smallest for the annual estimates.
Overall, estimates were closest for wet sulfate deposition with smaller annual and seasons!
percent errors as compared to the wet deposition of N03" and t-T.
95
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It was most important to estimate the wet deposition over the smaller thirteen site domain
accurately. For each of these sites the estimates were compared to the actual deposition. As
with the mean error across all sites, the aggregation technique usually yielded better results
for the annual estimates at individual sites. Reasons for this were suggested in the discussion
concerning the spatial plots of wet deposition. The percent errors for wet SO4" deposition are
shown in Figure 30. Positive values indicate an over-estimate and negative ones under-
estimation. On an annual basis the error was within 20 percent at all thirteen sites^The
estimates were most uncertain at Big Moose, NY and Fort Wayne, IN with errors of -17 and 18
percent respectively. At the other locations uncertainty was less than 11 percent. The most
uncertain aggregate estimate occurred at Tunkhannock, PA during the winter half of the year.
The percent error was 54 percent. In general, the seasonal estimates were within 30 percent
across the thirteen-site region with the exception of Clearfield, KY in both seasons and
Winterport, ME in the winter. The uncertainty in the seasonal estimates at Clearfield and
Tunkhannock appear to balance during an annual time frame so that the longer-term estimates
are more certain. The most consistent bias in uncertainty occurred at Fort Wayne where for
both seasons and for the annual period the estimates were high. The aggregate estimates
contained the smallest amount of uncertainty in Ontario, Ohio, southern Indiana, Massachusetts
and Vermont.
Figure 31 shows the level of uncertainty associated with the aggregate estimates of wet
nitrate deposition. Errors appear to be somewhat more variable from region to region and the
amount of uncertainty is greater than it was for sulfate. Biases towards over or under predic-
tion were also more consistent across seasons. At Tunkhannock and Turner Falls, MA the
uncertainty was skewed towards under-estimation. While at Zanesville and Fort Wayne depo-
sition was over-estimated by around 20 percent. At Big Moose the uncertainty changed sign
from winter to summer as it did at Underhill, VT and Dorset, ON. At these sites the altering
direction in uncertainty resulted in more stable annual estimates but a Big Moose the annual
estimate remained relatively uncertain. In Kentucky, Michigan, North Carolina. Maine and
southern Indiana and southern Ontario the aggregate estimates contained relatively little
uncertainty for wet nitrate deposition.
Levels of uncertainty in estimates of total acidity deposition were generally between those
of nitrate and sulfate. This was shown in Table 9 and is also apparent in Figure 32. In Ohio,
Indiana. North Carolina. Michigan, Vermont and Ontario the degree of uncertainty was relatively
small (generally within 20 percent). Confidence in acidity deposition was somewhat less and
biased low over Massachusetts, Pennsylvania, Maine and New York. Overall the aggregate
estimates of wet acid deposition were most uncertain at Tunkhannock.
Compared to the observed depositions the level of certainty in the aggregate estimates
varied between sites and seasons. The magnitude of the uncertainty indicated how well the 30
episodes reproduced the mean behavior of the categories they were chosen to represent. The
96
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larger values indicated that the concentration of pollutants in the precipitation for some of the
episodes selected were significantly different than the mean volume-weighted concentration of
the categories used. In general, it only required a couple of unusual events to noticeable effect
the aggregate estimate. Considering that the same 30 episodes were used to estimate deposition
at all locations across eastern North America the results were quite good. Certainly it would be
possible to produce better estimates if separate events were used for each site. This would
significantly increase the number of RADM runs necessary and is therefore not an option. The
real root of uncertainty lies not in the accuracy of the specific predictions but in the choice of
categories or meteorological situations represented. To place confidence in the outcome of the
aggregation study and in the long-term estimates of deposition derived through the RADM
project the episodes selected must vary meteorologically and chemically. They must also
represent the types of situations that contribute a reasonable portion of the total deposition at
any given location. These aspects have been continually tested throughout this study to insure
confidence in the estimates and in the reaction of the estimates to altered emission scenarios.
D Annual
Summer B Winter
p 60%
E
R *0% +
C
E 2 0 % 4-
N
E -20% i
R
R -40% ••
O
R -60%
4^
1
MA PA OH S-IN N-N NC M KY ME VT NY N-ON S-ON
Precipitation Chemistry Site
(by state/province)
Figure 30. The uncertainty ranges for annual and seasonal wet SO4" deposition due to
estimation using the aggregation technique.
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Annual
Summer ES Winter
MA PA OH S-IN N-IN NC MJ KY ME VT NY N-ON S-ON
Precipitation Chemistry Site
(by state/province)
Figure 31. The uncertainty ranges for annual and seasonal wet NO3" deposition due to
estimation using the aggregation technique.
Annual
Summer 5 Winter
MA PA OH S-IN N-IN NC Ml KY ME VT NY N-ON S-ON
Precipitation Chemistry Site
(by state/province)
Figure 32. The uncertainty ranges for annual and seasonal wet H* deposition due to
estimation using the aggregation technique.
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3.4. SOURCE-RECEPTOR RELATIONSHIPS
The use of the meteorological categories was a key step in the aggregation project. In
addition to providing accurate estimates of deposition it was also assumed that sampling from the
dominant categories (in terms of total amount of wet deposition contributed) would insure -that
the important source-receptor relationships were represented. The category-based method of
event selection and the specific 30 selected episodes have thus far only been evaluated in terms
of observed wet deposition. The use of these categories, the behavior of which were quantified in
terms of deposition and seasonality, also allowed for the application of scaling and weighting
factors during aggregation. The actual episodes selected from the categories were also shown to
provide good estimates of annual and seasonal wet deposition at a majority of the sites and to
reproduce the spatial patterns well.
This section focuses on the differences in source-receptor relationships between cate-
gories, the benefits of event selection by category and on the actual source-receptor relation-
ships represented by the 30 episodes. It was initially anticipated that because the categories
represent different 850 mb wind-flow patterns they should also be associated with different
source-receptor relationships. This implies that the winds at 850 mb (-1500 meters} were
related to the winds responsible for transporting and dispersing pollutants in the atmospheric
boundary layer (surface to 1500 meters). Evidence to support this hypothesis was uncovered
in the testing of the wet deposition behavior of the categories. A possible explanation for their
differences in terms of wet deposition is that they were associated with different source-
receptor relationships. This should result in different depositions between categories because
the chemical composition of precipitation depended upon transport. To investigate this, mixed-
layer back-trajectories and Q-fie!ds for three of the monitoring sites; Big Moose, New Ycrk;
Gaylord, Michigan; and Raleigh, North Carolina were analyzed to provide a more direct measure
of source-receptor relationships.
3.4.1. CUmatoloaical Wind Flow Patterns during Precipitation
Over eastern North America the air generally moves from west to east advectir.g air-
bourne pollutants along with it. At any particular time and place, however, the flow deviates
markedly from this pattern. Over the course of a year or longer certain upwind areas will have
a stronger influence upon each location. It is these long-term tendencies that play an key role
in the spatial pattern of seasonal and annual deposition of SO4", NO3' and H+ and in the specific
amount that any given receptor experiences. In terms of long-term wet deposition it is the wind
flow during and shortly before each event that is important.
To identify which areas have the greatest tendency to affect Big Moose, Gaylord and Raleigh
the trajectories associated with all of the precipitation events during 1982-1986 were
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combined into overall Q-fields for each site. Figures 33 to 35 show the mean probability of
transport to Big Moose, Gaylord and Raleigh, respectively (units are 10"8 km"2). The air
arriving at each location had the highest probability of passing over areas immediately adjacent
to the receptor and the probability decreases with distance. The rate of decrease depends upon
direction and as expected the gradient is somewhat sharper toward the east. This is because the
winds from the east were less common than those from the west. At Raleigh, however, a part cf
the reason for the rapid decrease towards that east was probably due to the lack of data over the
ocean. Compared to Big Moose, the boundary layer winds during precipitation events at Gaylord
and Raleigh had a greater tendency to come from the north and south and did not favor a westerly
direction as much. At Raleigh, northeast and southeast winds were more common than at the
other sites and in general, the wind appears to be lighter there.-' This behavior was indicated by
the size of the area encompassed by the 25 x 10"8 contour. The Q-field for Gaylord shows that
northwest and southwest winds were relatively more prominent. Relatively close to the
receptors (within about 500 km) it appears that any sources to west and east would contribute
more at Big Moose and Gaylord while at Raleigh no such tendency was apparent. In general, the
probability fields for all events (with and without precipitation - not shown) tended to be more
skewed towards the west because non-precipitating events were even less often associated with
easterly and southerly winds.
3.4.2. Wind flow Associated with Sulfate Wet Deposition
To obtain an idea of where the air originated during events with higher levels of SO4" in
precipitation the Q-fields were re-calculated using the same set of trajectories but with
deposition-related weights attached. The weighted Q-fields are shown in Figures 36 to 38.
Subtle differences between these fields and the non-weighted fields were discernabie by
comparing the figures.
At Big Moose the weighting appeared to have resulted in increased probabilities towards
the south to southwest and locally. The values decreased to the west, northwest and east. The
effect of the weighting for Gaylord showed up as slight increases to the south and north and
decreases to the west to northwest. At Raleigh the probabilities increased very slightly to the
north and it is more noticeable over Virginia. There were decreases in probabilities to the
southwest, west and northeast. These changes suggested a possible relationship to the spatial
distribution of SO2 emissions across the Northeast. To highlight the differences between the
weighted and non-weighted fields the potential source contribution functions (PSCF) were
determined. These new fields clearly show which regions were most often affiliated with high
and low deposition amounts and can be more easily compared to the emissions field.
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Flaure 33. The mean probability (10'8 km'2) for transport on precipitation days to Big
Moose. NY, during the period 1982-1985.
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Flgur» 34. The mean probability (10~8 km"2) for transport on precipitation days to
Gaylord. Ml. during the period 1982-1985.
102
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Figure 35. The mean probability (10'8 km"2) for transport on precipitation days to
Raleigh, NC, during the period 1982-1985.
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Figure 36. The SO4" wet deposition weighted probability (10~8 km"2) for transport to Big
Moose, NY during the period 1982-1985.
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Figure 37. The SO4" wet deposition weighted probability (10"8 km'2) for transport to
Gaylord, Ml, during the period 1982-1985.
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Figure 38. The SO4" wet deposition weighted probability (10~8 km'2) for transport to
Raleigh. NC. during the period 1S82-1985.
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3.4.3. Potential Source Contribution Functions
Figure 39 illustrates the transport bias field for Big Moose, NY. Values are positive
(more often associated with above average 804" wet deposition) to the south to southwest and to
the northwest to north-northwest. When airmasses originated over Big Moose and over areas
immediately to the south extending across Pennsylvania to southeast Ohio the resulting
depositions were most likely to be large. Large positive values close to Big Moose are indicative
of high depositions during stagnant conditions when the ambient S02 and SO4* levels were likely
to be rather homogeneous over large areas. In general the regions with positive PSCF values
agreed with locations of known SO2 emissions. The main exception is off the coast of Virginia
where the PSCF is large. It is possible that this area was linked to above average depositions
because of synoptic situation or because of the importance of precipitation amount in
determining wet deposition amount there. In Figure 39 it can be seen that when the air was
from the east, northeast, northwest and west depositions were more often below average at Big
Moose.
Sulfate wet deposition amounts were used to weight the Q-fields in the numerator o? the
PSCF's hence, one could argue that the results were as much a function of large precipitation
amounts as they are enhanced SO4* concentrations (i.e. more precipitation fell when the winds
were from the southwest). To test for this possibility the PSCF field for precipitation (i.e.
numerator weighted by precipitation amount) was determined. The result for Big Moose is
shown in Figure 40. In this case the largest values were located to the east where values
approached a minimum in the S04* weighted PSCF field. There was overlap between the positive
areas in the precipitation and SO4° weighted fields in the vicinity of Big Moose and to the north
and south. However, along the Ohio River valley and over most of Ohio and southeastern Indiana
the SO4" weighted field indicated above average contribution (positive) and the precipitation
weighted field showed below average (negative) contribution. This discrepancy highlights the
importance of the large sources of anthropogenic SO2 in the Midwest to the wet deposition of
SO4" at Big Moose. These two figures clearly indicate that SO4* wet deposition is not driven
solely by precipitation amount.
The results of the PSCF analysis for Gaylord, Ml are shown in Figures 41 and 42. Over
all wet events (Figure 41), wind flow from the southwest and south-southeast were linked with
relatively high SO4" deposition events. The area most often tied to large events extended from
Gaylord across southeastern Michigan, Ohio, Kentucky and western West Virginia and
encompassed much of the Ohio River valley. Winds from the north also appeared to have
frequently resulted in above average SO4* wet deposition. Flow from the southwest to northwest
and the east to southeast did not seem to be systematically related to high deposition events.
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Figure 39. The bias for transport associated with large SO4* wet deposition at Big Moose. NY,
during the period 1982-1985. Positive values represent regions more often
associated with above average SO4* wet deposition amounts .
There is better agreement between the SO4* weighted and precipitation weighted fields at
Gaylord than was seen at Big Moose. As with S04", there was a greater tendency for relatively
large precipitation events when the air arrived from the south and north (Figure 42). The
magnitudes of the PSCF values over these areas were significantly smaller than those generated
with SO4" weighting. It is interesting to note that fairly strong winds from the southwest seem
to have been associated with high precipitation events more often. As well, winds from the
north-northwest across Lake Superior and the Straights of Mackinac were associated with
relatively large amounts of water deposition. Some of these events could be associated with cold
outbreaks during the winter that pick up large amounts of moisture over the relatively warm
lake water.
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Figure 40. The bias for transport associated with large water deposition at Big Moose, NY,
during the period 1982-1985. Positive values represent regions more often
associated with above average precipitation .
Similar analyses for Raleigh, NC were hampered by the fact that many of the trajectories
from the eastern quadrants ended abruptly over the ocean. It is therefore difficult to assess the
relative importance of airmasses approaching from these areas. For all of the wet events during
1982 to 1986 the PSCF field is displayed in Figure 43. The regions most often leading to
relatively high wet SO4* depositions are to the northwest and towards the east to southeast. The
reason for the large values to the east is uncertain. It could be due to a greater influx of
moisture and precipitation associated with easterly winds and/or due to polluted airmasses from
the northeast that move out over the ocean and then back inland south of anti-cyclones. It could
also be partly due to the difficulty discussed above. The region above 0.05 (5%) across
Virginia, West Virginia and Ohio is more certain. Flow from this area correlates well with high
S02 emission regions in the Ohio River valley. This area takes on more significance when
compared to the precipitation weighted PSCF fields for Raleigh (Figure 44). Clearly, these
areas do not have a tendency to be associated with the large precipitation events. These events
usually result with flow from the east and southeast.
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Figure 41. The bias for transport associated with large SO4" wet deposition at Gaylord, Ml,
during the period 1982-1985. Positive values represent regions more often
associated with above average precipitation .
To highlight the airmass source regions that were most often associated with high SO4=
wet deposition events the PSCF fields for Big Moose, Gaylord and Raleigh were combined. This
new -product' field is shown in Figure 45. With the individual PSCF fields it is not possible to
determine where along the trajectories the S04= has originated. The intersection of the PSCF
fields values from these three locations, however, is able to indicate more specifically which
regions were consistently associated with relatively large amounts of SO4" wet deposition.
Areas above 0.25 or below -0.25 are the most significant.
In Figure 45 it can be seen that when the airmasses originated over Ohio, northeastern
Kentucky, western Pennsylvania and northwestern West Virginia they lead to relatively large
S04" wet depositions at all three sites. This pattern is rather good agreement with the spatial
distribution of SO2 sources. In contrast airflow from the northwest and northeast were not
systematically leading to high depositions. Another area of that shows up as being important to
wet SO4" deposition is located off the coasts of North Carolina and Virginia. This could have been
due to the availability of moisture over the Atlantic and/or the easterly flow on the south side of
anti-cyclones and north side of cyclones.
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Figure 42. The bias for transport associated with water deposition at Gaylord, Ml, during the
period 1982-1985. Positive values represent regions more often associated
with above average precipitation .
Every location in eastern North America will experience its own unique relationship with
the various source regions that contribute pollutants to the atmosphere. From day to day and
even year to year these relationships are likely to change. Over the relatively long time period
of 5 years the significant short-term variations are essentially smoothed over. These clima-
tological Q-fields were generated and analyzed for three opposing receptors. At each of the three
locations studied the upwind regions most often associated with precipitation events were
different, however, throughout the five years it was possible for the flow to come from almost
any direction regardless of location.
The aggregate estimates of seasonal and annual deposition were compared to the observed
values during 1982-1985. Similarly, aggregate Q-fields were compared to the actual wind
flow over a longer period of time. Before such comparisons could be made it was necessary to
decide which flow pattern should be reproduced. Since wet deposition is of primary interest the
precipitation only Q-fields should be the main goal. However, the event selection protocol was
designed to identify storms with a high likelihood of producing S04= wet deposition at multiple
locations across eastern North America. This implies that flows from areas most often leading
to relatively high SQ4" are desirable. Therefore, an aggregate of the wind flow from the selected
storms should most resemble the SO4* weighted Q-fields.
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Figure 43. The bias for transport associated with large S04" wet deposition at Raleigh, NC,
during the period 1982-1985. Positive values represent regions more often
associated with above average wet deposition.
3.5. THE VARIATION IN SOURCE-RECEPTOR RELATIONSHIPS BETWEEN
CATEGORIES
Over all events (wet and dry) some categories favored flow from the north to northwest
(categories 7. 8), others favored the south to southwest (1, 5, 11) and still others showed bras
towards the west (6, 10). Category 9 was associated with low wind flow situations and category
10 with higher wind speeds. Wet differential probability fields for these categories are
included in Figures 46-54. Figure 46 shows the wet differential field for category 20
(unbiased set of missing days) and illustrates the magnitudes one can expect due to random
fluctuations. Close to the site, the differential was as high as 30 x 10~8 km"2 but further away
the random fluctuations resulted in differentials less than 10"8 km"2. Wet differential proba-
bilities seen in Figures 47 to 54 are greater than these values and, hence, can be significant.
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Figure 44. The bias for transport associated with large water deposition at Raleigh, NC,
during the period 1982-1985. Positive values represent regions more often
associated with above average precipitation .
The wet differential probabilities for these eight categories were similar to those calcu-
lated over all events (dry and wet) which were summarized above. The main differences
appearing in the wet on.ly cases are outlined in Table 10. In terms of where the air was most
and least likely to originate just before and during precipitation there were identifiable
differences between categories. Selection from a collection of these categories should he!p
insure that the resulting aggregate estimates of wet deposition are not biased towards a small
number of potential source regions.
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Figure 45. The ensemble bias for transport associated with large S04* wet deposition at Big
Moose, NY; Gaylord, Ml; and Raleigh, NC, during the period 1982-1985.
Positive values represent regions consistently associated with above average
SO4° deposition amounts.
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Figure 46. Difference between climatological Q field (1982-1985) and the Q field for
category 20 (unbiased set of missing days).
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Figure 47. Difference between climatological Q field (1982-1985) and the Q field for
category 1.
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Figure 48. Difference between climate-logical Q field (1982-1985) and the Q field for
category 5.
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Figure 49. Difference between climatological Q field (1982-1985) and the Q field for
category 6.
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Figure 50. Difference between climatological Q field (1982-1985) and the Q field for
category 7.
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Figure 51. Difference between climatological Q field (1982-1985) and the Q field for
category 8.
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Figure 52. Difference between climatological Q field (1982-1985) and the Q field for
category 9.
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Figure 53. Difference between climatological Q field (1982-1985) and the Q field for
category 10.
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Figure 54. Difference between climatological Q field (1982-1985) and the Q field for
category 11.
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TABLE 10. DESCRIPTION OF SOURCE-RECEPTOR RELATIONSHIPS FOR SELECTED
METEOROLOGICAL CATEGORIES.
CATEGORY
DESCRIPTION
Category 1:
Category 5:
Category 6:
Category 7:
Category 8:
Category 9:
There area that the air was more likely to originate from extended to the
southwest and south covering the Ohio River Basin and parts of Virginia and
North Carolina and did not seem to be associated with Indiana and Ohio. The
low-probability region extends a good distance to the west.
This category is different than Category 1 in that the flow bias is stronger
and aligned toward the south of the receptor. This category might be more
likely to be influenced by east coast sources than would category 1 The
region less likely to influence the airmass over Big Moose extends westward
to encompass Michigan.
The biases in this pattern are relatively weak with a slight enhancement in
probability of wind flow from the west. The area less-likely-to-contribute
included a large area around the receptor itself and extends to the south and
east slightly. The wind flow was stronger for precipitation events than for
dry events.
There is a pronounced positive bias towards the northwest and a little to the
'west. There is some likelihood.of light wind situations during wet events in
category 7. This is indicated by a large, positive bias region immediately to
the north of Big Moose.
Category 8 has a relatively strong bias toward air flow from the north,
though the differences do not extend far from the receptor indicating a weak
flow pattern. Category 8 exhibited lower concentrations of SO4" than did
other predominantly summertime categories.
Category 9, which exhibited a relatively high SO4" wet deposition to
precipitation ratio, is associated with a moderately pronounced differential
probability field. This field decreases fairly fast with distance suggesting a
tow wind flow associated with the category. There is bias toward transport
from the southwest in category 9. For precipitation events the maximum
positive area moves further away from the receptor in the southwest. There
may therefore be slightly stronger winds associated with precipitation
events. A negative bias towards the north is rather pronounced.
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TABLE 10 (CONT.). DESCRIPTION OF SOURCE-RECEPTOR RELATIONSHIPS FOR SELECTED
METEOROLOGICAL CATEGORIES.
CATEGORY DESCRIPTION
Category 10: The relatively large negative bias suggests the transport was less likely to
be associated with low wind speed situations. The weak positive bias to the
west suggests that air flow to the receptor had a somewhat enhanced chance of
arriving from the upper midwestern United States and extreme southern
Ontario.
Category 11: The differences between the overall wet probability field and the wet prob-
ability field associated with category 11 were small throughout eastern
North America. This suggests that the wind flow pattern common to this
category is similar to the wind flow associated with the typical precipitation
event at Big Moose.
3 6. SELECTING EPISODES TO ESTIMATE SOURCE-RECEPTOR
RELATIONSHIPS
The differences in the wind flow between categories suggests that using them as event
selection guidelines should result-in a better representation of the long-term S04" weighted
source-receptor relationships. In the context of this study better implies relative to selecting
events -at random. For the estimation of annual depositions category-based selection and
weighting was shown to improve upon random and seasonal selection methods. This was
demonstrated by selecting and aggregating a number of nineteen-events ensembles using a
variety of methods. A similar approach was developed for testing how well categories dc in
helping to reproduce the expected source-receptor relationships.
The episode selection criteria outlined for estimating deposition were followed. Nineteen
were selected from categories 1W, 3W. 5W, 6W, 7W, 8W, 9W and 11W. The random versus
strata comparisons were not based upon 30 episodes because it was the original nineteen that
were selected to best reproduce deposition and wind flow. The other eleven were included to
cover more unusual situations. Random selection of events was limited to these events in the
wet halves of the categories. The same episodes were expected to estimate deposition at locations
across eastern North America so each ensemble of episodes was tested in terms of its ability to
reproduce the wet-weighted fields at Big Moose, Gayiord and Raleigh collectively. Twenty-five
individual ensembles were sampled and aggregated using the two techniques (random and
strata). For the random case it was assumed that there was no way to weight the various
episodes. The only option involved whether or not to use all days or just the wet days. When
categories were employed, however, there were a number of potential approaches to weighting
the episodes.
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Keep all days or days with reported precipitation only and combine with no weights.
Keep ail days and weight them according to the frequency of occurrence of their
respective categories.
• Employ only the days with precipitation and weight them with the amount of
deposition on each day and category frequency.
• Employ only the days with precipitation and weight with expected deposition (mean
category deposition) and frequency.
^f
The Q-fields against which our selection and aggregation methods were tested were the
SO4" wet deposition weighted fields shown in Figures 36 to 38. These Q-fields served as the
base-line for all comparisons. Two of the above approaches were tested. In the first case both
the precipitating and non-precipitating days were retained and both the random and category-
based ensembles were aggregated without weights. It was assumed that the way in which the
category selections were prescribed implicitly included a weighting since those categories
known to produce a large percentage of the deposition across eastern North America were
sampled according to importance and frequency. All days were kept because the occurrence or
non-occurrence of precipitation at the actual receptor is not indicative of the processes
occurring over the surrounding area. The movement of airmasses during the entire evolution of
a storm system will'effect the chemistry of the resulting precipitation. The results of
comparing the random and category-based ensembles in this situation indicated which approach
better represented the wind flow associated with long-term regional deposition. Figure 55
shows the percent RMS errors, ranked from smallest to largest, for the random and category-
based ensembles.
»j. *^
The G-fields were compared over all areas with probabilities greater than 10 km'* and
over the Midwest. Figure 55 indicates that selection by category is consistently better than
random selection in reproducing the source-receptor relationships important to regional SO4"
wet deposition. The two methods were compared over the Midwest because flow from this region
is frequently associated with significant amounts of S04" wet deposition (Figure 45). Errors
are smaller over the Midwest for both selection routines and range from 20 to 45% when
categories are used to help select the episodes.
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— C —R —C-MW —R-MW
100
I—i—\—I—I—i—I—I—I—i—I
1 23456789 10111213141516171819202122232425
RANKED ENSEMBLES
Figure 55. Percent RMS errors, ranked from smallest to largest, for the random and
category-based ensembles using trajectories from all days in 30 episode sample.
125
H—I—I 1—I-
12345678 910111213141516171819202122232425
RANKED ENSEMBLES
Figure 56. Percent RMS errors, ranked from smallest to largest, for the random and
category-based ensembles using trajectories from precipitating days only in 30
episode sample.
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In the second case only days with precipitation at the receptors were included when
generating the estimated Q-fieids (third option listed above). In this approach the comparisons
are more specific to each location and not as indicative of the situation over larger areas. The
results are also more sensitive to the stochastic nature of precipitation. Each ensemble was
selected at random either with or without the guidance of categories and the number of wet days
available for source-receptor evaluation varied between ensembles and sites. Weights were
applied when aggregating each ensemble to an estimated long-term Q-field. For the category-
based ensembles information on the expected deposition and on the frequency of occurrence of
the particular categories sampled were used determine the weights. The randomly selected
ensembles were weighted according to the amount of deposition occurring with each event
selected. The comparison between the random and strata based approaches is shown in Figure
56. Over the high probability areas (>10~4 km"2) the method of pure random sampling did
slightly better than the category guided selections. Over the Midwest regions, however, the
outcome was just the opposite. In general, neglecting the days without precipitation resulted in
a decrease in the quality of the estimates of between 10 and 20%. Considering the results of
both of the cases tested, the use of the meteorological categories appears to offer improvement
when attempting to represent the long-term source-receptor relationships with a finite
number of episodes.
3.7. SOURCE-RECEPTOR RELATIONSHIPS ASSOCIATED WITH THE 30
. EPISODES '
Improvement in source-receptor representation due to category-guided event selection is
a fundamental assumption of the aggregation technique developed during this project. The
previous section corroborated this assumption. Most important, in terms of applying the
outcome of this study to RADM. is the ability of the actual 30 episodes to reproduce the ciima-
tological source-receptor relationships. These episodes were recommended for RADM simula-
tion. They were carefully examined for source-receptor representativeness at Big Moore,
Gaytord and Raleigh. Mixed-layer trajectories were combined to produce Q-fields for each of
these sites. As in the previous section, the long-term wind flow patterns were approximated
using ail of the days (non-weighted) and with only the wet days (weighted). The resulting fields
were compared to the long-term wet sulfate deposition weighted Q-fields.
Of the 30 events selected for RADM simulation two did not have trajectory information.
The estimated Q-fields discussed here were generated with up to 28 events instead of 30. The
effect of missing this information was not known but it was assumed that with more trajectories
the Q-fields would be more accurate. The non-weighted Q-fields for Big Moose, Gaylord and
Raleigh are shown in Figures 57 to 59. Qualitatively, they appeared similar to the base-line
fields. The probabilities decreased away from the receptors and tended to fall-off quicker to the
east. Comparing the actual field to the estimated field for Big Moose it can be seen that the
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estimation was not as smooth. This behavior was expected since the number of days included was
Fiaure 57 Source-receptor analysis (Q fields) for Big Moose, NY derived from both
precipitating and non-precipitating days within the 30 selected episodes.
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Figure 58. Source-receptor analysis (Q fields) for Gaylord, Ml derived from both
precipitating and non-precipitating days within the 30 selected episodes.
130
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131
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Figure 60. Bias in source-receptor analysis resulting from use of 30-everrt sample instead
of 4-year sample at Big Moose, NY. Bias is expressed as a percent of predicted
Q-field from the 4-year analysis. Both precipitating and non-precipitating days
• during the 30 selected episodes are included in the analysis.
relatively small and it was common to all three sites. To quantitatively compare the actual to
the estimated fields the percent difference between the two was determined over all areas with
probabilities greater than 10"* km"2. Throughout this area the mean percent RMS error was
46% for Big Moose, 48% for Gaylord and 37% for Raleigh. Contrasting these values to those in
Figure 55 we see that the actual episodes selected were reproducing the wind flow patterns
about as well as can be expected.
Figure 60 shows the spatial distribution of errors for Big Moose. Positive values indicate
over-representation and negative imply under-representation. The wind flow from the
northeast was significantly over-emphasized but throughout the remainder of the region
analyzed the flow is represented to within -20%. Along the Ohio River valley the bias was
towards under-representation and was generally equal to or less than 20%. Northeast of Big
Moose the main source area was southwestern Quebec (Montreal). This area will likely be
over-emphasized by RADM simulations using the 30 days. These sources were less important
than those in the Midwest where the 30 day estimates were representing the flow well.
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Figure 61. Bias in source receptor analysis resulting from use of 30-event sample instead
. of 4-year sample at Gaylord, fvH. Bias is expressed as a percent of predicted Q-
field from the 4-year analysis. Both precipitating and non-precipitating days
during the 30 episodes are included in the analysis.
The same type of plot for Gaylord is included in Figure 61. The pattern of over and under
representation was more uniform than for Big Moose. The southern section of the midwest
including the Ohio River valley was under-emphasized as was southern Ontario and New York.
Flow from the north was given more weight. Throughout a rather large area the differences
were within 20%. Of the areas shown to be most often related to higher than average deposi-
tions (Figure 41) the sources along the western end of the Ohio River valley are most likely to
be misrepresented.
The flow resulting from the combination of all 30 days at Raleigh overpredicted towards
the north and east and underpredicied to the west and south (Figure 62). Basically, the percent
differences were within 20% over much of the continent with the exception of the east coast
from east of Raleigh to the north across Pennsylvania. Over the midwest the representation was
quite good. RADM simulations using these 30 days will likely give sources from this region
proper emphasis while those associated with the eastern seaboard could be significantly over-
emphasized.
133
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Figure 62. Bias in source receptor analysis resulting from use of 30-event sample instead
of 4-year sample at Raleigh, NC. Bias is expressed as a percent of
field from the 4-year analysis. Both precipitating and non-precipitating days
during the 30 episodes are included in the analysis.
The long-term wind flow at each site was also estimated for precipitation events only.
The results of these fields were fairly sensitive to the weighting scheme applied. The estimated
fields evaluated here were generated by weighting each of the days with precipitation by the
actual amount of S04" deposition reported and by the frequency of occurrence of the category i
was selected from. Use of weights may be redundant since they are indirectly accounted for by
the breakdown of categories sampled and in some cases neglecting weights and just averaging the
trajectories associated with precipitation resulted in a more accurate Q-field. The percent
difference fields generated for the wet-only estimates and are shown in Figures 63 to 65. rhe
overall RMS errors attributed the the 30 days were in the same range as those shown in Figure
56. It was 61% for Big Moose, 52% for Gaylord and 76% for Raleigh. As in the previous
section the estimates of the flow deteriorated when only precipitation days were included. Of the
28 available episodes at Big Moose 19 resulted in deposition. Thus, only 25 days were used to
134
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produce the estimated Q-field as opposed to 84 days when non-precipitating days were included.
At Gaylord, 16 episodes had deposition and at Raleigh only 15. This translated to 25 and 23 wet
days at Gaylord and Raleigh respectively.
The estimated flow during precipitation events at Big Moose (Figure 63) indicates that
sources to the north were under-represented by up to 60%. Sources in the central Ohio River
valley were over-represented by about the same amount. Flows from areas to the west and
areas south of Big Moose but north of the Ohio River were portrayed reasonably well consid-
ering the number of days included. For Gaylord (Figure 64), the sources to the south to
southeast were significantly over-emphasized. Elsewhere the differences were basically less
than 30%. At Raleigh, the 23 days used to approximate the long-term wind flow on precipi-
tating days were yielding large positive biases towards the north and northeast (Figure 65).
Sources towards the midwest were being under-represented by anywhere from 10 to 60
percent while over the eastern Ohio River valley, an area shown of major importance (Figures
43 and 45) the estimated flow was within 30%.
figure 63. Bias in source receptor analysis resulting from use of 30-event sample instead
of 4-year sample at Big Moose, NY. Bias is expressed as a percent of predicted
Q-field from the 4-year analysis. Only days with precipitation reported at Big
Moose are included in the analysis.
135
-------
Figure 64. Bias in source receptor analysis resulting from use of 30-event sample instead
of 4-year sample at Gaylord, Ml. Bias is expressed as a percent of predicted Q-
field from the 4-year analysis. Only days with precipitation reported at Gaylord
are included in the analysis.
136
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Figure 65. Bias in source receptor analysis resulting from use of 30-event sample instead
of 4-year sample at Raleigh, NC. Bias is expressed as a percent of predicted Q-
field from the 4-year analysis. Only days with precipitation reported at Raleigh
are included in the analysis.
137
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4. SUMMARY
4.1. ACCOMPLISHMENTS
A method for aggregating episodic deposition estimates of the Regional Acid Deposition
Model (RADM) has been developed. The method is based upon the stratification of three-day
periods into categories of similar wind flow at 850 mb (roughly 1500 meters above mean sea
level). The stratification has been used to identify which meteorological situations merit
simulation by RADM based on their likelihood of producing sulfur deposition at multiple loca-
tions across eastern North America, their frequency of occurrence, and their seasonality. The
aggregation technique has been simulated using four years of precipitation chemistry data to
demonstrate that uncertainties in estimates of seasonal and annual sulfur deposition using the
aggregation technique is significantly less than those obtained from random (non-stratified)
sampling of the data.
The following specific tasks have been completed as part of this project:
(1 ) An extensive data base with daily precipitation and wet deposition of sulfate, nitrate
and total H* was developed. The data base covered the years 1982-1985 and
included 23 sites in the northeastern, southern and midwestern U.S. and southern
Canada. Meteorological information to be used in subsequent analysis was also
collected.
(2) Techniques of cluster analysis were used to stratify 3-day events over the 4-year.
period into categories based on the meteorological data described above. Four
independent stratification schemes were investigated: stratification by 850 mb
wind field, stratification by 850 mb geopotential and mean surface pressure,
stratification by 850 mb temperature advection and 500 mb vorticity, and
stratification by 500 mb vorticity advection and 850 mb relative humidity. These
four stratification schemes were evaluated to find the combination of meteorological
parameters which best explained variations in sulfur wet deposition.
( 3 ) A set of 19 meteorological categories was compiled based on similarity in wind flow
at 850 mb. These 19 categories were each subdivided into "wet" and "dry" cases,
making 38 categories. These 38 categories were used as the basis for selection of
individual days and derivation of aggregated estimates for annual deposition.
138
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( 4 ) Accuracy and validity of the 38-category stratification was evaluated based on the
ability to predict annual sulfate deposition at eastern and midwestem sites from a
finite sample of 3-day events. We compared estimates for annual deposition
derived from a random sample of 3-day events with estimates from a cluster-based
sample of events. The standard deviation between estimated and actual deposition
was lower by 25% with cluster-based selection.
(5) A preliminary selection of 30 individual 3-day events for in-depth study with
RADM was made. Selection of individual days was designed to satisfy the following
criteria:
(a) representation of the meteorological categories that account for the bulk of
dry deposition in the eastern U.S.
(b) Inclusion of days representing a wide variety of circulation patterns,
including relatively rare winter circulation patterns.
(c) Inclusion of a number of days with very little wet deposition, to allow for
simulation of dry deposition processes.
(d) Selection of individual events within each category by a random process,
rather than prescribing specific events.
(e) Accurate representation of total annual deposition at all northeastern and
midwestem sites from the ensemble of individual events.
The selected ensemble allows for accurate estimation of summer, winter, and total annual
deposition at all sites in the northeast and midwest.
4.2. RECOMMENDATIONS FOR FUTURE WORK
4.2.1. Evaluation of the 30 Episodes and the Meteorological Categories
Meteorological categorization was employed to help select episodes for RADM simulation
with the purpose of trying to represent the dominant wind flow situations. Categorization based
on the 850 mb wind flow was used because it was assumed that the ensuing categories would best
segregate general meteorological situations and the deposition events. These assumptions were
tested. The categories were shown to explain a good degree of the variance in wet deposition (S5
% confidence level) and in terms of their mean 850 mb wind flow they were seen to differ
systematically from each other and resemble common meteorological patterns. In the context of
offering improved aggregate estimates the categorization by 850 mb wind flow was shown to be
139
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warranted. Nonetheless, within categories there exist large variations from the mean flow and
the mean deposition pattern. The following tasks investigate the magnitude and causes of those
variations.
4.2.1.1. Source-Receptor Relationships
The hypothesis that the categories would differ chemically (at any given site) was based
upon the assumption that the categories represented systematically different wind flow patterns
and hence would differ with respect to source-receptor relationships. This concept is very
important to the overall RADM project. The collection of events selected from the various
categories should adequately represent the potential influence of all the major source regions
effecting eastern North America. In addition, the frequenc/'with which a particular source
region impacts upon any given receptor must be as is in nature. If the overall source-receptor
behavior represented by the collection of events is biased then the aggregate of RADM runs
resulting from various emission change scenarios will also be biased.
The source-receptor relationships associated with the categories and with the 30 days
selected for simulation were tested during the past year of research. Results indicated that for
Big Moose, NY; Raleigh, NC; and Gaylord, MI the categories did favor different upwind regions
and the 30 three-day episodes also differed in terms of where the air was most likely to have
originated. How well the wind flow over the 30 days represented -the expected wind flow was
also tested, however, results of these comparisons are still being investigated.
During the next year a new method for evaluating the various approaches (30 day
aggregate vs. actual vs. random 30 days) will be applied to aid in this aspect of the source-
receptor testing. This new method will be based on a slightly modified version of the "threat
score" (Anthes 1983) and should supply a more meaningful gauge for assessing the effective-
ness of the selected days.
In terms of the source-receptor relationships and whether the specific source regions are
given proper weight, the categories and the 30 days have yet to be evaluated. This aspect, which
will require a more detailed look at the meteorology associated with the episodes and categories,
will be explored in the upcoming year. In studying the 30 days the approach will be to examine
the winter and summer situations separately. The flow patterns are more distinct in the winter
and thus the source receptor relationships for the associated categories should be more clear-
cut. As a result, it is hypothesized that the wind flow for the days selected to represent this
time of year should do a better job at reproducing the overall pattern (as compared to the
reproduction of the summer half). If this is shown to be the case then it will only be necessary
to concentrate on improving the categorization and selection of warm season events.
4.2.1.2. Chemical and Meteorological Behavior of the Categories
In general, the categories were shown to differ chemically and meteorologically.
140
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However, as it was pointed out earlier there remains a large amount of variability. While the
current methodology has been shown to improve upon a random selection of episodes, the ability
of our specific events to represent the expected range of situations is still clouded by the within
category variability. This variability is great enough to allow some overlap. Events from
different categories can be more similar than different and also not resemble the mean category
pattern at all. These effects can lead to selecting events which do not possess the desired
pollutant transport paths and the under or over representation of certain meteorological
situations.
The within category variability is most notable in the summer when just a few categories
are predominant. There are clearly other mechanisms previously not accounted for that govern
the precipitation and pollutant deposition behavior. To understand and include these effects the
meteorology of these situations needs to be examined in more detail. The goal will be to better
understand what differentiates the events in these more ubiquitous categories. This knowledge
could be used to further stratify some categories leading to better weighting factors for some of
our selected days. It will also identify any key situations that are not presently being modeled.
In the process of testing the nineteen categories for their ability to explain the variation
in wet deposition a number of regression models were developed. These models are capable of
relating depositions and concentrations to specific parameters in quantitative terms. The
importance of a variety of meteorological factors, in terms of their influence on precipitation
chemistry, can be quantitatively assessed in this way. This approach can be used in conjunction
with additional stratification to uncover the other dominant mechanisms controlling deposition
and to improve the aggregate estimates. Using the results of the regression models alternate
aggregation schemes can also be explored and techniques that can better account for the effects of
meteorological variability can be incorporated. As discussed above, specific knowledge of the
source-receptor relationships associated with the categories and the 30 events have not been
determined. This information is especially lacking for the light wind categories which are
associated with high deposition to precipitation ratios and relatively large numbers of events.
The regression studies and sub-stratification along with careful meteorological analysis will
add more to our knowledge of the source receptor relationships under these types of situations.
4.2.2. Application of aggregation to other atmospheric phenomena
4.2.2.1. Dry Deposition
A method for aggregating dry deposition estimates from the RADM simulations has not
been developed. This problem is difficult to tackle because the amount of suitable dry deposition
data is very limited. In addition, the mechanisms associated with dry deposition processes are
not completely understood. A variety of factors such as boundary layer turbulence and surface
characteristics are known to influence the amount of pollutants that are dry deposited. In order
to apply similar aggregation methods to dry deposition it will be necessary to study the behavior
141
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of the various contributing mechanisms and the ambient levels of SO4", SO2 and NOX with
respect to the meteorological categories.
The data gathered during the RADM intensive evaluation period (Summer, 1988) can
provide some of the information needed to address the dry deposition issue. With a better
understanding of the within versus between category variation in ambient concentrations it will
be possible to evaluate the suitability of estimating the long-term patterns of dry deposition
using a small subset of episodes.
In the present selection six of thirty episodes were selected to enhance dry deposition
representation. The only criteria used in selecting these episodes was that they be associated
with a small amount of wet deposition across the Northeast as aT whole (< 50 mgm"2). However,
with no dry deposition or ambient concentration data it has been very difficult to evaluate the
days chosen. The information gained through the ideas mentioned above will help test these days
and also indicate whether or not more episodes need to be selected to estimate dry deposition
patterns.
4.2.2.2. Visibility
Visibility in the eastern United States is known to be largely governed by the concentra-
tions of SO4*. Therefore, the methods developed thus far could be applied to visibility. Specific
aspects of the problem that need to be addressed include:
• ' Identification of the sources of visibility data across eastern North America;
• Examination of methods for reducing meteorological variability in visibility data
due to humidity;
• Examination of the behavior of visibility associated with the different meteorolog-
ical categories;
• Evaluate need for additional meteorological categories based on differences in
visibility;
• Identification of some of the conditions (categories) that are conducive to good poor
visibilities. Study of the specific relationships existing between these conditions
and visibility.
• Test performance of the current 30 days in predicting visibility behavior over
seasonal and annual time periods.
• Selection of specific episodes useful in predicting long-term visibility behavior
through an aggregate technique. This could include:
142
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— Episodes to supplement the existing 30 days, or
— An independent set of episodes,
4.2.3. Quantification of Aggregation Sensitivity
There are a number of factors that influence the aggregate estimates. In particular, the
variability of deposition-to-precipitation ratios within categories, between categories and
between years can affect the estimate. The variation in the frequency and amount of precipita-
tion associated with the categories and variation in the frequency of occurrence of the actual
categories also affects the estimates. The sensitivity of the aggregate estimates to these types of
factors needs to be quantified and the factors that have the most impact on the results need to be
highlighted. This will also enable us to determine what time scale the aggregation scheme
handles best (i.e. one year averages, four year averages) and the amount of inter-annual
variability associated with the average estimates.
143
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APPENDIX A
Mean 850 mb Wind Flow Patterns
for the 19 Categories
-------
EASTERN NORTH AUCRICAN MEAN asows w»e rr.o - a
TRAJECTORY O.USTER 1 J.ex. N-
100 93 SO 33 10
70 Si
EASTERN NORTH AUE.R;CAN UEAN asous WM: FXLD - 19 CLUST
TRAJECTORY C.USTES 1 J.sr.. N-
100 9S gc IS 10 73 70 63
A-2
-------
EASTERN NORTH AMERICAN WEAN JJOMB WIND
TRAJECTORY CLUSTER 1
» O.USTERS
3.6S. N- O
100 9i 90 ai ao
Ai - X«»n 850 sb vind flov
for t-i« thr«« days la Ci-star 1. The
clusters were exirac'ei Ircnv a see of ;cr.aecutive
three-day periods during 1919, 1931 and 1933. N
ccrreSFcr.is ta t.w.e r.un^er of bccurrer.ces of clu.ite:
1 durir.g that period. The percer.t cf ti.-e (out ci
ail cases) the cluster occurred is also showr. in
tlJ« upper right. Wind vectors represent six hours
of motion at reported wind speed.
A-3
-------
EASTERN NORTH AMERICAN MEAN SiOUB WWC HEl^ - 4 CLUSTERS
TRAJECTORY CLUSTER 2 JJJ. M-
100 as ao as *o
70 85
EASTES.H NOR'TH AMERICAN WEAN asoue WIND rE'.2 - a CL-STESS
TRAJECTORY CLUSTE.R 2 3.;-. N- 12
-------
EASTERN NORTH AUERICAN MEAN sioue WNO FELO - » CLUSTCRS
TRAJECTORY O.USTEH 2 3,27.. W- 12
100
71
rigur« X2 - M«an 850 =b wind flow p«tr«r=a
for tJs« thr«« days in Cluster 2 . T.".e
clusters were ex^rac.cd frcr. a se* o£ ccr.jecutive
t.s.ree-day periods during 1979, 1981 and 1983. N
corresponds to the nunoer of occurrences of cluster
2 during that period. The percent of tine (out of
all cases) the cluster occurred is also shown in
the upper right. Wind vectors represent six hours
of motion at reported wir.d speed.
A-5
-------
EASTERN NORTH AMERICAN y£AN 850U8 WNC FCLD - « CLUSTERS
TRAJECTORY CLUSTER 317S. N- i
co si so as to a
EASTERN NORTH AMCRJCAN WEAN sioue WIND F
TRAJECTORY CLUSTER 2
is CLUSTE.RS
1.7-. N- 6
100
70 e:
A-6
-------
EASTERN NORTH AMERICAN MEAN tioua wsfO ndfl - » CLUSTERS
TRAJECTORY C1.USTER 3 t.7X. N- (
X3 - >t«»= 850 nis wind flow p»ttar=s
for th« thr««
-------
EASTERN NORTH AMERICAN MEAN asoua wo FCLD - » CUSTESS
TRAJECTORY CLUSTER « 2.2X. H- a
13 iO 75 70 iS
EASTERN NORTH AUESICAN MEAN 850M8 WIND FiELD - IS CLUS*E?.S
TRAJECTORY CLUSTER 4 2.2". N- 8
100 »3
A-8
-------
CASTESN NORTH AMERICAN MEAN aS
-------
EASTERN NORTH AMERICAN MEAN (sous WMO FCLD - » CLUSTERS
TRAJECTORY CLUSTER 3 i.9X. M- 25
EASTERN NORTH AMERICAN WEAN aiouB WIND FIELD - » C.USTERS
TRAJECTORT CLUSTER 5 6.95. N- 25
100 91
A-10
-------
EASTERN NORTH AMERICAN MEAN asous WIND
TRAJECTORY CLUSTER 5
It CLUSTERS
«.«. M- 25
-123
•3 90 85 «0 75 70 t-
A3 - M««n 850 mb wind flow patttms
for th« thr«« days In Cluster 5. The
clusters were extracted from a set of consecutive
three-day periods during 1979, 1981 and 1933. N
corresponds to the nusvfcer of occurrence; of cluster
5 during that period. The percent of tir.e (out of
all cases) the cluster occurred is also showr. in
the upper right. Wi.-.d vectors represent six hcur:
of =otion at reported wi.-.d speed.
A-ll
-------
EASTERN NORTH AMERICAN utAN asoua w*c FCLD - » CLUSTERS
TRAJECTORY CLUSTER « §.OX. N- 2»
100 13 90 tO 7S 70 «1
EASTERN NORTH AMERICAN MEAN asoua wwo FELB - « c.us*ERS
TRAJECTORY CLUSTER 6 j OI M_ 29
100 92
A-12
-------
EASTERN NORTH AMERICAN MEAN asous w»c PELD - a
TRAJECTORY CLUSTER I g.ox. M- 2S
100
AS - Haan 850 mb »ind flow pattama
for tha thraa days in Cluster £. T.w.e
clusters were extracted £rom a set of ccnaecutive
three-day periods during 1919, 1981 and 1983.- N
corres?cr.dj to the nirrier of occurre.-.ces of cluster
6 during that period. The percer.t of tiae (out cf
all cases) the cluster occurred is also shown i.-.
the upper right. wind vectors represent six hours
of motion at reported wind speed.
A-13
-------
EASTERN MOUTH AUERICAN KAN »sous w»e mo - » CLUSTERS
TRAJECTORY CLUSTER 7 g.gx. N- 32
75 70 8S
EASTERN NORTH AMERICAN MEAN 850U3 WIND FIELD - IS CLUSTERS
TRAJECTORY C-'JSTER 7 g.8S. N- 32
* 10
100 »; to 13 *o ' 70 e:
A-14
-------
EASTERN NORTH AMERICAN WEAN esous w»c fELD - » D.USTEKS
TRAJECTORY CLUSTER 7 8.85. N- 32
100
81 90
AT - Maaa 850 ab wind flow ptt-«rti«
for ti« tir«« d*y» In Clustaz 7. T^-.e
clusters were exrracte'i frcn: a set of consecutive
three-day periods during 1979, 1931 ar.d 1932. N
corresponds to the r.u-ii-er of occurrences of cluster
7 during that period. The percent of tiM (out of
all cases) the cluster occurred is also shown in
the upper right. Wind vectors represent six hours
of motion at reported wind sp-eed.
A-15
-------
EASTERN NORTH AMERICAN MEAN SSOUB WINS mo - T» CLUSTERS
TRAJECTORY CLUSTER 8 s.ox. N- w
100 K 10
80 75
91
EASTERN NORTH AUIR.-CAN MEAN SSOUB WIND FIELD - is c^s'^s
TRAJECTORY CLUSTER 8 s.os. N- 18
iOO »S
•3 10 75 70 15
A-16
-------
EASTERN NORTH AFRICAN WEAN JSOUB WIND rtto - « CLUSTERS
TRAJECTORY CLUSTER 8 5.ox. M-
100 »1 90 IS 10 7! 70
ricrur« AS - Main 850 mb wind flow patterns
for ti« t_hr«« days In Cluster 8 . The
clusters were exrrac-ed Irom a set of consec-.tive
three-day periods during 1979, 1931 ar.d 19S3. N
corresponds to the p.usrer of occurrences of cluster
8 during that period. The percent of tijr.e (out cf
•11 cases) the cluster occurred is also shown i-
the upper right. Wind vectors represent six hours
of motion at reported wind speed.
A-17
-------
EASTERN NORTH AMERICAN MEAN asous w»« rtiB - i» CLUSTERS
TRAJECTORY CLUSTER » ».«. M- 33
100 »S
EASTERN NORTH AUERXAN U£AN 850W8 W»C FIELD - IS CLL'STESS
TRAJECTOSr CLuSTER 9 9.:-, N- 33
100 »5
»0 7S 70 6i
A-18
-------
EASTERN NORTH AMERICAN MEAN lious WIND ncLD - n CLUSTERS
TRAXCTORY CLUSTER i « 1X. N- 33
100 91 90 «J 13 75 70
Fig^r* A9 - M««n 8£0 ab wind flew patterns
2or th« thr«« diva ii Cluat»r 9. The
cluster: were extracted free, a set ef consecutive
three-day periods during 1979, 1981 and 1983. N
corresponds to the nu.-r.reT of occurrences of cluster
9 during that period. T.-.e percent cf tirr.e (cut cf
• 11 cases) the cluster occurred is ilsc showr. in
the upper right. Wind vectors represent six hcurs
of inction at repcrtec wir.i speed.
A-19
-------
EASTERN NORTH AyERCAN HAN eSOuS WM) FTLD - « CLUSTERS
TRAJECTORY O.USTE* X5 j.jr.. N- a
ioc
EASTERN NORTH AMERICAN UEAN 850U6 WIND r£L2 - IS CLUSTERS
TRAJECTO*- CLUSTER 10 2.2r.. N- S
1 2=
A-20
-------
EASTERN NORTH AMERICAN UEAN esoua WIND FIELD - a CLUSTERS
TRAJECTORY CLUSTER TO j.2r.. N- 8
IOC
90
rig-ur« JUO - Maaa 850 nb wind «io» p»ttar=3
•or th« thr««
-------
EASTERN NORTH AMERICAN MEAN asoua wno fits - 19 CLUSTERS
TRAJECTORY auSTER n 7.47.. N. 57
ico es gc i: ic
o a:
EASTERN NORTH AMERICAN WEAN SSOUB WIND FIE.D -
TRAJECTCRr C.USTER n j t- N_ 2-
A-22
-------
EASTESN NORTH AMERICAN uf AN 550U8 WIND HELD - is CLUSTERS
TRAJECTORY CLUSTER 11 7.*S. N- 27
rig-ur« *•" ~ H*»n 85° °*> wind flo« patterns
for ti« tixraa days ia Cluster 11. The
clusters vere ext.racrei frcr. a jet cf ssr.sec-tive
three-day pericis durir.g 19"5, 155: ar.i 19 = 2. N
ccrresponds t= the nuster cf occurrer.ces of cluster
11 during that pericc. The percent of tinie (cut of
all cases) the cluster occurred is also shown in
the upper right. Wir.d vectors represe.-.t six hours
of motion at reported wir.i spesi.
A-23
-------
EASTERN NORTH AMERICAN MEAN asous WIND rzio - n CLUSTERS
TRAJECTORY CLUSTER 12 4.IS. H- 15
100 93 1C 6S 10 73 70 l-
£>ST£SN NORTH AMERICAN UCAN 850U8 WINS FX^D - IS C»US*E»S
TRAJECTORr CLUSTER 12 4 •£. s. 1S
100
70 e:
A-24
-------
EASTCRN NORTH AFRICAN WEAN aicuB WIND FCJ - w CLUSTERS
TRAJECTORY CLUSTCR 12 4 m N.
K
A12 - M«.n 850 ab »ind flow
for the three d*ya ia Cluster 12. T.le
Clusters were extracted from a set of c=r.ae;utive
three-day period during 19"9, 1951 a--.d 1983. V
corresponds to the nuri«r of occurrences of cluster
12 during that period. The percent of tine (out of
all cases) the cluster occurred is also shown in
the upper right. wind vectors represent six hours
of motion at reported wind speed.
A-25
-------
EASTERN NORTH AUCRICAN MEAN asous WIND rcj - » CLUSTERS
TRAJECTORY CXUSTER 13 2.2X. N- 8
100
EASTERN NORTH AMERICAN MEAN esous WINO ncj - is C^USTE=S
TRAJECTO=r CLUSTER U 2.;-. N- a
- —
100
•° is «o ?: 70 t:
A-26
-------
EASTERN NOflTH AMERICAN MEAN JSOuS WHO fTLD - W CLUSTERS
TRAJECTORY CLUSTER U J.2S. N- 1
100
it BO
1C 7S 70 65
i.13 - Main 650 ai vind flow
.for th« thr«« days in Cluster 13.
clustera were er^racted frc=
a set of C3
three-day periois during 1979, 1931 a.-.d 1953. N
corresponds to the ni^rier of occurrer.cei of cluster
13 during that period. The percent of time (out of
•11 cases) the cluster occurred is also shown in
the upper right. Kind vectors represent six hcurs
of notion at reported wind sp«ed.
A-27
-------
EASTERN NORTH AUCRJCAN WEAN tsoua WINO rtLD - » CLUSTERS
TRAJECTORY CLUSTER H V4X N. 5
too »:
ro a:
EASTERN NORTH AMCRCAS MEAN esous WINO FXLO - is CIUSTE^S
TRAJECTORY CLUSTER H 1-4- N, «
100 91
A-28
-------
E.ASTESN NORTH AULRICAN UCAN 850U8 WIND FIELD - 1J CLUSTERS
TRAJtCTORY CLUSTER 14 t*-. M. S
ICO IS BO 12 10 73 70 IS
riyur« A14 - >U»n BIO ais wind fiov p»tt«rns
for th« tfcz«« days in Cluster 1«. The
clusters were extracted frori a set of consecutive
three-day periods during 1579, 1981 and 1983. N
corresponds to the nusier of occurrences of cluster
14 during that period. The percent of ti-Tie (out of
all cases) the cluster occurred is also shown in
the upper right. Wind vectors represent six hours
of motion at reported wind speed.
A-29
-------
EASTERN NORTH AMERICAN UEAN .«soue w>c FCLD
TRAJCCTORT C.USTES B
CLUSTERS
N. 3
100
EASTERN NORTH AMERICAN U£AN BiOug W5O FIELD - ?9 CLUSTERS
TRAJECTORY O.USTLR 13 ..*-. N. «
100 IS tO 13 tO
A-30
-------
EASTERN NORTH
MEAN »soua wwo ncto - a CLUSTERS
TRAJECTORY CLUSTER 13 ^z. N- 5
A15 - Haan 850 nb wind flow p«tt«r=a
for ti« tir«« itys in Cluster 15 . T.w.e
clusters were ext.racte£ free a se* ci rcr.iecu;iv«
three-day periida duri.-.g 1979, 19B1 and 1963. N
correspor.da to the r.uz-ier cf occurrence: cf cluster
. 15 during that period. The percent of tirr.e (cut cf
all cases) the cluster occurred is also shewn in
the upper right. Wind vectors represent six hours
of notion at reported wind speed.
A-31
-------
EASTERN NORTH AMERICAN MEAN tioue w»c FCLD - w CLUSTERS
TRAJECTORY O.USTER W 17* N.
103 f.
EASTERN NORTH AMERICAN UEAN SSOMB WIND
TRAJECTORY CLUSTER is
- is C.-JSTERS
- N_ 6
/ ^ y vx
100 •! tO |0 71
A-32
-------
EASTERN NORTH AMERICAN MEAN 850UB WIND FELD - 1» CLUSTERS
TRAJECTORY C1.USTEK tt \7~, N- (
loo is sc
10 7S 70 61
rig-ar« A16 - Haan 850 =b wind flow p*--en:3
for »^» rhr«« days i- Cl--s-«r 16. The
clusters were extracted lrcr> a set of ccr.secutive
three-<±ay p«rioda during 1979, 1981 ar.d 1982. N
correspcr.ds to the r.urier of occurrences of cluster
16 durir-.g that period. The perce.-.t of fine (cut cf
all cases) the cluster occurred is also showr. ir.
the vrpp«r right. Wind vectors represent six hour:
of seticn at reported wind sp«ed.
A-33
-------
EASTERN NORTH AMERICAN MEAN ajous WIND
TRAJECTORY CLUSTER 17
it CLUSTERS
j.j-. s- u
100 e:
EASTERN NORTH AMERICAN MEAN esoua WIND FIELO - is CLUSTERS
TRAJECTOR-r CLUSTER 17 J.sr.. N. '.:
100
-------
EASTERN NORTH AMERICAN MEAN 850MB WIND TELO - « CLUSTERS
TRAJECTORY CLUSTER 17 J.ss. N- O
ioo 8:
XI7 - K..= «£0 mb wind flow p«««»a
for th« thr.« diys in Cluat.r 17.
eluacera were ex-.r3c-.ed frcrr. a «'. of "r.secu--^
-ree-d^y period during 1979, 1981 ar.i 1983 >
correapcnd, to the nusfcer o£ occurrence^ - c.«. .
17 during t.-.« period. The percent of t^-.e (ou. of
.11 ca^es) the cluster occurred is .lao shcwr. ir.
the upper right. Hind vectors ze?re3er.t 3-x ..cur:
of action .t reported wind speed.
A-35
-------
EASTERN NORTH AUtSiCAN UEAN eSOuB WIND FIELD - IS CLUSTERS
TRAJECTORY CLUSTER 18 2.6-. N- U
ICO s:
STERN NCSTH AUE=:CAM MEAN aicua WINS FXLD - '.s c^.S'E'.s
TSAJECTOPr CL'JSTE8 'S J.6". N- O
:co .
A-36
-------
EASTERN NORTH AMERICAN WEAN JSOUB WIND FIELD - 11 CLUSTERS
TRAJECTORY CLUSTER 18 3.S-. N- U
ICC
SJ 10 IS »0
rig-ur« A18 - M«»n 850 mb wind flow patterns
for tb« t!ir«« days is Clustar IS. The
clusters were ex-racied frsm a set of consecutive
three-day periods during 1979, 1981 ar.d 1982. N
corresponds to the nu.-rier of occurrences of cluster
18 during that period. The percent of ti--ne (out cf
all cases) the cluster occurred is also shewn in
the upper right. Hind vectors represent six hours
of motion at reported wind speed.
A-37
-------
EASTERN NORTH AMERICAN MEAN asoua wmo FCLO - is CLUSTERS
TRAJECTORY CLUSTER 19 7.8^. N- 10
:so s; sc i: ic 7: re s:
EASTERN NORTH AMERICAN UEAN 8icu3 WIMC rxL3 - is C^L'STLRS
TRAJECTORY CLUSTER 19 ;..- N. -c
100
A-38
-------
EASTERN NORTH AMERICAN I«*N ssoue WIND FIELD - u CLUSTERS
TRAJEC'ORY CLUSTER 19 2.ST.. N- 10
X19 -
i. thr..
« 850
Clu.t.r 19.
- = r ti. thr.. i*7» ia Cu.t.r .
L3-. = S were extrac.ec fr« a ,e^ o£ c=r.« = u-.ive
chree-d^y period, &rir.5 1S79. 196: : -;e/c,./....
S'LM.S"«K P;;-';,:^";"'--^ ';:;';;
f notion a- repcr-.ed -ir.d spesd
A-39
-------
APPENDIX B
85th Percentile Wet Sulfate
Deposition Plots for the
19 Categories
B
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 1
Figure Bl - 85th Percentile Three-day Total
Deposition Pattern due to Cluster 1 Cases.
The 85th percentile at each site was derived fror.
the distribution of all available three-day total
deocsitior. cases within the cluster.
B-2
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 2
Figure B2 - 85th Percantile Three-day Total
Deposition Pattern due to Cluster 2 Cases.
The 65th percer.tile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
B-3
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 3
Figure B3 - 8£th Percentile Three-day Total
Deposition Pattern due to Cluster 3 Cases .
The 85th percer.tile at each site was derived fro:?.
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 4
Figure B4 - 85th Percentile Three-day Total
Deposition Pattern due to Cluster 4 Cases.
The 85th percentile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
B-5
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 5
Figure 35 - 85th Percentile Three-day Total
Deposition Pattern due to Cluster 5 Cases.
The 85th percer.tiie at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 6
Figure B6 - 85th Percentile Three-day Total
Deposition Pattern due to Cluster 6 Cases.
The 85th percer.tile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
B-7
-------
85th Pcrcentile S04= Deposition (STEP 19) - CLUSTER 7
Figure B7 - 85th Percentile Three-day Total
Deposition Pattern due to Cluster 7 Cases.
The 85th percentile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 8
Figura B8 - 85th Parcentila Three-day Total
Deposition Pattern due to Cluster 8 Cases.
The 85th percer.tile at each site was derived fror.
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 9
Figure B9 - 85th Percentile Three-day Total
Deposition Pattern due to Cluster 9 Cases.
The 85th percer.tile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile 504= Deposition (STEP 19) - CLUSTER 10
Figure BIO - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 10
The 85th percer.tile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
B-ll
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 11
Figure Bll - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 11,
The 85th percer.tile at each site was derived frcrr.
the distribution of ail available three-day total
deposition cases within the cluster.
B-12
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 12
Figure B12 - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 12
The 85th percer.tile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
B-13
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 13
Figure B13 - 85th Parcentile Three-day
Total Deposition Pattern due to Cluster 13
The 85th percentile at each site was derived from
the distribution of all available three-day total
deposition cases within the cluster.
B-1A
-------
85th Percentile 504= Deposition (STEP 19) - CLUSTER 14
Figure B14 - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 14
The 85th percentile at each site was derived from
the distribution of ail available three-day total
deposition cases within the cluster.
B-15
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 15
Figure B15 - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 15.
The 85th percer.tile at each site was derived frcrr.
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 16
Figure B16 - 85th Percentile Three-day
Total Depoaition Pattern due to Cluster 16
th percentile at each site was derived f-om
the distribution of all available three-day total
deposition cases within the cluster.
B-17
-------
85th Percentile 504= Deposition (STEP 19) - CLUSTER 17
Figure B17 - 85th Percentile Three-day
Tot»l Deposition Pattern due to Cluster 17 .
The 85th percer.tile at each site was derived from
the distribution of ail available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 18
Figure B18 - 85th Percentile Three-day
Total Deposition Pattern dua to Cluster 18.
The 85th percentile at each site was derived from
the distribution of ail available three-day total
deposition cases within the cluster.
B-19
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 19
Figure B19 - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 19.
The 85th percentile at each site was derived frcrr.
the distribution of all available three-day total
deposition cases within the cluster.
-------
85th Percentile S04= Deposition (STEP 19) - CLUSTER 20
Figure B20 - 85th Percentile Three-day
Total Deposition Pattern due to Cluster 20
The 85th percent-le at each site was derived fror.
the distributicr. cf ail available three-day total
deposition cases within the cluster.
B-21
-------
APPENDIX C
Site Specific Tables of
Total and Percent of Total
Wet Sulfate Deposition
KEY
No. - Number of times cluster had precipitation
%ofDep - Percent of total deposition
Total - Total wet sulfate deposition (mgro )
AVG - Average deposition of wet events only jmgm
Max - Maximum deposition value reported (mgm )
S.D. - Standard deviation of the wet events (mgnf
-------
SITE 1
CL
9
11
7
6
5
8
12
1
3
10
19
2
17
16
14
i
15
18
13
20
No.
68
90
58
53
47
45
41
28
25
29
26
12
14
10
•
g
i
8
i
150
%ofDep
16.7
16.13
9.78
9.63
9.38
8.4
5.51
5.36
4.45
3.08
2.63
1.75
1.67
1.62
1.59
1.1
0.99
0.18
0.07
0
TOTAL
2612.81
2523.21
1529.61
1505.99
1467.35
1314.23
861.36
838.47
696.54
481.18
410.67
273.63
261.53
253.94
248.1
171.88
154.12
28.34
10.87
7160.86
AVG
38.424
28.036
26.373
28.415
31.22
29.205
21.009
29.945
27.862
16.592
15.795
22.802
18.681
25.394
35.443
21.485
38.53
3.543
2.717
47.739
MAX
230.09
150.78
192.73
117.5
150.78
192.73
80.43
11724
102.3
83.5
125.49
50.62
66.97
80.43
46.37
52.67
71.35
8.82
4.76
230.09
S.D.
45.458
24.801
35.594
29.668
27.58
37.178
20.564
28.932
26.133
16.24
23.99
14.462
18.981
23.95
10.67
20.802
36.021
2.692
1.838
44.174
SITE 2
CL
7
9
11
5
8
6
12
1
i
10
4
14
17
16
2
18
15
19
13
20
No.
58
55
67
40
35
53
23
19
24
24
12
12
12
6
12
13
c
12
•
160
%ofDep
20.66
16.76
12.43
10.06
9.42
8.42
4.43
4.38
3.43
2.55
1.45
1.39
1.18
1.08
0.91
0.55
0.53
0.35
0
0
TOTAL
3460.22
2807.97
2082.47
1685.34
1578.31
1410.03
741.24
734.31
574.71
426.5
243.19
232.36
197.68
181.06
153.12
92.29
89.26
58.67
0.5
6288.38
AVG
59.659
51.054
31.082
42.133
45.095
26.604
32.228
38.648
23.946
17.771
20.266
19.363
16.473
30.177
12.76
7.099
9.918
4.889
0.5
39.302
MAX
328.08
328.08
146.87
143.16
327.9
140.77
145.56
124.61
81.33
65.91
47.28
74.05
46.18
68.69
37.61
39.51
29.68
14.4
0.5
204.37
S.D.
73.2%
66.853
33.437
37.464
62.082
26.044
33.533
28.209
19.565
14.666
14.797
21.394
14.973
21.668
10.356
12.576
11.151
5.047
0
38.606
CM
-------
SITE 4
CL
6
7
9
5
11
8
1
3
10
4
14
12
2
17
19
16
18
13
15
20
No.
75
61
68
60
78
29
32
23
41
11
16
29
23
18
8
8
14
9
8
19?
%ofDep
15.9
14.15
13.91
11.7
9.44
7.51
4.51
4.47
4.11
2.77
2.49
1.93
1.91
1.62
1.25
0.66
0.62
0.53
0.51
0
TOTAL
3451.02
3071.06
3018.09
2538.01
2049.42
1629.42
978.58
969.31
892.98
601.1
541.44
417.97
414.33
351.88
271.91
144.18
135.39
113.97
111.29
7266.73
AVG
46.014
50.345
44.384
42J
26.275
56.187
30.581
42.144
21.78
54.645
33.84
14.413
18.014
19.549
33.989
18.023
9.671
12.663
13.911
37.651
MAX
180.65
164.53
40237
17036
152.91
44826
12321
14325
173.74
204.13
10821
50.52
53.99
68.09
87.3
58.71
51.49
43.51
56.64
230.92
S.D.
50.758
35.461
63.322
40.517
29.947
92.825
32.238
38.152
35.657
75.285
29.263
13.329
13.387
16.237
28.051
18.838
14.986
13.57
18.098
39.294
SITE 5
CL
6
7
9
5
11
10
14
1
2
8
3
19
17
4
16
15
12
13
18
20
No.
79
57
58
50
56
36
16
25
27
29
18
13
15
14
9
10
14
5
10
161
5ofDeo
15.36
13.25
11.88
11.26
6.91
6.59
5.76
5.59
3.85
3.72
3.35
2.76
2.32
2.15
1.86
1.4
1.03
0.61
0.35
0
TOTAL
3695.48
3187.9
2858.47
2710.61
1663.57
1584.97
1385.26
1346.05
927.13
895.62
805.53
663.97
559.47
518.18
447.4
337.53
248.24
146.07
84
9042.69
AVG
46.778
55.928
49.284
54.212
29.707
44.027
86.579
53.842
34.338
30.883
44.752
51.075
37.298
37.013
49.711
33.753
17.731
29.214
8.4
56.166
MAX
190.27
333.5
170.22
333.5
134.91
233.66
233.66
169.1
103.4
79.03
233.66
137.4}
164.68
103.3
175.01
92.98
38.87
64.08
17.09
280.62
S.D.
45.879
58.679
35.692
57.408
30.691
54.861
72.276
51.079
32.208
26.83
53.201
42.757
39.479
32.082
50.53
29.485
12.266
29.275
6.463
55.165
C-2
-------
SITE 7
CL
6
7
9
5
11
1
10
8
14
3
17
i
12
2
16
18
19
15
13
20
No.
71
57
61
43
48
28
33
20
15
17
17
11
15
13
1
8
i
5
<
152
%ofDc?
18.06
16.97
12.74
9.18
8.26
7.24
4.97
4.09
3.43
3.34
2.91
1.58
1.53
1.43
1.36
1.02
0.85
0.83
0.2
0
TOTAL
3466.08
3256.63
2445.27
1761.87
1584.62
1389.6
953.38
784.8
658.39
640.67
557.85
302.99
293.78
273.72
261
196.3
163.77
160.2
40.45
6801.45
AVG
48.818
57.134
40.086
40.974
33.013
49.629
28.89
39.24
43.893
37.686
32.815
27.545
19.585
21.055
37.286
24.539
40.943
32.04
10.113
44.746
MAX
234.88
186.23
160.3
226.18
128.68
160.55
13539
172.58
125.11
128.68
124.29
84.28
40.84
56.61
105.01
91.2
91.2
86.54
19.6
353.8°
S.D.
47.94
51.377
38.462
46.933
34.104
50.764
30.708
44.462
30.774
31.792
32.953
22.565
12.401
14.023
35.182
28.441
34.082
32.164
7.256
52.32
<;TTF 8
CL
11
9
8
7
<
3
^
19
12
i
10
17
i
14
i
15
1
1
1
2C
No.
75
66
39
30
31
22
25
16
19
23
20
16
11
10
20
i
6
i
14
%ofDeo
16.51
13.11
11.1
10.95
9.22
6.25
4.73
3.74
3.59
3,5
3.03
2.95
2.85
2.49
2.15
1.4
1.3
0.7
0.2
TOTAL
2037.28
1618.52
1369.53
1351.92
1138.2
771.6
584.13
461.26
443.45
432.04
373.63
364.34
352.28
307.79
264.98
172.63
171.63
95.06
32.76
4870.14
AVG
27.164
24.523
35.116
45.064
36.716
35.073
23.365
28.829
23.339
18.784
18.682
22.771
32.025
30.779
13.249
19.181
28.605
15.843
6.552
34.057
MAX
124.37
101.64
105.13
330.62
123.42
112.16
112.16
62.02
75.76
63.92
45.93
61.99
103.14
75.56
73.78
35.14
136.16
54.55
15
330.6
S.D.
26.377
24.459
27.159
59.368
29.691
29.844
28.098
20.264
21.268
15.175
' 13.447
16.072
37.131
22.365
18.247
12.736
52.909
19.654
4.992
44.688
C-3
-------
SITE 10
CL
6
5
9
7
11
3
10
1
2
8
14
17
19
4
12
16
18
13
15
20
No.
93
46
65
48
66
18
31
25
18
16
12
19
11
7
9
4
6
*
i
i
161
%ofDep
23.97
15.87
14.7
10.38
8.82
4.25
3.78
3.65
2.67
2.55
2.21
1.98
1.88
1.09
0.94
0.7
0.39
0.11
0.06
0
TOTAL
2674.48
1770.82
1640.54
1158.78
983.93
474.72
421.28
407.08
297.98
284.06
246.12
220.8
209.94
121.72
104.67
78.44
44.07
12.56
7.24
3949.21
AVG
28.758
38.496
25.239
24.141
14.908
26.373
13.59
16.283
16.554
17.754
20.51
11.621
19.085
17.389
11.63
19.61
7.345
6.28
3.62
24.529
MAX
202.95
215.16
117.38
96.74
151.13
65.66
48.57
75.59
99.25
64.03
58.59
44.05
31.49
70.31
47.83
46.9
31.49
6.28
6.28
105.14
S.D.
31.137
39.804
21.807
23.902
24.766
21.203
14.06
18.354
24.162
18.385
17.665
11.439
8.189
24.737
13.886
21.357
12.028
0
3.762
24.977
SITE 11
CL
9
11
7
t,
g
6
10
3
12
1
17
14
4
15
18
19
O
4.
16
13
20
No.
73
91
57
50
41
75
50
24
30
29
21
15
15
14
13
13
22
8
8
198
%ofDep
17.89
13.94
13.61
10.21
8.83
7.36
5.09
4.01
3.37
3.28
2.66
1.63
1.42
1.41
1.25
1.25
1.22
1
0.57
0
TOTAL
3886.21
3028.78
2957.89
2219.3
1918.94
1598.49
1105.56
870.54
732.01
712.52
578.51
354.81
307.72
306.08
272.34
271.42
265.21
218.25
123.28
9651.78
AVG
53.236
33.283
51.893
44.386
46.803
21.313
22.111
36.273
24.4
24.57
27.548
23.654
20.515
21.863
20.949
20.878
12.055
27.281
15.41
48.746
MAX
362.37
359.07
289.71
279.55
236.63
93.49
93.49
90.24
93.54
76.11
82.38
78.63
54.35
75.48
164.14
73.09
40.05
97.6
49.06
313.13
S.D.
75.088
50.239
71.324
46.443
56.984
18.214
21.848
30.889
24.753
19.603
21.964
19.039
14.127
20.465
43.535
23.808
10.7%
30.673
15.586
55.283
-------
SITE 12
CL
9
6
5
11
1
10
3
16
14
2
7
17
8
4
15
12
19
13
18
20
No.
36
51
22
32
26
25
15
8
11
13
12
8
11
10
6
t
4
j.
•
112
%ofDep
19.65
16.24
9.84
9.46
8.17
6.09
5.01
3.94
3.64
3.47
2.95
2.67
2.2
1.95
1.27
1.19
1.09
0.75
0.41
0
TOTAL
1912.42
1581.01
957.66
920.71
795.02
592.33
487.85
383.85
353.87
337.6
287.46
259.54
214.54
189.99
123.5
116.29
105.76
73.45
39.87
2898.22
AVG
53.123
31
43.53
28.772
30.578
23.693
32.523
47.981
32.17
25.969
23.955
32.443
19.504
18.999
20.583
23.258
26.44
18.363
13.29
25.877
MAX
214.56
214.56
227.57
73.69
84.84
84.84
75.55
93.06
74.14
74.14
64.49
75.55
33.13
44.03
40.1
46.81
30.25
28.6
18.67
206.37
S.D.
58.198
42.681
61.717
21.99
20.35
19.899
23.784
30.95
21.456
19.253
21.179
22.86
11.063
12.719
10.577
13.994
2.62
7.363
5.845
25.283
SITE 13
CL
11
6
5
9
7
8
12
1
19
10
3
16
17
15
A
i
18
14
13
20
No.
89
56
41
56
52
46
37
30
25
39
17
10
16
A
10
11
12
t
2
159
%ofDep
19.04
12.14
11.26
9.6
8.41
7.36
7.19
5.87
3.92
3.62
2.95
2.62
2.13
1.03
0.88
0.77
0.61
0.51
0.09
0
TOTAL
1917.71
1222.67
1133.82
966.99
847.03
740.93
723.62
590.96
394.69
364.35
297.27
263.89
214.96
103.33
88.78
77.51
61.07
51.12
9.37
3853.9
AVG"
21.547
21.833
27.654
17.268
16.289
16.107
19.557
19.699
15.788
9.342
17.486
26.389
13.435
51.665
8.878
7.046
5.089
10.224
4.685
24.238
MAX
107.33
112.77
104.32
94.55
122.59
65.45
80.42
127.76
38.82
47.87
54.33
85.68
50.05
67.85
29.01
16.35
17.7
24.77
8.9
112.46
S.D.
21.749
32.428
29.015
21.437
20.237
15.538
18.167
24.29
11.868
10.592
• 13.803
24.807
14.212
22.889
7.996
5.668
6.087
10.329
5.961
25.997
C-5
-------
SITE 14
CL
7
8
9
11
3
6
1
19
5
2
15
13
17
14
12
10
4
16
18
20
No.
49
46
57
71
20
29
28
10
26
27
11
6
18
10
14
15
9
4
4
145
%ofDcp
15.59
13.99
13.09
1132
5.42
5.1
5.06
4.71
4.58
4.12
3.44
2.86
2.63
2.16
2.14
1.89
1.15
0.56
0.19
0
TOTAL
1598.28
1434.15
1342.66
1160.97
555.53
523.48
518.41
482.62
469.58
422.42
352.46
293.4
269.72
221.58
219.61
194.26
118.1
57.42
19.9
3632.68
AVG
32.618
31.177
23.555
16.352
27.776
18.051
18.515
48.262
18.061
15.645
32.042
48.9
14.984
22.158
15.686
12.951
13.122
11.484
4.975
25.053
MAX
382.37
382.37
401.1
161.9
161.9
93.55
161.9
117.5
60.93
50.64
76.61
94.1
40.49
38.68
66.54
50.04
28.88
25.72
7.97
192.41
S.D.
66.725
56.898
52.593
21.579
39.317
23.803
29.578
43.327
16.052
11.834
26.095
34.743
11.928
14.754
15.969
14.347
8.866
9.526
2.451
35.95
SITE 15
CL
9
11
5
8
6
7
1
2
3
10
17
4
14
15
16
13
19
12
18
20
No.
55
71
39
41
48
45
27
30
19
29
17
16
13
12
9
9
11
9
7
163
<*ofDep
15.53
15.19
8.45
7.05
6.93
6.34
6.09
5.69
4.93
4.45
3.74
3.14
2.81
138
2.29
1.79
1.28
1.19
0.72
0
TOTAL
2178.93
2131.73
1185.32
989.22
972.52
889.8
854.55
798.5
691.84
624.59
525.45
440.56
394.43
334.41
321.8
250.59
179.86
167.03
100.4
4457.37
AVG
39.617
30.024
30.393
24.127
20.261
19.773
31.65
26.617
36.413
21.538
30.909
27.535
30.341
27.868
35.756
27.843
16.351
18.559
14.343
27.346
MAX
195.3
97.09
104
191.73
113.1
138.24
97.09
66.67
97.09
51.98
77.28
113.1
76.88
46.53
58.85
67.05
47.86
57.18
51.98
195.3
S.D.
44.448
24.506
21.955
32.844
21.555
28.245
21.717
17.771
26.502
17.231
20.933
29.693
21.75
17.51
14.836
20.709
13.35
17.234
18.208
29.072
C-6
-------
SITE 16
CL
7
9
11
1
6
5
8
10
4
2
3
19
16
17
15
14
12
13
18
20
No.
58
61
68
31
64
47
37
36
17
29
17
17
10
13
9
11
16
7
7
165
%ofDep
14.59
11.1
10.87
7.71
7.62
7.22
5.92
5.14
4.79
4.32
3.67
3.13
2.61
2.39
2.36
2.3
1.81
1.4
1.05
0
TOTAL
2236.6
1701.24
1665.93
1181.38
1168.07
1105.85
907.18
788.44
733.61
662.34
563.11
480.13
399.33
366.08
362.03
352.22
276.97
213.98
161.42
5373.42
AVG
38.562
27.889
24.499
38.109
18.251
23.529
24.518
21.901
43.154
22.839
33.124
28.243
39.933
28.16
40.226
32.02
17.311
30.569
23.06
32.566
MAX
254.14
216.22
123.19
155.75
12733
123.19
159.77
103.09
107.48
59.58
94.51
79.63
94.51
78.89
78.27
94.51
77.96
61.08
61.08
254.14
S.D.
55.72
43.099
27.446
38.107
20.374
33.177
28.676
20.814
33.154
14.161
31.03
26.77
26.184
26.887
26.732
26.869
21.343
22.702
19.623
41.579
SITE 17
CL
6
7
9
11
10
5
8
1
4
2
19
14
16
15
17
-
13
18
12
20
No.
74
47
52
52
30
38
35
27
14
17
21
12
11
12
12
12
8
9
10
151
%ofDeo
17.93
10.53
9.88
8.32
6.96
6.92
6.61
5.23
5.01
3.78
3.57
3.15
2.39
2.31
2.28
2.01
1.21
1.17
0.75
0
TOTAL
2403.38
1411.58
1324.33
1114.81
932.88
927.35
885.78
700.9
670.91
506.34
479.05
422.86
320.54
310.12
305.41
269.08
162.12
156.65
100.12
3893.04
AVG
32.478
30.034
25.468
21.439
31.0%
24.404
25.308
25.959
47.922
29.785
22.812
35.238
29.14
25.843
25.451
22.423
20.265
17.406
10.012
25.782
MAX
135.2
88.33
84.72
76.86
113.02
97.46
80.94
85
126.04
94.17
81.57
68.46
59.97
59.97
97.46
47.9
46.63
30.33
29.57
96.17
S.D.
28.669
29.137
21.505
17.428
27.888
22.599
23.077
17.977
32.045
26.456
18.538
19.949
14.742
14.612
30.809
13.533
12.927
8.119
9.429
22.375
C-7
-------
SITE
CL
9
6
7
8
5
19
14
10
11
4
1
16
3
2
15
17
18
13
12
20
18
No.
68
74
57
35
38
26
16
28
29
10
19
10
16
13
8
12
4
7
5
160
%ofDep
17.95
16.62
16.35
9.54
7.42
7.11
3.68
2.9
2.75
2.72
2.33
2.16
1.67
1.63
1.33
1.31
1.18
0.82
0.53
0
TOTAL
2504.82
2319.08
2281.78
1331.78
1035.05
992.43
512.95
404.95
384.26
379.44
324.57
301.18
233.3
227.7
185.1
183.29
164.09
114.54
73.43
5132.71
AVG
36.836
31.339
40.031
38.051
27.238
38.17
32.059
14.463
13.25
37.944
17.083
30.118
14.581
17.515
23.138
15.274
41.022
16.363
14.686
32.079
MAX
145.26
126.02
135.46
156.28
106.03
130.02
87.08
51.92
75.89
72.39
40.79
73.88
31.03
53.45
44.68
33.01
111.19
56.06
57.11
159.37
S.D.
35.551
27.616
34.781
39.16
26.236
30.397
23.176
13.294
15.53
27.4
13.605
20.043
9.34
12.952
12.481
10.35
47.472
18.794
23.794
33.882
SITE 19
CL
6
9
;
8
(
19
11
4
14
10
1
2
3
17
16
12
18
13
15
20
No.
63
57
44
29
21
18
11
6
3
1)
4
7
7
6
2
2
2
2
1
99
%ofDg>
27.16
23.11
14.57
10.61
7
5.86
1.96
1.88
1.56
1.27
1.1
1.06
0.86
0.74
0.71
0.26
0.18
0.06
0.05
0
TOTAL
1999.28
1701.4
1073.04
781.02
515.66
431.7
144.45
138.05
115.15
93.14
81.24
78.12
62.95
54.73
52.2
19.29
12.99
4.33
3.65
1859.52
AVG
31.735
29.849
24.387
26.932
24.555
23.983
13.132
23.008
38.383
8.467
20.31
11.16
8.993
9.122
26.1
9.645
6.495
2.165
3.65
18.783
MAX
106.62
145.26
135.46
120.2S
69.57
74.16
75.89
57.03
85.03
24.41
26.5
28.29
32.17
24.41
27.98
17.38
11.38
3.51
3.65
121.66
S.D.
27.301
33.868
29.394
35.05
22.898
20.769
21.185
23.856
42.413
8.068
11.031
11.871
11.389
8.495
2.659
10.939
6.908
1.902
0
21.779
C-8
-------
SITE 20
CL
7
11
6
9
5
8
10
1
12
19
17
16
3
4
2
14
18
15
13
20
No.
71
86
74
66
43
49
44
34
35
21
17
10
21
14
12
9
12
8
8
181
%ofDep
16.44
16.3
15.08
11.92
8.17
7.52
4.91
4.21
4.19
3.1
1.67
1.34
1.29
1.22
0.98
0.56
0.47
0.42
0.21
0
TOTAL
2670.1
2647.88
2448.57
1935.92
1326.61
1221.64
796.83
683.04
680.22
504.25
270.79
217.95
209.74
198.46
159.63
90.68
75.75
' 68.73
33.62
4810.84
AVG
37.607
30.789
33.089
29.332
30.851
24.931
18.11
20.089
19.435
24.012
15.929
21.795
9.988
14.176
13.303
10.076
6.313
8.591
4.203
26.579
MAX
151.18
233.2
264.74
15027
227.74
12536
93.31
10627
67.84
110.01
61.67
93.31
63.72
69.39
40.12
19.16
22.72
14.44
9.53
151.18
S.D.
39.701
40.89
54.4
28.83
42.302
29.064
20.992
26.842
21.604
27.256
20.752
28.287
13.247
19.714
11.309
6.289
6.762
3.954
3.26
28.728
SITE 21
CL
7
9
11
6
5
1
8
10
4
12
16
3
17
2
19
14
18
15
13
20
No.
65
74
79
78
45
33
44
38
13
29
9
21
14
17
17
9
12
5
5
185
<7cofDer
16.18
14.25
13.26
12.78
11.41
6.91
5.71
5.43
3.55
2.38
1.63
1.52
1.27
1.07
0.92
0.69
0.64
0.28
0.11
0
TOTAL
3248.21
2861.77
2662.01
2566.08
2289.95
1386.54
1145.87
1089.85
713.23
478.23
328.14
305.31
254.97
215.81
184.28
139.14
128.25
56.02
22.93
8019.24
AVG
49.972
38.673
33.6%
32.898
50.888
42.016
26.043
28.68
54.864
16.491
36.46
14.539
18.212
12.695
10.84
15.46
10.687
11.204
4.586
43.347
MAX
255.09
255.09
216.03
255.09
230.9
174.88
124.7
174.88
295.54
51.43
174.88
52.3
63.81
40.27
28.88
42.07
62.94
26.95
9.24
419.5
S.D.
60.174
42.0%
32.821
42.433
52.327
42!662
32.513
30.082
83.919
14.245
52.703
13.961
18.442
12.085
9.371
13.354
16.918
10.719
3.384
61.703
-------
SITE 27
CL
6
9
11
5
7
10
1
8
3
2
17
4
12
18
14
16
19
13
15
20
No.
83
67
63
47
53
38
25
32
20
15
13
14
16
11
9
4
i
6
t
147
%ofDep
21.07
14.94
14.22
14.21
6.98
6.19
6.14
3.23
2.47
2.21
1.92
1.91
1.35
0.85
0.75
0.56
0.45
0.29
0.25
0
TOTAL
3502.05
2483.48
2363.91
2361.19
1160.56
1029.34
1020.29
536.45
410.71
367.11
319.38
317.73
225.07
140.45
125.06
92.95
75.51
48.35
41.09
4906.59
AVG
42.193
37.067
37.522
50.238
21.897
27.088
40.812
16.764
20.536
24.474
24.568
22.695
14.067
12.768
13.8%
18.59
15.102
8.058
10.273
33.378
MAX
257.77
265.19
265.19
178.57
90
142.58
142.58
78.21
69.39
58.46
72.1
68.71
58.87
38.91
24.1
56.63
31.5
13.58
13.84
169.36
S.D.
42.839
40.573
46.135
45.409
18.766
29.25
38.687
18.639
17.61
19.959
26.25
21.186
15.713
14.1
9.052
21.37
12.733
5.453
6.233
28.888
SITE 28
CL
9
5
6
7
11
1
10
4
17
8
2
14
3
12
18
16
13
19
15
20
No.
65
48
78
41
68
28
43
14
18
19
15
14
12
8
9
6
8
4
5
138
<5k>fDep
18.9
14.12
12.93
12.55
9.74
5.61
5.31
3.69
3.52
2.88
2.43
2.25
2.16
1.38
0.8
0.63
0.53
0.35
0.23
0
TOTAL
3940.1
2943.62
2696.28
2616.8
2031.65
1169.49
1106.47
769.66
733.6
599.67
507.3
469.98
451.01
286.76
166.97
130.32
110.64
73.38
47.08
6274.74
AVG
60.617
61.325
34.568
63.824
29.877
41.767
25.732
54.976
40.756
31.562
33.82
33.57
37.584
35.845
18.552
21.72
13.83
18.345
9.416
45.469
MAX
458.42
391.45
117.02
193.36
180.94
157.2
88.17
141.18
183.09
. 103.26
73.43
78.65
125.6
86.81
61.57
33.65
32.66
32.55
15.73
458.42
S.D.
75.112
76.565
26.926
58.354
30.52
42.594
22.646
41.308
47.878
32.445
19.221
20.795
32.21
35.788
18.304
11.754
9.201
9.569
6.67
60.823
C-10
-------
APPENDIX D
Site Specific Tables of
Total and Percent of Total
Precipitation
KEY
No. - Number of times cluster had precipitation
%of Pre - Percent of total precipitation
Total - Total precipitation (cm)
AVG - Average precipitation for wet events^ only
Max - Maximum precipitation reported (mgm *)
S.D. - Standard deviation of precipitation
the wet events only(mgm" )
-------
SITE 1
CL
11
9
5
12
6
3
7
8
1
10
19
2
16
17
14
15
4
18
13
20
No.
104
75
55
43
66
27
69
47
33
41
28
18
11
18
9
5
12
12
6
179
%ofPre
18.95
12.02
8.88
8.21
6.78
6.46
6.31
5.89
5.06
3.92
3.77
3.52
2.69
2.57
1.88
1.02
0.74
0.74
0.59
0
TOTAL
179.41
1 13.74
84.06
77.67
64.19
61.17
59.74
55.78
47.87
37.06
35.73
33.28
25.47
24.35
17.83
9.61
7
6.99
5.62
428.41
AVG
1.725
1.517
1.528
1.806
0.973
2.266
0.866
1.187
1.451
0.904
1.276
1.849
2.315
1.353
1.981
1.922
0.583
0.582
0.937
2.393
MAX
16.13
5.58
6.09
4.55
4.06
4.58
5.05
5
4.39
3.55
8.38
8.25
3.81
4.11
3.35
4.19
1.52
2.39
2.74
19.82
S.D.
1.986
1.411
1.393
1.411
1.027
1.265
0.897
1.217
0.975
0.994
1.776
1.973
1.35
1.322
0.741
1.609
0.584
0.783
0.92
2.478
SITE 2
CL
11
9
;
6
t
8
3
12
1
10
2
14
17
16
4
18
19
13
15
20
No.
79
80
70
67
48
43
27
30
28
37
16
14
14
10
13
16
23
4
9
207
%ofPre
14.34
12.98
11.6
9.31
8.79
6.78
6.23
5.94
5.93
3.94
3.32
2.48
1.85
1.76
1.4]
1.04
0.98
0.77
0.56
0
TOTAL
105.79
95.81
85.59
68.72
64.84
50.02
45.96
43.81
43.76
29.13
24.5
18.29
13.67
13.02
10.38
7.64
7.26
5.65
4.16
321.48
AVG
1.339
1.198
1.223
1.026
1.351
1.163
1.702
1.46
1.563
0.787
1.531
1.306
0.976
1.302
0.798
0.478
0.316
1.413
0.462
1.553
MAX
11.63
11.63
5.07
11.63
4.01
5.07
5.08
4.06
6.38
4.27
7.49
4.83
3.13
3.43
2.49
1.52
1.48
3.76
1.37
13.28
S.D.
1.633
1.693
1.293
1.588
1.183
1.223
1.178
1.162
1.556
0.905
1.835
1.401
0.941
1.262
0.708
0.53
0.423
1.66
0.393
2.195
D-l
-------
SITE 4
CL
6
5
7
11
9
3
8
1
10
12
4
14
17
2
18
15
19
13
16
20
No.
83
66
73
92
77
28
38
35
47
34
16
18
23
25
18
10
10
12
9
205
%ofPre
14.63
11.01
10.8
9.88
9.42
6.79
6.28
5.97
5.69
3.35
3.27
3.06
2.77
2.57
1.11
1.01
0.95
0.77
0.63
0
TOTAL
117.48
88.43
86.71
79.35
75.66
54.49
50.46
47.96
45.72
26.91
26.28
24.6
22.25
20.65
8.93
8.1
7.66
6.17
5.08
264.87
AVG
1.415
1.34
1.188
0.863
0.983
1.946
1.328
1.37
0.973
0.791
1.642
1.367
0.967
0.826
0.4%
0.81
0.766
0.514
0.564
1.292
MAX
7.69
4.65
8.24
5.16
8.44
4.79
8.85
4.54
7.95
3.16
4.5
4.43
3.16
1.91
2.62
2.34
2.16
2.29
1.5
6
S.D.
1.94
1.308
1.248
1.013
1.331
1.333
1.708
1.221
1.567
0.828
1.382
1.311
0.915
0.557
0.606
0.613
0.746
0.593
0,492
1.249
SITE 5
CL
6
5
7
9
11
10
1
14
2
4
3
19
8
16
17
15
12
18
13
20
No.
91
57
63
70
68
43
31
19
30
18
22
15
31
10
20
11
15
11
I
195
%ofPre
17.88
10.11
9.13
7.86
7.61
7.41
7.15
6.49
4.86
3.8
3.58
3.19
3.09
2.26
2.06
1.38
0.78
0.77
0.58
0
TOTAL
185.49
104.84
94.75
81.51
78.98
76.86
74.16
67.28
50.39
39.45
37.16
33.13
32.01
23.45
21.38
14.34
8.1
8
6.03
383.06
AVG
2.038
1.839
1.504
1.164
1.161
1.787
2.392
3.541
1.68
2.192
1.689
2.209
1.033
2.345
1.069
1.304
0.54
0.727
0.861
1.964
MAX
11.23
9.07
9.07
5.92
4.83
7.29
8.58
7.38
4.78
9.19
4.62
4.32
3.81
5.86
5.16
2.59
2.39
4.09
2.08
18.56
S.D.
1.947
1.838
1.452
1.112
1.235
1.778
. 2.025
1.807
1.509
2.201
1.251
1.576
1.081
1.628
1.177
0.954
0.573
1.153
0.686
2.564
-------
SITE 7
CL
6
7
11
5
1
9
10
3
14
17
g
2
12
4
16
18
19
15
13
20
No.
95
72
82
61
36
73
43
26
17
28
31
24
27
12
11
17
8
7
9
195
%ofPre
19.08
13.36
10.54
9.93
8.15
7.97
5.27
4.23
3.87
3.18
2.99
2.35
2
1.98
1.4
1.15
1.03
0.94
0.56
0
TOTAL
161.69
113.27
89.38
84.18
69.07
67.52
44.69
35.89
32.79
26.99
25.35
19.95
16.98
16.76
11.88
9.79
8.74
7.94
4.77
258.24
AVG
1.702
1.573
1.09
1.38
1.919
0.925
1.039
1.38
1.929
0.964
0.818
0.831
0.629
1.397
1.08
0.576
1.092
1.134
0.53
1.324
MAX
8.94
6.35
6.48
11.91
7.87
5.08
3.4
4.17
4.32
3.81
8.05
3.17
3.28
3.56
3.68
2.67
3.35
2.49
1.65
7.8
S.D.
1.82
1.632
1.25
2.091
2.104
0.962
0.96
1.257
1.182
1.108
1.646
0.947
0.721
1.233
1.447
0.703
1.248
0.765
0.549
1.201
SITE 8
CL
11
9
8
2
5
7
3
1
12
17
10
6
19
4
15
14
13
18
16
20
No.
91
80
57
31
42
47
25
35
30
21
26
30
19
11
13
13
12
9
6
188
%ofPre
17.75
11.85
10.94
7.62
6.96
6.16
6.09
5.37
4.06
3.84
3.71
2.98
2.97
2.61
2.36
1.83
1.67
0.61
0.6
0
TOTAL
181.88
121.43
112.04
78.04
71.33
63.15
62.42
55.05
41.63
39.36
38
30.48
30.39
26.75
24.2
18.71
17.14
6.24
6.17
344.87
AVG
1.999
1.518
1.966
2.517
1.698
1.344
2.497
1.573
1.388
1.874
1.4$2
1.016
1.599
2.432
1.862
1.439
1.428
0.693
1.028
1.834
MAX
13.76
9.04
5.03
11.66
9.3
4.17
11.66
5.74
4.42
6.1
8.79
6.96
3.94
7.62
7.29
2.92
7.29
3.15
3.28
18.71
S.D.
2.126
1.69
1.533
2.492
1.704
1.204
2.556
1.359
1.243
1.552
1.858
1.596
1.254
2.794
1.983
0.961
1.945
0.982
1.16
2.14
D-3
-------
SITE 10
CL
6
5
9
11
7
10
1
3
8
17
14
2
12
4
19
16
18
15
13
20
No.
99
53
74
91
53
46
31
22
22
23
15
22
19
10
15
6
10
4
i
185
%ofPre
20.57
12.9
11.95
11.84
8.S6
6.23
5.47
4.16
3.25
2.75
2.37
2.3
2.1
2.04
1.08
0.87
0.76
0.28
0.23
0
TOTAL
133.98
84.02
77.8
77.09
57.72
40.6
35.62
27.08
21.14
17.93
15.44
14.95
13.67
13.29
7.02
5.64
4.93
1.8
1.47
238.42
AVG
1.353
1.585
1.051
0.847
1.089
0.883
1.149
1.231
0.961
0.78
1.029
0.68
0.719
1.329
0.468
0.94
0.493
0.45
0.49
1.289
MAX
8.38
8.13
3.99
5.59
4.09
4.15
5.16
3.6
4.24
3.1
2.54
1.88
2.51
4.08
1.45
1.98
1.45
0.66
0.66
5-26
S.D.
1.683
1.402
0.942
0.908
1.066
0.836
1.092
1.207
1.182
0.693
0.768
0.507
0.569
1.166
0.361
0.859
0.474
0.203
0.294
1.168
SITE 1 1
CL
n
9
5
6
7
1
8
30
3
12
14
17
2
4
15
19
16
18
13
20
No.
108
82
63
81
72
37
52
60
28
38
18
22
30
16
14
17
10
15
13
242
%ofPre
13.42
12.25
11.05
9.03
9.03
7.03
6.91
6.69
5.18
3.82
2.84
2.58
2.54
1.92
1.49
1.32
1.21
1.07
0.62
0
TOTAL
130.73
119.37
107.59
87.97
87.92
68.49
67.3
65.2
50.42
37.22
27.65
25.13
24.72
18.71
14.51
12.9
11.77
10.46
6.01
379.43
AVG
1.21
1.456
1.708
1.086
1.221
1.851
1.294
1.087
1.801
0.979
1.536
1.142
0.824
1.169
1.036
0.759
1.177
0.697
0.462
1.568
MAX
7.49
7.67
6.91
5.97
6.89
7.31
6.38
6.79
5.26
5.82
6.91
3.83
1.9
3.05
3.81
3.81
4.57
4.5
2.18
7.85
S.D.
1.396
1.738
1.61
1.141
1.359
1.534
1.577
1.236
1.503
1.176
' 1.74
0.978
0.558
0.913
1.044
0.939
1.318
1.097
0.576
1.717
D-4
-------
SITE 12
CL
6
9
11
1
5
10
14
3
2
4
8
7
16
19
17
15
12
18
13
20
No.
70
43
52
31
33
32
16
17
17
14
16
21
9
6
10
9
10
3
4
132
%ofPre
21.95
11.85
11.42
9.1
6.97
6.77
5.1
3.83
3.69
3.67
3.37
3.03
3.01
1.73
1.63
1.6
0.9
0.21
0.19
0
TOTAL
222.48
120.17
115.74
92.22
70.61
68.64
51.65
38.81
37.36
37.18
34.19
30.68
30.55
17.53
16.48
16.27
9.12
2.16
1.88
239.07
AVG
3.178
2.795
2.226
2.975
2.14
2.145
3.228
2.283
2.198
2.656
2.137
1.461
3.394
2.922
1.648
1.808
0.912
0.72
_ 0.47
1.811
MAX
33.7
33.7
7.11
9.45
8.26
9.45
9.35
9.35
8.81
8.81
6.07
6.1
9.35
6.14
3.3
6.1
2.24
1.02
0.63
7.09
S.D.
5.688
5.053
1.955
2.186
2.271
2.365
2.577
2.367
2.145
2.342
1.91
1.586
3.222
2.094
1.089
1.75
0.893
0.267
0.17
1.698
SITE 13
CL
11
12
9
5
7
8
6
3
19
10
1
16
17
2
14
15
4
18
13
20
No.
110
44
69
48
70
55
70
22
31
55
36
12
21
12
7
5
14
15
6
199
%ofPre
20.3
10.39
8.33
6.93
6.89
6.43
6.39
5.86
5.22
5.07
4.38
3.72
3.29
1.5
1.5
1.33
1.13
1.06
0.26
0
TOTAL
185.43
94.91
76.07
63.33
62.92
58.69
58.39
53.5
47.7
46.32
39.97
34
30.09
13.69
13.67
12.19
10.29
9.72
2.42
412.02
AVG
1.686
2.157
1.102
1.319
0.899
1.067
0.834
2.432
1.539
0.842
1.11
2.833
1.433
1.141
1.953
2.438
0.735
0.648
0.403
2.07
MAX
11.84
6.43
6.35
6.09
4.24
5.75
4.01
6.12
7.39
5.21
4.25
10.5
6.23
5
6.12
7.19
3.32
3.89
1.22
11.94
S.D.
1.964
1.903
1.37
1.366
0.953
1.222
0.9
1.632
1.899
1.126
0.919
2.959
1.738
1.374
2.571
2.796
0.941
1.053
0.483
2.694
D-5
-------
SITE 14
CL
11
9
7
8
2
1
3
5
6
14
10
19
17
15
4
13
12
16
18
20
No.
79
68
53
55
32
30
24
34
32
11
19
13
20
12
10
7
15
5
4
164
%ofPre
15.72
12.16
10.87
10.75
7.19
6.76
6.47
5.12
4.61
3.8
3.46
3.09
2.24
1.99
1.98
1.61
1.54
0.44
0.18
0
TOTAL
156.6
121.16
108.3
107.12
71.65
67.35
64.47
51.04
45.88
37.82
34.49
30.76
22.28
19.82
19.72
16
15.38
4.42
1.81
330.5
AVG
1.982
1.782
2.043
1.948
2.239
2.245
2.686
1.501
1.434
3.438
1.815
2.366
1.114
1.652
1.972
2.286
1.025
0.884
0.452
2.015
MAX
10.03
12.3
8.1
8.1
6.99
10
10.03
6.6
6.81
9.14
12.3
6.99
6.17
5.03
4.69
5.86
5.03
1.27
1.02
8.74
S.D.
2.236
2.348
2.214
1.806
1.994
2.29
2.849
1.742
1.962
2.711
3.336
2.204
1.339
1.269
1.523
2.187
1.327
0.245
0.386
2.06
SITE. 15
CL
11
9
6
1
2
8
5
10
3
7
14
17
4
16
15
19
12
13
18
20
No.
75
62
56
33
33
55
39
33
21
48
13
17
18
9
14
13
11
10
7
183
%ofPre
15.51
8.77
8.74
8.11
7.84
7.66
7.28
5.34
5.16
4.32
3.99
3.89
3.26
3.16
1.9
1.68
1.33
1.27
0.8
0
TOTAL
175.13
98.99
98.68
91.63
88.54
86.51
82.26
60.29
58.33
48.83
45.04
43.91
36.8
35.64
21.46
18.94
15.04
14.37
8.98
383.42
AVG
2.335
1.597
1.762
2.777
2.683
1.573
2.109
1.827
2.778
1.017
3.465
2.583
2.044
3.96
1.533
1.457
1.367
1.437
1.283
2.095
MAX
11.76
5.62
6.35
7.44
11.76
10.32
6.5
5.94
11.59
5.87
11.59
7.7
6.3
10.08
4.14
8
5.51
4.6
4.29
13.36
S.D.
2.118
1.505
1.741
2.121
2.542
1.985
1.913
1.997
2.622
1.202
' 2.974
2.114
1.569
3.534
1.459
2.229
1.804
1.572
1.563
2.156
-------
SITE 16
CL
6
11
9
7
1
5
4
10
8
2
14
3
19
16
17
15
12
18
13
20
No.
66
70
63
60
34
50
17
43
42
29
15
21
18
10
16
10
16
9
8
186
%ofPre
11.84
11.04
7.72
7.71
7.68
7.55
6.83
6.29
6.29
4.68
4.44
3.67
3.2
2.72
2.72
1.99
1.34
1.27
1.02
0
TOTAL
157.48
146.9
102.65
102.56
102.21
100.42
90.82
83.71
83.61
62.2
59.08
48.8
42.62
36.24
36.19
26.46
17.76
16.93
13.54
462.86
AVG
2.386
2.099
1.629
1.709
3.006
2.008
5.342
1.947
1.991
2.145
3.939
2.324
2.368
3.624
2.262
2.646
1.11
1.881
1.692
2.488
MAX
12.56
10.61
6.07
11.61
8.15
15.12
12.56
8.15
7.82
6.35
9.66
8.84
7.87
9.02
5.79
6.45
6.35
4.9
3.79
17.25
S.D.
2.749
2.38
1.738
2.161
2.446
2.965
4.024
2.283
2.143
1.634
3.585
2.531
2.565
2.767
1.901
2.438
1.773
1.322
1.564
3.145
SITE 17
CL
6
10
11
9
4
7
5
1
8
2
19
14
17
3
15
16
13
18
12
20
No.
87
37
58
61
17
49
44
30
41
20
21
13
15
13
15
11
9
9
10
162
%ofPre
23.11
7.96
7.82
7.46
7.24
7.13
6.47
6.42
5.08
4.42
3.19
2.71
2.46
2.22
2.07
1.55
1.1
0.9
0.68
0
TOTAL
274.05
94.39
92.67
88.42
85.86
84.5
76.75
76.07
60.26
52.37
37.81
32.13
29.2
26.35
24.59
18.4
13
10.71
8.08
286.63
AVG
3.15
2.551
1.598
1.45
5.051
1.724
1.744
2.536
1.47
2.619
1.8
2.472
1.947
2.027
1.639
1.673
1.444
1.19
0.808
1.769
MAX
13.09
16.7
5.33
7.72
16.7
8.35
5.82
11.68
7.62
16.7
5.39
5.54
5.46
4.52
4.11
4.11
3.76
2.79
3.1
9.45
S.D.
2.862
3.497
1.242
1.564
4.314
1.91
1.638
2.818
1.843
3.868
1.7
1.456
1.846
1.378
1.218
1.225
1.389
0.743
1.11
1.913
D-7
-------
SITE
CL
6
9
7
8
5
19
11
1
4
10
14
17
16
A
-
18
15
13
12
20
18
No.
96
71
63
40
47
29
41
27
11
36
17
21
12
19
18
•
10
•7
g
188
%ofPre
19.34
17.74
14.76
9.23
6.55
5.95
4.19
3.54
3.13
3.08
2.81
2.14
1.71
1.63
1.6
1.09
0.71
0.5
0.29
0
TOTAL
208.89
191.62
159.44
99.71
70.78
64.27
45.3
38.2
33.81
33.29
30.3
23.1
18.46
17.59
17.31
11.76
7.65
5.38
3.15
305.3
AVG
2.176
2.699
2.531
2.493
1.506
2.216
1.105
1.415
3.074
0.925
1.782
1.1
1.538
0.926
0.962
1.68
0.765
0.769
0.394
1.624
MAX
10.08
13.49
14.12
9.78
5.33
9.65
6.86
5/76
9.08
2.7
4.63
4.7
4.45
3.59
4.62
8.89
1.9
2.67
2.39
9.91
S.D.
2.321
2.763
2.543
2.384
1345
2.413
1.314
1.487
2.862
0.856
1.427
1.527
1.322
0.951
1.101
3.198
0.607
0.912
0.807
1.672
SITE 19
CL
6
9
7
8
5
19
11
14
10
4
1
4.
3
17
16
18
12
15
13
20
No.
90
82
73
41
39
24
33
10
29
8
15
14
11
17
4
6
8
-
<
149
%ofPre
26.43
19.93
14.93
8.24
7.1
4.36
3.05
2.64
2.62
2.6
2.52
1.35
1.35
1.19
0.63
0.39
0.36
0.17
0.14
0
TOTAL
217.13
163.74
122.71
67.73
58.35
35.84
25.07
21.69
21.56
21.4
20.67
11.11
11.09
9.75
5.16
3.18
2.93
1.38
1.15
238.39
AVG
2.413
1.997
1.681
1.652
1.4%
1.493
0.76
2.169
0.743
2.675
1.378
0.794
1.008
0.574
1.29
0.53
0.366
0.46
0.23
1.6
MAX
9.12
13.49
14.12
7.22
4.85
5.46
6.86
4.77
2.46
9.08
5.76
3.59
2.84
2.59
2.64
1.65
1.19
1.1
0.51
8.15
S.D.
2.314
2.755
2.285
2.033
1.31
1.253
1.306
1.598
0.814
3.705
1.917
0.922
0.857
0.685
0.914
0.606
0.375
0.557
0.221
1.894
D-8
-------
SITE
CL
11
7
9
6
12
8
5
10
1
19
3
17
2
16
4
18
14
15
13
20
20
No.
114
87
83
94
45
59
51
50
39
27
26
22
17
12
16
18
10
12
12
232
%ofPre
19.17
11.64
9.82
9.5
8.17
6.86
5.61
5.46
5.22
3.87
3.53
2.57
2.01
1.65
1.51
1.24
0.94
0.7
0.52
0
TOTAL
188.25
114.28
96.47
93.3
80.23
67.41
55.07
53.64
51.24
37.99
34.7
25.24
19.77
16.25
14.82
12.18
9.25
6.87
5.15
368.04
AVG
1.651
1.314
1.162
0.993
1.783
1.143
1.08
.073
.314
.407
.335
.147
1.163
1.354
0.926
0.677
0.925
0.572
0.429
1.586
MAX
8.28
8.77
8.13
4.8
7.11
9.02
4.95
5.36
3.83
3.49
3.71
5.63
4.38
3.49
5.31
2.59
1.93
1.72
1.85
9.02
S.D.
1.475
1.278
1.316
1.203
.459
.576
.071
.187
.142
.037
0.849
1.301
1.319
1.026
1.298
0.654
0.665
0.409
0.516
1.801
SITE 21
CL
11
6
7
9
5
10
1
12
8
3
2
17
4
18
19
16
14
13
15
20
No.
122
112
92
91
62
69
39
47
57
27
24
25
15
24
29
13
14
11
12
256
•fcofPre
16.6
10.92
9.78
9.35
9.17
9.11
6.8
5.86
4.26
3.89
2.31
2.26
2.05
1.75
1.7
1.62
1.23
0.73
0.62
0
TOTAL
190.93
125.59
112.51
107.51
105.5
104.82
78.21
67.39
49
44.7
26.62
25.97
23.6
20.14
19.53
18.68
14.17
8.36
7.12
416.18
AVG
1.565
1.121
1.223
1.181
1.702
1.519
2.005
1.434
0.86
1.656
1.109
1.039
1.573
0.839
0.673
1.437
1.012
0.76
0.593
1.626
MAX
7.66
7.29
6.45
7.29
7.66
14.45
6.17
5.45
3.99
5.1
6.07
5.24
6.37
3.62
1.99
3.73
3.4
1.6
1.65
14.4
S.D.
1.433
1.259
1.153
1.136
1.478
1.965
1.371
1.156
0.926
1.118
1.456
1.215
1.584
0.954
0.609
1.287
0.899
0.515
0.476
1.88
D-9
-------
SITE
CL
11
6
9
5
10
1
7
3
12
2
17
g
4
14
18
16
19
15
13
20
27
No.
Ill
121
106
65
61
37
81
27
43
27
27
51
17
16
21
12
17
6
8
248
%ofPre
16.24
14.77
11.77
9.94
8.3
6.9
6.57
3.84
3.78
3.49
2.84
2.84
2.37
1.82
1.58
0.99
0.82
0.65
0.48
0
TOTAL
159.66
145.19
115.73
97.69
81.6
67.84
64.62
37.78
37.2
34.29
27.91
27.88
23.32
17.91
15.54
9.77
8.05
6.41
4.71
281.27
AVG
1.438
1.2
1.092
1.503
1.338
1.834
0.798
1.399
0.865
1.27
1.034
0.547
1.372
1.119
0.74
0.814
0.474
1.068
0.589
1.134
MAX
6.05
5.84
5.82
5.84
5.52
5.36
4.19
4.11
3.27
3.82
4.06
3.13
3.83
4.51
2.82
1.71
2.44
3.21
1.53
5.86
S.D.
1.277
1.291
1.173
1.142
1.142
1.539
0.854
0.996
0.761
1.252
0.983
0.62
1.161
1.251
0.75
0.611
0.652
1.167
0.44
1.053
SITE 28
CL
6
9
11
5
7
10
1
3
17
4
2
12
8
14
19
18
13
16
15
20
No.
Ill
78
90
62
63
54
34
24
28
15
23
34
30
15
15
13
12
10
10
198
%ofPre
15.33
13
11.17
10.74
9.82
6.92
6.09
4.75
3.52
3.27
3.2
2.77
2.67
2.27
1.12
1.11
1.01
0.7
0.56
0
TOTAL
130.86
111
95.38
91.66
83.79
59.04
51.97
40.54
30.07
27.89
27.34
23.67
22.79
19.34
9.52
9.44
8.64
5.94
4.78
261.51
AVG
1.179
1.423
1.06
1.478
1.33
1.093
1.529
1.689
1.074
1.859
1.189
0.6%
0.76
1.289
0.635
0.726
0.72
0.594
0.478
1.321
MAX
5.17
9.42
5.36
8.36
6.56
4.29
6.6
5.16
5.46
4.18
3.32
2.12
3.22
2.86
2.54
1.63
1.76
1.25
2.3
9.42
S.D.
1.143
1.622
0.95
1.505
1.465
0.964
1.622
1.198
1.131
1.245
'0.853
0.626
0.933
0.823
0.802
0.422
0.569
0.547
0.675
1.391
D-10
------- |