1 

# Meteorological Model Performance for
Annual 2011 WRF v3.4 Simulation2

1. INTRODUCTION

The Weather Research and Forecasting model (WRF) wa s applied for the entire year of 2011 to generate meteorological data to support emissions a nd photochemical modeling applications for this year. The WRF meteorological fields will b e converted to air quality modeling input data and used to support assessments of ozone, PM2.5, vi sibility, and a variety of toxics.

The WRF model was applied to a 36 km continental Un ited States scale domain (36US) and a 12 km continental United States scale domain (12US2). Both model simulations were initialized directly from meteorological analysis data. Model p arameterizations and options outlined in
this document were chosen based on a series of sens itivity runs performed by U.S. Environmental Protection Agency (USEPA) Office of R esearch and Development that provided an optimal configuration based on temperature, mixi ng ratio, and wind field. All WRF simulations were done by Computer Sciences Corporat ion (CSC) under contract to the USEPA.

2. MODEL CONFIGURATION

2.1 Configuration of the 36US Domain

Version 3.4 of the WRF model, Advanced Research WRF (ARW) core (Skamarock, 2008) was used for generating the 2011 simulations 1 . Selected physics options include Pleim-Xiu land surface model, Asymmetric Convective Model version 2 planetary boundary layer scheme, KainFritsch cumulus parameterization utilizing the mois ture-advection trigger (Ma and Tan, 2009), Morrison double moment microphysics, and RRTMG long wave and shortwave radiation schemes (Gilliam and Pleim, 2010).

The 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. An alysis nudging for temperature, wind, and moisture was applied above the boundary layer o nly. The model simulations were conducted in 5.5 day blocks with soil moisture and temperature carried from one block to the next via the ipxwrf program (Gilliam and Pleim, 201 0). Landuse and land cover data were based on the 1992-1993 U.S. Geological Survey (USGS) data

. Figure 2.1 shows the 36US domain, which utilizes a Lambert conformal projection centered at (-97,40) with true latitudes of 33 and 45 degrees n orth. The domain contains 164 cells in the X
direction and 128 cells in the Y direction. All cel ls are 36 km 2 . As shown in Table 2.1, the

1 Version 3.4 was the most current version of WRF at the time the 2011 meteorological model simulations were performed.3

atmosphere is resolved with 35 vertical layers up t o 50 mb, 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 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 groun d level.4

Figure 2.1 Map of WRF model domain: 36US

2.2 Configuration of the 12US2 Domain

The 12km configuration is the same as the 36km doma in with two exceptions: the Group for High Resolution Sea Surface Temperatures (GHRSST) ( Stammer et al., 2003) 1km SST data was used to provide more resolved information compared to the more coarse data in the NAM analysis. Additionally, landuse and land cover data were based on the National Land Cover Database 2006 (NLCD 2006). Analysis nudging for tem perature, wind, and moisture was applied above the boundary layer only. The model simulation s were conducted in 5.5 day blocks with soil moisture and temperature carried from one bloc k to the next via the ipxwrf program (Gilliam and Pleim, 2010).

Figure 2.2 shows the 12US2 domain, which utilizes a Lambert conformal projection centered at (-97,40) with true latitudes of 33 and 45 degrees n orth. The domain contains 396 cells in the X
direction and 246 cells in the Y direction. All cel ls are 12 km 2 . The atmosphere is resolved with 35 vertical layers up to 50 mb (see table 2.1), wit h the thinnest layers being nearest the surface
to better resolve the PBL.5

Figure 2.2 Map of WRF model domain: 12US2.

3 MODEL PERFORMANCE DESCRIPTION

The WRF model simulations were evaluated to determi ne whether the output fields represent a reasonable approximation of the actual meteorology that occurred during the modeling period. Identifying and quantifying these output fields all ows for a downstream assessment of how the air quality modeling results are impacted by the me teorological data. For the purposes of this assessment, 2-meter temperature and mixing ratio, 1 0-meter wind speed and direction, and shortwave radiation are quantitatively evaluated. A qualitative evaluation of precipitation is also provided.

The observation database for surface-based temperat ure, wind speed and direction, and mixing
ratio is based on measurements made at United State s (i.e., National Weather Service) and Canadian (i.e., Environment Canada) airports. The o bservational dataset (ds472 network) is
available from NCAR. Monitors used for evaluation a re shown in Figure 3.1.6

Figure 3.1 Stations used for model performance: ds472 network.

Shortwave downward radiation measurements are taken at Surface Radiation Budget Network (SURFRAD) ( http://www.srrb.noaa.gov/surfrad ) and Integrated Surface Irradiance Study (ISIS) ( http://www.srrb.noaa.gov/isis/index.html ) monitor locations. The SURFRAD network consists
of 7 sites and the ISIS network consists of 9 sites across the United States (see Figure 3.2). Both networks are operated by the National Oceanic and A tmospheric Administration (NOAA), with SURFRAD sites existing as a subset of ISIS monitors that provide higher level radiation
information not used in this evaluation.7

Figure 3.2. Location of ISIS and SURFRAD radiation monitors.

Rainfall amounts are estimated by the Parameter-ele vation Relationships on Independent Slopes Model (PRISM) model, which uses an elevation -based regression model to analyze precipitation. PRISM’s horizontal resolution is app roximately 2 to 4 km and is re-projected to the WRF modeling domain for direct qualitative comp arison 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, a nd 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 becaus e they describe model performance in the measured units of the meteorological variable and a s a normalized percentage. Since wind direction is reported in compass degrees, estimatin g 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 use d to assess wind vector performance (Equation 1). Performance is best when these metric s approach 0.

(1) Wind displacement (km) = ( U M – U O + V M – V O )*(1 km/1000 m)*(3600 s/hr)*(1 hr)8

Rainfall performance is examined spatially using si de-by-side comparisons of monthly total rainfall plots. The WRF model outputs predictions a pproximately 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 ti mes taken at monitor stations. This should be considered when interpreting model performance m etrics.

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 fo r the 36US (Figure 3.1.1) and 12US2 (Figure 3.1.2) domains are shown below.

At 36km, wind speeds are generally overpredicted ac ross most hours of the day for all seasons, in terms of mean bias. In general, performance impr oves at 12km, with less overprediction. However, at 12km WRF tends to slightly overpredict wind speeds in the early morning and afternoon hours, while slightly underpredicting win d 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- 3.1.6) and daytime hours 2 (Figures 3.1.7-3.1.10) are also presented. No appr eciable difference is observed in the biases for daytime hours versus all hours. However, WRF tends to slightly overpredict wind speeds for areas in the eastern US and underpredicts wind speeds in the western US, particularly southern California. As no ted above, these biases persist regardless of changes in season.

2 1200 GMT to 0000 GMT9

Figure 3.1.1. Distribution of hourly bias by hour and hourly bias , error, fractional bias, and fractional error for wind speed by month for 36US d omain.10

Figure 3.1.2. Distribution of hourly bias by hour and hourly bia s, error, fractional bias, and fractional error for wind speed by month for 12US2 domain.11

Figure 3.1.3. Spatial distribution of wind speed bias (m/s) acro ss all hours for the months of January, February, and March (top to bottom) for th e 12US2 domain.12

Figure 3.1.4. Spatial distribution of wind speed bias (m/s) acro ss all hours for the months of April, May, and June (top to bottom) for the 12US2 domain.13

Figure 3.1.5. Spatial distribution of wind speed bias (m/s) acro ss all hours for the months of July, August, and September (top to bottom) for the 12US2 domain.14

Figure 3.1.6. Spatial distribution of wind speed bias (m/s) acro ss all hours for the months of October, November, and December (top to bottom) for the 12US2 domain.15

Figure 3.1.7. Spatial distribution of wind speed bias (m/s) acro ss daytime hours for the months of January, February, and March (top to bottom) for the 12US2 domain.16

Figure 3.1.8. Spatial distribution of wind speed bias (m/s) acro ss daytime hours for the months of April, May, and June (top to bottom) for the 12U S2 domain.17

Figure 3.1.9. Spatial distribution of wind speed bias (m/s) acro ss daytime hours for the months of July, August, and September (top to bottom) for the 12US2 domain.18

Figure 3.1.10. Spatial distribution of wind speed bias (m/s) acro ss daytime hours for the months of October, November, and December (top to bottom) for the 12US2 domain.19

Wind vector displacement (km) is presented below fo r the 36US (Figure 3.1.11) and 12US2 (Figure 3.1.12) domains utilizing the ds472 observa tion network described earlier. These plots show the entire distribution of hourly wind displac ement by month and by hour of the day. Overall, model performance is adequate in terms of wind vector differences. Both the 36- and 12-km simulations have a mean wind displacement of around 5km. Since this difference is less than the horizontal resolution, negligible impacts due to wind displacement are expected.20

Figure 3.1.11. Distribution of hourly wind displacement (km) by ho ur and month for the 36US
domain.21

Figure 3.1.12. Distribution of hourly wind displacement (km) by h our and month for the 12US2
domain.

3.2 Temperature22

Temperature estimates are compared to the ds472 obs ervation network described earlier and are presented below for the 36US (Figure 3.2.1) and 12US2 (Figure 3.2.2) domains.

Overall, WRF slightly underpredicts surface tempera ture at both 36- and 12-km for most hours, with a slight overprediction in the early morning h ours. In the summer months (June, July, and August), there appears to be less variability in bo th simulations, with the inner quartile range (IQR) more closely centered around zero in both sim ulations. Overall, with an average IQR of +/- 2 degrees Celsius (C), this is considered good mode l performance.

In Figures 3.2.3-3.2.6 and 3.2.7-3.2.10 the monthly spatial distributions of the temperature bias for the 12km simulation is presented for all hours and daytime only, respectively. Overall, a persistent slight underprediction of temperature is noted for most months. During daytime hours, a more significant underprediction of temper ature is noted across much of the central and eastern US when compared to all hours. In areas of the western US, there is a persistent slight overprediction of temperature, regardless of season.23

Figure 3.2.1. Distribution of hourly bias by hour and hourly bias , error, fractional bias, and fractional error for temperature by month for the 3 6US domain.24

Figure 3.2.2. Distribution of hourly bias by hour and hourly bia s, error, fractional bias, and fractional error for temperature by month for the 1 2US2 domain.25

Figure 3.2.3. Spatial distribution of temperature bias (C) acros s all hours for the months of January, February, and March (top to bottom) for th e 12US2 domain.26

Figure 3.2.4. Spatial distribution of temperature bias (C) acros s all hours for the months of April, May, and June (top to bottom) for the 12US2 domain.27

Figure 3.2.5. Spatial distribution of temperature bias (C) acros s all hours for the months of July, August, and September (top to bottom) for the 12US2 domain.28

Figure 3.2.6. Spatial distribution of temperature bias (C) acros s all hours for the months of October, November, and December (top to bottom) for the 12US2 domain.29

Figure 3.2.7. Spatial distribution of temperature bias (C) acros s daytime hours for the months of January, February, and March (top to bottom) for th e 12US2 domain.30

Figure 3.2.8. Spatial distribution of temperature bias (C) acros s daytime hours for the months of April, May, and June (top to bottom) for the 12US2 domain.31

Figure 3.2.9. Spatial distribution of temperature bias (C) acros s daytime hours for the months of July, August, and September (top to bottom) for the 12US2 domain.32

Figure 3.2.10. Spatial distribution of temperature bias (C) acros s daytime hours for the months of October, November, and December (top to bottom) for the 12US2 domain.33

3.3 Mixing Ratio

Water mixing ratio estimates are compared to the ds 472 observation network described earlier and are presented below for the 36US (Figure 3.3.1) and 12US2 (Figure 3.3.2) domains.

In either simulation, no significant positive or ne gative bias is observed. However, WRF tends to be slightly drier in the early afternoon hours rela tive to the rest of the day. Additionally, there is more uncertainty in model predictions during the sp ring and summer months. This increase in error is explained by the increased convective acti vity and influx of moist air masses that are typical of that time of year. In general, WRF perfo rmance was adequate for water vapor mixing
ratio.

The monthly spatial distributions of the mixing rat io bias for the 12km simulation are shown in Figures 3.3.3-3.3.6 (all hours) and 3.3.7-3.3.10 (d aytime). Little appreciable difference is observed in the biases either across all hours or j ust daytime. This is to be expected since water vapor mixing ratio has less temporal variability wh en compared to other variables (i.e., temperature). In the central and western US, mixing ratio is generally underpredicted across all months of the year, whereas a slight overprediction is observed in the eastern states for most months. In October and November, WRF exhibits a gen eral underprediction for most locations across the country.34

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 mo nth for the 36US domain.35

Figure 3.3.2. Distribution of hourly bias by hour and hourly bia s, error, fractional bias, and fractional error for water vapor mixing ratio by mo nth for the 12US2 domain.36

Figure 3.3.3. Spatial distribution of water vapor mixing ratio b ias (g/kg) across all hours for the months of January, February, and March (top to bott om) for the 12US2 domain.37

Figure 3.3.4. Spatial distribution of water vapor mixing ratio b ias (g/kg) across all hours for the months of April, May, and June (top to bottom) for the 12US2 domain.38

Figure 3.3.5. Spatial distribution of water vapor mixing ratio b ias (g/kg) across all hours for the months of July, August, and September (top to botto m) for the 12US2 domain.39

Figure 3.3.6. Spatial distribution of water vapor mixing ratio b ias (g/kg) across all hours for the months of October, November, and December (top to b ottom) for the 12US2 domain.40

Figure 3.3.7. Spatial distribution of water vapor mixing ratio b ias (g/kg) across daytime hours for the months of January, February, and March (top to bottom) for the 12US2 domain.41

Figure 3.3.8. Spatial distribution of water vapor mixing ratio b ias (g/kg) across daytime hours for the months of April, May, and June (top to bott om) for the 12US2 domain.42

Figure 3.3.9. Spatial distribution of water vapor mixing ratio b ias (g/kg) across daytime hours for the months of July, August, and September (top to bottom) for the 12US2 domain.43

Figure 3.3.10. Spatial distribution of water vapor mixing ratio b ias (g/kg) across daytime hours for the months of October, November, and December ( top to bottom) for the 12US2 domain.44

3.4 Precipitation

Monthly total rainfall is plotted for each grid cel l 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 36US domain (Figures 3.4.1 through 3. 4.4) and 12US2 domain (Figures 3.4.5 through 3.4.8).

In general, WRF performs adequately in terms of the spatial patterns and magnitude of precipitation across the US throughout the year. Bo th simulations, however, tend to overestimate precipitation in elevated terrain (e.g ., northern CA and the Pacific Northwest). The 12km simulation tends to generate slightly higher p recipitation amounts compared to the 36km simulation, and at times generates amounts higher t han observed values (e.g., the southeast in July). Isolated amounts of overpredicted or underpr edicted precipitation in the summer months relative to the observations is likely due to uncer tainty in the convective parameterization
scheme utilized.45

Figure 3.4.1. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for January, February, and March.46

3.4.2. PRISM analysis (left) and WRF (right) estimated mo nthly total rainfall (in) for April, May,
and June.47

Figure 3.4.3. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for July, August, and September.48

Figure 3.4.4. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for October, November, and December.49

Figure 3.4.5. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for January, February, and March.50

Figure 3.4.6. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for April, May, and June.51

Figure 3.4.7. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for July, August, and September.52

Figure 3.4.8. PRISM analysis (left) and WRF (right) estimated mon thly total rainfall (in) for October, November, and December.

3.5 Solar Radiation

Photosynthetically activated radiation (PAR) is a f raction of shortwave downward radiation and is an important input for the biogenic emissions mo del 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 perf ormance evaluation also gives an indirect assessment of how well the model captures cloud for mation during daylight hours.53

Shortwave downward radiation estimates are compared to surface based measurements made at SURFRAD and ISIS network monitors for the 36US ( Figure 3.6.1) 12US2 (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. Bias es tend to grow during the spring and peak in the summer, though the spread in overpredictions te nds to be less than 100 W/m 2 on average, with a median bias close to zero.

More variability is noted on an hourly basis. WRF t ends to overpredict early morning to early afternoon shortwave radiation, while underpredictin g the late afternoon and early evening values. The median overprediction at the time of gr eatest incoming solar radiation is near 100 W/m 2 . In the late afternoon and evening hours, the medi an bias is close to -50 W/m 2 . These errors are likely attributable to the model being u nable to accurately simulate cloud features at subgrid (<12km) scales. This assumption is based on the slight improvement in predictions at
12km versus 36km.54

Figure 3.5.1. Distribution of hourly bias for shortwave radiation (W/m 2 ) by month (top) and by hour of the day (bottom) for the 36US domain.55

Figure 3.5.2. Distribution of hourly bias for shortwave radiation (W/m 2 ) by month (top) and by hour of the day (bottom) for the 12US2 domain.

4 CLIMATE REPRESENTATIVENESS OF 2011

Figures 4.1 and 4.2 show the divisional rankings fo r observed temperatures across the US for 2011. A climatic representation of the precipitatio n for 2011 is shown in Figures 4.3 and 4.4. These plots are useful in determining the represent ativeness of 2011 in terms of certain climatological variables compared to historical ave rages.

Temperatures in 2011 were average to above average for most of the central and eastern US throughout the spring and summer months. Some areas in the southern and southeastern portions of the country exhibited near record warmt h from June through August. Conversely, February through July tended to be cooler than aver age for the western US with near-record
cold exhibited in the Pacific Northwest.56

The spring and summer months experienced below aver age precipitation for much of the southern and southeastern US, whereas wetter condit ions than average were experienced across the northern tier states. During the fall an d winter months, most of the eastern US experienced average to above average precipitation, while the western and southern US remained generally below average.57

Figure 4.1 Climatic temperature rankings by climate division: January to June 2011. http://www.ncdc.noaa.gov/temp-and-precip/maps.php58

Figure 4.2 Climatic temperature rankings by climate division: July to December 2011. http://www.ncdc.noaa.gov/temp-and-precip/maps.php59

Figure 4.3 Climatic rainfall rankings by climate division: Jan uary to June 2011. http://www.ncdc.noaa.gov/temp-and-precip/maps.php60

Figure 4.4 Climatic rainfall rankings by climate division: Jul y to December 2011. http://www.ncdc.noaa.gov/temp-and-precip/maps.php61

5 REFERENCES

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Carlton, A.G., Baker, K.R., 2011. Photochemical Mod eling of the Ozark Isoprene Volcano: MEGAN, BEIS, and Their Impacts on Air Quality Predi ctions. Environmental Science & Technology 45, 4438-4445.

Cooper, O.R., Stohl, A., Hubler, G., Hsie, E.Y., Pa rrish, D.D., Tuck, A.F., Kiladis, G.N., Oltmans, S. J., Johnson, B.J., Shapiro, M., Moody, J.L., Lefohn, A. S., 2005. Direct Transport of Midlatitude Stratospheric Ozone into the Lower Troposphere and Marine Boundary Layer of the Pacific Ocean. Journal of Geophysical Research – Atmosphere s 110, D23310, doi:10.1029/2005JD005783.

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Stammer, D., F.J. Wentz, and C.L. Gentemann, 2003, Validation of Microwave Sea Surface Temperature Measurements for Climate Purposes, J. Climate, 16, 73-87.