H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc Revised Draft Protocol

2002 ANNUAL MM5 SIMULATIONS TO SUPPORT WRAP CMAQ VISIBILITY MODELING

FOR THE SECTION 308 SIP/TIP

Prepared for

The Western Regional Air Partnership

Prepared by

ENVIRON International Corporation 101 Rowland Way, Suite 220

Novato, CA 94945-5010

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

December 2004December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 1 INTRODUCTION

In 1999, the U.S. Environmental Protection Agency (EPA) announced a major effort to improve air quality in national parks and wilderness areas. Its Regional Haze Rule (RHR) calls for state and federal agencies to work t ogether to improve visibility in 156 national parks and wilderness areas (referred to as “Class I” areas). The objectiv e of the RHR is to achieve natural visibility conditions in these federally protected lands by th e year 2064. The rule requires the states to develop and implement air quality protection plans to reduce the pollution th at causes visibility impairment. The Western Regional Air Partnershi p (WRAP) is a consortium of federal, state, and tribal agencies charged with implementing re gional planning processes to improve visibility in all western Class I areas. To meet this goa l, WRAP is providing the necessary technical and policy tools to help states and tribes 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 is performing th e modeling and analysis necessary to develop the Section 309 SIPs/TIPs and begin the analysis fo r the Section 308 SIPs/TIP s. This project is focused on seasonal-annual regional visibility mo deling using a deterministic photochemical grid model capable of simulating the formation, transport, and fate of tropospheric particulate matter (PM) on time scales of one hour. The RMC is utilizing the EPA Models-3/CMAQ system for this purpose. The model is applied for the enti re 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 Class I areas. The Fifth Genera tion Mesoscale Model (MM5), developed and maintained by the Pennsylvania State University and National Center for Atmospheric Research (PSU/NCAR), is being used to develop hourly meteorological fiel ds on these two grids. The EPA Sparse Matrix Operating Kernel Emissions (SMOKE) system is being used to develop the anthropogenic and biogenic gas and PM emission rate estimates for CMAQ.

This document provides the protocol for the meteorological modeling component using MM5. First, a brief description of MM5, th e WRAP modeling domain, the MM5 physical configuration, and model applicatio n approach is presented. The pr otocol then presents the plan for evaluating the performance of the model in replicating the evolut ion of observed winds, temperature, humidity, and boundary layer morphol ogy to the extent that resources and data availability allow; this will se rve as the primary approach to assess the reliability of the meteorological fields to adequate ly characterize the state of the atmosphere for input to CMAQ.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 2 METEOROLOGICAL MODELING APPROACH

The CMAQ model requires inputs of three-dime nsional gridded wind, temperature, humidity, cloud/precipitation, and boundary layer parameters. The current version of CMAQ can only utilize output fields from th e PSU/NCAR MM5 meteorological model. MM5 is a state-of-thescience atmosphere model that has proven useful for air quality applications and has been used extensively in past local, stat e, regional, and national modeli ng efforts. MM5 has undergone extensive peer-review, with all of its components continua lly undergoing development and scrutiny by the modeling community. The MM5 m odeling system software is freely provided and supported by the Mesoscale Prediction Group in the Mesoscale and Microscale Meteorology Division of NCAR. For these reasons, MM5 is the most widely used public-domain prognostic model. In-depth descriptions of MM5 can be found in Dudhia ( 1993) and Grell et al. (1994), and

at http://www.mmm.ucar.edu/mm5

.

A meteorological database suitable for air qu ality modeling with CMAQ will be generated by developing a year-long run with MM5. The master domain for this run was determined by the Regional Planning Organization (RPO) Modelin g Discussion Group, and consists of the

continental United States modeled at a resolution of 36 km. This domain is required because of the regional nature of haze and the need to cr eate a continental-scale meteorological modeling database that can be used seamlessly with ot her RPO modeling efforts around the country. EPA has recently sponsored the development of an annual MM5 simulation on this grid for the year 2001. The Iowa Department of Natural resour ces (IDNR) and Baron Advanced Meteorological Systems (BAMS) have also run MM5 on this grid for the entire year of 2002 for eventual air quality modeling by the Midwest RPO (MRPO) and the Visibility Improvement State and Tribal Association of the Southeast (V ISTAS), respectively. For the cu rrent application, an additional western regional domain at 12 km resolution is added by WRAP to encompass the western U.S.

Class I areas.

Description Of MM5

The PSU/NCAR MM5 is a limited-area, terrai n-following (sigma-coordinate), prognostic meteorological model. It solves the full suit e of non-hydrostatic prognostic primitive equations for the three-dimensional wind, temperature, water (i n all phases), and pressure fields. It can be run with multiple one-way or two-way nested grids to resolve a range of atmospheric processes and circulations on spatial scales ranging from one to several thousands of kilometers. The model is highly modular, facilitating the interc hange of physics and data assimilation options. Several options exist for boundary layer schemes; resolved and sub-grid cloud and precipitation treatments; soil heat budget models, and radiativ e transfer. The model equations are solved horizontally on an Arakawa-B grid structure defi ned on a number of available map projections. The Lambert conformal conic projection is used for air quality applications in the U.S. The

vertical coordinate is a terrain -following normalized pressure coor dinate, referred to as a “sigmap”. Typically, 30-50 vertical levels are used to resolve the tropos phere and lower stratosphere to

~15 km.

The model is supported by several pre- and post- processing programs, which are referred to collectively as the MM5 modeling system. The MM5 modeling system software is mostly written in Fortran, and has been developed at Penn State and NCAR as a community mesoscaleDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 3 model with contributions from users worldwide. The pre- and post-processi ng tools facilitate the development of various model inputs, and the analysis of model output.

Because MM5 is a limited-area model, it requir es lateral boundary conditions that define the space- and time-varying conditions at the peri phery of the coarsest domain throughout the simulation. Both initial and boundary conditions are generally specified using observational analyses, and may be supplemented by additional surface or upper air observations. These data

sources can be obtained from a va riety of routine analysis systems, from several global analysis products to higher resolution (time and space) fo recast initialization fiel ds prepared by the

National Weather Service or other entities. Most datasets are available directly from NCAR.

The model may be constrained during the simulation to relax toward observed temperature, wind and humidity observations through the use of fo ur dimensional data assimilation, known as FDDA (Stauffer and Seaman 1990, 1991). FDDA amounts to adding an additional term to the prognostic equations that serves to “nudge” the m odel solution toward objective analysis fields

and/or individual observati ons. This has been shown to signi ficantly reduce drift in the solution for simulations of several days or more. Drift may be caused by (among other effects)

inaccuracies in the initial conditions , the effects of discretization, or errors in the formulation of various parameterizations.

MM5 Configuration for the 36-km Grid

The WRAP MM5 modeling system configuratio n for the 2002 annual simulation builds on the prior IDNR 2002 application (Johns on, 2003), which was in turn set according to the optimal MM5 physics options that resulted from an in-dep th sensitivity project carried out by IDNR and the Lake Michigan Air Directors Consortium (LADC o). While these earlier simulations utilized MM5 version 3.5, the application to be carried out for WRAP will use the latest available version of the model (v3.6.2). Additional modifications to the physics configuration and application methodology were made for the WRAP simulation based on the latest information from EPA, IDNR, LADCo, and others. An initial 2002 WRAP annual run was made with this configuration, and evaluated. Then, further sens itivity tests were made to identify an MM5 configuration that would addre ss performance issues identified from the initial annual run. These sensitivity tests are described further below.

Modeling Domain

For the WRAP annual run, MM5 is configured with two grids: the continental-scale RPO National Grid with 36 km grid point spacing, an d a WRAP-specific western regional grid with 12 km grid point spacing. The model is run on both grids simultaneously, but in a “one-way” nesting mode. In this approach, information fr om the 36-km grid is transferred to the 12-km domain through boundary conditions during the simu lation, but there is no feedback of the 12- km fields up-scale to the 36-km domain. The or iginal approach was to first run MM5 on the 36- km grid, and then to extract initial and boundary conditions from the resulting fields for the 12- km simulation and run the finer grid as a complete ly separate simulation. Another alternative is “two-way” nesting, wherein MM5 is run for both grids simultaneously and information propagates both down and up-scale between the two. Our experience suggests that the choice of one- vs. two-way nesting does not have a signifi cant impact on model performance for regionalDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 4 applications with coarse grid spacing. However, this has not necessarily been the case for small mesoscale grids (~100 km scale) with high resolu tion (1-4 km). In any event, CMAQ modeling guidance from EPA suggests that each grid to be run in the air quality model should be provided with MM5 meteorological fields th at are developed independently in 1-way nested mode. Thus, our approach follows this suggestion.

The RPO National Grid is defined on a Lamber t conformal projection, with true latitudes at

33 ° N and 45 ° N, and the central latitu de and longitude at 40 ° N and 97 ° W, respectively. The grid point spacing is 36 km. The continental expanse of this domain results in a grid of 165 (eastwest) by 129 (north-south) dot points, and 164 (east-west) by 128 (north-south) cross points (Figure 1). Overall, the do main covers 5904 km by 4608 km.

The WRAP regional grid is defined on the same Lambert projection, but covers the immediate area of interest to this project with 12 km gr id point spacing. Table 1 shows the relationship between the MM5 and CMAQ regional grids. Th e CMAQ grid will be contained within the slightly larger MM5 grid with a 12-point overlap (6 points on each side). This meets the EPA’s suggestion that the MM5 domain should provide at least 3-5 boundary points of overlap to alleviate any numerical boundary artifacts that may be present in the MM5 output fields. The coverage of the MM5 12-km regi onal grid is shown in Figure 2.

Table 1. Relationship between the WRAP CMAQ and MM5 12-km regional grids.
CMAQ MM5 Dot points 208 x 187 220 x 199 Cross points 207 x 186 219 x 198 SW corner coordinate -2376, -936 -2248, -1008 NE corner coordinate 108, 1296 180, 1368

Vertical Grid Structure

In order to ensure consistency among the 2001 EPA simulations and the 2002 IDNR simulations, we adopted the same 34 layer vertical structure. The layer structure is summarized in Table 2.

The altitudes above sea level ar e estimated according to standard atmosphere assumptions used in MM5 (surface pressure of 1000 mb, model to p at 100 mb, surface temperature of 275 K, and log-pressure lapse rate of 50 K/ln[p]).

Physical Treatments and FDDA

We first describe the setup for the initial WRAP 2002 applic ation, and then describe its performance issues and our subsequent modifi cations to the original configuration. As mentioned above, the 2002 annual WRAP MM5 applic ation was initially configured in most aspects according to the optimum arrangement identified by the IDNR sensitivity simulations (Johnson, 2003). The physical options selected fo r the initial WRAP app lication are shown in Table 3. One departure from Johnson (2003) in is the use of the Reisner II cloud microphysics scheme (Reisner et al. 1998). Although this scheme is more expensive than the “simple ice” option used by IDNR, the EPA recommends that a mixed phase ice scheme be employed in MM5 to drive aqueous chemistry and wet scavengi ng in CMAQ. This is because the simple iceDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 5 Figure 1. Spatial coverage of the RPO National Grid with 36 km grid point spacing.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 6 Figure 2. Coverage of the WRAP western regional domain with 12 km grid point spacing.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 7 Table 2. MM5 vertical layer structure for the WRAP 2002 application.

Level Sigma Pressure (mb) Height (m) 34 – top 0.000 100 14662

33 0.050 145 12822

32 0.100 190 11356

31 0.150 235 10127

30 0.200 280 9066

39 0.250 325 8127

28 0.300 370 7284

27 0.350 415 6517

26 0.400 460 5812

25 0.450 505 5160

24 0.500 550 4553

23 0.550 595 3948

22 0.600 640 3448

21 0.650 685 2942

20 0.700 730 2462

19 0.740 766 2095

18 0.770 793 1828

17 0.800 820 1569

16 0.820 838 1400

15 0.840 856 1235

14 0.860 874 1071

13 0.880 892 911

12 0.900 910 753

11 0.910 919 675

10 0.920 928 598

9 0.930 937 521

8 0.940 946 445

7 0.950 955 369

6 0.960 964 294

5 0.970 973 220

4 0.980 982 146

3 0.985 987 109

2 0.990 991 73

1 0.995 996 36 0 - ground 1.000 1000 0

Table 3. Physics options selected for the 2002 WRAP MM5 simulation. Physics Option Parameterization Cloud Microphysics Reisner II

Cumulus Parameterization Kain-Fritsch II Planetary Boundary Layer Asymmetr ic Convective Mixing Model Land Surface Model Pleim-Xiu (no INTERPPX, no soil moisture nudging) Radiation Rapid Radiative Transfer Model

Shallow Convection None Varying SST with time? Yes Thermal Roughness Garratt Snow Cover Simple snow modelDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 8 approach treats all condensed water forms as a single liquid variable, which, when passed to CMAQ, overstates the quantity of liquid cloud a nd precipitation water available for chemistry and removal. Furthermore, the most recent release of CMAQ (v4.4) includes separate contributions from precipitating ice (graupel), so it is advantageous to prepare the meteorological fields with this new capability in mind. Anot her difference in physics options from Johnson’s (2003) is the use of the Kain-Fritsch II cumulus parameterization. We have seen in short tests for July 2002 that the use of this newer algorithm leads to improve d precipitation performance in the desert southwest, along with commensura te improvements to temperature and humidity performance (Kemball-Cook et al. 2004b).

By selecting the Pleim-Xiu land surface model (Xiu and Pleim 2000) we are required to use the Asymmetric Convective PBL option (Pleim and Ch ang 1992), as these two are directly coupled. The Pleim-Xiu (P-X) parameterization is a predic tive/interactive soil temperature and moisture budget model that responds to atmospheric proc esses that affect the thermodynamics of the surface (e.g., rainfall) while in tu rn dictating the surface fluxes of momentum, heat and moisture into the boundary layer to further affect atmospheri c processes. This allo ws the P-X approach to maintain a historical “memory” of the soil conditi ons over the course of a continuous simulation.

However, that memory is interrupted whenever the simulation is broken into separate multi-day runs of the model. Therefore, P-X utilizes the external INTERPPX processor, which reinitializes the soil temperature and moisture fields for the next model run according to the conditions at the end of the previous run. While this option prevents discontinuities in the soil fields during the reinitialization of the model, it can lead to surface temperature and moisture biases that the model physics cannot overcome if the model is initialized during a period of extreme cold (Olerud and Sims, 2003). It was found that removing the co ntinuous soil moisture option from the application improved the temperat ure bias problem. This finding was revealed to MM5 users after the IDNR had completed th eir 2002 annual simulatio ns using the INTERPPX option. Accordingly, the INTERPP X option will not be used fo r the 2002 WRAP application. The P-X scheme is instead run for each simulation segment with the initial soil moisture interpolated from the input objec tive analyses (described further below). The vegetation is not be allowed to grow during the simulation, and so il moisture nudging is turned off for the PleimXiu land surface module.

MM5 is configured to utilize its FDDA capabili ties to nudge the model toward observed wind, temperature, and moisture fields on both 36-km and 12-km grids throughout the 2002 annual simulation. Specifically, analysis (or grid) nudgi ng is performed at 3-hour ly intervals both for

the two-dimensional surface fields and for the th ree-dimensional fields aloft, excluding the boundary layer depth. Exclusion of the boundary layer in the FDDA process removes the potential for damping out resolved mesoscale fo rcings in the model that are important to boundary layer development and thus the vertical fluxes of momentum, heat, and moisture into the free atmosphere and to the surface. Tw o-dimensional surface nudging is based on EDAS surface analyses, but is enhanced via the “little_R” program using hourly NWS surface

observations from NCAR dataset ds.472.

Analysis nudging coefficients for the initial appl ication are shown in Tabl e 4. Use of FDDA to nudge toward observational data at individual measurement sites (i .e., “station” or “observation” nudging) nudging is not performe d. Usually, this option is be st suited for smaller highresolution grids in which data from a very de nse network of measurem ent sites are available (such as an intensive field study).December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 9 Table 4. FDDA analysis nudging coefficients (s -1 ).

36 km 12 km

Wind 2.5 × 10 -4 1.0 × 10 -4
Temperature 2.5 × 10 -4 1.0 × 10 -4
Water vapor 1 × 10 -5 1.0 × 10 -5

MM5 performance in the initial 2002 application as described in Tables 3 and 4 is described in the report: “ 2002 Annual MM5 36 KM Simulation to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP: Preliminary Report on the Initial 2002 36 km MM5 Simulation Performed during 2003” (Kemball-C ook et al. 2004a). The initial 2002 36-km WRAP simulation results showed that MM5 perfor ms better in the Central and Eastern U.S. than in the West, and performs generally better in winter than in summer. Specifically, in the western U.S., the amplitude of the diurnal surface temperature cycle was persistently underestimated during the summer, especially in th e southwest. In the desert southwest, the surface humidity was greatly overestimated during the summer, and there was a pronounced cold bias. The excessive humidity and weak di urnal temperature cycle may be linked to the over prediction of summer precipitation by MM5, esp ecially in the southwest. This can have serious repercussions for CMAQ modeling sinc e 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 with MM5 to converge upon a set of physics options that would ameliorate the above MM5 pe rformance problems in the west. These tests are described in the report: “ 2002 Annual MM5 36 KM Simulati on to Support WRAP CMAQ Visibility Modeling for the Sec tion 308 SIP/TIP: MM5 Sensitivity Simulations to Identify a More Optimal MM5 Configuration for Simulating Meteor ology in the Western United States” (Kemball-Cook et al. 2004b). The more optimal model confi guration developed through the

tests is shown in Table 5.

Table 5. Modified MM5 configuration for the 36-km grid.

A nalysis FDDA

LSM PBL Cumulus Microphysics 3D Surface Obs FDDA

Pleim-Xiu ACM Betts-Miller Reisner II W/T/H W Wind

MM5 runs made with these physic s options selected showed:

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

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

• More accurate representation of the diurnal te mperature cycle in the desert southwest;

• Improvements in the index of agreement fo r temperature, 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.; andDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 10 • Runs made with these phys ics options generally represented an improvement in performance throughout the year, making it unnecessary to select different physics

schemes for different seasons.

In summary, the new MM5 configuration repr esents a significant improvement in model performance relative to the original 2002 36-km MM5 run, and will be used for the final annual 2002 MM5 simulation to support WRAP’s regional haze modeling.

MM5 Configuration for the 12-km Grid

We allowed for the possibility that the 12-km grid might require a different set of physics options than the 36-km grid. Of particular co ncern was the cumulus parameterization. At the 12 km scale, the problem of cumulus parameteri zation is not well-posed, as there is no clear spectral gap between the resolved grid-scale pro cess and the scale of the parameterized process (Arakawa and Chen 1987). Molinari (1993) s uggests that parameterization of cumulus convection for grid cell sizes of 2-20 km cannot be addressed with either the fully explicit (i.e. no cumulus parameterization) method or a hybrid parameterization approach. However, our ultimate goal of modeling visibility with C MAQ requires us to be pragmatic and configure MM5 in a physically reasonable way, which pr oduces the most accurate representation of the 2002 meteorology. Our plan for the 12-km grid cumulus scheme, therefore, was to conduct several sensitivity tests to determine which cumulus scheme (or no cumulus scheme at all) gives the best performance in terms of rainfa ll, surface temperature, humidity, and wind.

The poor 36-km MM5 model performance in the western US relative to the eastern U.S. may also be due in part to the different (drier) western soil characteristics. Therefore, we performed a series of 12-km MM5 simulations in which the soil moisture nudging and the method of soil moisture specification were varied . These sensitivity st udies are documented in the report: “ 2002 Annual MM5 36 KM Simulation to Support WRAP CMAQ Visibility Modeling for the Section 308 SIP/TIP: MM5 Sensitiv ity Simulations to Identify a More Optimal MM5 Configuration for Simulating Meteorology in the Western United States” (KemballCook et al. 2004b). The other physics options for th e 12-km grid were identical to those used on the 36-km grid. Table 6 shows the selected MM5 configuration for the 12-km application.

Table 6. MM5 configuration for the 12-km grid.

A nalysis FDDA

LSM PBL Cumulus Microphysics 3D Surface Obs FDDA

Pleim-Xiu ACM None Reisner II W/T/H W Wind

Procedure to Simulate the Year 2002

Preprocessing

Several preprocessing steps are necessary to prepare input data for an MM5 simulation. The MM5 modeling system provides all of the tools n ecessary to prepare topo graphic, vegetative, initial condition, boundary condition, and FDDA nudging input files. These tools are

summarized below.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 11 Program Description

TERRAIN Defines the MM5 horizontal domain specifications, including nested grid re lationships; processes raw topographic, vegetative, and soil type data to all MM5 grids.

REGRID (2 sub-programs) PREGRID Extracts raw pressure-l evel meteorological objective analyses from various sources and writes them to intermediate file formats for further processing.

REGRIDDER Processes/blends PREG RID output to the MM5 horizontal grids for a user-defined peri od; data remain on pressure

levels.

RAWINS/LITTLE_R Blends various raw mete orological observational data into the analyses developed fro m REGRID to improve the

characterization of coarse-re solution analyses on higherresolution MM5 grids, performs data QA checks, and reports data outliers to the user; produces a 2-D surface

FDDA file.

INTERPF Translates 3-D analysis fields derived from REGRID or RAWINS/LITTLE_R from pressu re levels to the final MM5 sigma-p structure; generates initial, lateral and surface boundary condition files, and 3-D FDDA analysis nudging files.

Global topographic data at 10-mi nute (latitude/longitude) resoluti on are used to define terrain elevations on the 36-km grid; higher 5-minute reso lution from the same dataset are used for the

smaller 12-km domain. Land use distribution on the MM5 domains is defined from the 24- category USGS vegetation data with a resolution of 10 minutes. The TERRAIN processor is run with options invoked to process all of the addition al terrestrial information necessary to run the

P-X land surface model.

The underlying objective analyses used for the 2002 WRAP simulation is taken from the Eta Data Assimilation System (EDAS). Available from NCAR, EDAS contains 3-hourly objective analysis initialization and forecast fields from the National Center for Environmental Prediction’s (NCEP) Eta model, which is the current short-term national operational forecasting platform. The EDAS analyses are provided on a standardized continental-scale pressure-level Lambert grid with ~40 km horizontal grid poi nt spacing. The EDAS analyses are developed from a wide variety of observational sour ces, including standard surface and upper air measurements, profiler networks, radar- and sa tellite-derived measur ements, and ship and aircraft reports. The wide array of data s ources, coupled with th e high time- and spatial resolution provided by EDAS, result in an analysis product that far exceed s the level of detail found in traditional global-scale analyses. The E DAS analysis files with the extension “.tm12” are not used since they ar e the “cold start” files.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 12 The REGRID system of programs is used to process the EDAS analyses for use by MM5 as initial/boundary conditions, and for three-dimens ional analysis nudging in the FDDA package. The PREGRID program is configured to enhance the raw EDAS pressure-level input data with additional non-standard pressure le vels at 950, 925, 900, 800, 750, 650, 600, 550, 450, and 350 mb. Sea surface temperatures (SSTs) are appr oximated by Eta skin temperatures, and the SSTs are allowed to vary over the cour se of the simulation. In order to improve the EDAS SSTs near coastlines, the NCEP Global Tropospheric Analysis datasets is processed within PREGRID to incorporate observed SSTs.

Given the large amount and variety of data that is ingested into EDAS, and the fact that the spatial resolution is commensurate with th e MM5 continental-scale domain, the use of LITTLE_R or RAWINS to improve the EDAS fiel ds for use in MM5 is considered to be redundant. However, these programs also cons truct the two-dimensional surface nudging fields for FDDA, which are considered necessary for the WRAP 2002 annual simulation. Therefore, the LITTLE_R program is used to develop the 2-D FDDA fields since it provides additional capabilities over the older RAWINS processor. The measurement datasets used as inputs to LITTLE_R include the NCEP ADP Global Surface Observations and the NCEP ADP Upper Air

Observations. A modified version of the FETC H utility, which re-processes the raw observation data into a format suited to LITTLE_R, is used to ensure surface FDDA operates only at three hourly intervals, as hourly surface nudging ma y lead to model instability (Johnson, 2003).

Model Application

The annual simulation is made in sequential 5- day run segments, each with an initial spinup period of 12 hours that overlap the last 12 hours of the preceding run. This is done so that the air quality model can be started at either 0Z or 12Z without including the MM5 re-initialization period. MM5 is re-initialized at the beginning of each 5-day period to reduce error propagation through the simulation. The 2002 annual simulation includes the final two weeks of December 2001 to allow for sufficient spin-up time for photoc hemical/visibility applications with start dates at the beginning of January 2002. A list of the run segments and their date/time durations are provided at http://pah.cert.ucr.edu/aqm/308/mm5.shtml . The model is run with 90 and 30 second time steps on the 36-km and 12-km grids, respectively.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 13 EVALUATION OF THE 2002 ANNUAL RUN

The goal of the evaluation is to determine whet her the meteorological fields are sufficiently accurate to properly characterize the transport, chemistry, and removal processes in CMAQ. If errors in the meteorological fields are too large, the ability of the air quality model to replicate regional pollutant levels over the entire base y ear will be severely hampered and the predicted impacts from future year growth and controls will be highly questio nable. To provide a reasonable meteorological charac terization to the photochemical/v isibility model, MM5 must represent with some fidelity the:

• Large-scale weather patterns (i.e., synoptic patterns depicted in the 850-300 mb height fields), as these are key forci ngs for mesoscale circulations;

• Mesoscale and regional wind, temperature, PB L height, humidity, and cloud/precipitation

patterns;

• Mesoscale circulations such as sea br eezes and mountain/drainage circulations;

• Diurnal cycles in PBL depth, temperature, and humidity.

For visibility applications, the moisture and conden sate fields are particularly important as they significantly impact PM chemical formation, rem oval, and light scattering efficiency. In addition, cloud and precipitation fi elds are a good measure of the integrated performance of the model since these are model-derived quantities and not nudged to observati ons. Because of the model’s coarse resolution of 36 and 12 km, the m odel cannot be expected to faithfully simulate the pattern or variability of the convective precipitation, but should reproduce the synoptic precipitation and cloud patterns.

As Johnson (2003) points out, there are no rigid guidelines for systematically and objectively evaluating the quality of me teorological simulations. However, sound comprehensive philosophies exist, such as discussed by Te sche et al. (1994 and 2001), who classify the evaluation into two components: an operational evaluation and a scientific evaluation. The scientific evaluation requires rigorous examin ations of the model formulation and algorithm development, which is usually beyond the scope of most air quality modeli ng projects. Previous peer-reviewed documentation of MM5 formulati on, testing, and evaluation must provide the basis for the scientific evaluation. Accordingl y, our evaluation will be limited to operational testing of the model, which entails an assessmen t of the model's ability to correctly estimate surface and boundary layer wind, temp erature, and moisture largel y independent of whether the actual process descriptions in the model are accura te. The operational evaluation essentially tests whether the predicted meteorological fields are reasonable, consiste nt, and agree adequately with available observations in time and space. The process provides only limited information about

whether the results are correct from a scientific perspective or whether th ey are the fortuitous product of compensating errors; thus a “successf ul” operational evaluation is a necessary but insufficient condition for achieving a sound, reliable performance testing exercise.

In this study, the basis for the operational performance assessment will entail a comparison of the predicted meteorological fields to available surf ace and aloft data that are collected, analyzed, and disseminated by the National Weather Service. This will be carried out both graphically and statistically to evaluate m odel performance for winds, temperatures, humidity, and the placement, intensity, and evolution of key weathe r phenomena. A specific set of statistics has been identified for use in establishing benchmarks for acceptable model performance (Emery etDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 14 al., 2001), with the idea that these benchmarks, si milar to current EPA guidance criteria for air quality model performance, allow for a cons istent comparison of various meteorological simulations for important variables at the surface and in the boundary layer.

Graphical Evaluation

The first step in the operational evaluation will be the preparation of graphics to display the predicted meteorological fields at the surface and for selected levels aloft. This will allow for a qualitative assessment of model performance by comparing results to commonly available sounding data, satellite imagery and analysis maps of wind, temperature, pressure, and precipitation patterns available from several entities, including the NWS and others (e.g., http://weather.unisys.co m, http://www.spc.noaa.gov/obswx/maps, and http://lwf.ncdc.noaa.gov/servlets/GoesBrowser) . Examples of these products are shown in Figures 3 through 5. The purpose of these eval uations will be to establish a first-order acceptance/rejection of the simulation in adequate ly replicating the gro ss weather phenomena in the region of interest. Thus, this approach sc reens for obvious model flaws and errors. Given the enormous volume of data available for these types of evaluations, only spot checks will be performed throughout the simulated year.

In this study, maps of MM5 results will be pr epared for the surface and 500 mb levels. These

levels are chosen to facilita te the comparison to common anal yses from the NWS. Specific parameters that will be plotted include wind v ectors, temperature, sea level pressure (surface maps), geopotential height (aloft maps), and precipitation (to compare with radar mosaics). Cloud predictions will be subjectively co mpared against satellite imagery.

Because of the regional nature of the haze problem, and the fact that most pollutant transport of interest occurs above the surface, evaluation of the MM5 upper air meteorological fields assumes particular importance in this study. Analysis of MM5 tropospheric output fields is made more difficult by the spatial and temporal resolution of the upper air sounding network, which is sparse in time and space compared with the surface obse rving network. Radiosonde measurements are usually taken twice per day by an observing network of approximately 120 stations in the continental U.S. Hourly radar profiler measur ements are available from NOAA, but are lacking in spatial coverage. Additional plotting capabilities have been developed by the IDNR (RAOBPLOT) to compare sounding data at indivi dual sites and times to predicted soundings. This allows for a site-by-site comparison of wi nd, temperature, and humidity profiles, and also provides the user the capability to diagnose and evaluate the heights, strengths, and depths of key stability regimes (e.g., boundary layer depths, no cturnal inversion heights) from observational soundings and MM5 predictions. The observed s oundings will come from the Forecast Systems Laboratory (FSL)/ National Climatic Data Center (NCDC) Dataset. An example of the resulting graphics is provided in Figure 6.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 15 Figure 3. Example of a surface weather chart downloaded from the Unisys Weather Site ( http://weather.unisys.com) , depicting station observations, fronts, radar-derived precipitation, and sea-level pressure patterns.

Figure 4. Example of a 500 mb weather chart downloaded from the NOAA Weather Site ( http://www. Spc.noaa.gov/obswx/maps) , depicting rawinsonde station observations, geopotential heights contours, and temperature contours.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 16 Figure 5. Example of an infrared satellite im age downloaded from the NCDC GOES Browser Site ( http://lwf.ncdc.noaa.gov/servlets/GoesBrowser ).

Statistical Evaluation

Johnson (2003) provides an articulate synopsis of the challenges associated with undertaking an objective and meaningful performance evaluatio n. Climatic variability, complex mesoscale phenomenon, stochastic variabilit y, and scientific limitations c ontribute to unique performance issues in each meteorological modeling exercise, thereby forcing modelers to take a subjective approach to model performance evaluation. Objec tive statistics that offer a quantitative model assessment exist, but implementation of the metr ics is subjective to a degree. For example, defining the area over which domain-averaged metr ics are calculated is a subjective decision, buffered only through guidelines. In general, me trics averaged over larg e modeling domains are avoided, as error cancellation dilutes relevance. Conversely, splitting th e modeling domain into small analysis sub-domains can render sample sizes unrepresentative. The logical approach falls well within the bounds of the extremes, leaving optimum sub-domain definitions open to interpretation.

Within the statistical degrees of freedom availabl e to the meteorological modeler, a subset of standard statistical measures has emerged, as de scribed below. These metrics are calculated on hourly and daily time frames for wind speed, wind direction, temperature, and humidity at the

surface. Below we list and describe the various statistical measures that will be employed. While no strict criteria establishing acceptable m odel performance exist, general guidelines have been developed by Emery et al. ( 2001) and are described later.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 17 Figure 6. Sample sounding comparison from RAOBPLOT for January 5, 2002 for DVN.

Mean Observation (M

o ) : calculated from all sites with valid data within a given analysis region and for a given time period (hourly or daily):

where O i j is the individual obs erved quantity at site i and time j , and the summations are over all sites ( I

) and over time periods ( J

).

Mean Prediction (M p ): calculated from simulation results that are interpolated to each observation point used to calculate the mean observation (hourly or daily): 

## ∑∑

= = = J

j I

i i
j o O IJ M

1 1 1



# ∑∑

= = = J

j I

i i
j p P IJ M

1 1 1December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 18 where P i j is the individual pred icted quantity at site i and time j . Note that mean observed and predicted winds are vector-avera ged (for east-west component u and north-south component v ), from which the mean wind speed and m ean resultant direction are derived.

Least Square Regression : performed to fit the prediction set to a linear model that describes the

observation set for all sites with valid data with in a given analysis region and for a given time period (daily or episode ). The y-intercept a and slope b of the resulting st raight line fit are calculated to describe the regres sed prediction for each observation:

The goal is for a 1:1 slope and a “0” y-inter cept (no net bias over the entire range of observations), and a regression coefficient of 1 (a perfect regression). The slope and intercept

facilitate the calculation of several erro r and skill statistics described below.

Bias Error (B) : calculated as the mean difference in pred iction-observation pairings with valid data within a given analysis region and fo r a given time period (hourly or daily):

Gross Error (E) : calculated as the mean absolute difference in predictio n-observation pairings with valid data within a given analysis regi on and for a given time period (hourly or daily):

Note that the bias and gross error for winds are calculated from the predicted-observed residuals in speed and direction (not from vector components u and v ). The direction error for a given prediction-observation pairing is limited to range from 0 to ± 180 °

.

Root Mean Square Error (RMSE) : calculated as the square root of the mean squared difference in prediction-observation pairings with valid data within a give n analysis region and for a given time period (hourly or daily):

The RMSE, as with the gross er ror, is a good overall measure of model performance. However, since large errors are weighted heavily (due to squari ng), large errors in a small subregion may produce a large RMSE even though the errors may be small and quite acceptable elsewhere.

Systematic Root Mean Square Error (RMSE S ) : calculated as the square root of the mean squared

difference in regressed prediction-observation pairings with in a given analysis region and for a given time period (hourly or daily): i
j i
j bO a P + = ˆ



# ( ) ∑∑

= = − = J

j I

i i
j i
j O P IJ B

1 1 1



# ∑∑

= = − = J

j I

i i
j i
j O P IJ E

1 1 1



# ( ) 2 / 1

1 1 2 1      

− = 

## ∑∑

= = J

j I

i i
j i
j O P IJ RMSEDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 19 where the regressed prediction is estimated fo r each observation from the least square fit

described above. The RMSE S estimates the model's linear (or sy stematic) error; hence, the better the regression between predictions and obser vations, the smaller the systematic error.

Unsystematic Root Mean Square Error (RMSE U ) : calculated as the square root of the mean squared difference in prediction-regressed predicti on pairings within a gi ven analysis region and for a given time period (hourly or daily):

The unsystematic difference is a measure of how much of the discrepancy between estimates and observations is due to random pro cesses or influences outside the legitimate range of the model.

A "good" model will provide low values of the RMSE, explaining most of the variation in the observations. The systematic error should a pproach zero and the unsystematic error should approach RMSE since:

RMSE 2 = RMSE S 2 + RMSE U 2

It is important that RMSE, RMSE S , and RMSE U are all analyzed. For example, if only RMSE is estimated (and it appears acceptable) it could cons ist largely of the systematic component. This error might be removed through improvements in the model inputs or use of more appropriate options, thereby reducing the error transferred to the photochemical mo del. On the other hand, if the RMSE consists largely of the unsystematic component, this indicate s that further error reduction may require model refinement (new algor ithms, higher resolution grids, etc.), or that the phenomena to be replicated can not be fully addressed by the model. It also provides error bars that may be used with the input s in subsequent sensitivity analyses.

Index of Agreement (IOA) : calculated following the approach of Willmont (1981). This metric

condenses all the differences between model esti mates and observations within a given analysis region and for a given time period (hourly and daily) into one statistical quan tity. It is the ratio

of the total RMSE to the sum of two differe nces – between each prediction and the observed mean, and each observation and the observed mean: 

# ( ) 2 / 1

1 1 2 ˆ 1      

− = 

## ∑∑

= = J

j I

i i
j i
j S O P IJ RMSE

   

 

   

 

− +

−

⋅

− = 

## ∑∑

= = J

j I

i o i
j o i
j M O M P RMSE

IJ IOA

1 1 2 1 

# ( ) 2 / 1

1 1 2 ˆ 1      

− = 

# ∑∑

= = J

j I

i i
j i
j U P P IJ RMSEDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 20 Viewed from another perspective, the index of agreement is a measure of the match between the departure of each prediction from the observed m ean and the departure of each observation from the observed mean. Thus, the correspondence betw een predicted and obser ved values across the domain at a given time may be qua ntified in a single metric and di splayed as a time series. The index of agreement has a theoretical range of 0 to 1, the latter sc ore suggesting perfect

agreement.

Most of statistics used to evaluate meteorologica l model performance are gi ven in absolute terms (e.g., wind speed error in m/s), rather than in relative terms (percent error) as is commonly shown for air quality assessments. The major reason for this is that a very different significance is associated with a given relative error for diffe rent meteorological parameters. For example, a 10% error for wind speed measured at 10 m/s is an absolute error of 1 m/s, a minor error. Yet a 10% error for temperature at 300 K is an absolute error of 30 K, a ridiculo usly large error. On the other hand, pollutant concentration errors of 10% at 1 ppb or 10 ppm carry practically the same significance.

The METSTAT Analysis Package

ENVIRON has developed a statistic al analysis software package to calculate and graphically present the statistics described above. The p ackage is comprised of a single Fortran program (METSTAT) to generate observati on-prediction pairings and to calculate the statistics, and a Microsoft Excel macro (METSTAT.XL S) that plots the results.

The Fortran program reads MM5 output prediction files and surface observational data files. The program reads either MM5 observation FDDA input files directly, or obs ervation data in an ASCII format. The program then spatially and temporally pairs MM5 predictions with observations for a user-defined time and space window. Since the surface layer in MM5 is usually rather thick relative to the heights at which the observational data were recorded, the METSTAT program includes a micro-meteorologi cal module that scales mid-layer predicted winds to 10 m heights, and mid-layer predicte d temperatures to 2 m heights, using common stability-dependent similarity relationships. Th e horizontal analysis range can be given for an entire MM5 grid, by an LCP coordi nate box, or as a list of speci fic site identifiers (such as WBAN or AIRS numbers), as labele d on the observational file. This allows for an evaluation at a single site, a subset of specific sites (e.g., those along a coastline that would be difficult to select by defining an LCP box) or over an entire regional domain.

The program then proceeds to calculate the stat istics described above for each hour and for each day of the time window. The following parameters are determined:

Wind Speed, Temperature, Humidity:

- Mean Observed

- Mean Predicted

- Bias

- Gross Error

- RMSE

- RMSE S - RMSE U - IOA Wind Direction:

- Mean Observed

- Mean Predicted

- Bias

- Gross ErrorDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 21 The RMSE and IOA have not been typically used to quantify error for wind direction, and thus are not calculated by the program.

Separate ASCII files containing the hourly a nd daily statistics are generated, formatted specifically to facilitate import into the Excel macr o. The Excel macro is used to plot the data. The hourly statistics are plotted as time seri es, to show the diurna l variation of model performance. The daily statisti cs are plotted as bar charts to show daily performance over an episode. The macro also allows the daily results from multiple MM5 runs to be plotted together to ease the inter-comparison of performance. Figure 7 shows an example output graphic for hourly wind performance.

Figure 7. Example of METSTAT graphics from the WRAP 2002 run.

Statistical Benchmarks

Emery et al. (2001) have derive d and proposed a set of daily performance “benchmarks” for typical meteorological model performance. Thes e standards were based upon the evaluation of a variety of about 30 MM5 and RAMS air quality applicati ons in the last few years, as reported by Tesche et al. (2001). The purpose of these benchm arks was not necessarily to give a passing or WRAP 36 km MM5 Run. July 21-30. SE.

Observed/Predicted Windspeed

0 1 2 3 4 5

7/21 7/22 7/23 7/24 7/25 7/26 7/27 7/28 7/29 7/3m/s ObsWndSpd PrdWndSpd

Bias Windspeed

-1 -0.5

0 0.5

1 1.5

7/21 7/22 7/23 7/24 7/25 7/26 7/27 7/28 7/29 7/3m/s BiasWndSpd

RMSE Windspeed

0 1 2 3 4

7/21 7/22 7/23 7/24 7/25 7/26 7/27 7/28 7/29 7/3m/s RMSEWndSpd RMSESWndSp RMSEUWndSp

IOA Windspeed

0 0.2 0.4 0.6 0.8

1

7/21 7/22 7/23 7/24 7/25 7/26 7/27 7/28 7/29 7/3 IOAWndSpdDecember 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 22 failing grade to any one particular meteorological model application, but rather to put its results into the proper context. For example, expectatio ns for modeling the U.S. west coast might not be as high as a simpler domain located over the Mi dwest. The key to the benchmarks is to understand how poor or good the results are relative to the universe of other model applications run for various areas of the U.S. Certainly, an important criticism of the EPA guidance statistics for acceptable photochemical performance is that they are relied upon much too heavily to establish an acceptable (to the EPA) model simulati on of a given area and episode. Often lost in

the statistical evaluation is the n eed to critically evaluate all as pects of the model via diagnostic and process-oriented approaches. The same must stressed for the meteorological performance

evaluation.

Emery et al. (2001) carefully considered the appropriateness and adequacy of the proposed benchmarks based upon the results of numerous meteorological simulations reported by Tesche et al (2001) and other MM5 si mulations performed in Texas by ENVIRON. The final daily proposed benchmarks are given in Table 7.

Table 7. Statistical benchmarks for evaluating meteorological model performance. Wind Speed Wind Direction Temperature Humidity

RMSE ≤ 2 m/s

Mean Bias ≤ ± 0.5 m/s ≤ ± 10 ° ≤ ± 0.5 K ≤ ± 1 g/kg

Gross Error ≤ 30 ° ≤ 2 K ≤ 2 g/kg Index of Agreement ≤ 0.6 ≤ 0.8 ≤ 0.6

Surface Statistical Analyses for WRAP

The statistical evaluation of MM5 surface fields will be performed using NCAR dataset ds472, which contains hourly observations of the comm only measured variables from airports in the

U.S. and Canada. Dataset variables include temperature, dewpoint, wind speed/direction and gusts, cloud cover fraction and cloud base for mul tiple cloud layers, visu al range, precipitation rates and snow cover, and a descri ptive weather code. The key data of interest will be extracted for the various sub-domains, and processed in to the appropriate formats for METSTAT.

As described above, some care must be taken in selecting an area for averaging. The problem with evaluating statistics is that the more data pa irings that are summarized in a given metric, the better the statistics generally look, and so calculating a si ngle set of st atistics for a very large area (e.g., the entire 36-km domain) w ould not yield significant insight in to performance. Therefore, a balance must be struck between taking a larg e enough area to create a representative sample and choosing such a large area as to smear out the signal of inte rest. Johnson (2003) suggests the use of the subdomains shown in Figure 8, and th ose subdomains will be adopted for use in this analysis.

For each subdomain, METSTAT will be used to calculate hourly and daily statistical measures, and these will be compared against benchmarks for acceptable model performance set forth by Emery et al. (2001) as summarized in Table 7. Potential reasons for poor statistical performance for certain variables, times, and sub-regions will be investigated and documented in the evaluation report.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 23 Figure 8 . METSTAT subdomains for the WRAP 2002 MM5 performance evaluation.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 24 REFERENCES

Arakawa, A. and J.-M. Chen, 1987: Closure assumptions in the cumulus parameterization problem. Short and Medium Range Numerical Prediction , Collection of Papers Presented at the WMO/IUGG NWP Symposium, Tokyo. T. Matsuno, Ed. Meteor. Soc. Japan. 107-131.

Dudhia, J., 1993: A non-hydrostatic version of the Penn State/NCAR Mesoscale Model:

validation tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev

. 121 , pp.1493-1513.

Emery, C.A., E. Tai, and G. Yarwood, 2001: Enhanced Meteorol ogical Modeling and Performance Evaluation for Two Texas Ozone Ep isodes. Prepared for the Texas Natural

Resource Conservation Commi ssion, by ENVIRON International Corporation, Novato,

CA.

Grell, G.A., J. Dudhia, and D.R. Stauffer, 1994: A description of the Fifth Generation Penn

State/NCAR Mesoscale Mode l (MM5). NCAR Technica l Note, NCAR TN-398-STR, 138 pp.

Johnson, M., 2003: Meteorologi cal modeling protocol: IDNR Annual MM5 Application. Prepared for the Iowa Department of Natural Resources Air Quality Bureau.

Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang, G. Tonnesen, 2004a: 2002 Annual MM5 36 KM Simulation to Support WRAP CM AQ Visibility Modeling for the Section

308 SIP/TIP: Preliminary Report on the Initial 2 002 36 km MM5 Simulation Performed during 2003. Prepared for the Western Regional Air Partnership.

Kemball-Cook, S., Y. Jia, C. Emery, R. Morris, Z. Wang, G. Tonnesen, 2004b: 2002 Annual MM5 36 KM Simulation to Support WRAP CM AQ 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.

Molinari, J., 1993. An overview of cumulus parameterization in mesoscale models. The Representation of Cumulus Convection in Numerical Models . K. Emanuel and D. Raymond, Ed. American Meteorological Society, p.155-159.

Olerud, D. and A. Sims, 2003: MM5 Sensitivity modeling in support of VISTAS (Visibility Improvement-State and Tribal Association) (DRAFT). Prepared for Mike Abraczinskas of the VISTAS Technical Analysis Wo rkgroup by Baron Advanced Meteorological Systems, LLC.

Pleim, J.E. and J.S. Chang, 1992: A non-loca l closure model for vertical mixing in the convective boundary layer. Atmos. Env

., 26A , pp. 965-981.

Reisner, J.R., R.M. Rasmussen, and R.T. Bruin tjes, 1998: Explicit forci ng of supercooled liquid

water in winter storms usi ng the MM5 mesoscale model. Quart. J. Roy. Met. Soc.

, 124B

, pp. 1071-1107.December 2004

H:\WRAP RMC\2002MM5\Modeling_Protocol\ WRAP_modeling_protocol_revsd.doc 25 Stauffer, D.R. and N.L. Seaman , 1990: Use of four-dimensional data assimilation in a limitedarea mesoscale model. Part I: experiments with synoptic data. Mon. Wea. Rev

. 118, pp.1250-1277.

Stauffer, D.R. and N.L. Seaman , 1991: Use of four-dimensional data assimilation in a limitedarea mesoscale model. Part II: effects of da ta assimilation within the planetary boundary layer. Mon. Wea. Rev

., 118, pp.734-754.

Tesche, T., 1994: Evaluation Procedures for Regional Emi ssions, Meteorological, and

Photochemical Models. Presented at the 86 th Annual Meeting fo the Air and Waste Management Association, 14-18 June, Denver, CO.

Tesche, T., D. McNally, C. Emery, and E. Tai, 2001. Evaluation of th e MM5 Modeling Over the Midwestern U.S. for Three 8-hour Oxidant Ep isodes. Prepared for the Kansas City Ozone Technical Workgroup, by Alpine Geophysics, LLC, Ft. Write, KY, and ENVIRON International Corp., Novato, CA.

Xiu, A. and J.E. Pleim, 2000: Development of a land surface model. Part I: application in a mesoscale meteorology model. J. App. Met.

, 40 , pp. 192-209.