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Meteorological Model Performance for Annual
2016 Simulation WRF v3.8
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EPA-454/R-19-010
July 2019
Meteorological Model Performance for Annual 2016 Simulation WRF v3.8
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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Meteorological Model Performance for
Annual 2016 Simulation WRF v3.8
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1. INTRODUCTION
The Weather Research and Forecasting model (WRF) was applied for the entire year of 2016 to
generate meteorological data to support emissions and photochemical modeling applications
for this year. The WRF meteorological fields will be converted to air quality modeling input data
and used to support assessments of ozone, PM2.5, visibility, and a variety of toxics.
The WRF model was applied to 36 km North America (36NOAM) and 12 km continental United
States (12US) scale domains. Both model simulations were initialized directly from
meteorological analysis data. Model parameterizations and options outlined in this document
were chosen based on a series of sensitivity runs performed by U.S. Environmental Protection
Agency (USEPA) Office of Research and Development that provided an optimal configuration
based on temperature, mixing ratio, and wind field. All WRF simulations were done by CSRA
under contract to the USEPA.
2. MODEL CONFIGURATION
Version 3.8 of the WRF model, Advanced Research WRF (ARW) core (Skamarock, 2008) was
used for generating the 2016 simulation1. Selected physics options include Pleim-Xiu land
surface model, Asymmetric Convective Model version 2 planetary boundary layer scheme, Kain-
Fritsch cumulus parameterization utilizing the moisture-advection trigger (Ma and Tan, 2009),
Morrison double moment microphysics, and RRTMG longwave and shortwave radiation
schemes (Gilliam and Pleim, 2010).
The 36NOAM WRF model was initialized using the 0.25-degree GFS analysis and 3-hour forecast
from the 00Z, 06Z, 12Z, and 18Z simulations. The 12US WRF model was initialized using the
12km North American Model (12NAM) analysis product provided by National Climatic Data
Center (NCDC). Where 12NAM data was unavailable, the 40km Eta Data Assimilation System
(EDAS) analysis (ds609.2) from the National Center for Atmospheric Research (NCAR) was used.
Analysis nudging for temperature, wind, and moisture was applied above the boundary layer
only. The model simulations were conducted continuously. The 'ipxwrf' program was used to
initialize deep soil moisture at the start of the run using a 10-day spinup period (Gilliam and
Pleim, 2010). Landuse and land cover data were based on the USGS for the 36NOAM simulation
and the 2011 National Land Cover Database (NLCD 2011) for the 12US simulation. Sea surface
temperatures were ingested from the Group for High Resolution Sea Surface Temperatures
(GHRSST) (Stammer et al., 2003) 1km SST data.
1 Version 3.8 was the most current version of WRF at the time the 2016 meteorological model simulations were
performed.
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Additionally, lightning data assimilation was utilized to suppress (force) deep convection where
lightning is absent (present) in observational data. This method is described by Heath et al.
(2016) and was employed to help improve precipitation estimates generated by the model.
Figures 2.1 and 2.2 show the 36NOAM and 12US domains, which utilize a Lambert conformal
projection centered at (-97,40) with true latitudes of 33 and 45 degrees north. The 36NOAM
domain contains 184 cells in the X direction and 160 cells in the Y direction. The 12US domain
contains 412 cells in the X direction and 372 cells in the Y direction. The atmosphere is resolved
with 35 vertical layers up to 50 mb (see table 2.1), with the thinnest layers being nearest the
surface to better resolve the planetary boundary layer (PBL).
WRF
Layer
Height
(m)
Pressure
(mb)
Sigma
35
17,556
5000
0.000
34
14,780
9750
0.050
33
12,822
14500
0.100
32
11,282
19250
0.150
31
10,002
24000
0.200
30
8,901
28750
0.250
29
7,932
33500
0.300
28
7,064
38250
0.350
27
6,275
43000
0.400
26
5,553
47750
0.450
25
4,885
52500
0.500
24
4,264
57250
0.550
23
3,683
62000
0.600
22
3,136
66750
0.650
21
2,619
71500
0.700
20
2,226
75300
0.740
19
1,941
78150
0.770
18
1,665
81000
0.800
17
1,485
82900
0.820
16
1,308
84800
0.840
15
1,134
86700
0.860
14
964
88600
0.880
13
797
90500
0.900
12
714
91450
0.910
11
632
92400
0.920
10
551
93350
0.930
9
470
94300
0.940
8
390
95250
0.950
7
311
96200
0.960
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6
232
97150
0.970
5
154
98100
0.980
4
115
98575
0.985
3
77
99050
0.990
2
38
99525
0.995
1
19
99763
0.9975
Surface
0
100000
1.000
Table 2.1 WRF layers and their approximate height above ground level
Figure 2.1 Map of WRF model domain: 36NOAM,
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f
307 -
290 -
273 -
256 -
239 -
222 -
205 -
188 -
171 -
154 -
137 -
120 -
103 -
86 -
69 -
52 -
35 -
i
53
—i—
105
—i—
157
—i—
209
—i—
261
—i—
313
—i—
365
—i—
417
—r
469
Figure 2,2 Map of WRF model domain: 12US.
3 MODEL PERFORMANCE DESCRIPTION
The WRF model simulations were evaluated to determine whether the output fields represent a
reasonable approximation of the actual meteorology that occurred during the modeling period.
Identifying and quantifying these output fields allows for a downstream assessment of how the
air quality modeling results are impacted by the meteorological data. For the purposes of this
assessment, 2-meter temperature and mixing ratio, 10-meter wind speed and direction, and
shortwave radiation are quantitatively evaluated. A qualitative and quantitative evaluation of
precipitation is also provided.
The observation database for surface-based temperature, wind speed and direction, and mixing
ratio is based on measurements made at United States (i.e., National Weather Service) and
Canadian (i.e., Environment Canada) airports. The observational dataset (ds472 network) is
available from NCAR. Monitors used for evaluation are shown in Figure 3.1.
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Figure 3.1 Stations used for model performance: ds472 network.
Shortwave downward radiation measurements are taken at Surface Radiation Budget Network
(SURFRAD) (https://www.esrl.noaa.gov/grmd/grad/surfrad/index.html) and SOLRAD (formerly
ISIS) (https://www.esrl.noaa.gov/gmd/grad/solrad/index.html) monitor locations. The
SURFRAD network consists of 7 sites and the SOLRAD network consists of 9 sites across the
United States (see Figure 3.2). Both networks are operated by the National Oceanic and
Atmospheric Administration (NOAA), with SURFRAD sites existing as a subset of SOLRAD
monitors that provide higher level radiation information not used in this evaluation.
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Figure 3.2. Location of SOLRAD and SURFRAD radiation monitors.
Rainfall amounts are estimated by the Parameter-elevation Relationships on independent
Slopes Model (PRISM) model, which uses an elevation-based regression model to analyze
precipitation. PRISM's horizontal resolution is approximately 2 to 4 km and is re-projected to
the WRF modeling domain for direct comparison to model estimates. The rainfall analysis is
limited to the contiguous United States as the model utilizes elevation and measured
precipitation data at automated weather stations.
Model performance (i.e., temperature, wind speed, and mixing ratio) is described using
quantitative metrics: mean bias, mean (gross) error, fractional bias, and fractional error (Boylan
and Russell, 2006). These metrics are useful because they describe model performance in the
measured units of the meteorological variable and as a normalized percentage. Since wind
direction is reported in compass degrees, estimating performance metrics for wind direction is
problematic as modeled and observed northerly winds may be similar but differences would
result in a very large artificial bias. For example, the absolute difference in a northerly wind
direction measured in compass degrees of 1° and 359° is 358° when the actual difference is only
2°. To address this issue, wind field displacement, or the difference in the U and V vectors
between modeled (M) and observed (O) values, is used to assess wind vector performance
(Equation 1). Performance is best when these metrics approach 0.
(1) Wind displacement (km) = (Um ~ Uo + Vm - Vo)*(l km/1000 m)*(3600 s/hr)*(l hr)
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Rainfall performance is examined spatially using side-by-side comparisons of monthly total
rainfall plots. The WRF model outputs predictions approximately 15 meters above the surface
while observations are at 10 meters. WRF generates output at near instantaneous values (90
second time step) as opposed to longer averaging times taken at monitor stations. This should
be considered when interpreting model performance metrics.
3.1 Model Performance for Winds
WRF-predicted wind speed estimates are compared to surface-based measurements made in
the ds472 network described earlier. The results for the 36NOAM (Figure 3.1.1) and 12US
(Figure 3.1.2) domains are shown below.
At 36km, wind speeds are generally overpredicted across most hours of the day for all seasons,
in terms of mean bias. In general, performance improves at 12km with less overprediction
relative to the 36km simulation. However, at 12km WRF tends to slightly overpredict wind
speeds in the early morning and afternoon hours, while slightly underpredicting wind speeds in
the late evening and overnight hours. There is no significant seasonal variability at either
resolution in terms of wind speed.
The monthly spatial distributions of the wind speed biases (m/s) for all hours (Figures 3.1.3-
3.1.10) and daytime hours2 (Figures 3.1.11-3.1.18) are also presented, as well as the hourly
average distribution of observed and predicted wind speeds by season and region (Figure
3.1.19). The previously mentioned overprediction of wind speeds at 36km is noticeable,
primarily across the eastern coastal areas of the US and upper Midwest and Great Lakes
regions. This overprediction improves significantly at 12km, though it still persists across the
eastern US. The WRF simulations tend to underpredict wind speeds in the western US, though
this underprediction is muted slightly during the daytime hours. As noted above, these biases
generally persist regardless of changes in season.
2 12UTC to OOUTC
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Wind Speed Bias
"i 1 1 1 1 1 1 1 1 1 1 1 1 r
10 11 12 13 14 15 10 17 18 19 20 21 22 23
Hour of day (GMT)
Wind Speed Bias
1
1 1 1 1
1
III,
Wind Speed Error
T 1 1 1 T I 1 1 I I 1 1
J FMAMJ JASOND
Wind Speed Fractional Bias
Wind Speed Fractional Error
o
O -f
CM
J FMAMJ JASOND
Figure 3.1.2. Distribution of hourly bias by hour and hourly bias, error, fractional bias, and
fractional error for wind speed by month for 36NOAM domain.
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Wind Speed Bias
=> a B — B
1 1
1 1 1 1
1 1
3 a a c
acai=iai=iaac=i
1 1
1 1 1 1
1 1
1 1 1 1
' i
1 1 1 1 1 1 1
1
0
1 1 1 1 1 I
2 3 4 5 6 7
1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
9 10 11 12 13 14 15 18 17 18 1 9 20 21 22 23
Hour of day (GMT)
Wind Speed Bias
1
¦ i i i i i i ¦
1
i —1 i i I i
Wind Speed Error
BBS
e e b
~r~
M
~r~
M
Wind Speed Fractional Bias
i i i
n-j-j r 1 .
—i i ' i i ' i i ' ' i—1—"
i | ' 1—¦—> i [
j tj_—i i' ¦! eh &
i i i i
Wind Speed Fractional Error
©
O H
CM
*
J FMAMJJASOND
Figure 3.1.2, Distribution of hourly bias by hour arid hourly bias, error, fractional bias, and
fractional error for wind speed by month for 12US domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-01-01 AND 2016-02-01
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-02-01 AND 2016-03-01
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-03-01 AND 2016-04-01
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Figure 3.1.3. Spatial distribution of wind speed bias (m/s) across all hours for the months of
January, February, and March (top to bottom) for the 36NOAM domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-04-01 AND 2016-05-01
-2.5
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-05-01 AND 2016-06-01
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% «
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Figure 3.1.4. Spatial distribution of wind speed bias (m/s) across all hours for the months of
April, May, June (top to bottom) for the 36NOAM domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-07-01 AND 2016-08-01
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Figure 3.1.5. Spatial distribution of wind speed bias (m/s) across all hours for the months of
July, August, September (top to bottom) for the 36NOAM domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-10-01 AND 2016-11-01
/«•
-2.5
-1.5
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-11-01 AND 2016-12-01
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-12-01 AND 2017-01-02
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Figure 3.1.6. Spatial distribution of wind speed bias (m/s) across all hours for the months of
October, November, December (top to bottom) for the 36NOAM domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-01-01 AND 2016-02-01
•
-3
•
-2.5
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-02-01 AND 2016-03-01
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-03-01 AND 2016-04-01
Figure 3.1.7. Spatial distribution of wind speed bias (m/s) across all hours for the months of
January, February, and March (top to bottom) for the 12US domain.
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• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
1.5
• 2
• 2.5
• 3
•
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•
-2.5
•
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•
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•
-1
-0.5
0
0.5
1
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•
2
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•
3
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-04-01 AND 2016-05-01
•
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•
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•
-1
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0.5
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3
»-05-01 AND 2016-06-
01
Mean bias of Wind Speed (m/s) Date:
01
BETWEEN 2016
-06-01 AND 2016-07-
Figure 3.1.8. Spatial distribution of wind speed bias (m/s) across all hours for the months of
April, May, and June (top to bottom) for the 12US domain,
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Mean bias of Wind Speed (mis) Date: BETWEEN 2016-07-01 AND 2016-08-01
---• ' h'i
.v J .T.fr ¦
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-08-01 AND 2016-09-01
. -i \
-2.5
-2
-1.5
-0.5
0.5
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-09-01 AND 2016-10-01
•2.5
-1.5
-0.5
0.5
2.5
V ?¦
Figure 3.1.9. Spatial distribution of wind speed bias (m/s) across all hours for the months of
July, August, and September (top to bottom) for the 12US domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-10-01 AND 2016-11-01
• •
rj • * • • •
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-11-01 AND 2016-12-01
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-12-01 AND 2017-01-01
Figure 3.1.10. Spatial distribution of wind speed bias (m/s) across all hours for the months of
October, November, and December (top to bottom) for the 12US domain.
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-01-01 AND 2016-02-01
-3
-2.5
-2
-1.5
¦0.5
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-02-01 AND 2016-03-01
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-03-01 AND 2016-04-01
Figure 3.1,11, Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of January, February, and March (top to bottom) for the 36NOAM domain.
23
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-04-01 AND 2016-05-01
-3
-2.5
f * j -¦ - r
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-05-01 AND 2016-06-01
vj y
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-06-01 AND 2016-07-01
-2.5
-2
-1.5
-0.5
0.5
2.5
Figure 3.1.12. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of April, May, and June (top to bottom) for the 36NOAM domain.
24
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Mean bias of Wind Speed (mis) Date: BETWEEN 2016-07-01 AND 2016-08-01
^ W .
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-08-01 AND 2016-09-1
vo V ,
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-09-01 AND 2016'
vo -y
«£ •',J I t
iJji
• * . j. '• s *. " !*i V-vw V
**jT *•* * \zs
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Figure 3.1.13. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of July, August, and September (top to bottom) for the 36NOAM domain.
25
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Mean bias of Wind Speed (mis) Date: BETWEEN 2016-10-01 AND 2016-11-01
frr* • • •
f j. • • * •
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-11-01 AND 2016-12-01
• 7* % # • * • "•
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itsT
Figure 3.1.14. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of October, November, and December (top to bottom) for the 36NOAM domain.
26
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• -3
• -2.5
• -2
• -1.5
• -1
-0.5
• 0
0.5
1
1.5
• 2
• 2.5
• 3
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
1.5
• 2
• 2.5
• 3
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
• 0
0.5
1
• 1.5
• 2
• 2.5
• 3
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-01-01 AND 2016-02-01
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-02-01 AND 2016-03-01
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-03-01 AND 2016-04-01
Figure 3.1.15. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of January, February, and March (top to bottom) for the 12US domain.
27
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Mean bias of Wind Speed (mis) Date: BETWEEN 2016-04-01 AND 2016-05-01
* 1 * Sv "" \ -i n:
\ \ \ I VI
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-05-01 AND 2016-06-
ft/. #
V " *
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¦ \r ¦•
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
• 0
0.5
1
1.5
• 2
• 2.5
• 3
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-06-01 AND 2016-1
• "j*D *- /jf <*" ^ \ " #
^v—vrj^H^O-4
JPT
Figure 3.1.16. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of April, May, and June (top to bottom) for the 12US domain.
28
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Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-07-01 AND 2016-08-01
-3
-2.5
-1.5
-0.5
0.5
2.5
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-08-01 AND 2016-09-01
-2.5
¦0.5
0.5
2.5
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-09-01 AND 2016-10-01
Y* AV-
-3
-2.5
-2
-1.5
-0.5
0.5
2.5
Figure 3.1.17. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of July, August, and September (top to bottom) for the 12US domain.
29
-------
Mean bias of Wind Speed (mw. twSST
•v. • ,i->- • M'\:-
Mean bias of Wind Speed (m/s) Date: BETWEEN 2016-12-01 AND 2017-01-01
YX -.-v .•:• --I-v V. . m
^ .
•*. kM, J*.*. V
Figure 3.1.18. Spatial distribution of wind speed bias (m/s) across daytime hours for the months
of October, November, and December (top to bottom) for the 12US domain.
30
-------
31
-------
Figure 3.1.19. Hourly average distribution of observed arid predicted wind speeds for the 12US
domain in the Central, East-North Central, Northeast, Northwest, South, Southeast, Southwest,
West, and West-North Central (top to bottom) regions for each season (Spring, Summer, Fall,
Winter, L-R).
32
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Climate Region
Season
Mean Obs
Mean Mod
MB
MAE
NMB
NME
RMSE
Northeast
Spring
5.09
5.69
0.60
0.82
11.79
16.12
1.09
Northeast
Summer
11.95
12.12
0.17
1.05
1.42
8.79
1.45
Northeast
Fall
7.35
7.54
0.19
0.70
2.57
9.47
0.96
Northeast
Winter
2.74
3.14
0.40
0.51
14.55
18.50
0.71
West-North Central
Spring
4.77
4.89
0.12
0.63
2.50
13.16
0.87
West-North Central
Summer
9.97
10.14
0.18
1.19
1.77
11.97
1.64
West-North Central
Fall
5.85
5.87
0.02
0.65
0.36
11.12
0.91
West-North Central
Winter
2.34
2.45
0.10
0.34
4.44
14.68
0.47
Northwest
Spring
5.37
5.45
0.09
0.65
1.61
12.15
0.87
Northwest
Summer
7.07
7.06
-0.01
0.83
-0.11
11.77
1.14
Northwest
Fall
6.05
6.11
0.06
0.68
0.92
11.25
0.89
Northwest
Winter
3.87
4.09
0.22
0.49
5.80
12.59
0.64
Central
Spring
6.72
7.23
0.50
0.88
7.51
13.15
1.20
Central
Summer
14.81
15.07
0.25
1.25
1.71
8.46
1.70
Central
Fall
8.74
8.64
-0.10
0.80
-1.13
9.10
1.08
Central
Winter
3.20
3.42
0.22
0.45
6.77
14.22
0.63
South
Spring
9.53
9.74
0.21
1.02
2.22
10.68
1.45
South
Summer
16.37
16.44
0.07
1.41
0.40
8.61
1.98
South
Fall
11.21
10.94
-0.27
1.05
-2.43
9.34
1.48
South
Winter
5.14
5.03
-0.10
0.63
-2.02
12.20
0.88
Southeast
Spring
9.15
9.35
0.20
0.97
2.16
10.57
1.31
Southeast
Summer
16.55
16.71
0.17
1.45
1.00
8.74
1.91
Southeast
Fall
10.81
10.67
-0.14
1.06
-1.31
9.80
1.43
Southeast
Winter
5.62
5.69
0.07
0.70
1.29
12.50
0.98
Southwest
Spring
3.93
4.09
0.16
0.68
4.08
17.40
0.95
Southwest
Summer
7.94
8.41
0.46
1.34
5.85
16.91
1.81
Southwest
Fall
5.37
5.55
0.17
0.84
3.23
15.67
1.17
Southwest
Winter
2.88
2.94
0.06
0.50
2.25
17.45
0.68
East-North Central
Spring
5.05
5.47
0.42
0.71
8.39
14.07
1.01
East-North Central
Summer
12.11
12.42
0.32
1.10
2.62
9.08
1.53
East-North Central
Fall
7.30
7.35
0.05
0.64
0.68
8.74
0.88
East-North Central
Winter
2.32
2.43
0.11
0.29
4.54
12.54
0.39
West
Spring
6.22
6.20
-0.02
0.78
-0.34
12.54
1.14
West
Summer
7.67
7.84
0.17
1.08
2.23
14.04
1.55
West
Fall
6.43
6.54
0.10
0.95
1.60
14.79
1.34
West
Winter
5.02
4.93
-0.08
0.71
-1.68
14.12
0.98
Table 3.1.1. Mean observed, mean modeled, mean bias (MB), mean absolute error (MAE),
normalized mean bias (NMB), normalized mean error (NME), and root mean square error
(RMSE) for wind speed (m/s) for the 12US simulation.
33
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Wind vector displacement (km) is presented below for the 36NOAM (Figure 3.1.20) and 12US
(Figure 3.1.21) domains utilizing the ds472 observation network described earlier. These plots
show the entire distribution of hourly wind displacement by month and by hour of the day.
Overall, model performance is adequate in terms of wind vector differences. The average wind
displacement for the WRF simulation is around 5km for all months and hours of the day. The
interquartile ranges are roughly 2-10km. As the displacement is generally less than the
resolution of the model, minimal impacts due to displacement of wind vectors are expected.
Wind Displacement
ID
CM
O
CM
~l I I I I I I I I I I I I I I I I I I I I I I l~~
0 1 2 3 4 5 6 7 8 9 11 13 15 17 19 21 23
Hour of day (GMT)
Wind Displacement
o
CM
LO _
i i i i i i i i i i i r
J F MAMJ JASO ND
Figure 3.1.20. Distribution of hourly wind displacement by hour and month for the 36NOAM
domain.
34
-------
Wind Displacement
o
EN
I I I I I I I I I I I I I I I I I I I I I I I T"
0 1 2 3 4 5 6 7 8 9 11 13 15 17 19 21 23
Hour of day (GMT)
Wind Displacement
o
o _
CM
1 1 1 1 1 1 1 1 1 1 r
J FMAMJ JASOND
Figure 3.1.21. Distribution of hourly wind displacement by hour and month for the 12US
domain.
3.2 Temperature
Temperature estimates are compared to the ds472 observation network described earlier and
are presented below for the 36NOAM (Figure 3.2.1) and 12US (Figure 3.2.2) domains.
35
-------
Overall, WRF slightly underpredicts temperatures at both 36km and 12km across most hours
and months, with a small underprediction during the mid-afternoon hours. The range of biases
decreases slightly during the summer months compared to the rest of the year, with the inner-
quartile range (IQR) becoming more tightly centered around zero. Overall, with an average IQR
of +/-1 degree, this is considered adequate model performance.
In Figures 3.2.3-3.2.6 and 3.2.7-3.2.10, spatial distribution of monthly biases is presented across
all hours for the 36km and 12km simulations, respectively. In 3.2.11-3.2.14 and 3.2.15-3.2.18,
the monthly spatial distributions of the temperature bias for the 36km and 12km simulations is
presented for daytime hours only, respectively. Additionally, the hourly average distribution of
observed and predicted temperatures for each season and region is presented in Figure 3.2.19.
Overall, a persistent slight underprediction of temperature is noted for most of the year, with a
transition to a slight overprediction in some areas during the summer months. A more
persistent underprediction of temperature is noted during daytime hours specifically, with a
slight improvement in that underprediction observed during the summer months. In areas of
the western US, performance for temperature is mixed, with persistent significant
overpredictions and underpredictions observed in varying locations.
36
-------
Temperature Bias
O o
"i 1 1 1 1 1 1 1 1 1 i 1 1 1 1 1 1 1 1 i 1 1 r~
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 18 20 21 22 23
Hour of day (GMT)
Temperature Bias
T
i
i
i
1
i
i
-
-
1 1 J 1 1 1
-
—
-
i
j
i
F
1
M
1
A
l 1 1 1
M J J A
Temperature Error
1
S
1
O
1
N
1
D
¦
e
i
i
a
-y
1 i ! 1
T
¦y
¦y
i
i
i
j
1
F
i
M
i
A
i 1 1 1
M J J A
Temperature Fractional Bias
1
S
i
o
i
N
1
D
T
-3=- -=b- ~ZET" -=b-
-=b-
-zcr1
y o
Temperature Fractional Error
Figure 3.2.1. Distribution of hourly bias by hour arid hourly bias, error, fractional bias, and
fractional error for temperature by month for the 36NOAM domain.
37
-------
Temperature Bias
i 1 1 1 1 1 1 r
23456789
~l 1 1 1 1 1 1 1 1 1 1 1 1 T
10 11 12 13 14 15 10 17 18 19 20 21 22 23
Hour of day (GMT)
Temperature Bias
I
J
i
F
1
M
1
A
1 1 1 1
M J J A
Temperature Error
i
s
1
o
1
N
1
D
1
i
T
7-
a
B
e
i
j
1
F
i
M
1
A
i i i i
M J J A
Temperature Fractional Bias
i
s
1
o
1
N
i
D
-r~
i
j
1
F
1
M
1
A
1 1 1 1
M J J A
Temperature Fractional Error
1
s
1
o
1
N
i
D
£
Figure 3.2.2. Distribution of hourly bias by hour and hourly bias, error, fractional bias, and
fractional error for temperature by month for the 12LJS domain.
38
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-01-01 AND 2016-02-01
_T _
'#mr
-3
-2.5
-2
-1.5
V . A-
-0.5
0.5
2.5
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-02-01 AND 2016-03-01
-3
¦0.5
0.5
2.5
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-03-01 AND 2016-04-01
vo v
Figure 3.2.3. Spatial distribution of temperature bias (C) across all hours for the months of
January, February, and March (top to bottom) for the 36NOAM domain.
39
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-04-01 AND 2016-05-01
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
1.5
• 2
• 2.5
• 3
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-05-01 AND 2016-06-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-06-01 AND 2016-07-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Figure 3.2,4. Spatial distribution of temperature bias (C) across all hours for the months of April,
May, and June (top to bottom) for the 36NOAM domain.
40
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-07-01 AND 2016-08-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-08-01 AND 2016-09-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-09-01 AND 2016-10-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
-0.5
0
0.5
1
1.5
•
2
•
2.5
•
3
Figure 3.2.5. Spatial distribution of temperature bias (C) across all hours for the months of July,
August, and September (top to bottom) for the 36NOAM domain.
41
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-10-01 AND 2016-11-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-11-01 AND 2016-12-01
W7
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-12-01 AND 2017-01-02
Figure 3.2,6. Spatial distribution of temperature bias (C) across all hours for the months of
October, November, and December (top to bottom) for the 36NOAM domain.
42
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-01-01 AND 2016-02-01
«• ¦
rV.
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-02-01 AND 2016-03-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-03-01 AND 2016-04-01
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
1.5
• 2
• 2.5
• 3
Figure 3.2.7. Spatial distribution of temperature bias (C) across all hours for the months of
January, February, and March (top to bottom) for the 12US domain.
43
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-04-01 AND 2016-05-01
—- J , " '-V - • }•. •»
:¦ l^SSj£3$fa»
• I J. •,./SVi>aa J V*'• 3&'
> w.:
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
-0.5
0
0.5
1
1.5
•
2
•
2.5
•
3
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-05-01 AND 2016-06-01
TT i "~" - 4
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-06-01 AND 2016-07-01
v H & I'.V
\ *
;>/v*
y?w
—i v ?
< i •_. '
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
-0.5
0
0.5
1
1.5
•
2
•
2.5
•
3
Figure 3.2,8. Spatial distribution of temperature bias (C) across all hours for the months of April,
May, and June (top to bottom) for the 12US domain,
44
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-07-01 AND 2016-08-01
> „ ¦ /w 5Tv3v
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-08-01 AND 2016-09-01
.---r r-r-v. wv
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-09-01 AND 2016-10-01
£
\ r ... -|
y J
>• . n .
rtv *<:•?.
Kn ;— ¦ "w
¦•'sr
Figure 3.2.9, Spatial distribution of temperature bias (C) across all hours for the months of July,
August, and September (top to bottom) for the 12US domain.
45
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-10-01 AND 2016-11 -01
-2.5
-2
-1.5
-0.5
0.5
2.5
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-11-01 AND 2016-12-01
-2.5
-2
-1.5
-0.5
0.5
2.5
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-12-01 AND 2017-01-01
-2.5
-1.5
-0.5
0.5
2.5
Figure 3.2.10. Spatial distribution of temperature bias (C) across all hours for the months of
October, November, and December (top to bottom) for the 12US domain.
46
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-01-01 AND 2016-02-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-02-01 AND 2016-03-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-03-01 AND 2016-04-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Figure 3.2.11. Spatial distribution of temperature bias (C) across daytime hours for the months
of January, February, and March (top to bottom) for the 36NOAM domain,
47
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-04-01 AND 2016-05-01
V0 v
•• •
V* ••
' --T f-i-
\ V kip te-
\ Nftf • >/. *£f§; J
5* * " I L k , -1^ X
jgs; ":Js
- r / .• VA . ^
V..
v J? *• f'•
\T »• * vU
* 7 ^ *v
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-05-01 AND 2016-06-01
. .
*« •—1—t-h v. • • •• •• %?• ^
« •*.• * i« L . - '/~rf -v '¦
v.
^ ^ -* •. i*;*
\b
Figure 3.2.12. Spatial distribution of temperature bias (C) across daytime hours for the months
of April, May, and June (top to bottom) for the 36NOAM domain.
48
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-07-01 AND 2016-08-01
-2.5
-1.5
-0.5
0.5
1.5
2.5
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-08-01 AND 2016-09-01
T>
-2.5
-0.5
0.5
2.5
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-09-01 AND 2016-10-01
M 4 ;\
.. '
• -3
• -2.5
• -2
-0.5
0.5
2.5
Figure 3.2.13. Spatial distribution of temperature bias (C) across daytime hours for the months
of July, August, and September (top to bottom) for the 36NOAM domain.
49
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-10-01 AND 2016-11-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-11-01 AND 2016-12-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-12-01 AND 2017-01-01
Figure 3.2.14. Spatial distribution of temperature bias (C) across daytime hours for the months
of October, November, and December (top to bottom) for the 36NOAM domain.
50
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-01-01 AND 2016-02-01
fv„ \
r*5** t %. • • /a- •» • •» •
V II— ~r « vV
wt J"' > * « • * \
I^ ^ J • a. m —• -—L • ¦ ••• <«. -r~i? :
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-02-01 AND 2016-03-01
.,3*
Jn
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-03-01 AND 2016-04-01
• • *. ¦ •' » *
^Vv. \r-, ¦ ^
X>Ti '' '• ft
Figure 3.2.15. Spatial distribution of temperature bias (C) across daytime hours for the months
of January, February, and March (top to bottom) for the 12US domain.
51
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-04-01 AND 2016-05-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-05-01 AND 2016-06-01
• 2.5
• 3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
1.5
•
2
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-06-01 AND 2016-07-01
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Figure 3.2.16. Spatial distribution of temperature bias (C) across daytime hours for the months
of April, May, and June (top to bottom) for the 12US domain.
52
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-07-01 AND 2016-08-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-08-01 AND 2016-09-01
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-09-01 AND 2016-10-01
Figure 3.2.17. Spatial distribution of temperature bias (C) across daytime hours for the months
of July, August, and September (top to bottom) for the 12US domain.
53
-------
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-10-01 AND 2016-11-01
y
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-11-01 AND 2016-12-01
.• 7v • ....
, f r## • • • •
• / *
^ x • • " * .* • *
.. J.> • . . •. •.
Mean bias of 2 m Temperature (C) Date: BETWEEN 2016-12-01 AND 2017-01-01
VV.
*•
S*,
v-W&lMtk
v/^
V3 *'' S ]
1
> q\.
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Figure 3.2.18. Spatial distribution of temperature bias (C) across daytime hours for the months
of October, November, and December (top to bottom) for the 12US domain.
54
-------
55
-------
S-
¦S •" ¦'III* &-
i fHM mm »«» s t ii I? up fiiipii iffiii 11 if ?r?iifif if mi: i nmm iwu mm
^
I|#|II|iS|i?I™|Infill !|!i?|fii||iiil!!^ip 5 v tfifllr- »-?r*«!s ""
»- ' """*"I
l|I|g||f|f||l||f;HrlI|l »¦-
|||||. J
'5-
ft- 1
*- '"-1
ijijiftirii6ii|ii||f}iii
l|ljflllls||Sf|Slf!|,|l|i
Figure 3.2,19, Hourly average distribution of observed arid predicted temperature for the 12US
domain in the Central, East-North Central, Northeast, Northwest, South, Southeast, Southwest,
West, and West-North Central (top to bottom) regions for each season (Spring, Summer, Fall,
Winter, L-R).
56
-------
Climate Region
Season
Mean Obs
Mean Mod
MB
MAE
NMB
NME
RMSE
Northeast
Spring
282.51
282.26
-0.25
1.59
-0.09
0.56
2.13
Northeast
Summer
295.16
295.46
0.30
1.35
0.10
0.46
1.85
Northeast
Fall
285.68
285.95
0.27
1.52
0.09
0.53
2.05
Northeast
Winter
272.41
272.20
-0.21
1.74
-0.08
0.64
2.33
West-North Central
Spring
281.38
281.48
0.10
1.57
0.03
0.56
2.05
West-North Central
Summer
294.20
294.44
0.24
1.44
0.08
0.49
1.94
West-North Central
Fall
283.30
283.45
0.15
1.64
0.05
0.58
2.15
West-North Central
Winter
268.53
268.25
-0.29
1.92
-0.11
0.72
2.56
Northwest
Spring
283.93
283.99
0.06
1.51
0.02
0.53
2.00
Northwest
Summer
292.38
292.45
0.07
1.61
0.02
0.55
2.15
Northwest
Fall
284.15
284.27
0.12
1.57
0.04
0.55
2.08
Northwest
Winter
274.55
274.85
0.30
1.79
0.11
0.65
2.44
Central
Spring
286.08
285.99
-0.09
1.47
-0.03
0.51
1.94
Central
Summer
297.50
297.77
0.27
1.18
0.09
0.40
1.61
Central
Fall
288.66
288.77
0.12
1.39
0.04
0.48
1.80
Central
Winter
273.75
273.54
-0.21
1.53
-0.08
0.56
1.99
South
Spring
291.40
291.57
0.18
1.38
0.06
0.48
1.83
South
Summer
300.64
300.82
0.18
1.09
0.06
0.36
1.53
South
Fall
293.40
293.52
0.12
1.32
0.04
0.45
1.76
South
Winter
281.71
281.65
-0.06
1.60
-0.02
0.57
2.06
Southeast
Spring
291.16
291.10
-0.06
1.39
-0.02
0.48
1.82
Southeast
Summer
299.70
299.89
0.19
1.18
0.06
0.39
1.60
Southeast
Fall
292.44
292.42
-0.02
1.43
-0.01
0.49
1.88
Southeast
Winter
281.87
281.74
-0.13
1.65
-0.05
0.58
2.13
Southwest
Spring
284.65
284.75
0.10
1.79
0.03
0.63
2.36
Southwest
Summer
296.61
296.70
0.09
1.87
0.03
0.63
2.54
Southwest
Fall
286.91
287.20
0.29
1.91
0.10
0.67
2.52
Southwest
Winter
274.86
275.13
0.27
2.30
0.10
0.84
3.13
East-North Central
Spring
281.56
281.39
-0.16
1.50
-0.06
0.53
1.99
East-North Central
Summer
294.26
294.60
0.34
1.27
0.12
0.43
1.71
East-North Central
Fall
284.74
284.85
0.12
1.36
0.04
0.48
1.81
East-North Central
Winter
267.78
267.51
-0.27
1.52
-0.10
0.57
2.02
West
Spring
288.61
288.40
-0.21
1.61
-0.07
0.56
2.17
West
Summer
296.97
296.75
-0.22
1.81
-0.07
0.61
2.46
West
Fall
290.17
290.06
-0.11
1.84
-0.04
0.63
2.47
West
Winter
282.54
282.58
0.03
1.81
0.01
0.64
2.47
Table 3.2.1. Mean observed, mean modeled, mean bias (MB), mean absolute error (MAE),
normalized mean bias (NMB), normalized mean error (NME), and root mean square error
(RMSE) for temperature (K) for the 12US simulation.
57
-------
3.3 Mixing Ratio
Water mixing ratio estimates are compared to the ds472 observation network described earlier
and are presented below for the 36NOAM (Figure 3.3.1) and 12US (Figure 3.3.2) domains.
In either simulation, no significant positive or negative bias is observed. However, WRF tends to
be slightly drier in the morning and early afternoon hours relative to the rest of the day.
Additionally, there is more uncertainty in model predictions during the spring and summer
months. This increase in error is explained by the increased convective activity and influx of
moist air masses that are typical of that time of year. In general, WRF performance was
adequate for water vapor mixing ratio.
The monthly spatial distributions of the mixing ratio bias across all hours for the 36km and
12km simulation are shown in Figures 3.3.3-3.3.6 and 3.3.7-3.3.10, respectively. The spatial
distribution of the mean bias during daytime hours for the 36km and 12km simulations are
shown in Figures 3 3.3.11-3.3.14 and 3.3.15-3.3.18, respectively. Lastly, the hourly average
distribution of observed and predicted water vapor mixing ratio for each season and region is
presented in Figure 3.3.19. Little appreciable difference is observed in the biases either across
all hours or just daytime. This is to be expected since water vapor mixing ratio has less temporal
variability when compared to other variables (i.e., temperature). A slight overprediction persists
across the eastern US during the late Winter through late Summer before transitioning to a
slight underprediction through early Winter. Mixing ratio performance is generally unbiased to
slightly underpredicted across the western states throughout the year.
58
-------
Mixing Ratio Bias
-0 -0-0—0—0—0—0—H—0——B S~0—B- 0-8-0-
! « ! ¦ '¦''!!!!! 1 '•!!!!!!! I
~i 1 1 r
2 3 4 5
"I 1 1 r
6 7 6 9
i 1 1 1 1 1 1 1 1 1 1 1 1 r~
10 11 12 13 14 15 18 17 18 19 20 21 22 23
Hour of day (GMT)
Mixing Ratio Bias
T X ;
sjs Hja I—|—I L_l
i
i
r"-1
1
I—1 1—1 E=3 == c^]
~r
M
Mixing Ratio Error
Mixing Ratio Fractional Bias
~r~
M
© _
co
* = I
Figure 3.3.1. Distribution of hourly bias by hour and hourly bias, error, fractional bias, and
fractional error for water vapor mixing ratio by month for the 36NOAM domain.
Mixing Ratio Fractional Error
~i i i i i i i i i i i r
J FMAMJ J ASOND
59
-------
Mixing Ratio Bias
0 0 a s e h a e $ $ $ 8 $ $ $ $ h b a s a s s 0
! ! ! ! ' I > ' 1 ' 1 ! 1 1 1 1 ! ! ! ! ! ! ! !
H 1 1 1 1 1 1 1 1 ! 1 1 1 1 1 1 1 1 1 1 1 1 1 r~
D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 10 17 18 1 9 20 21 22 23
Hour of day (GMT)
Mixing Ratio Bias
1—I I-1-! [ ] —1
i__i i | i i 1 M-i
Mixing Ratio Error
th b a a
~r~
M
Mixing Ratio Fractional Bias
Mixing Ratio Fractional Error
Figure 3.3.2, Distribution of hourly bias by hour and hourly bias, error, fractional bias, and
fractional error for water vapor mixing ratio by month for the 12US domain.
60
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-01-01 AND 2016-02-01
-2.5
-1.5
" 2^-'
* . 1
" ~w, ¦[ / I
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-02-01 AND 2016-03-01
-3
-2.5
-1.5
*/-
-0.5
n
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-03-01 AND 2016-04-01
-3
-0.5
I 1
0.5
2.5
Figure 3.3.3. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of January, February, and March (top to bottom) for the 36NOAM domain.
61
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-04-01 AND 2016-05-01
vj
j; '
—r
_4 S •/
V3 -UT
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-05-01 AND 2016-06-01
V# /• t v /
. t, v
sj
|y ;L- j) _
i—{ i *
¥..VV-'V
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
• 0
0.5
1
i
1.5
• 2
• 2.5
• 3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-06-01 AND 2016-07-01
) < , >; - ^
V i S i, 1 . I * >•- - J. li, •
' ••• . £'
«••['. t * ,
* .
: -< ; ••<.; •
V ^
a«\
Figure 3.3.4. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of April, May, and June (top to bottom) for the 36NOAM domain,
62
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-07-01 AND 2016-08-01
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-08-01 AND 2016-09-01
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-09-01 AND 2016-10-01
Figure 3.3.5. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of July, August, and September (top to bottom) for the 36NOAM domain,
63
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-10-01 AND 2016-11 -01
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
3
Mixing Ratio {g/kg)
Date: BETWEEN 2016-11-01 AND 2016-12-01
Mean bias of
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-12-01 AND 2017-01-02
-3
-2.5
-1.5
-0.5
0.5
2.5
Figure 3.3.6. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of October, November, and December (top to bottom) for the 36NOAM domain.
64
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-01-01 AND 2016-02-01
I • / I • U a * V '*
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-02-01 AND 2016-03-01
T~ ''fttAxf.' >
/ r i. ^ *,\-
: >'
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-03-01 AND 2016-04-01
Figure 3.3.7. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of January, February, and March (top to bottom) for the 12US domain.
65
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-04-01 AND 2016-05-01
Wi.-
\t.
^•jl • ".j; #
rv. . •'¦••v'..
1 • i—i i •»» iy
. i ,¦ —r
\' *">
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-05-01 AND 2016-06-01
* TRT •• . \m— ' A
V$«/* t • i .i
• f -*• I u
,1 w(#^
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-06-01 AND 2016-07-01
,r a#
t" .'V •>*
:;i-
?i V\. ,
U \
\
> A*\
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
•
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Figure 3.3.8, Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of April, May, and June (top to bottom) for the 12US domain.
66
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-07-01 AND 2016-08-01
hi "4
T*D* - *
•••-• !*. _ r*
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
: 1.5
• 2
• 2.5
• 3
Mean bias of Mixing Ratio (g/kg) Data; BETWEEN 2016-08-01 AND 2016-09-01
—P* • 1—_
Y'
; ^ • ' -)!; ¦*¦ m
"»*y .>=^r Wy''
ji. '*•" \y*°* 'v'• • aV
^rh&
-xJ •
— x^y
\ , ^ • • - )
.W/.
• /
\' ^
> a*\
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
1.5
• 2
• 2.5
• 3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-09-01 AND 2016-10-01
• v. 3^,'^ ¦
¦ /vr- f •.*.! • I,- . .
>4-7- ^L-^cm'
i • V^/ x<:x
•• Z-—-j &1-.V
f^l •• *• ••*/. •• v- •
j • - L ^ • • ** . ] 1 T' - . 1 ^
. J ^w ^^-v
Figure 3.3.9. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of July, August, and September (top to bottom) for the 12US domain.
67
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-10-01 AND 2016-11 -01
•*V- ':
• . vf .
<*¦
' - i,.
\ _ * V'
,i '¦• i s-t-i . : f * "« V
r *-vj> r.$\.w* >>
.• - • •* ;^M^c4r
V
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
0
0.5
1
» 1.5
• 2
• 2.5
• 3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-11-01 AND 2016-12-01
_ V.•.-* -« . .' >. v.
-—¦^ .T, •.
<2l
-
H
-v,
w
'_JLL
> I\
• -3
• -2.5
• -2
• -1.5
• -1
• -0.5
• 0
0.5
1
1.5
• 2
• 2.5
• 3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-12-01 AND 2017-01-01
Figure 3.3.10. Spatial distribution of water vapor mixing ratio bias (g/kg) across all hours for the
months of October, November, and December (top to bottom) for the 12US domain.
68
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-01-01 AND 2016-02-01
-3
-2.5
-1.5
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-02-01 AND 2016-03-01
1,
-3
-2.5
-1.5
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-03-01 AND 2016-04-01
-2.5
-1.5
-0.5
0.5
1.5
2.5
Figure 3.3.11. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for the months of January, February, and March (top to bottom) for the 36NOAM domain.
69
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-04-01 AND 2016-05-01
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-05-01 AND 2016-06-01
-3
-2.5
-0.5
0.5
1.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-06-01 AND 2016-07-01
Figure 3.3.12. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for the months of April, May, and June (top to bottom) for the 36NOAM domain.
70
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-07-01 AND 2016-08-01
; «•, -'Mi1 % . y
>•¦ x TS5..r r-1 f x&k^mk'
p.- .t ti V
• . t si h. '4 "¦/ V
—J** »=; v > | . .'r
'v/.;-v £\
J
^ A \
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
• 0
0.5
1
• 1.5
• 2
• 2.5
• 3
V3 V/
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-08-01 AND 2016-09-01
t w m —gj\
-.—T•,*-
r "TTfc* - * iv ' j-
; r > • > /.
>1jA I * ;*
'. ¦ •» j •># •¦'. k^^SF
i i -taI *v
.'r '~\f>
V V • • cJ *
, ^
¦v
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-09-01 AND 2016-10-01
r. v •
V .i ¦•• ~
• .S 5 • • *
Figure 3.3.13. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for the months of July, August, and September (top to bottom) for the 36NOAM domain.
71
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-10-01 AND 2016-11-01
-3
-2.5
,f"-n •
-1.5
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-11-01 AND 2016-12-01
-2.5
-1.5
f •: .
-0.5
.
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-12-01 AND 2017-01-01
-3
-2.5
-2
-1.5
-0.5
0.5
2.5
Figure 3.3.14. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for October, November, and December (top to bottom) for the 36NOAM domain,
72
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-01-01 AND 2016-02-01
-3
-2.5
-2
-1.5
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-02-01 AND 2016-03-01
,. yi. ¦ r 1 >.r sv
_ X'
I 4v4/ 1 u ^
I „ ' . .
\ ^
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-03-01 AND 2016-04-01
.J \ /* v '-1'
•y — --
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-04-01 AND 2016-05-01
V
I—H I «V» iy
\ ¦' 7
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-05-01 AND 2016-06-01
r- —^ - —v* L ¦ • •
¦¦
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-06-01 AND 2016-07-01
• * i_- , i ( 1
' V.v
. -A \ - * A •
.J -ViT- y.
f ¦•< >* y-
J>*.7
Figure 3.3.16. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for the months of April, May, and June (top to bottom) for the 12US domain,
74
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-07-01 AND 2016-08-01
SSt 14 • • •
\? f\ 1 v • • •' '• Iy
W* *• *. \**i1• 1 V >4 -A • ¦ 'X^y-vM-
- jv
• f
": ./* [T\' , T,"'
r V' * '* \
^ 9?
•
-3
•
-2.5
•
-2
•
-1.5
•
-1
-0.5
0
0.5
1
•
1.5
•
2
•
2.5
•
3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-08-01 AND 2016-09-
3:'\
I ./* •*' ife !>
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-09-01 AND 2016-10-01
j«-. ...w>
Figure 3.3.17. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for the months of July, August, and September (top to bottom) for the 12US domain.
75
-------
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-10-01 AND 2016-11-01
• -3
• -2.5
• -2
• -1.5
• -1
-0.5
• 0
0.5
1
1.5
• 2
• 2.5
• 3
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-11-01 AND 2016-12-01
-3
-2.5
-1.5
-0.5
0.5
2.5
Mean bias of Mixing Ratio (g/kg) Date: BETWEEN 2016-12-01 AND 2017-01-01
Figure 3.3.18. Spatial distribution of water vapor mixing ratio bias (g/kg) across daytime hours
for the months of October, November, and December (top to bottom) for the 12US domain,
76
-------
i in hi i- ii i ,li j
IJl|!!ff|l8fffffffffl'fl Ifl|I|l|||llfllllSfpp *" |Pfliflflinfill;|||f|i
77
-------
Figure 3.3,19. Hourly average distribution of observed arid predicted water vapor mixing ratio
for the 12US domain in the Central, East-North Central, Northeast, Northwest, South,
Southeast, Southwest, West, and West-North Central (top to bottom) regions for each season
(Spring, Summer, Fall, Winter, L-R).
78
-------
Climate Region
Season
Mean Obs
Mean Mod
MB
MAE
NMB
NME
RMSE
Northeast
Spring
4.13
4.10
-0.03
1.26
-0.74
30.43
1.81
Northeast
Summer
3.57
3.57
0.00
1.11
0.12
31.21
1.63
Northeast
Fall
3.87
4.00
0.14
1.22
3.56
31.55
1.78
Northeast
Winter
4.46
4.59
0.13
1.40
2.89
31.42
2.01
West-North Central
Spring
5.32
4.75
-0.57
1.41
-10.67
26.42
1.88
West-North Central
Summer
4.49
4.18
-0.31
1.32
-6.90
29.36
1.81
West-North Central
Fall
4.75
4.45
-0.31
1.29
-6.46
27.18
1.74
West-North Central
Winter
5.31
4.63
-0.68
1.51
-12.81
28.42
2.06
Northwest
Spring
4.01
3.72
-0.29
1.33
-7.32
33.24
1.76
Northwest
Summer
3.80
3.53
-0.27
1.19
-7.08
31.36
1.56
Northwest
Fall
3.69
3.43
-0.26
1.31
-7.00
35.45
1.72
Northwest
Winter
3.67
3.32
-0.35
1.41
-9.61
38.25
1.87
Central
Spring
4.16
4.21
0.05
1.11
1.16
26.58
1.47
Central
Summer
3.18
3.19
0.01
0.95
0.18
29.74
1.28
Central
Fall
3.62
3.84
0.22
1.00
5.94
27.60
1.32
Central
Winter
4.41
4.45
0.04
1.08
0.90
24.49
1.43
South
Spring
4.54
4.39
-0.15
1.23
-3.32
27.14
1.66
South
Summer
3.81
3.75
-0.06
1.09
-1.68
28.51
1.48
South
Fall
3.86
3.82
-0.04
1.02
-0.92
26.54
1.37
South
Winter
4.28
4.13
-0.15
1.10
-3.47
25.74
1.47
Southeast
Spring
3.57
3.78
0.21
1.11
5.87
31.09
1.46
Southeast
Summer
3.04
3.05
0.01
1.01
0.26
33.33
1.36
Southeast
Fall
3.31
3.49
0.19
1.05
5.72
31.86
1.41
Southeast
Winter
3.65
3.94
0.29
1.15
7.93
31.53
1.53
Southwest
Spring
4.68
4.12
-0.55
1.60
-11.80
34.21
2.17
Southwest
Summer
4.08
3.54
-0.55
1.54
-13.43
37.78
2.09
Southwest
Fall
4.16
3.61
-0.55
1.47
-13.25
35.35
2.02
Southwest
Winter
4.19
3.64
-0.55
1.55
-13.21
37.03
2.18
East-North Central
Spring
4.46
4.52
0.06
1.14
1.29
25.50
1.49
East-North Central
Summer
3.67
3.90
0.22
1.07
6.12
29.01
1.42
East-North Central
Fall
4.09
4.47
0.38
1.12
9.21
27.41
1.48
East-North Central
Winter
4.69
4.75
0.06
1.17
1.20
24.93
1.55
West
Spring
4.19
3.88
-0.31
1.40
-7.38
33.52
1.89
West
Summer
4.08
3.64
-0.45
1.33
-10.94
32.54
1.77
West
Fall
3.68
3.34
-0.35
1.32
-9.39
35.91
1.80
West
Winter
3.53
3.28
-0.25
1.39
-7.12
39.20
1.90
Table 3.3.1. Mean observed, mean modeled, mean bias (MB), mean a
normalized mean bias (NMB), normalized mean error (NME), and root
(RMSE) for water vapor mixing ratio (g/kg) for the 12US simulation.
Dsolute error (MAE),
mean square error
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3.4 Precipitation
Monthly total rainfall is plotted for each grid cell to assess how well the model captures the
spatial variability and magnitude of convective and non-convective rainfall. As described earlier,
the PRISM estimations for rainfall are only within the continental United States. WRF rainfall
estimates by month are shown for all grid cells in the domain. Monthly total estimates are
shown for the 36NOAM domain (Figures 3.4.1 through 3.4.12) and 12US domain (Figures 3.4.13
through 3.4.24).
In general, WRF performs adequately in terms of the spatial patterns and magnitude of
precipitation across the US throughout the year. Both simulations, however, have difficulty
simulating precipitation in areas of complex terrain (e.g., northern CA and the Pacific
Northwest). Both simulations tend to underpredict precipitation during periods of increased
convective activity (Jun - Oct), with notable underpredictions observed in the eastern US. In
general, both simulations overpredict precipitation across the western areas of the country
during most months, with notable overpredictions of precipitation in the southwest during July
and August. Overpredictions are observed during the Spring and Fall months across the
Northeast and Mid-MS/OH Valley regions. General underpredictions are observed across the
Deep South and Southern Plains throughout much of the year.
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PRISM, January 2016
WRF, January 2016
0 3 6 9 12 15 18
WRF - Prism, January 2016
Inches
Figure 3.4.1, PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for January for the 36NOAM simulation.
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PRISM, February 2016
WRF, February 2016
0 3 6 9 12 15 18
WRF - Prism, February 2016
n a—
Inches
Figure 3.4.2. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for February for the 36NOAM simulation.
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PRISM, March 2016
WRF, March 2016
0 3 6 9 12 15 18
WRF - Prism, March 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.3. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for March for the 36NOAM simulation.
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PRISM, April 2016 WRF, April 2016
0 3 6 9 12 15 18
WRF - Prism, April 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.4, PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for April for the 36NOAM simulation.
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PRISM, May 2016
WRF, May 2016
WRF- Prism, May 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.5. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for May for the 36NOAM simulation.
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PRISM, June 2016
WRF, June 2016
0 3 6 9 12 15 18
WRF - Prism, June 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.6. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for June for the 36NOAM simulation.
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PRISM, July 2016
WRF, July 2016
0 3 6 9 12 15 18
WRF - Prism, July 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.7. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for July for the 36NOAM simulation.
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PRISM, August 2016
WRF, August 2016
0 3 6 9 12 15 18
WRF - Prism, August 2016
n fr—i
ZLfta
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.8. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for August for the 36NOAM simulation.
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PRISM, September 2016 WRF, September 2016
WRF - Prism, September 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.9. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for September for the 36NOAM simulation.
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PRISM, October 2016
WRF, October 2016
0 3 6 9 12 15 18
WRF - Prism, October 2016
n
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.10. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for October for the 36NOAM simulation.
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PRISM, November 2016
WRF, November 2016
0 3 6 9 12 15 18
WRF - Prism, November 2016
-I—
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.11. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for November for the 36NOAM simulation,
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PRISM, December 2016
WRF, December 2016
0 3 6 9 12 15 18
WRF - Prism, December 2016
i
n
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.12. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for November for the 36NOAM simulation.
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PRISM, January 2016
WRF, January 2016
WRF- PRISM, January 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4,13. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for January for the 12US simulation.
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PRISM, February 2016
WRF, February 2016
0 3 6 9 12 15 18
WRF - PRISM, February 2016
—
—t.
¦ %
-5-4-3 -2 -1 1 2 3 4 5
Inches
3.4.14. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in) and
the difference (bottom) for February for the 12LJS simulation.
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PRISM, March 2016
WRF, March 2016
0 3 6 9 12 15 18
WRF - PRISM, March 2016
I—-
—5 -4 -3 —2 -1 1 2 3 4 5
Inches
Figure 3.4.15. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for March for the 12US simulation.
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PRISM, April 2016
WRF, April 2016
o 3 6 9 12 15 18
WRF - PRISM, April 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.16. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for April for the 12US simulation.
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PRISM, May 2016
WRF, May 2016
o 3 6 9 12 15 18
WRF-PRISM, May 2016
m-
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4,17. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for May for the 12US simulation.
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PRISM, June 2016
WRF, June 2016
0 3 6 9 12 15 18
WRF - PRISM, June 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.18. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for June for the 12US simulation.
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PRISM, July 2016
WRF, July 2016
0 3 6 9 12 15 18
WRF - PRISM, July 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.19. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for July for the 12US simulation.
99
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PRISM, August 2016
WRF, August 2016
0 3 6 9 12 15 18
WRF - PRISM, August 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.20, PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for August for the 12US simulation.
100
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PRISM, September 2016
WRF, September 2016
WRF - PRISM, September 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.21, PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for September for the 12US simulation.
101
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PRISM, October 2016
WRF, October 2016
0 3 6 9 12 15 18
WRF - PRISM, October 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.22. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for October for the 12US simulation.
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WRF, November 2016
PRISM, November 2016
0 3 6 9 12 15 18
WRF - PRISM, November 2016
-5 -4 -3 -2 -1 1 2 3 4 5
Inches
Figure 3.4.23. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for November for the 12US simulation.
103
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PRISM, December 2016
WRF, December 2016
0 3 6 9 12 15 18
WRF - PRISM, December 2016
-5
-3
-1 1
Inches
Figure 3.4.24. PRISM analysis (top left) and WRF (top right) estimated monthly total rainfall (in)
and the difference (bottom) for December for the 12US simulation.
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3.5 Solar Radiation
Photosynthetically activated radiation (PAR) is a fraction of shortwave downward radiation and
is an important input for the biogenic emissions model for estimating isoprene (Carlton and
Baker, 2011). Isoprene emissions are important for regional ozone chemistry and play a role in
secondary organic aerosol formation. Radiation performance evaluation also gives an indirect
assessment of how well the model captures cloud formation during daylight hours.
Shortwave downward radiation estimates are compared to surface-based measurements made
at SURFRAD and SOLRAD network monitors for the 36NOAM (Figure 3.6.1) 12US (Figure 3.6.2)
domains.
Overall, both the 36- and 12km simulations show WRF has little bias in shortwave radiation
predictions during the fall and winter months. Biases tend to grow during the spring and peak in
the summer, though the spread in overpredictions tends to be less than 100 W/m2 on average,
with a median bias close to zero.
More variability is noted on an hourly basis. WRF tends to overpredict early morning to early
afternoon shortwave radiation, while underpredicting the late afternoon and early evening
values. The median overprediction at the time of greatest incoming solar radiation is near 100
W/m2 in the 36km simulation and closer to 50 W/m2 in the 12km simulation. In the late
afternoon and evening hours, the median bias is close to -50 W/m2 in both simulations. These
errors are likely attributable to the model being unable to accurately simulate cloud features at
subgrid (<12km) scales. This assumption is based on the slight improvement in predictions at
12km versus 36km.
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Shortwave Radiation Bias: 36NOAM 2016
IT)
o
ID
O
O
in
J F M A M J J A S 0 N D
Shortwave Radiation Bias: 36NOAM 2016
-
T
1
1
1 T
1 i-U -=f- -a- -s- -s- -s- -s- -a- -s- -s-
I
I
i
I
j_.
I
I
i
i
\
i
_L
Lt
1
1
1
1
_i
-T
T
T
T
-
±
i
I
I
I
J.
j_ 1
j.
T
-T
012345678 910 12 14 16 18 20 22
Hour of day (GMT)
Figure 3.5.1. Distribution of hourly bias for shortwave radiation (W/m2) by month (top) and
hour of the day (bottom) for the 36NOAM domain.
106
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Shortwave Radiation Bias: 12US 2016
Shortwave Radiation Bias: 12US 2016
Hour of day (GMT}
Figure 3.5.2. Distribution of hourly bias for shortwave radiation (W/rn2) by month (top) and by
hour of the day (bottom) for the 12US domain.
4 CLIMATE REPRESENTATIVENESS OF 2016
Figures 4,1 and 4.2 show the divisional rankings for observed temperatures across the US for
2016. A climatic representation of the precipitation for 2016 is shown in Figures 4.3 and 4.4.
These plots are useful in determining the representativeness of 2016 in terms of certain
climatological variables compared to historical averages.
Temperatures in 2016 were above average to much above average across several months of
the year, with record warmth observed in many areas of the country at varying times of the
year. Cooler than average conditions were noted in the eastern US in January, central and
eastern US in May, Pacific Northwest in July, southwest in August, and the Intermountain West
and Northwest in December.
107
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Drier than normal conditions were observed across the southeastern US for most of the year
with the exception of February, May, September, and October. In the northeast, abnormally dry
conditions also persisted throughout much of the year, except in February, August, October,
and December. Near-record precipitation was observed in the Deep South and Upper Great
Lakes in March and August, and Pacific Northwest in October.
108
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Divisional Average Temperature Ranks Divisional Average Temperature Ranks
January 2016 February 2016
H [=~ (Z=l UZ2 M H
Much Below Near Above Much Record
Below Average Average Average Above SVannest
Average Average
Divisional Average Temperature Ranks
March 2016
Period: 1895-2016
Much Below Near Above Much
Below Average Average Average Above
Average Average
Divisional Average Temperature Ranks
May 2016
Period: 1895-2016
Much Below Near Above Much
Below Averaoe Average Average Above
Average Average
Divisional Average Temperature Ranks
April 2016
Period: 1895-2016
Much Below Near Above Much
Below Average Average Average Above
Average Average
Divisional Average Temperature Ranks
June 2016
Period: 1895-2016
Much Record Record Much Below Near Above
Above Warmest Coldest Below Average Average Average
Average Average
Figure 4.1 Climatic temperature rankings by climate division: January to June 2016.
http://www.ncdc.noaa.eov/temp-and-precip/maps.php
109
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Divisional Average Temperature Ranks Divisional Average Temperature Ranks
July 2016 August 2016
~
{=I
Much Bo tow
Beon Average
Above Much Record
Average Abov® Wannest
Average
Much
Below
Average
Mudi
Divisional Average Temperature Ranks
September 2016
Divisional Average Temperature Ranks
October 2016
Period: 1895-2016
¦¦ B E3 ~ LZZl ~ ~
Much Record Record Much Below Near Above
Above Warmest Coldest Below Average Average Average
Average Average
National Centers (or
Divisional Average Temperature Ranks Divisional Average Temperature Ranks
November 2016 December 2016
Period: 1895-2016 Period: 1895-2016
Much
Below
Average
Much Record
Mxw Warmest
Much
Below
Average
IZZ1
Below
Average
. .
Average
Figure 4.2 Climatic temperature rankings by climate division: July to December 2016.
http://www.ncdc.noaa.eov/temp-and-precip/maps.php
110
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TnuFoO 42016
Divisional Precipitation Ranks Divisional Precipitation Ranks
January 2016 February 2016
Divisional Precipitation Ranks
March 2016
Period: 1895-2016
Divisional Precipitation Ranks
April 2016
Period: 1895-2016
Mud)
Below
Average
Much
..¦'•coy:
[=l
Mud)
Below
Average
Average
Divisional Precipitation Ranks
May 2016
Period: 1895-2016
Divisional Precipitation Ranks
June 2016
Abo*1
Record Much
Driest Below
Average
~ ~
Near Above
Average- Average
Figure 4.3 Climatic rainfall rankings by climate division: January to June 2016.
http://www.ncdc.noaa.eov/temp-and-precip/maps.php
111
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Divisional Precipitation Ranks Divisional Precipitation Ranks
July 2016 August 2016
Divisional Precipitation Ranks
September 2016
Divisional Precipitation Ranks
October 2016
Period: 1895-2016
Average
Much
Below
Average
Divisional Precipitation Ranks
November 2016
Period: 1895-2016
Divisional Precipitation Ranks
December 2016
Period: 1895-2016
H B H _ 1Z3 CZZ1
Much Record Record Much Below Near Above Much
Above Wettest Driest Below Average Average Average Above
Average Average Average
Figure 4.4 Climatic rainfall rankings by climate division: July to December 2016.
https://www.ncdc.noaa.gov/sotc/
112
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Otte, T.L., Pleim, J.E., 2010. The Meteorology-Chemistry Interface Processor (MCIP) for the
CMAQ modeling system: updates through MCIPv3.4.1. Geoscientific Model Development 3,
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Skamarock, W.C., Klemp, J.B., Dudhia, J., Gill, D.O., Barker, D.M., Duda, M.G., Huang, X., Wang,
W., Powers, J.G., 2008. A Description of the Advanced Research WRF Version 3.
Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-19-010
Environmental Protection Air Quality Assessment Division July 2019
Agency Research Triangle Park, NC
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