Revised Report

2002 ANNUAL MM5 SIMULATION TO SUPPORT WRAP CMAQ VISIBILITY MODELING FOR THE SECTION 308 SIP/TIP:

MM5 Sensitivity Simulations to Identify a More Optimal MM5 Configuration for Simulating Meteorology in the Western United States

Prepared for

The Western Regional Air Partnership www.wrapair.org

Prepared by

Sue Kemball-Cook Yiqin Jia Chris Emery Ralph Morris ENVIRON International Corporation 101 Rowland Way, Suite 220

Novato, CA 94945-5010

Zion Wang Gail Tonnesen University of California at Riverside College of Engineering Center for Environmental Research and Technology Riverside, CA 92507

December 10, 2004

101 Rowland Way, Suite 220, Novato, CA 94945 415.899.0700December 2004

i TABLE OF CONTENTS

EXECUTIVE SUMMARY .................................................................................................... ES-1

1. INTRODUCTION.......................................................................................................... 1-1

Background.................................................................................................................... .. 1-1 Overview of Approach..................................................................................................... 1-3

2. ADDITIONAL MM5 SENSITIVTY TESTS .............................................................. 2-1

Analysis Procedures......................................................................................................... 2-1 MM5 Sensitivity Test Results.......................................................................................... 2-3

3. SUMMARY OF 36-KM RESULTS ............................................................................. 3-1

Comparison to Previ ous Simulations............................................................................... 3-1 Evaluation of Other Subdomains..................................................................................... 3-6 Summary........................................................................................................................ .. 3-8

4. SUMMARY OF 12-KM SENSITIVITY TESTS ........................................................ 4-1

Cumulus Parameterization Tests...................................................................................... 4-1 Soil Moisture Nudging Tests ........................................................................................... 4-6

REFERENCES..................................................................................................................... ..... R-1

TABLES

Table 1-1. MM5 Configurations from original (Run 0) and modified (Run 5) MM5 runs. .............................................................................................. 1-2 Table 2-1. Summary of additi onal MM5 sensitivity tests..................................................... 2-2 Table 2-2. METSTAT subdomain abbreviations.................................................................. 2-3 Table 3-1. Surface model performance evaluation statistics summary................................. 3-6 Table 4-1. MM5 configuration for the 12-km grid for cumulus sensitivity tests.................. 4-2 Table 4-2. Summary of cumulu s sensitivity configurations.................................................. 4-2 Table 4-3. Physics options common to all 12-kmdomain soil moisture sensitivity tests..... 4-6 Table 4-4. Physics options varied fo r 12-km domain sensitivity tests.................................. 4-6

FIGURES

Figure 2-1. Definition of Metstat subdomains........................................................................ 2-3 Figure 2-2a. Wind scatterplot for cumulus sensitivity test....................................................... 2-5 Figure 2-2b. Temperature scatterplot for cumulus sensitivity test. .......................................... 2-5 Figure 2-2c. Humidity scatterplot for cumulus sensitivity test. ............................................... 2-6December 2004

ii Figure 2-3. Precipitation comparison for the cumulus parameterization sensitivity test. Run 2a : Betts-Miller, Run 2b:Grell, Run 5: Kain-Fritsch II.......................................................................................... 2-6 Figure 2-4a. Wind scatterplot for LSM/PBL sensitivity tests .................................................. 2-8 Figure 2-4b. Temperature scatterplot for LSM/PBL sensitivity tests....................................... 2-8 Figure 2-4c. Humidity scatterplot fo r LSM/PBL sensitivity tests............................................ 2-9 Figure 2-5. Precipitation comparison for the LSM/PBL parameterization sensitivity tests. Run 2a: PX/ACM, Run 3b:5-layer/M-MRF, Run 1ba: NOAH/M-MRF. ........................................ 2-10 Figure 2-6a. Wind speed scatterplo t for FDDA sensitivity test. ............................................ 2-11 Figure 2-6b. Wind speed/direction scatterp lot for FDDA sensitivity test.............................. 2-12 Figure 2-6c. Temperature scatterplo t for FDDA sensitivity test............................................ 2-12 Figure 2-6d. Humidity sc atterplot for FDDA se nsitivity test................................................. 2-13 Figure 2-7a. Wind scatterplot for NOAH/ M-MRF nudging sensitivity test. ......................... 2-13 Figure 2-7b. Temperature scatterplot for NOAH/M-MRF nudging sensitivity test..................................................................................................... 2-14 Figure 2-7c. Humidity scatterplot for NOAH/M-MRF nudging sensitivity test.................... 2-14 Figure 2-8. Precipitation comparison for the FDDA sensitivity tests. Observed (top right) Run 2a: PX/ACM (top left), Run 3b: 5-layer/M-MRF (bottom left) a nd, Run 1ba: NOAH/M-MRF (bottom right)... 2-15 Figure 2-9a. Scatterplots for seasona l wind comparisons for Run 2ae .................................. 2-17 Figure 2-9b. Scatterplots for seas onal wind comparison for Run 1bb ................................... 2-17 Figure 2-9c. Temperature scatterplo t for Run 2ae seasonal test. ........................................... 2-18 Figure 2-9d. Temperature scatterp lot for Run 1bb seasonal test............................................ 2-18 Figure 2-9e. Humidity scatterplot for Run 2ae seasonal test.................................................. 2-19 Figure 2-9f. Humidity scatterplot for Run 1bb seasonal test. ................................................ 2-19 Figure 2-10a. CPC observed precipitation for the 5-day modeled segment in each season. ..................................................................................... 2-20 Figure 2-10b. Seasonal precipitation test for Run 2ae (PX)..................................................... 2-21 Figure 2-10c. Seasonal precipitation test for Run 1bb (M-MRF). ........................................... 2-22 Figure 3-1a. Wind speed scatterplot for comparison of original, interim, and final MM5 runs ............................................................................................. 3-2 Figure 3-1b. Wind direction scatterplot fo r comparison of original, interim, and final MM5 runs. ............................................................................................ 3-2 Figure 3-1c. Temperature scatterplot fo r comparison of original, interim, and final MM5 runs. ............................................................................................ 3-3 Figure 3-1d. Humidity sca tterplot for comparison of original, interim, and final MM5 runs. ................................................................................................... 3-3 Figure 3-2a. July 1-5 MM5 predicted ve rsus observed surface temperature time series for Run 0............................................................................................ 3-4 Figure 3-2b. July 1-5 MM5 predicted ve rsus observed surface temperature time series for Run 2ae. ....................................................................................... 3-4 Figure 3-3. Precipitation comparison fo r July for original, interim, and final MM5 runs. ................................................................................................... 3-5 Figure 3-4a. Wind scatterplot for comparis on of original, interim, and final MM5 runs for subdomains outside WRAP.......................................................... 3-7 Figure 3-4b. Temperature scatterplot for comparison of original, interim, and final MM5 runs for subdomains outside WRAP. ................................................ 3-7December 2004

iii Figure 3-4c. Humidity scatterplot for comparison of original, interim, and final MM5 runs fo r subdomains outside WRAP........................................... 3-8 Table 4-1a. Wind speed scatterplot for comp arison of 12-km cumulus test runs................... 4-3 Table 4-1b. Temperature scatterplot for co mparison of 12-km cumulus test runs ................. 4-3 Table 4-1c. Humidity scatterplot for comp arison of 12-km cumulus test runs ...................... 4-4 Figure 4-2. Precipitation comparison for the cumulus parameterization sensitivity test ....... 4-5 Figure 4-3a. Time series of observe d and MM5 surface wind, temperature and humidity for the SW domain......................................................................... 4-8 Figure 4-3b. Time series of observe d and MM5 surface wind, temperature and humidity for the DesertSW domain. ............................................................. 4-9 Figure 4-3c. Time series of observe d and MM5 surface wind, temperature and humidity for the PacificNW domain........................................................... 4-10December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_Report\ Revised\EXECUTIVE SUMMARY_revsd.doc ES-1 EXECUTIVE SUMMARY

The WRAP Regional Modeling Cent er (RMC) is in the process of performing the analyses needed for the Section 308 SIPs/TIPs due in 2007/2008. The first Section 308 SIP will need to project visibility from 2000-2004 to 2018 to demo nstrate progress toward natural conditions to be achieved by 2064. Visibility is projected usin g mathematical models to simulate emissions, meteorology and air quality. The RMC is utiliz ing the EPA’s Models-3 Community Multi-scale Air Quality (CMAQ) modeling system to simulate particulate matter (PM) from which visibility degradation is obtained. CMAQ will be applied for the entire year of 2002 on two grids: a continental-scale domain with 36 km grid spacing, and a regional-scale domain with 12 km grid spacing covering the western United States. The Fifth Generation Mesoscale Model (MM5), developed and maintained by th e Pennsylvania State Univers ity and National Center for Atmospheric Research (PSU/NCAR) , is being used to develop hour ly meteorological fields on these two grids. The CEP Sparse Matrix Operat or Kernel Emissions (SMOKE) system is being used to develop the anthropogenic and biogenic gas and PM emission rate estimates for CMAQ.

An MM5 Modeling Protocol has been prepared (ENVIRON and UCR, 2004) , which describes in detail the MM5 model and the setup and evaluati on methods used in the 2002 modeling effort. The model performance evaluation for an initial 2002 MM5 run on a single continental-scale 36- km grid, and results from preliminary MM5 sensitiv ity tests, have been di scussed by Morris et al. (2004a), Kemball-Cook et al. (2004), and Emery et al. (2004) . This document describes additional MM5 sensitivity simulations pe rformed to identify a more optimal MM5 configuration for simulating meteorology on a 36-km grid in the western United States. Once a particular configuration for the 36-km simulation has been selecte d, further tests are described to assess whether the new configuration is appr opriate for use at 12 km resolution.

The initial 2002 36-km WRAP si mulation results showed that MM5 performs better in the Central and Eastern U.S. than in the West, and performs generall y better in winter than in summer. Specifically, in the western U.S., th e amplitude of the diurnal surface temperature cycle was persistently un derestimated during the summer, especi ally in the southwest. In the desert southwest, the surface humidity was grea tly overestimated during the summer, and there was a pronounced cold bias. The excessive humid ity and weak diurnal temperature cycle may be linked to the overprediction of su mmer precipitation by MM5, especially in the southwest. This can have serious repercussions for CMAQ mode ling since too much rain can "wash out" pollutants, while the too cool, humid and cloudy environment may lead to incorrect pollutant chemistry and aerosol thermodynamics.

A series of sensitivity tests was carried out w ith MM5 to converge upon a set of physics options which would ameliorate the above MM5 performa nce problems in the west. The more optimal model configuration for the 36-km application was:

A nalysis FDDA LSM PBL Cumulus Microphysics 3D Surface Obs FDDA Pleim-Xiu ACM Betts-Miller Reisner II W/T/H W/T/H WindDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_Report\ Revised\EXECUTIVE SUMMARY_revsd.doc ES-2 MM5 runs for the test period of July 1-5, 2002 made with these physics opti ons selected showed:

• A dramatic reduction in the summertime co ld, wet bias in the desert southwest;

• Surface temperature and humidity performan ce now within benchmarks for all WRAP subdomains except desert southwest for temperature;

• More accurate representation of the diurnal temperature cy cle in the desert southwest;

• Improvements in the index of agreement for te mperature, humidity and wind speed for all WRAP subdomains;

• A more realistic precipitation pattern over the western U.S.;

• Better model performance in the eastern U.S.; and

• Runs made with these phys ics options generally repres ented an improvement in performance throughout the year, making it unn ecessary to select different physics schemes for different seasons.

In summary, the new configuration of MM5 re presents a significant improvement in model performance relative to the original 2002 36-km MM5 run.

We then conducted a series of tests to determin e the optimal MM5 configuration for the nested 12-km grid of the WRAP modeling domain. In these tests, we varied only the physics options that we expected to ameliorate the biases in the WRAP region seen in the 36-km simulation. The over prediction of summer rainfall and associat ed surface humidity bias suggested that the cumulus parameterization might need to be chan ged. Another possible reason for the wet bias might be the soil moisture initia lization. Therefore, we focused on sensitivity tests of the soil moisture specification and the cumulus scheme.

The main results from the 12-km runs over the test period of July 1-5, 2002 are as follows:

• Use of soil moisture nudging degrades temp erature and humidity model performance.

• Initializing the soil moisture with the 36-km domain MM5 output degrades temperature and humidity model performance.

• Better temperature and humidity performance was obtained by initializing the soil model with the EDAS analysis fields.

Based upon our evaluation of surface variable an d rainfall performance, we selected a configuration with the Betts-Mi ller cumulus scheme running on the 36-km grid and no cumulus parameterization on the 12-km gri d. This was among the best performers in terms of surface wind, temperature, and humidity, and performed the best for rainfall. We have also chosen to remove surface analysis nudging of temperatur e and humidity from both grids, based on guidance from reviewers that nudging to thes e variables can degrad e performance aloft.

This final 36/12-km configur ation is now being used for the annual 2002 MM5 simulation to support WRAP’s regional haze modeling.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec1_revsd.doc 1-1 1. INTRODUCTION

The U.S. Environmental Protection Agency (EPA ) Regional Haze Rule (RHR) calls for state and federal agencies to work togeth er to improve visibility in 156 Federally mandated Class I areas that include national parks and w ilderness areas. The objective of the RHR is to achieve natural visibility conditions in these fe derally protected lands by the y ear 2064. The rule requires the states to develop and implement air quality pr otection plans to reduce the pollution that causes visibility impairment. The Western Regional Air Partnership (WRAP) is a consortium of federal, state, and tribal agencies charged with implementing regional planning processes to improve visibility in western US Class I areas. To meet this goal, WRAP is providing the necessary technical and policy tools to help states and tr ibes implement the RHR.

The WRAP has formed the Regional Modeling Cent er (RMC), consisting of the University of California at Riverside (UCR), ENVIRON Intern ational Corporation, a nd the University of North Carolina (UNC). The RMC performed the m odeling and analysis necessary to develop the Section 309 SIPs/TIPs and is in the process of performing the an alyses needed for the Section 308 SIPs/TIPs due in 2007/2008. The first Section 308 SIP will need to project visibility from 2000-2004 to 2018 to demonstrate progress toward natural conditions to be achieved by 2064. Visibility is projected using mathematical models to simulate emissions, meteorology and air quality. The RMC is utilizing the EPA’s M odels-3 Community Multi-scale Air Quality (CMAQ) modeling system to simulate particulat e matter (PM) from which visibility degradation is obtained. CMAQ will be applied for the entire year of 2002 on two grids: a continental-scale domain with 36 km grid spacing, and a regional -scale domain with 12 km grid spacing covering the western United States. Th e Fifth Generation Mesoscale M odel (MM5), developed by the Pennsylvania State University and maintained by National Center for Atmospheric Research (PSU/NCAR), is being used to develop hourly meteorological fiel ds on these two grids. The CEP Sparse Matrix Operator Kernel Emissions (SMOKE) system is being used to develop the anthropogenic and biogenic gas and PM emission rate estimates for CMAQ.

An MM5 Modeling Protocol has been prepared (ENVIRON and UCR, 2004) , which describes in detail the MM5 model and the setup and evaluati on methods used in the 2002 modeling effort. The model performance evaluation for an initial 2002 MM5 run on a single continental-scale 36- km grid, and results from preliminary MM5 sensitiv ity tests, have been di scussed by Morris et al. (2004a), Kemball-Cook et al. ( 2004), and Emery et al. (2004) . This document describes additional MM5 sensitivity simulations pe rformed to identify a more optimal MM5 configuration for simulating meteorology on a 36-km grid in the western United States. Once a particular configuration for the 36-km simulation has been selecte d, further tests are described to assess whether the new configuration is appr opriate for use at 12 km resolution.

BACKGROUND

MM5 simulations are being carried out by the WR AP Regional Modeling Center (RMC) for the entirety of 2002 to support CM AQ visibility modeling for the S ection 308 SIP/TIP. During the fall of 2003, the RMC made an initial MM5 run fo r the year 2002 on the single unified National RPO 36-km grid. The model configuration for this run was based upon a prior 2002 MM5 application undertaken by the Iowa Departme nt of Natural Resources (IDNR; Johnson, 2003 Personal Communication), which was in turn set according to the optimal MM5 physics optionsDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec1_revsd.doc 1-2 that resulted from an in-depth sensitivity projec t carried out by IDNR and the Lake Michigan Air Directors Consortium (LADCo). While the I DNR simulations used MM5 version 3.5, the applications carried out for WRAP used the late st version of the model (v3.6.1) available at the end of 2003. Additional modifications to the physics configuration and application methodology were made for the WRAP simulation based on th e latest information from EPA, IDNR, LADCo, and others. First, the RMC opted to use the Reisner II mixed-phase cloud microphysics package according to suggestions from EPA/ORD. Second, the INTERPPX option, which allows for continuous soil moisture initialization from one run segment to the next, was not used based on poor performance reported by IDNR and LADCo.

The initial 2002 36-km WRAP simu lation (referred to hereafter as Run 0) results showed that MM5 performs better in the Central and Eastern U. S. than in the West, and performs generally better in winter than in summer(Morris et al., 2004a; Kemball-C ook et al., 2004). In the western U.S., the amplitude of diurnal temperature cy cle was persistently underestimated during the summer, especially in the sout hwest. In the desert southw est, the humidity was greatly overestimated during the summer as well, and ther e was a pronounced cold bias. Some of these problems appear to be linked to the excessive simulated precipitation generated by MM5 during the summer, especially in the southwest. This can have serious repercussions for CMAQ modeling since too much rain can “wash out” po llutants, while the too cool, humid and cloudy environment may lead to incorrect polluta nt chemistry and aerosol thermodynamics. Temperature and humidity problems overshadowed the surface wind performance, which is not particularly good, but is likely aff ected by smaller-scale topographi c influences that are not well represented even at the finer 12 km resolution. Wind performance improved quickly with height above the surface, suggesting th at regional transport speeds/ directions are reasonably represented.

Initial sensitivity tests were undertaken for a 5-day period in July 2002 for the 36-km National domain in an attempt to find alternative MM5 configurations that would improve the poor summertime performance at 36 km. Furthermore, in-depth analyses were conducted to compare our performance against 36-km MM5 runs made for VISTAS, and against operational EDAS fields. From these analyses, we found that the use of the Kain-Fritsch II scheme (as used in the VISTAS 2002 MM5 modeling) improved, but did not entirely solve, the precipitation over prediction problem. We also found that remova l of soil moisture nudging improved temperature and humidity performance for the short summertime tests. The evaluation of the EDAS fields used in the MM5 four-dimensi onal-data-assimilation (FDDA) re vealed that EDAS did not exhibit the summer time cold wet bias in the southwest so the bias was not introduced by the FDDA. A new model configuration (Run 5) was identified from these initial tests for new annual 2002 simulations, and the WRAP MM5 modeli ng protocol was updated to reflect this, as shown in the Table 1-1.

Table 1-1 . MM5 Configurations from original (Run 0) and modified (Run 5) MM5 runs.

LSM PBL Cumulus Microphysics Analysis FDDA 3D Surface Obs FDDA Soil Moisture Nudging Run 0 PX ACM KF I Reisner II W/T/H W/T/H None Yes Run 5 PX ACM KF II Reisner II W/T/H W/T/H None NoDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec1_revsd.doc 1-3 Although the configuration identified in the ne w MM5 modeling protocol did improve humidity, temperature, and precipitation performance over the short July test period (especially where improvement was needed most), the desert southw est continued to exhibit unsatisfactory levels of precipitation and humidity. As stated above, th ese issues will likely play crucial roles in CMAQ performance.

These initial MM5 sensitivity simulations at tempted to identify a better performing MM5 simulation that can be used to simulate all of 2002 but retain the Pleim-Xiu (PX) LSM and ACM PBL options. When results of the Run5 MM5 mo del performance were presented at the May 24- 25, 2004 National RPO modeling meeting in Denver, Colorado (Emery et al, 2004), there were still concerns about the 2002 MM5 model performance in the western US. Thus, WRAP requested that the Regional Modeling Center (R MC) perform further MM5 sensitivity test to identify a better performing configuration, including investigating alternative LSM/PBL configurations to PX/ACM and include the possib ility of using different MM5 configurations for different times of year.

OVERVIEW OF APPROACH

We carried out additional 36-km MM5 test simula tions in an attempt to further improve MM5 performance. The latest available version of MM5 (v3.6.2) was used. Since the most severe problems for the initial WRAP 2002 MM5 annual simulation are the wet bias and precipitation overestimation in summer, we first investigated th ese issues for the 5-day July test period (July 1-5, 2002) on the 36-km grid to maximize computat ional efficiency. We tested four physics options: (1) the cumulus parameterization; (2) th e land surface models (LSM); (3) the planetary boundary layer (PBL) models; and (4) the fou r-dimensional data assimilation (FDDA).

The first group of tests focused on the cumu lus parameterization, the major MM5 component responsible for the overstated summertime rainfall. Most of the summer rainfall in the U.S. is due to convective clouds; stratiform rain produced by mid-latitude cyclones is a smaller fraction of the total rainfall in summer. The performance of the Kain-Fritsch II, Betts-Miller, and Grell cumulus schemes was examined.

Next, we conducted a series of te sts using different combinations of PBL and LSM options. In other MM5 applications around th e country, including in Califo rnia, we have achieved good model performance using the historically sta ndard 5-layer slab mode l as well as the NOAH LSM. Our choices for PBL models include th e ETA and our "modified" MRF (M-MRF). The standard MRF available with MM5 is known to contai n deficiencies that lead to overestimates of mixing depth and diurnal phase la gs in wind speed due to over es timates of surface momentum flux – our simple modifications have been shown to fix these problems (ATMET, 2003).

In the past, we have avoided performing MM5 se nsitivity tests that altered the Pleim-Xiu/ACM LSM/PBL configuration because it is recomm ended by EPA and it has been shown by other RPOs (e.g., VISTAS) to provide the best overall meteorological model performance and best CMAQ performance (Olerud and Sims, 2003; Morris et al, 2004b). Also, it a llows the use of the Pleim-Xiu dry deposition scheme in CMAQ, which is technically superior and performs better than the alternative (Wesely scheme). However, most of the MM5 Pleim-Xiu tests to date have focused on the eastern U.S. and, given the im portance of the MM5 LSM/PBL scheme to itsDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec1_revsd.doc 1-4 performance, we tested altern ative LSM/PBL schemes to see if they would provide better performance in the western US.

Once the cumulus and LSM/PBL schemes had been selected, we performed a series of experiments to determine the best FDDA configur ation. Finally, because the above sensitivity tests were done for July only, we ran 5-day segm ents in winter, spring, and fall to assess whether the performance showed improvement in other seasons besides summer. Once we had an optimal configuration, we went back and tested it against the original MM 5 run from the initial WRAP 2002 36-km modeling (Table1-1, Run 0), and also our in terim Run 5 configuration from Table 1-1. Then, we examined the non-WRAP subdomains to determine how the new set of physics options affected MM5’s performance in the eastern U.S.

We then undertook a series of tests to determin e the optimal MM5 configuration for the nested 12-km grid of the WRAP modeling domain. In these tests, we varied only the physics options that we expected to ameliorate the biases in the WRAP region seen in the 36-km simulation. The over prediction of summer rainfall and associat ed surface humidity bias suggested that the cumulus parameterization might need to be chan ged. Another possible reason for the wet bias might be the soil moisture initia lization. Therefore, we focused on sensitivity tests of the soil moisture specification and the cumulus scheme.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-1 2. ADDITIONAL MM5 SENSITIVTY TESTS

Table 2-1 lists the additional MM5 sensitivity te sts beyond those reported earlier (Morris et al, 2004a; Kemball-Cook et al, 2004; Emery et al; 2004) to identify a more optimal MM5 configuration for simulating meteorology in the west ern U.S. These sensitivity tests differed in the choice of Land Surface Model (LSM), Plan etary Boundary Layer (PBL) scheme, cumulus parameterization, explicit cloud microphysics and type and level of four-dimensional data assimilation (FDDA). Below is listing of the di fferent schemes tested in this study and an explanation of the abbrevia tions used in Table 2-1.

Land Surface Models (LSM)

• NOAH LSM

• Pleim-Xiu LSM (PX)

• 5-layer LSM

Planetary Boundary Layer (PBL) Schemes

• Eta PBL

• Modified-MRF PBL

• Asymmetric Convective Mixing Model (ACM) PBL

Four Dimensional Data Assimilation (FDDA)

• W: Wind

• T: Temperature

• H: Water vapor mixing ratio

Cumulus Schemes

• Betts-Miller (BM)

• Grell

• Kain-Fritsch II (KFII)

Cloud Microphysics Schemes

• Simple Ice Microphysics

• Reisner II Microphysics

ANALYSIS PROCEDURES

The MM5 evaluation was performed by examini ng surface performance statistics using the METSTAT evaluation software, and by compari ng the output MM5 tota l (convective + largescale) precipitation fields with gr idded observed precipitation amounts.

Observed precipitation data was derived from the CPC (Climate Prediction Center) gridded precipitation amount dataset. This data set is available from the National Weather Service's Climate Prediction Center website at: http://www.cpc.ncep.noaa.gov/products /precip/realtime/retro.html.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-2 Table 2-1. Summary of additional MM5 sensitivity tests. Analysis FDDA LSM PBL Cumulus Microphysics 3D Surface Obs FDDA Run 0 PX ACM KF I Reisner2 W/T/H W/T/H Run 5 PX ACM KF II Reisner2 W/T/H W/T/H Run2a PX ACM BM Reisner2 W/T/H W/T/H Run2aa PX ACM BM Reisner2 W/T/H W Run2ac PX ACM BM Reisner2 W/T/H W/T/H W/T/H Run2ad PX ACM BM Reisner2 W/T/H W W Run2ae PX ACM BM Reisner2 W/T/H W/T/H W Run2b PX ACM Grell Reisner2 W/T/H W/T/H Run1ba NOAH M-MRF BM Reisner2 W/T/H W/T/H Run1bb NOAH M-MRF BM Reisner2 W/T/H W/T/H W Run3b 5-Layer M-MRF BM Reisner2 W/T/H W/T/H No soil nudging was performed for the above runs

The CPC daily precipitation amounts are provided on a grid that covers the U.S. mainland at a resolution of 0.25° x 0.25°, and are ramped down to zero immediately offshore. The advantage of this field is that it has a reasonably high resolution, which is especially important when it comes to resolving the effects of orography on precipitation over the western U.S.

The performance statistics for surface wind, temp erature, and humidity were calculated using METSTAT with the NCAR ds472 surface observations data set. METSTAT is comprised of a single Fortran program, which ge nerates observation-prediction pairings and calculates the statistics, and a Microsoft Excel macro that plots th e results. This program is described in detail in the WRAP MM5 Modeling Protocol (ENVIRON and UCR, 2004).

For the purpose of organizing the analysis, “S occer Plots” are used that display average performance statistics for each subdomain for th e modeled time period. “Soccer Plots” present average bias versus error for a particular meteorological variable. Model performance benchmarks or goals are plotted on the Soccer Plot to see whether the performance of the variable meets the goal. Subdomain definitions are shown in Figure 2-1, and Table 2-2 lists the numbers of the METSTAT domains shown in Fi gure 2-1 and the corresponding abbreviations used in the discussion below.

Soccer Plots are shown for wind speed bias vers us wind speed RMSE, temperature bias versus temperature error, and humidity bias versus humid ity error. In each plot , a solid line indicates the benchmark. The benchmarks were develope d by Emery and Tai. (2001), and were based upon the evaluation of more than 30 MM5 and RA MS meteorological simu lations in support of air quality applications performed in the last few years, as reported by Tesche et al. (2002). A data point that falls inside the box represents a model run that meets the performance benchmark. Perfect model performance is indica ted by a data point at (0,0). Th e closer a data point is to the origin, the better the model’s perf ormance. It should be emphasi zed that the benchmarks are not used as an acceptance/rejection criteria of the MM5 model simula tion. Rather they put the MM5 model performance into perspective and allow th e identification of poten tial problems in the MM5 fields.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-3 Figure 2-1. Definition of Metstat subdomains.

Table 2-2. METSTAT subdomain abbreviations. METSTAT Subdomain Name Abbreviation 1 PacificNW 2 SW 3 North 4 DesertSW 5 CenrapN 6 CenrapS 7 Great Lakes 8 Ohio Valley 9 SE 10 NE 11 MidAtlantic

MM5 SENSITIVITY TEST RESULTS

Cumulus Parameterization Sensitivity Test

We began by testing MM5’s sensitivity to the choi ce of cumulus parameterization. The original 2002 MM5 run (Run 0) used the Kain-Fritsch I scheme. Results from VISTAS 36-km MM5 modeling showed improved surface fields relative to Run 0 using Kain-Fritsch II, so KainFritsch II (KFII) was the first sche me tested here (Run 5). Next, we compared runs that used theDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-4 Grell (Run 2b) and Betts-Miller (Run2a) paramete rizations to Run 5. All of these runs were made using the PX/ACM LSM/PBL schemes and W/T/H analysis nudging at the surface and aloft and no nudging to observations (obs nudging).

Figure 2-2a summarizes the wind pe rformance for the July 2002 test period. All three cumulus schemes show a low wind speed bias. For the SW, PacificNW, and North subdomains, the Grell scheme has a smaller error than the other scheme s, and is similar in bias. In the DesertSW subdomain, the Betts-Miller scheme s lightly outperforms the others in both bias and error, but all three schemes are far from th e benchmark for DesertSW.

Figure 2-2b shows the results for temperature. Al l three schemes show a ge neral cold bias. The Betts-Miller scheme has the smallest overall bias , and does slightly better than the Grell scheme for error. The KFII scheme has the smallest e rror, except in the PacificNW subdomain, in which it is an outlier. The Betts-Miller scheme has the best overall temper ature performance. It has the smallest bias, and, although it ha s a slightly larger error than the KFII scheme for DesertSW, SW, and North, it does significantly better th an KFII in the Pacific Northwest.

The humidity performance is shown in Figure 2-2c . Here, again, the Bett s-Miller scheme shows the best overall performance. For the SW and PacificNW subdomains, the performance of the three schemes is comparable, but the Betts-Miller scheme is clearly better for the North and DesertSW subdomains.

Figure 2-3 is a plot of observed versus MM5 precipitation for th e modeled time period. All three runs are too wet across the northern U.S. Kain-Fritsch II has a str ong wet bias, particularly in the SW and DesertSW subdomains, and also underpre dicts the precipitation maximum over Texas. Both the Grell and Betts-Mille r schemes produce a maximum over Texas, although they translate it to the southwest of the observed location. Ov er the southwestern U.S., the Betts-Miller precipitation field is closer to th e observations than that of Grell, which generates too much rain in this region. Based on the improvement in performance over the southwest and Texas, we conclude that the Betts-Miller scheme produced th e best simulation of precipitation for this time period.

Of the three cumulus packages tested in the WRAP region, Betts-Miller is the best-performing scheme for temperature, humidity, and precipita tion. We therefore sele cted the Betts-Miller scheme and use it in all subsequent runs as we continue with further sensitivity tests.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-5 Figure 2-2a. Wind scatterplot for cumulus sensitivity test.

Figure 2-2b. Temperature scatterplot for cumulus sensitivity test. WRAP 36 km July Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Run 2a: Betts-Miller Run 2b: Grell Run 5: Kain-Fritsch II Cumulus Parameterization Sensitivity Test

DesertSW

PacNW

SW

North

WRAP 36 km July Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Run 2a: Betts-Miller Run 2b: Grell Run 5: Kain-Fritsch II Cumulus Parameterization Sensitivity Test

DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-6 Figure 2-2c. Humidity scatterplot for cumulus sensitivity test.

Figure 2-3. Precipitation comparison for the cumulus parameterization sensitivity test. Run 2a: Betts-Miller (top left), Run 2b:Grell (bottom left) and, Run 5: Kain-Fritsch II (bottom right). Observed (top right). WRAP 36 km Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Run 2a:Betts-Miller Run 2b: Grell Run 5: Kain-Fritsch II Cumulus Parameterization Sensitivity Test

DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-7 LSM Sensitivity Test

Next, we tested the land surface and planetary bo undary layer (PBL) parameterizations. All of the runs described in this section used th e Betts-Miller cumulus parameterization, W/T/H Analysis nudging at the surface and aloft a nd no obs nudging. We begin by comparing the NOAH LSM (Run 1ba) with the 5-layer model (Run 3b). In both of these runs, the modified MRF (M-MRF) PBL scheme was used.

Figure 2-4a shows the wind performance for the R un 1ba and 3b LSM sensitivity tests. Both the 5-layer and NOAH runs show a low wind speed bias, with the bias more pronounced in the 5- layer case. For both runs, the SW and Pacifi cNW subdomains lie within the benchmark, while North and DesertSW do not. The NOAH run performs better for all subdomains except for the DesertSW. For DesertSW, the NOAH run has a s lightly smaller bias, but a larger error.

The LSM temperature comparison is shown in Figur e 2-4b. Both runs have a cold bias, but the bias is remarkably pronounced for the NOAH run in the PacificNW and North subdomains. The error is smaller for all subdomains in the 5-layer run, as is the bi as in PacificNW and North. For the SW and DesertSW domains, the NOAH run has a sm aller bias, but larger error. Overall, the 5-layer model temperature performance was an improvement over that of the NOAH run.

Figure 2-4c shows the humidity performance for thes e runs. There is an ove rall wet bias, except in the SW subdomain. For humidity, the NOAH run meets the performance goal, while the 5- layer run lies entirely outside the benchmark. The NOAH run performs better in the North and PacificNW than in the SW and DesertSW domains.

The precipitation performance is shown in Fi gure 2-5. Run 1ba does slightly better here, producing less rain over th e desert southwest states than does run 3b; Run 1ba is therefore closer to the observed rainfall field than Run 3b.

Overall, the NOAH model outperfor med the 5-layer model for this case. The 5-layer model introduces a large positive humidity bias in the DesertSW and SW, which is one of the main problems from the original Run 0 we are trying to ameliorate in this study. Having eliminated the 5-layer LSM model from further consider ation, we now test the NOAH/M-MRF combination and compare it to the run which us ed the PX/ACM configuration. The results of these two runs are also shown in Figures 2-4 and 2-5.

Figure 2-4a displays the wind comparison for the NOAH/M-MRF and PX/ACM runs. The two runs show similar performance for error, and the NOAH/M-MRF run has a smaller overall bias. For temperature (Figure 2-4b), neither run meet s the performance benchmark, but the PX/ACM run is a clear improvement in both bias and e rror over the NOAH/M-MRF run. The cold bias is far more pronounced in the NOAH/M-MRF run for the PacificNW and North subdomains.

For humidity (Figure 2-4c) there is not much difference between the two runs, except in DesertSW, where PX/ACM has a higher bias and error. For precipitation, the PX/ACM run showed some slight improvement over the NOAH/ M-MRF case over the southwestern U.S. The intensity of the maximum over Texas is less we ll simulated in the PX/ACM run, however. Performance was roughly equivalent elsewhere.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-8 Figure 2-4a. Wind scatterplot for LSM/PBL sensitivity tests.

Figure 2-4b. Temperature scatterplot for LSM/PBL sensitivity tests. WRAP 36 km July Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Run 1ba: NOAH-MRF Run 3b: 5 lyr-MRF Run 2a: PX-ACM Run 1bb: NOAH-MRF, OBS Nudged LSM/PBL Sensitivity Test

DesertSW

PacNW

SW

North

WRAP 36 km July Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Run 1ba: NOAH-MRF Run 3b: 5 lyr-MRF Run 2a: PX-ACM 1bb: NOAH-MRF, OBS Nudged LSM/PBL Sensitivity Test

DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-9 Figure 2-4c. Humidity scatterplot for LSM/PBL sensitivity tests. WRAP 36 km Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Run 1ba: NOAH-MRF Run 3b: 5 lyr-MRF Run 2a: PX-ACM Run 1bb: NOAH-MRF, OBS Nudged LSM/PBL Sensitivity Test

DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-10 Figure 2-5. Precipitation comparison for the LSM/PBL parameterization sensitivity tests. Observed (top right) Run 2a: PX/ACM (top left), Run 3b:5-layer/M-MRF (bottom left) and, Run 1ba: NOAH/M-MRF (bottom right).

Overall, there was no single factor that made it easy to select one LSM/PBL scheme over the other. Because there was no significant improvement in going to the NOAH/M-MRF configuration, we propose to use the PX/ACM c onfiguration because it a llows the use of the Pleim dry deposition algorithm in subsequent CM AQ modeling, is endorsed by EPA, and allows consistency with the other RPO’s.

FDDA Sensitivity Test

Having selected a cumulus parameterization and an LSM/PBL combination, we turn now to the FDDA configuration. The runs discussed in this section differ only in their FDDA, and all use the PX/ACM, Betts-Miller, and Reisner2 schemes (see Table 2-1 for run definitions).

Figure 2-6a is a comparison of the wind perf ormance for the runs with different FDDA configurations. For all runs, all domains except PacificNW (2a and 2aa) and SW (2a) lie outside the benchmark. There is low wind speed bias for all runs and all subdomains. Run 2a shows the smallest bias, while 2ac had the lowest overall error. It is difficult to sa y which runs exhibit the best wind performance based on this figure. Fi gure 2-6b shows the wind direction error versusDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-11 the wind speed RMSE. In this figure, the e ffects of obs nudging on the surface wind field are apparent. Runs which had obs nudging lie together closer to the benchmark than the runs which were not obs nudged.

For temperature (Figure 2-6c) Run 2ae clearly sh ows the best performance. All subdomains but one are within the benchmark; Run 2ae also ha s the smallest bias and error. For humidity (Figure 2-6d), performance is comparable among the runs, and all subdomains except DesertSW lie within the benchmark. Differences in the precipitation fields for all the FDDA sensitivity runs were small (not shown) and did not help to distinguish among th e runs in terms of performance.

On the basis of its superior temperature perfor mance and wind performance, which was similar to the best-performing runs, Run 2ae was dete rmined to have the best FDDA configuration.

Next, to allow for the possibility that nudgi ng the NOAH/M-MRF case might cause significant improvement, we made another run (Run 1bb), whic h was identical to Run 2ae except that the NOAH/M-MRF LSM/PBL scheme was used instead of PX/ACM. The re sults are shown in Figure 2-7, with the more weakly nudged Run 1ba included to show the effects of nudging. For wind speed bias and error, Run 1bb (NOAH) is a slight improvement, except for the SW subdomain, for which it shows marked improvement over Run 2ae (PX). For temperature, Run 2ae (PX) had a smaller bias and error than the other runs (NOAH). For humidity and precipitation, there is no run that clearly performed better than th e others. We selected Run 2ae (PX) as the best-performing run overall, because the temperature performance is deemed to be more important than the wind performance, give n that the terrain in the western U.S. may prevent good agreement between modeled and observed surface winds at 36 km resolution.

Figure 2-6a. Wind speed scatterplot for FDDA sensitivity test. WRAP 36 km July Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae FDDA Sensitivity Test

DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-12 Figure 2-6b. Wind speed/direction scatterplot for FDDA sensitivity test.

Figure 2-6c. Temperature scatterplot for FDDA sensitivity test. WRAP 36 km July Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

0 20 40 60
Wind Direction Error (Degrees)WindSpeed RMSE (m/s)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae DesertSW

PacNW

SW

North

WRAP 36 km July Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae

FDDA Sensitivity Test

DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-13 Figure 2-6d. Humidity scatterplot for FDDA sensitivity test.

Figure 2-7a. Wind scatterplot for NOAH/M-MRF nudging sensitivity test. WRAP 36 km Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Run 2a Run 2aa Run 2ac Run 2ad Run 2ae

FDDA Sensitivity Test

DesertSW

PacNW

SW

North

WRAP 36 km July Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Run 1ba Run 1bb Run 2ae DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-14 Figure 2-7b. Temperature scatterplot for NOAH/M-MRF nudging sensitivity test.

Figure 2-7c. Humidity scatterplot for NOAH/M-MRF nudging sensitivity test. WRAP 36 km July Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3 3.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Run 1ba 1bb Run 2ae DesertSW

PacNW

SW

North

WRAP 36 km Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Run 1ba Run 1bb Run 2ae DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-15 Figure 2-8. Precipitation comparison for the FDDA sensitivity tests. Observed (top right) Run 2a: PX/ACM (top left), Run 3b:5-layer/M-MRF (bottom left) and, Run 1ba: NOAH/M-MRF (bottom right).December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-16 Tests in Other Seasons

Having determined a more optimal MM5 configurat ion based on the July sensitivity tests, we move now to other parts of the year, leaving op en the possibility that we may use a different MM5 configuration for different times of year for the final year-long 2002 simulation. The two best-performing runs overall (Runs 2ae and 1bb) were run for 5-day periods in January, April, and October, representing the winter, spring, and fall seasons.

Figures 2-9a-f show the performance of R uns 2ae and 1bb during the four 5-day runs representing the four seasons. We begin by co mparing the wind performance of the Runs 2ae and Run 1bb for all four seasons by examining Fi gures 2-9a and 2-9b. For January, shown in blue, performance in the PacificNW, SW, a nd DesertSW is similar, while Run 1bb shows significant improvement in the North subdomain. In both April (light blue) and October (red), Run 1bb has generally lower bias and error, wi th the 4 subdomains clustered closer to the benchmark than in Run 2ae. In July (magenta ), Run 1bb has a smaller bias, while Run 2ae has a smaller error. This is true for all subdomains except PacificNW, for which Run 2ae has a smaller bias than Run 1bb. Overall, then R un 1bb performs better for wind throughout the year, with the points clustered closer to the benchmark. Note however, that all points lie outside the benchmark for both simulations except for January PacificNW and North for Run 1bb.

For temperature (Figures 2-9c and 2-9d) in Ja nuary, Run 2ae has a smaller bias and error for North and DesertSW. For SW, performance is similar, and for PacificNW, Run 1bb has a smaller bias and error. In April, Run 1bb does better; in Run 2ae, Paci ficNW and North have a strong cold bias and lie far out side the benchmark. In July and October, Run 2ae performs better, with most of the subdomains situated within the benchmark, while in Run 1bb all subdomains lie in a halo around the benchmark.

For humidity (Figures 2-9e and 2-9f), performa nce between the two runs is comparable. The precipitation performance (not shown) was simila r enough that differences in the precipitation fields were not useful in choosing between the two runs.

Figure 2-10 compares the observed versus estimated precipitation for the four seasonal tasks and Runs 2ae and 1bb. There is good agreement between the observed and estimated precipitation in January. Note that the observed precipitation is just for the U.S. Both MM5 runs overstate the extent of the precipitation in Ap ril and July, but agree with th e observations fairly well in October. The precipitation performance fo r Runs 2ae and 1bb are nearly identical.

Because the regional haze events critical fo r WRAP CMAQ modeling tend to occur during the summer, we select Run 2ae, which gives better temperature performance during the summer, as the best-performing run overall. In the Ju ly simulation, the winds and humidity were comparable. The performance improvement in Run 1bb in January and April was insufficient reason to switch the LSM/PBL scheme in wint er/spring because the improvement brought only one additional subdomain point into the be nchmark, and there was no improvement in temperature or humidity. We therefore stay wi th PX/ACM throughout the year to get the best summer simulation and to allow the use of the Pleim dry deposition algorithm in CMAQ throughout the year.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-17 Figures 2-9a. Scatterplots for seasonal wind comparisons for Run 2ae.

Figure 2-9b . Scatterplots for seasonal wind comparison for Run 1bb. WRAP 36 km Run 2ae Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Jan Apr Oct Jul DesertSW

PacNW

SW

North

WRAP 36 km Run 1bb Seasonal Wind Performance Comparison

0 0.5

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3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Jul Jan Apr Oct DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-18 Figure 2-9c. Temperature scatterplot for Run 2ae seasonal test.

Figure 2-9d. Temperature scatterplot for Run 1bb seasonal test. WRAP 36 km Run 2ae Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Jul Jan Apr Oct DesertSW

PacNW

SW

North

WRAP 36 km Run 1bb Seasonal Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Jul Jan Apr Oct DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-19 Figure 2-9e. Humidity scatterplot for Run 2ae seasonal test.

Figure 2-9f. Humidity scatterplot for Run 1bb seasonal test. WRAP 36 km Run 2ae Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Jul Jan Apr Oct DesertSW

PacNW

SW

North

WRAP 36 km Run 1bb Seasonal Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Jul Jan Apr Oct DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-20 Figure 2-10a. CPC observed precipitation for the 5-day modeled segment in each season.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-21 Figure 2-10b. Seasonal precipitation test for Run 2ae (PX).December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec2_revsd.doc 2-22 Figure 2-10c. Seasonal precipitation test for Run 1bb (M-MRF).December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-1 3. SUMMARY OF 36-KM RESULTS

COMPARISON TO PREVIOUS SIMULATIONS

A graphical representati on of the differences between the in itial (Run 0) (Morris et al., 2004a; Kemball-Cook et al., 2004), interim (Run 5) (Emery et al., 2004), and fina l (Run 2ae) MM5 runs is provided in Figures 3-1 through 3-3. Figure 3- 1a shows the wind bias and error for all three runs. For the DesertSW subdomain, there is improve ment in both bias and error. For the other three subdomains, as we progress from Run 0 to Run 5, there is a reduction in error and an increase in the low wind speed bias. All poi nts but one (Run 0, PacificNW) lie outside the benchmark. Examination of Figure 3-1b, on which is plotted wind direction bias and gross error, shows that Run 2ae also produces improvements in the wind direction fiel d relative to Run 0 and 5.

For temperature (Figure 3-1c), all subdomains but DesertSW now lie within the benchmarks for Run 2ae, and Run 2ae is clea rly the best-performing run fo r both error and bias in all subdomains. The most noticeable change in going from Run 0 to Run 2ae is that DesertSW is no longer an outlier, but now lies close to the be nchmark for error, and within it for bias.

Figures 3-2a and 3-2b are time series of predic ted (MM5) and observed temperature for Run 2ae and Run 0. In Run 0, the amplitude of the predic ted diurnal temperature cycle is smaller than observed. This was attributed to the presence of excess water at the surface, indicated by the wet bias in DesertSW (Figure 3-2b) and the excessi ve precipitation seen in Run 0 in Figure 3-3. Since water has a higher specific he at than soil, the presence of excess water at the surface will tend to dampen the amplitude of daily excurs ions in temperature. There is a marked improvement in Run 2ae. The amplitude of the diurnal cycle in Run 2ae more nearly matches that of the observations, and the substantial cold bias has been re placed by a slight warm bias.

For humidity (Figure 3-1d), all subdomains now lie within the benchmark. Again, DesertSW has gone from being an outlier, with wet bias far outside of the benchmark, to falling within the benchmark. For North, going from Run 0 to R un 5 and from Run 5 to Run 2ae brings the subdomain inside the benchmark. For SW, Run 2 ae is an improvement in error but not bias.

The precipitation field for Run 2ae is notable fo r its improvement over Runs 0 and 5 (Figure 3- 3). The general overprediction of precipitation is less severe in Run 2ae than in the other two runs. The WRAP subdomains in particular are now seeing rainfall amounts closer to observed; this is reflected in the humidity scatterplot (Figur e 3-1c) that shows a reduc tion of the wet bias in the DesertSW seen in Runs 0 and 5. The observed precipitation maximum over Texas is simulated reasonably well in Run 2ae, but is underpredicted in Run 0 and 5.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-2 Figure 3-1a. Wind speed scatterplot for comparison of original, interim, and final MM5

runs.

Figure 3-1b . Wind direction scatterplot for comparison of original, interim, and final MM5

runs. WRAP 36 km Run 0, Run 5, and Run 2ae Wind Performance Comparison

0 0.5

1 1.5

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3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Run 0 Run 5 Run 2ae DesertSW

PacNW

SW

North

WRAP 36 km Run 0, Run 5, and Run 2ae Wind Direction Performance Comparison

0 10 20 30 40 50

-15 -10 -5 0 5 10 15
Wind Direction Bias (m/s)Wind Direction Gross Error (m/s)

Benchmark Run 0 Run 5 Run 2ae DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-3 Figure 3-1c. Temperature scatterplot for comparison of original, interim, and final MM5

runs.

Figure 3-1d. Humidity scatterplot for comparison of original, interim, and final MM5

runs

. WRAP 36 km Run 0, Run 5, and Run 2ae Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3 3.5

4

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Run 0 Run 5 Run 2ae DesertSW

PacNW

SW

North

WRAP 36 km Run 0, Run 5, and Run 2ae Humidit y Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Run 0 Run 5 Run 2ae DesertSW

PacNW

SW

NorthDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-4 Figure 3-2a. July 1-5 MM5 predicted versus observed surface temperature time series for Run 0.

Figure 3-2b. July 1-5 MM5 predicted versus observed surface temperature time series for Run 2ae. Observed/Predicted Temperature

285 290 295 300 305 310 315

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5K ObsTemp PrdTemp Observed/Predicted Temperature

285 290 295 300 305 310 315

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5K ObsTemp PrdTempDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-5 Figure 3-3. Precipitation comparison for July for original, interim, and final MM5 runs.

Table 3-1 is an overview of the change in surf ace MPE statistics going from the original WRAP 2002 Annual run (Run 0) through the improved version (Run 5) to the final version (Run 2ae) in the western subdomains. Statistics of particular intere st are highlighted. There is significant improvement in going from Run 0 to Run 2ae. No te the increases in IOA in SW and DesertSW. These are the subdomains that had a substantial deficiency in the original run, with a large wet bias. Note that the wet bias in the DesertSW subdomain has been more than halved in going from Run 0 to Run 2ae. The gross humidity error is also greatly reduced in these two subdomains. PacificNW and SW also show improve ment in humidity gross error, but show a slight degradation in bias pe rformance. Overall, the new Run 2ae model configuration has resulted in a greatly improved simulati on of humidity over the western U.S.

There is also a reduction in the cold bias appare nt in Run 0. In DesertSW, the cold bias was originally –2.07 ° C, and drops to 0.2 ° C in Run 2ae. Reductions in th e cold bias are also seen in the other subdomains. Temperature error is al so reduced in all subdom ains, although not enough to bring it entirely within the performance benchmark. Corres ponding improvements are seen in the temperature index of agreement.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-6 Table 3-1. Surface model performance evaluation statistics summary.
DesertSW North PacificNW SW Benchmark Run0 Run5 Run2ae Run0 Run5 Run2ae Run0 Run5 Run2ae Run0 Run5 Run2ae Wind Spd Bias < ± 0.5 -1.47 -1.30 -1.22 -0.90 -0.85 -0.94 -0.34 -0.35 -0.59 -0.82 -0.77 -0.94

Wind Spd RMSE <2.0 2.69 2.51 2.21 2.32 2.2 1.88 1.97 2.05 1.67 2.16 1.98 1.85

Wind Spd IOA ≥ 0.6 0.55 0.57 0.70 0.72 0.73 0.83 0.67 0.65 0.77 0.68 0.70 0.77

Wind Dir Bias < ± 10 0.05 4.79 3.13 -3.22 6.23 2.19 -3.75 9.23 5.18 - 11.85 -2.28 -2.23

Wind Dir Gross E <30 50.5 50.3 32.8 42.2 43.4 28.3 45.9 46.6 34.2 49.3 47.3 34.2

Temp Bias < ± 0.5 -2.07 -0.4 0.2 -0.8 -0.26 0.22 -0.56 -1.28 -0.10 -0.64 -0.39 -0.26 Temp Gross Error <2 3.42 2.19 2.07 2.32 2.09 1.77 2.14 2.8 1.86 2.37 2.72 2.02

Temp IOA ≥ 0.8 0.89 0.96 0.96 0.94 0.95 0.97 0.94 0.89 0.96 0.95 0.99 0.97 Humidity Bias < ± 1 2.73 1.90 1.00 1.08 0.75 0.40 -0.23 -0.41 -0.30 -0.28 -0.24 -0.47

Humidity Gross E <2 3.14 2.40 1.91 1.80 1.60 1.30 0.92 0.96 0.88 1.72 1.41 1.42

Humidity IOA ≥ 0.6 0.65 0.75 0.80 0.73 0.79 0.83 0.70 0.69 0.72 0.62 0.73 0.77

EVALUATION OF OTHER SUBDOMAINS

The tests described above focused on the western U.S., and the final configuration was selected on the basis of those results. This leaves open the possibility that we have optimized the MM5 configuration for the western U.S., causing degr adation of performance in other parts of the country. To investigate this, we compared model performance in the non-WRAP subdomains for Run 0, Run 5, and Run 2ae.

Figure 3-4a displays the wind perf ormance comparison. Run 2ae is clearly the best of the three runs here, with a lower error than Run 0 or R un 5 for all subdomains. For midAtl, cenrapN, and NE, Run 2ae also has the smallest bias (note that the point for cenrapN coincides with the point for midAtl for Run 2ae). For cenrapS subdomain, Run 2ae has a smaller bias than Run 0, but a slightly larger bias than Run 5. For OhioVa l Subdomain, the bias for Run 0 and Run 2ae is identical, and is slightly la rger than that of Run 5.

For temperature (Figure 3-4b), Run 2ae has a lo wer or equal error for all subdomains, and a smaller bias for midAtl, and cenrapS. For the othe r subdomains, the bias in 2ae is slightly larger than in Run 5, but still falls well within the benc hmark. All points show a warm bias in Run 2ae, which is a shift from Run 0, for which all s ubdomains have a cold bias. Run 5 shows the subdomains equally split between warm and cold biases.

For humidity (Figure 3-4c), Run 2ae shows improve ment over Runs 0 and 5 for all subdomains, lowering or eliminating the wet bias seen in R uns 0 and 5. Changes in error are less pronounced, but where they occur, Run 2ae has a smaller error.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-7 July 1-5, 2002 36 km: Run 0, Run 5, and Run 2ae Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5
Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark Run 0 Run 5 Run 2ae cenrapN

cenrapS

SE

midAtl

NE

OhioVal x o

Figure 3-4a. Wind scatterplot for comparison of original, interim, and final MM5 runs for subdomains outside WRAP.

Figure 3-4b. Temperature scatterplot for comparison of original, interim, and final MM5 runs for subdomains outside WRAP. Jul y 1-5, 2002 36 km: Run 0, Run 5, and Run 2ae Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark Run 0 Run 5 Run 2ae cenrapN

cenrapS

SE

midAtl

NE

OhioVal x oDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-8 Figure 3-4c. Humidity scatterplot for comparison of original, interim, and final MM5 runs for subdomains outside WRAP.

SUMMARY

In this study, we made a series of sensitiv ity tests and arrived at a revised 36-km MM5 configuration that resulted in significant improvements in th e precipitation, temperature, humidity and wind fields, all of which are likel y to be helpful in producing more accurate CMAQ results.

MM5 runs made with the new more optimal configuration showed:

• A dramatic reduction in the summertime co ld, wet bias in the desert southwest;

• Surface temperature and humidity performan ce now within benchmarks for all WRAP subdomains except desert southwest for temperature;

• More accurate representation of the diurnal temperature cy cle in the desert southwest;

• Increases in the Index of Agreement (IOA) fo r temperature, humidity and wind speed for all WRAP subdomains;

• A more realistic precipitation pattern over the western U.S.;

• Improved model performance in the eastern U.S.; and

• Runs made with these phys ics options generally repres ented an improvement in performance throughout the year, making it unn ecessary to select different physics schemes for different seasons.

Some performance issues with the final Run 2ae remain. For example, Run 2ae still lies outside the benchmarks for wind for all WRAP subdomain s, and the temperature error borders on or Jul y 1-5, 2002, 36 km: Run 0, Run 5, and Run 2ae Humidit y Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark Run 0 Run 5 Run 2ae cenrapN

cenrapS

SE

midAtl

NE

OhioVal x oDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitivity_ Report\Revised\Sec3_revsd.doc 3-9 exceeds the benchmark value for 3 of the four WR AP subdomains. MM5 is still producing too much rainfall in the northern half of the U.S. during the July period. Although use of the BettsMiller scheme improved the rainfall field, the DesertSW and CenrapN and CenrapS areas, in particular, are still too wet.

However, the sensitivity tests s uggest that getting pe rfect MM5 performance for all variables in the western U.S. is probably beyond the ski ll of the MM5 model. The existing run 2ae configuration is probably as clos e to optimal as can be achieved within the time constraints of this project.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-1 4. SUMMARY OF 12-KM SENSITIVITY TESTS

We now describe a series of te sts undertaken to determine th e optimal MM5 configuration for the nested 12-km grid of the WRAP modeling domai n. In these tests, we varied only the physics options that we expected to ameliorate the biases in the WRAP region seen in the 36-km simulation. The over prediction of summer rain fall and associated su rface humidity bias suggested that the cumulus parameterization might need to be changed. Another possible reason for the wet bias might be the soil moisture in itialization. Therefore, we focused on sensitivity tests of the soil moisture specification and the cumulus scheme.

CUMULUS PARAMETERIZATION TESTS

At the 12 km scale, the problem of cumulus para meterization is not well-posed, as there is no clear spectral gap between the resolved grid-scale process and the scale of the parameterized process (Arakawa and Chen 1987). Molinari (19 93) suggests that parameterization of cumulus convection for grid spacings of 2-20 km cannot be a ddressed with either the fully explicit (i.e. no cumulus parameterization) method or a hybrid parameterization approach. However, our ultimate goal of performing CMAQ visibility modeling requires us to be pragmatic and configure MM5 in a physically reasonable way th at produces the most accu rate representation of the 2002 meteorology. Our plan for the 12-km grid cumulus scheme, therefore, was to conduct several sensitivity tests to determine which cumu lus scheme (or no cumulus scheme at all) gives the best performance in terms of rainfall and surface temperature, humidity, and wind.

We tested the Betts-Miller scheme, which gave the best performance on the 36-km grid over the WRAP region, as well as the Kain-Fritsch I and II schemes. The Kain-Fritsch schemes include a parameterization of convective downdrafts. Th e Betts-Miller scheme, which was originally designed for use in general circul ation (i.e. large-scale) models, does not. We might then expect that the Kain-Fritsch schemes should do a better job of handling convection on the 12-km grid, particularly in cases where convec tion is severe and vert ical transport of energy and moisture in downdrafts is critical.

A third alternative was to use no cumulus pa rameterization, and let the cloud microphysics scheme and core model dynamics explicitly simu late convection. This approach has some limitations. Molinari and Dudek (1992) have shown that the explicit simulation of convection in mesoscale models can cause an unphysical phase delay in the onset of convective rainfall because condensation cannot occur until the model beco mes saturated at the resolved grid scale. In the real world, saturation o ccurs on a scale that is smaller than mesoscale model grid resolution; in a parameterization, the cloud model must saturate the column, but the grid need not be saturated. Molinari and Dudek also show th at explicit convection in mesoscale models, which cannot contain an updraft and a downdraft in the sa me grid cell, can cause difficulty simulating the moist static energy profile in a convecting grid cell. This can create an unrealistic representation of the rem oval of the convective instability, lead ing to inaccuracies in the timing and amount of predicted convective rainfall.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-2 For the purposes of the sensitivity tests, we configured MM5 so that the 12-km setup was identical to that of the 36-km run, and varied only the cumulus parameterization on both grids (Table 4-1).

Table 4-1. MM5 configuration for the 12-km grid for cumulus sensitivity tests. Radiation Microphysics Cumulus LSM/PBL RRTM Reisner II Kain Fritsch I PX/ACM

For example, the simulation denoted by KFII ran with KFII as the cumulus parameterization on the 12-km grid; the 12-km run was made using boundary conditions from a 36-km run which also used the KFII scheme. Likewise, boundary c onditions for the KF and BM 12-km runs were driven from 36-km runs that used the KF and BM schemes, respectively. The BM0 run had the Betts-Miller scheme running on the 36-km grid, and no cumulus parameterization on the 12-km grid. The BM0a run is identical in its configuration to BM0, except that no surface analysis nudging of temperature or humidity was performe d in BM0a. A summary of the different configurations is shown in Table 4-2.

Table 4-2. Summary of cumulus sensitivity configurations. Run Name 36-km Cumulus Scheme 12-km Cumulus Scheme KF I Kain-Fritsch I Kain-Fritsch I KF II Kain-Fritsch II Kain-Fritsch II BM Betts-Miller Betts-Miller BM0 Betts-Miller None BM0a Betts-Miller None

Figure 4-1a is a summary of the surface wind sp eed performance. For the DesertSW subdomain, the RMSE is comparable among all the simulatio ns, but the BM and BM0 runs have a smaller bias. For the other three WRAP domains, ther e is no significant diffe rence in performance, although the BM0 has a slightly larger RMSE and bias than the other runs. It is difficult to say whether one scheme outperforms the others for wind speed.

For temperature (Figure 4-1b), the KF run is a clear outlier for the DesertSW. For the North subdomain, the BM, BM0, and KFII runs are compar able, and KF and BM0a have larger bias and RMSE. For PacificNW and SW, performance is comparable, with BM0a having a slightly larger RMSE than the other schemes. Overall, BM0 and KFII are the best-performing simulations for temperature.

The humidity performance is shown in Figure 4-1c . For DesertSW, KF is again an outlier, along with KFII. For DesertSW and North, the th ree BM configurations (BM, BM0, and BM0a) perform better, with BM0a having the smallest RM SE and bias of all schemes. For the Pacific NW and SW subdomains, performance is comparable among all the schemes. Overall, then, the BM0a configuration performs best for humidity.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-3 Figure 4-1a. Wind speed scatterplot for comparison of 12-km cumulus test runs.

Figure 4-1b . Temperature scatterplot for comparison of 12-km cumulus test runs. WRAP 12 km July Wind Performance Comparison

0 0.5

1 1.5

2 2.5

3

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 Wind Speed Bias (m/s)WindSpeed RMSE (m/s)

Benchmark BM BM0 KF KFII BM0a Cumulus SensitivityTest

WRAP 12 km July Temperature Performance Comparison

0 0.5

1 1.5

2 2.5

3 3.5

-2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 Temperature Bias (K)Temeprature Error (K)

Benchmark BM BM0 KF KFII BM0a

Cumulus Sensitivity TestDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-4 Figure 4-1c. Humidity scatterplot for comparison of 12-km cumulus test runs

.

We turn now to an evaluation of the precipita tion performance over the July 1-5 time period on the 12-km grid. Figure 4-2 shows the total (convective+resolved) rainfall for the period for each MM5 run as well as the CPC precipitation observa tions. The KF and KFII runs overestimate the rainfall over the SW and DesertSW, and North subdomains. They predict light rainfall over large areas of the domain where the observations show no rainfall at a ll. In the runs where BM is used in the 36-km simulations, there is a smaller overprediction of rainfall over these three subdomains. Of the three configurations with BM running at 36-km, the BM0a scheme shows the best performance. It has the smallest over prediction and provides the best simulation of all runs of the feature located over Montana. The KF I and KF II schemes, however, do a better job with the feature over northern Washington. This is under predicted in the Betts-Miller scheme

runs.

Based on the surface variable and ra infall performance, then, we select the BM0a configuration. The configuration with Betts-Mil ler running on the 36-km grid a nd no cumulus parameterization on the 12-km grid was among the best performers in terms of surface wind, temperature, and humidity, and performed the best for rainfall . We choose the BM0a configuration over BM0 based on guidance from reviewers that surface an alysis nudging of temperature and humidity degrades performance aloft.

It is interesting to note that the Betts-Miller scheme, which is not recommended for use in MM5 at resolutions less than 30 km , performs better than the KF and KFII schemes, which were designed with higher resolution simulations in mi nd. It is also notewor thy that the explicit WRAP 12 km Humidity Performance Comparison

0 1 2 3 4 5

-3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 Humidity Bias (g/kg)Humidity Error (g/kg)

Benchmark BM BM0 KF KFII BM0a

Cumulus Sensitivity TestDecember 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-5 Figure 4-2. Precipitation comparison for the cumulus parameterization sensitivity test.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-6 12-km simulation also outperformed KF and KFII. We must bear in mind that these tests were performed over a short time period in July. Th is is a time period when MM5 is producing too much convective rainfall. The BM0a configuratio n may not perform as well as the others for other seasons. In winter, for example, overpredi ction of convective rainfall is not a performance issue. Most of the rainfall is stratiform pr ecipitation produced by mid- latitude cyclones, and MM5 does a reasonably good job of simulating the wint er rainfall in 2002. It is unclear whether BM0a would still be the best-performing simulati on for this season. Furt her testing, however, is limited by time constraints, and so we select the BM0a configuration for the 12-km domain simulation.

SOIL MOISTURE NUDGING TESTS

The poor 36-km MM5 model performance in the west ern U.S. relative to th e eastern U.S. may be due in part to the different (dri er) western soil characteristics. Therefore, we performed a series of 12-km MM5 simulations for the period July 1-5 2002 in which the soil moisture nudging and the method of soil moisture specification were va ried. All of these runs used the same physics options shown in Table 4-3 and the KFI cumulu s scheme. In Run 1, the 36-km MM5 simulation output was used to generate in itial and boundary conditions for the 12-km run. In Run 2, the Pleim-Xiu LSM was initialized from the defa ult land use lookup table (LANDUSE.TBL), and the soil moisture was not nudged. In Run 3, th e PX LSM was initialized with the 36-km MM5 output, while in Run 4, the PX LSM was initialize d using the EDAS analysis fields. No soil moisture nudging was performed in Runs 3 and 4. These are listed in Table 4-4.

Table 4-3. Physics options common to all 12-km domain soil moisture sensitivity tests. Cloud Microphysics Cumulus PBL/LSM Radiation Reisner II Kain-Fritsch I ACM/PX RRTM

Table 4-4. Physics options varied for 12-km domain sensitivity tests. Soil Moisture Nudging Soil Moisture Initialization 12km_run1 Yes 36km output 12km_run2 No LANDUSE.TBL 12km_run3 No 36km output 12km_run4 No EDAS analysis

The time series of hourly statistics for wi nd, temperature and humidity over the WRAP subdomains SW, PacificNW, and DesertSW are di splayed in Figure 4-3. Figure 4-3a shows time series of surface wind, temperature, and humidity for the SW subdomain. For winds, there is not much difference among the four runs. For temp erature, Run 4 performs the best, and Run 1 underestimates the amplitude of the diurnal cycle, giving the worst performance. For humidity, all runs show a spurious diurnal cycle, and Run 4 is the best performing run.

For DesertSW (Figure 4-3b), it is hard to say whet her one run is better th an the others for wind. For temperature, however, Run 2 has the smallest bi as. For humidity, all 4 runs have a wet bias, which is most pronounced in Run 1. The wet bi as is less pronounced in Runs 2 and 4.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-7 For PacificNW (Figure 4-3c), the temperature and wind time series are inconclusive, but for humidity, it is clear than Run 4 has the smallest bias. We conclude then, that Run 4 is the best performing run overall. These tests show th at turning off the soil moisture nudging has a positive impact on performance.

The main results from the 12-km Runs 1-4 for the western subdomains over the period July 1-5, 2002 are as follows:

• Use of soil moisture nudging degrades temp erature and humidity model performance.

• Initializing the soil moisture with the 36-km domain MM5 output degrades temperature and humidity model performance.

• Better temperature and humidity performance was obtained by initializing the soil model with the EDAS analysis fields.December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-8 Figure 4-3a . Time series of observed and MM5 surface wind, temperature and humidity for the SW domain. Observed/Predicted Windspeed

0 2 4 6 8

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5m/s

ObsWndSpd run1 run2 run3 run4 run5

Observed/Predicted Wind Direction

0 60 120 180 240 300 360

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5deg

ObsWndDir run1 run2 run3 run4 run5

Observed/Predicted Temperature

275 280 285 290 295 300 305 310

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5K

ObsTemp run1 run2 run3 run4 run5

Predicted/Observed Humidity

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7/ 1 7/ 2 7/ 3 7/ 4 7/ 5g/kg

ObsHum run1 run2 run3 run4 run5December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-9 Figure 4-3b. Time series of observed and MM5 surface wind, temperature and humidity for the DesertSW domain. Observed/Predicted Windspeed

0 2 4 6 8

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5m/s

ObsWndSpd run1 run2 run3 run4 run5

Observed/Predicted Wind Direction

0 60 120 180 240 300 360

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5deg

ObsWndDir run1 run2 run3 run4 run5

Observed/Predicted Temperature

275 280 285 290 295 300 305 310

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5K

ObsTemp run1 run2 run3 run4 run5

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0 2 4 6 8 10 12 14

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5g/kg

ObsHum run1 run2 run3 run4 run5December 2004

G:\WRAP RMC\2002MM5\MM5_Sensitiv ity_Report\Revised\Sec4.doc 4-10 Figure 4-3c. Time series of observed and MM5 surface wind, temperature and humidity for the PacificNW domain. Observed/Predicted Windspeed

0 2 4 6 8

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5m/s

ObsWndSpd run1 run2 run3 run4 run5

Observed/Predicted Wind Direction

0 60 120 180 240 300 360

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5deg

ObsWndDir run1 run2 run3 run4 run5

Observed/Predicted Temperature

270 275 280 285 290 295 300

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5K

ObsTemp run1 run2 run3 run4 run5

Predicted/Observed Humidity

0 1 2 3 4 5 6 7 8

7/ 1 7/ 2 7/ 3 7/ 4 7/ 5g/kg

ObsHum run1 run2 run3 run4 run5December 2004

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