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 2004
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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 together to improve visibility in 156 national parks and wilderness 

areas (referred to as “Class I” areas). The objective of the RHR is to achieve natural visibility 

conditions in these federally protected lands by the year 2064. The rule requires the states to 

develop and implement air quality protection 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 all western Class I areas. To meet this goal, WRAP is providing the necessary technical and 

policy tools to help states and tribes implement the RHR. 

The WRAP has formed the Regional Modeling Center (RMC), consisting of the University of 

California at Riverside (UCR), ENVIRON International Corporation, and the University of 

North Carolina (UNC). The RMC is performing the modeling and analysis necessary to develop 

the Section 309 SIPs/TIPs and begin the analysis for the Section 308 SIPs/TIPs. This project is 

focused on seasonal-annual regional visibility modeling 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 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 Class I areas. The Fifth Generation 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 fields 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, the WRAP modeling domain, the MM5 physical 

configuration, and model application approach is presented. The protocol then presents the plan 

for evaluating the performance of the model in replicating the evolution of observed winds, 

temperature, humidity, and boundary layer morphology to the extent that resources and data 

availability allow; this will serve as the primary approach to assess the reliability of the 

meteorological fields to adequately characterize the state of the atmosphere for input to CMAQ. 
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METEOROLOGICAL MODELING APPROACH 

The CMAQ model requires inputs of three-dimensional gridded wind, temperature, humidity, 

cloud/precipitation, and boundary layer parameters. The current version of CMAQ can only 

utilize output fields from the 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, state, regional, and national modeling efforts. MM5 has undergone 

extensive peer-review, with all of its components continually undergoing development and 

scrutiny by the modeling community. The MM5 modeling 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 quality 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) Modeling 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 create a continental-scale meteorological modeling 

database that can be used seamlessly with other 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 resources (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 (VISTAS), respectively. For the current 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, terrain-following (sigma-coordinate), prognostic 

meteorological model. It solves the full suite of non-hydrostatic prognostic primitive equations 

for the three-dimensional wind, temperature, water (in 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 interchange 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 radiative transfer. The model equations are solved 

horizontally on an Arakawa-B grid structure defined 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 coordinate, referred to as a “sigmap”. Typically, 30-50 vertical levels are used to resolve the troposphere 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 mesoscale 
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model with contributions from users worldwide. The pre- and post-processing tools facilitate the 

development of various model inputs, and the analysis of model output. 

Because MM5 is a limited-area model, it requires lateral boundary conditions that define the 

space- and time-varying conditions at the periphery 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 variety of routine analysis systems, from several global analysis 

products to higher resolution (time and space) forecast initialization fields 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 four 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 model solution toward objective analysis fields 

and/or individual observations. This has been shown to significantly 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 configuration for the 2002 annual simulation builds on the 

prior IDNR 2002 application (Johnson, 2003), which was in turn set according to the optimal 

MM5 physics options that resulted from an in-depth sensitivity project carried out by IDNR and 

the Lake Michigan Air Directors Consortium (LADCo). 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 sensitivity tests were made to identify an MM5 

configuration that would address 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, and 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 from the 36-km grid is transferred to the 12-km 

domain through boundary conditions during the simulation, but there is no feedback of the 12-

km fields up-scale to the 36-km domain. The original 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 completely 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 significant impact on model performance for regional 
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applications with coarse grid spacing. However, this has not necessarily been the case for small 

mesoscale grids (~100 km scale) with high resolution (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 that are developed independently in 1-way nested mode. Thus, 

our approach follows this suggestion. 

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

33°N and 45°N, and the central latitude 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 domain 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 grid point spacing. Table 1 shows the relationship 

between the MM5 and CMAQ regional grids. The 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 regional 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 are estimated according to standard atmosphere assumptions used 

in MM5 (surface pressure of 1000 mb, model top 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 application, and then describe its 

performance issues and our subsequent modifications to the original configuration. As 

mentioned above, the 2002 annual WRAP MM5 application was initially configured in most 

aspects according to the optimum arrangement identified by the IDNR sensitivity simulations 

(Johnson, 2003). The physical options selected for the initial WRAP application 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 scavenging in CMAQ. This is because the simple ice 
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Figure 1. Spatial coverage of the RPO National Grid with 36 km grid point spacing. 
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Figure 2. Coverage of the WRAP western regional domain with 12 km grid point spacing. 
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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 Asymmetric 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 model 
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approach treats all condensed water forms as a single liquid variable, which, when passed to 

CMAQ, overstates the quantity of liquid cloud and 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. Another 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 improved precipitation performance in 

the desert southwest, along with commensurate 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 Chang 1992), as these two are directly coupled. 

The Pleim-Xiu (P-X) parameterization is a predictive/interactive soil temperature and moisture 

budget model that responds to atmospheric processes that affect the thermodynamics of the 

surface (e.g., rainfall) while in turn dictating the surface fluxes of momentum, heat and moisture 

into the boundary layer to further affect atmospheric processes. This allows the P-X approach to 

maintain a historical “memory” of the soil conditions 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 continuous soil moisture 

option from the application improved the temperature bias problem. This finding was revealed 

to MM5 users after the IDNR had completed their 2002 annual simulations using the INTERPPX 

option. Accordingly, the INTERPPX option will not be used for the 2002 WRAP application. 

The P-X scheme is instead run for each simulation segment with the initial soil moisture 

interpolated from the input objective analyses (described further below). The vegetation is not 

be allowed to grow during the simulation, and soil moisture nudging is turned off for the PleimXiu land surface module. 

MM5 is configured to utilize its FDDA capabilities 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) nudging is performed at 3-hourly intervals both for 

the two-dimensional surface fields and for the three-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 forcings 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. Two-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 application are shown in Table 4. Use of FDDA to 

nudge toward observational data at individual measurement sites (i.e., “station” or “observation” 

nudging) nudging is not performed. Usually, this option is best suited for smaller highresolution grids in which data from a very dense network of measurement sites are available 

(such as an intensive field study). 
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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-Cook et al. 2004a). The initial 2002 36-km 

WRAP simulation 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. Specifically, in the 

western U.S., the amplitude of the diurnal surface temperature cycle was persistently 

underestimated during the summer, especially in the 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 diurnal temperature cycle may be linked to the 

over prediction of summer precipitation by MM5, especially in the southwest. This can have 

serious repercussions for CMAQ modeling 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 with MM5 to converge upon a set of physics 

options that would ameliorate the above MM5 performance problems in the west. These tests 

are described in the report: “2002 Annual MM5 36 KM 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” 

(Kemball-Cook et al. 2004b). The more optimal model configuration developed through the 

tests is shown in Table 5. 

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

Analysis 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 physics 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 temperature cycle in the desert southwest; 

• Improvements in the index of agreement for 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.; and 
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• Runs made with these physics 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 represents 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 concern was the cumulus parameterization. At the 

12 km scale, the problem of cumulus parameterization 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 (1993) suggests 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 CMAQ requires us to be pragmatic and configure 

MM5 in a physically reasonable way, which produces 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 rainfall, 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 studies are documented in 

the report: “2002 Annual MM5 36 KM 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” (KemballCook et al. 2004b). The other physics options for the 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.

Analysis 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 necessary to prepare topographic, vegetative, 

initial condition, boundary condition, and FDDA nudging input files. These tools are 

summarized below. 
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 Program Description

TERRAIN Defines the MM5 horizontal domain specifications, 

including nested grid relationships; processes raw 

topographic, vegetative, and soil type data to all MM5 

grids. 

 REGRID (2 sub-programs) 

PREGRID Extracts raw pressure-level meteorological objective 

analyses from various sources and writes them to 

intermediate file formats for further processing. 

REGRIDDER Processes/blends PREGRID output to the MM5 horizontal 

grids for a user-defined period; data remain on pressure 

levels. 

RAWINS/LITTLE_R Blends various raw meteorological observational data into 

the analyses developed from REGRID to improve the 

characterization of coarse-resolution 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 pressure 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-minute (latitude/longitude) resolution are used to define terrain 

elevations on the 36-km grid; higher 5-minute resolution 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 additional 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 point spacing. The EDAS analyses are developed 

from a wide variety of observational sources, including standard surface and upper air 

measurements, profiler networks, radar- and satellite-derived measurements, and ship and 

aircraft reports. The wide array of data sources, coupled with the high time- and spatial 

resolution provided by EDAS, result in an analysis product that far exceeds the level of detail 

found in traditional global-scale analyses. The EDAS analysis files with the extension “.tm12” 

are not used since they are the “cold start” files. 
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The REGRID system of programs is used to process the EDAS analyses for use by MM5 as 

initial/boundary conditions, and for three-dimensional 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 levels at 950, 925, 900, 800, 750, 650, 600, 550, 450, and 350 

mb. Sea surface temperatures (SSTs) are approximated by Eta skin temperatures, and the SSTs 

are allowed to vary over the course 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 the MM5 continental-scale domain, the use of 

LITTLE_R or RAWINS to improve the EDAS fields for use in MM5 is considered to be 

redundant. However, these programs also construct 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 FETCH 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 may 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 photochemical/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. 
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EVALUATION OF THE 2002 ANNUAL RUN 

The goal of the evaluation is to determine whether 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 year will be severely hampered and the predicted 

impacts from future year growth and controls will be highly questionable. To provide a 

reasonable meteorological characterization to the photochemical/visibility 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 forcings for mesoscale circulations; 

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

patterns; 

• Mesoscale circulations such as sea breezes and mountain/drainage circulations; 

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

For visibility applications, the moisture and condensate fields are particularly important as they 

significantly impact PM chemical formation, removal, and light scattering efficiency. In 

addition, cloud and precipitation fields are a good measure of the integrated performance of the 

model since these are model-derived quantities and not nudged to observations. Because of the 

model’s coarse resolution of 36 and 12 km, the model 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 meteorological simulations. However, sound comprehensive 

philosophies exist, such as discussed by Tesche et al. (1994 and 2001), who classify the 

evaluation into two components: an operational evaluation and a scientific evaluation. The 

scientific evaluation requires rigorous examinations of the model formulation and algorithm 

development, which is usually beyond the scope of most air quality modeling projects. Previous 

peer-reviewed documentation of MM5 formulation, testing, and evaluation must provide the 

basis for the scientific evaluation. Accordingly, our evaluation will be limited to operational 

testing of the model, which entails an assessment of the model's ability to correctly estimate 

surface and boundary layer wind, temperature, and moisture largely independent of whether the 

actual process descriptions in the model are accurate. The operational evaluation essentially tests 

whether the predicted meteorological fields are reasonable, consistent, 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 they are the fortuitous 

product of compensating errors; thus a “successful” 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 surface 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 model performance for winds, temperatures, humidity, and the 

placement, intensity, and evolution of key weather phenomena. A specific set of statistics has 

been identified for use in establishing benchmarks for acceptable model performance (Emery et 
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H:\WRAP RMC\2002MM5\Modeling_Protocol\WRAP_modeling_protocol_revsd.doc 14

al., 2001), with the idea that these benchmarks, similar to current EPA guidance criteria for air 

quality model performance, allow for a consistent 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.com, 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 evaluations will be to establish a first-order 

acceptance/rejection of the simulation in adequately replicating the gross weather phenomena in 

the region of interest. Thus, this approach screens 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 prepared for the surface and 500 mb levels. These 

levels are chosen to facilitate the comparison to common analyses from the NWS. Specific 

parameters that will be plotted include wind vectors, temperature, sea level pressure (surface 

maps), geopotential height (aloft maps), and precipitation (to compare with radar mosaics). 

Cloud predictions will be subjectively compared 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 observing 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 measurements 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 individual sites and times to predicted soundings. 

This allows for a site-by-site comparison of wind, 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, nocturnal inversion heights) from observational 

soundings and MM5 predictions. The observed soundings 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 

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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 

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Figure 5. Example of an infrared satellite image 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 evaluation. Climatic variability, complex mesoscale 

phenomenon, stochastic variability, and scientific limitations contribute to unique performance 

issues in each meteorological modeling exercise, thereby forcing modelers to take a subjective 

approach to model performance evaluation. Objective statistics that offer a quantitative model 

assessment exist, but implementation of the metrics is subjective to a degree. For example, 

defining the area over which domain-averaged metrics are calculated is a subjective decision, 

buffered only through guidelines. In general, metrics averaged over large modeling domains are 

avoided, as error cancellation dilutes relevance. Conversely, splitting the 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 available to the meteorological modeler, a subset of 

standard statistical measures has emerged, as described 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 model performance exist, general guidelines have 

been developed by Emery et al. (2001) and are described later. 
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Figure 6. Sample sounding comparison from RAOBPLOT for January 5, 2002 for DVN. 

Mean Observation (Mo): calculated from all sites with valid data within a given analysis region 

and for a given time period (hourly or daily): 

where Oi

j is the individual observed quantity at site i and time j, and the summations are over all 

sites (I) and over time periods (J). 

Mean Prediction (Mp): 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

o Oj IJ

M

1 1

1

∑∑= =

=

J

j

I

i

i

p Pj IJ

M

1 1

1
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H:\WRAP RMC\2002MM5\Modeling_Protocol\WRAP_modeling_protocol_revsd.doc 18

where Pi

j is the individual predicted quantity at site i and time j. Note that mean observed and 

predicted winds are vector-averaged (for east-west component u and north-south component v), 

from which the mean wind speed and mean 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 within a given analysis region and for a given time 

period (daily or episode). The y-intercept a and slope b of the resulting straight line fit are 

calculated to describe the regressed prediction for each observation: 

The goal is for a 1:1 slope and a “0” y-intercept (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 error and skill statistics described below. 

Bias Error (B): calculated as the mean difference in prediction-observation pairings with valid 

data within a given analysis region and for a given time period (hourly or daily): 

Gross Error (E): calculated as the mean absolute difference in prediction-observation pairings 

with valid data within a given analysis region 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 given analysis region and for a given 

time period (hourly or daily): 

The RMSE, as with the gross error, is a good overall measure of model performance. However, 

since large errors are weighted heavily (due to squaring), 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 (RMSES): calculated as the square root of the mean squared 

difference in regressed prediction-observation pairings within a given analysis region and for a 

given time period (hourly or daily): 

i

j

i Pˆ

j = a + bO

∑∑( ) = =

= −

J

j

I

i

i

j

i Pj O

IJ

B

1 1

1

∑∑= =

= −

J

j

I

i

i

j

i Pj O

IJ

E

1 1

1

( )

1/ 2

1 1

1 2





 



 = ∑∑ − = =

J

j

I

i

i

j

i Pj O

IJ

RMSE
December 2004 

H:\WRAP RMC\2002MM5\Modeling_Protocol\WRAP_modeling_protocol_revsd.doc 19

where the regressed prediction is estimated for each observation from the least square fit 

described above. The RMSES estimates the model's linear (or systematic) error; hence, the better 

the regression between predictions and observations, the smaller the systematic error. 

Unsystematic Root Mean Square Error (RMSEU): calculated as the square root of the mean 

squared difference in prediction-regressed prediction pairings within a given 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 processes 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 approach zero and the unsystematic error should 

approach RMSE since: 

RMSE2

 = RMSES

2

 + RMSEU

2

It is important that RMSE, RMSES, and RMSEU are all analyzed. For example, if only RMSE is 

estimated (and it appears acceptable) it could consist 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 model. On the other hand, if 

the RMSE consists largely of the unsystematic component, this indicates that further error 

reduction may require model refinement (new algorithms, higher resolution grids, etc.), or that 

the phenomena to be replicated cannot be fully addressed by the model. It also provides error 

bars that may be used with the inputs in subsequent sensitivity analyses. 

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

condenses all the differences between model estimates and observations within a given analysis 

region and for a given time period (hourly and daily) into one statistical quantity. It is the ratio 

of the total RMSE to the sum of two differences – between each prediction and the observed 

mean, and each observation and the observed mean: 

( )

1 / 2

1 1

2 ˆ 1 



 



 = ∑∑ − = =

J

j

I

i

i

j

i

S Pj O

IJ

RMSE

























− + −

⋅ = −

∑∑= =

J

j

I

i

o

i

o j

i Pj M O M

IJ RMSE IOA

1 1

2

1

( )

1 / 2

1 1

2 ˆ 1 



 



 = ∑∑ − = =

J

j

I

i

i

j

i

U Pj P

IJ

RMSE
December 2004 

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Viewed from another perspective, the index of agreement is a measure of the match between the 

departure of each prediction from the observed mean and the departure of each observation from 

the observed mean. Thus, the correspondence between predicted and observed values across the 

domain at a given time may be quantified in a single metric and displayed as a time series. The 

index of agreement has a theoretical range of 0 to 1, the latter score suggesting perfect 

agreement. 

Most of statistics used to evaluate meteorological model performance are given 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 different 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 ridiculously 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 statistical analysis software package to calculate and graphically 

present the statistics described above. The package is comprised of a single Fortran program 

(METSTAT) to generate observation-prediction pairings and to calculate the statistics, and a 

Microsoft Excel macro (METSTAT.XLS) 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 observation 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-meteorological module that scales mid-layer predicted 

winds to 10 m heights, and mid-layer predicted temperatures to 2 m heights, using common 

stability-dependent similarity relationships. The horizontal analysis range can be given for an 

entire MM5 grid, by an LCP coordinate box, or as a list of specific site identifiers (such as 

WBAN or AIRS numbers), as labeled 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 statistics 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 

- RMSES

- RMSEU

- IOA 

Wind Direction: 

- Mean Observed 

- Mean Predicted 

- Bias 

- Gross Error 
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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 and daily statistics are generated, formatted 

specifically to facilitate import into the Excel macro. The Excel macro is used to plot the data. 

The hourly statistics are plotted as time series, to show the diurnal variation of model 

performance. The daily statistics 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 derived and proposed a set of daily performance “benchmarks” for 

typical meteorological model performance. These standards were based upon the evaluation of a 

variety of about 30 MM5 and RAMS air quality applications in the last few years, as reported by 

Tesche et al. (2001). The purpose of these benchmarks 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 7/3

m/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 7/3

m/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 7/3

m/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

IOAWndSpd 
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failing grade to any one particular meteorological model application, but rather to put its results 

into the proper context. For example, expectations for modeling the U.S. west coast might not be 

as high as a simpler domain located over the Midwest. 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 simulation of a given area and episode. Often lost in 

the statistical evaluation is the need to critically evaluate all aspects 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 simulations 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 commonly 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 multiple cloud layers, visual range, precipitation 

rates and snow cover, and a descriptive weather code. The key data of interest will be extracted 

for the various sub-domains, and processed into 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 pairings that are summarized in a given metric, the 

better the statistics generally look, and so calculating a single set of statistics for a very large area 

(e.g., the entire 36-km domain) would not yield significant insight into performance. Therefore, 

a balance must be struck between taking a large enough area to create a representative sample 

and choosing such a large area as to smear out the signal of interest. Johnson (2003) suggests the 

use of the subdomains shown in Figure 8, and those 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 

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Figure 8. METSTAT subdomains for the WRAP 2002 MM5 performance evaluation. 
December 2004 

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