September 29, 2000

METEOROLOGICAL MODELING OF 1996

FOR THE UNITED STATES WITH MM5 

EPA TASK ORDER NUMBER CAA689805 

Prepared for 

Norman Possiel, Client Representative 

U.S. EPA/OAQPS 

Prepared by 

Donald Olerud (MCNC) 

Kiran Alapaty (MCNC) 

Neil Wheeler (Sonoma Technology, Inc.) 

MCNC–Environmental Programs 

P.O. Box 12889 

Research Triangle Park, NC 27709-2889 

Meteorological Modeling of 1996 for the United States with MM5 

Contents 

1. Introduction ........................................................................................................................... 1

2. Model Description ................................................................................................................. 3

3. Model Configuration............................................................................................................. 6

3.1 Domains.......................................................................................................................... 6 

3.2 Physical Options............................................................................................................. 8 

4. Model Inputs and Preprocessing........................................................................................ 11

4.1 Preprocessing System................................................................................................... 11 

4.1.1 Terrain............................................................................................................. 11 

4.1.2 Datagrid........................................................................................................... 12 

4.1.3 Rawins............................................................................................................. 12 

4.1.4 Interp............................................................................................................... 12 

4.2 Lateral Boundary Conditions....................................................................................... 12 

4.3 Data Assimilation......................................................................................................... 13 

5. Model Performance Evaluation Methodology.................................................................. 14

5.1 Data .............................................................................................................................. 14 

5.2 Procedures.................................................................................................................... 14 

6. Results................................................................................................................................... 17

6.1 General Observations................................................................................................... 17 

6.2 Spatial Analyses........................................................................................................... 17 

6.2.1 Upper-Level Winds......................................................................................... 17 

6.2.2 Surface Winds................................................................................................. 19 

6.2.3 Surface-Level Temperatures........................................................................... 20 

6.2.4 Surface-Level Water Vapor Mixing Ratio...................................................... 24 

6.2.5 Mixed-Layer Depths....................................................................................... 26 

6.2.6 Rainfall............................................................................................................ 27 

6.3 Point-specific Analyses................................................................................................ 29 

6.3.1 Analyses of Meteorological Variables at Surface Observational Data 

Sites................................................................................................................. 29 

6.3.2 Statistical Analyses of Meteorological Variables........................................... 31 

7. Closing Observations .......................................................................................................... 39

References.................................................................................................................................... 40

Appendices................................................................................................................................... 41

Appendix A – Model Configuration File .............................................................................. 41 

Appendix B – Supplemental Plots......................................................................................... 42 

Appendix C – Supplemental Color Graphic Plots................................................................. 57 

iii 

Meteorological Modeling of 1996 for the United States with MM5

1. INTRODUCTION 

The Air Quality Modeling Group (AQMG) of the U.S. Environmental Protection Agency 

(EPA) is responsible for providing air quality modeling assessments and databases to support the 

development of guidance, policies, and regulations on ozone, particulate matter, toxics, and other 

pollutants. As part of this mission, AQMG is performing a nationwide assessment of the 

beneficial effects of proposed emissions reductions from cars and trucks on ozone 

concentrations, particulate concentrations, visibility, and acid deposition. This assessment 

requires developing meteorological and other related inputs to air quality simulation models, 

performing simulations using a regional ozone model for the western United States, and 

performing annual simulations of particulate matter. 

The work documented in this report is referred to as “Task 2” in the original task order. 

The purpose of this task was to run the Pennsylvania State University (PSU)/National Center for 

Atmospheric Research (NCAR) Mesoscale Model version 5, or MM51, 2, at 108-km and 36-km

resolutions for the entirety of 1996 over the entire continental United States. This provides data 

necessary to drive air pollution models such as the variable-grid Urban Airshed Model 

(UAM-V3

), the Comprehensive Air Quality Model with extensions (CAMx

4

), the Regulatory 

Modeling System for Aerosols and Deposition (REMSAD5

), the Community Multi-scale Air 

Quality (CMAQ6

) modeling system, or the Multiscale Air Quality Simulation Platform 

(MAQSIP7

). This report documents the model inputs, model configuration, output formats, and 

results of these MM5 simulations. 

The work to be performed under Task 2 included the following activities: 

1. Developing and quality assuring all inputs to the required MM5 runs for 1996; 

2. Running MM5 and quality assuring the results; 

3. Delivering the outputs in electronic form; and 

4. Providing documentation of the inputs, assumptions, and results. 

These tasks were further broken down into eight subtasks: 

1. Acquire Data—Purchase data from the National Center for Atmospheric Research 

(NCAR) and the National Climatic Data Center (NCDC) suitable for initializing, 

providing boundary conditions, performing four-dimensional data assimilation 

(FDDA), and evaluating MM5 simulations for the year 1996. 

2. Set up Domains—Working with the Client Representative, finalize the 

meteorological modeling domains and set up MM5 to run on them. 

3. Configure Model—Working with the Client Representative, finalize the 

configuration of MM5, including selection of boundary layer scheme and other 

physics options. Make modifications to MM5 to output additional variables if 

needed. 

4. Preprocessing—Configure and run MM5 preprocessors. 

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Meteorological Modeling of 1996 for the United States with MM5

5. Quality Assure Inputs—Review preprocessor logs and output files as needed to 

confirm that the MM5 inputs are reasonable. 

6. Run MM5—Prepare scripts to run MM5 and archive results. Provide staff and 

dedicated computing resources to run MM5. Monitor MM5 execution and take 

corrective actions should problems arise. 

7. Quality Assure/Evaluate MM5 Output—Review MM5 simulation logs. Visualize 

MM5 output and review visualizations. Calculate model performance metrics and 

generate graphics depicting model performance. Metrics will include bias, error, 

correlation coefficient, and index of agreement for wind speed and direction, 

temperature, cloud cover, and moisture. Graphical analyses will include 

comparisons of observed and modeled parameters as time-series at specific 

locations and contour plots of modeled values with observed values overplotted. 

[Note: Precipitation plots have been substituted for cloud cover, since total cloud 

cover is not an MM5 output variable. Given the amount of data involved, the time 

series requirement was deemed not to be applicable for this portion of the task. ] 

8. Deliver outputs—Copy MM5 output files to magnetic tapes in a suitable format 

and ship the tapes to a location in the United States, to be specified by the Client 

Representative. 

9. Documentation—Prepare a report describing the inputs, configuration, and results 

of this task. The report will be provided in both hard and soft copy. 

Three appendices are included in this report. Appendix A contains the model 

configuration file. Appendix B contains supplemental analysis/statistical plots. Appendix C 

describes the supplemental color graphic files. 

2 
Meteorological Modeling of 1996 for the United States with MM5

2. MODEL DESCRIPTION 

MM5 is one of the leading three-dimensional prognostic meteorological models available 

for air quality studies. It uses an efficient split semi-implicit temporal integration scheme and has 

a nested-grid capability that can use up to ten different domains of arbitrary horizontal 

resolution. This allows MM5 to simulate local details with high resolution (as fine as ~1 km), 

while accounting for influences from great distances, with horizontal resolutions ranging to 

about 200 km. 

MM5 uses a terrain-following nondimensionalized pressure, or sigma-P, vertical 

coordinate similar to that used in many operational and research models. In the nonhydrostatic 

MM5, the sigma levels are defined according to an initial hydrostatically balanced reference state

so that these levels are also time-invariant. The vertical sigma-coordinate is determined in terms 

of pressure: 

σp = (p – pt)/(ps – pt), 

where ps and pt are the surface and top pressures of the model, pt being a constant. The meteorological fields can also be used in other air quality models with different coordinate systems by 

performing a vertical interpolation followed by a mass-consistency reconciliation step. 

The model contains several types of planetary boundary layer (PBL) parameterizations 

suitable for air quality applications, which represent subgrid-scale turbulent fluxes of heat, 

moisture, and momentum. A modified Blackadar PBL8

 scheme uses a first-order eddy diffusivity 

formulation for stable and neutral environments and a nonlocal-closure scheme for unstable 

regimes. The Gayno-Seaman PBL9

 scheme uses a prognostic equation for the second-order 

turbulent kinetic energy, while diagnosing the other key boundary layer terms. This is referred to 

as a 1.5-order PBL, or level-2.5, scheme

10. While not specifically developed for air quality 

applications, the model contains the Hong and Pan Mid-Range Forecast (MRF11) scheme, which 

has the advantage of calculating the vertical exchange coefficient, Kv, that is used as input to 

several common air quality models. 

Initial and lateral boundary conditions are specified for real-data cases from mesoscale 

3-D analyses performed at 12-h intervals on the outermost grid mesh selected by the user. 

Surface fields are analyzed at 3-h intervals. A Cressman-based technique is used to analyze 

standard surface and radiosonde observations, using the European Center for Medium-range 

Weather Forecasting (ECMWF) Tropical Ocean and Global Atmosphere (TOGA) analysis as a 

first guess. The lateral boundary data are introduced using a relaxation technique applied in the 

outermost five rows and columns of the coarsest grid domain. 

For most traditional (1-h standard) high-ozone episodes, precipitation is not the dominant 

factor. On the other hand, precipitation events may have a greater impact on 8-h average ozone 

episodes. The MM5 contains five convective parameterization schemes (Kuo, Betts-Miller, 

Fritsch-Chappell, Kain-Fritsch, and Grell). It also has an explicit resolved-scale precipitation 

scheme that solves prognostic equations for cloud water/ice (qc) and larger liquid or frozen 

hydrometeors (qr). In addition, the model contains a short- and long-wave radiation 

parameterization12. 

3 
Meteorological Modeling of 1996 for the United States with MM5

The horizontal grid has an Arakawa-Lamb B-staggering of the velocity variables with 

respect to the scalars (Figure 2-1). It can be seen that the scalars (T, q, etc.) are defined at the 

center of the grid square, while the eastward (u) and northward (v) velocity components are 

collocated at the corners. The center points of the grid squares are referred to as cross points, and 

the corner points are dot points. Hence, horizontal velocity is defined at dot points, for example, 

and when data are input to the model the preprocessors do the necessary interpolations to assure 

consistency with the grid. 

Figure 2-1. Schematic representation showing the horizontal Arakawa B-grid staggering 

of the dot (O) and cross (x) grid points. The smaller inner box is a representative mesh 

staggering for a 3:1 ratio of coarse-grid distance to fine-grid distance. (Grell et al., 1994) 

The nonhydrostatic model coordinate uses a reference-state pressure to define the 

coordinate rather than the actual pressure that is used in the hydrostatic model. It can be seen 

from Figure 2-2 that σ is zero at the top and one at the surface, and each model level is defined 

by a value of σ. The model vertical resolution is defined by a list of values between zero and one 

that do not necessarily have to be evenly spaced. Commonly the resolution in the boundary layer 

is much finer than above, and the number of levels may vary from 10 to 40, although in principle 

there is no limit. 

4 
Meteorological Modeling of 1996 for the United States with MM5

Figure 2-2. Schematic representation of the vertical structure of the model. The example 

is for 15 vertical layers. Dashed lines denote half-sigma levels, solid lines denote fullsigma levels. (Grell et al., 1994) 

5 
Meteorological Modeling of 1996 for the United States with MM5

3. MODEL CONFIGURATION 

For this project we used MM5 Version 2.12 with modifications to allow the output of the 

vertical exchange coefficient (Kv) for use in the UAM-V air quality model. We also modified 

MM5 to use the Meteorology-Coupler (MCPL) (http://envpro.ncsc.org/projects/ppar/mcpl.html) 

module to output selected variables in the Input/Output Applications Programming Interface (I/O 

API) (http://envpro.ncsc.org/products/ioapi/index.html) format. This allowed for rapid review of 

model results using MCNC’s Package for Analysis and Visualization of Environmental data 

(PAVE) (http://envpro.ncsc.org/EDSS/pave_doc/Pave.html). The model was run for the period 

from 0000 UTC January 1 through 1200 UTC December 30, 1996. 

3.1 DOMAINS 

The domain for MM5 covers the entire United States at 108-km and 36-km resolutions, 

and the western half of the United States at 12-km resolution (Figure 3-1). The horizontal grid 

sizes are shown in Table 3-1. 

Figure 3-1. MM5 domains for the 1996 simulations. 

6 
Meteorological Modeling of 1996 for the United States with MM5

Table 3-1. Horizontal structure of the MM5 modeling system 

Domain Resolution E-W Cells N-S Cells 

E-W Starting 

Cell 

N-S Starting 

Cell 

D01 108-km 66 88 1 1

D02 36-km 114 168 15 18

D03 12-km 189 165 35 21

The vertical domain extends from the surface to the100-mb pressure surface in 23 layers. 

The first layer has a thickness of approximately 38 m. The vertical structure is described in 

further detail in Table 3-2, where the heights are calculated using standard atmospheric 

conditions.

Table 3-2. Vertical structure of the MM5 modeling system

Level Sigma Height (m) Pressure (mb) Thickness (m) 

 0 1.000 0.0 1000.0 0.0 

 1 0.995 38.0 995.5 38.0 

 2 0.988 91.5 989.2 53.5 

 3 0.980 152.9 982.0 61.4 

 4 0.970 230.3 973.0 77.3 

 5 0.956 339.5 960.4 109.2 

 6 0.938 481.6 944.2 142.1 

 7 0.916 658.1 924.4 176.4 

 8 0.893 845.8 903.7 187.8 

 9 0.868 1053.9 881.2 208.1 

 10 0.839 1300.7 855.1 246.8 

 11 0.808 1571.4 827.2 270.7 

 12 0.777 1849.6 799.3 278.2 

 13 0.744 2154.5 769.6 304.9 

 14 0.702 2556.6 731.8 402.1 

 15 0.648 3099.0 683.2 542.4 

 16 0.582 3805.8 623.8 706.8 

 17 0.500 4763.7 550.0 957.9 

 18 0.400 6082.5 460.0 1318.8 

 19 0.300 7627.9 370.0 1545.5 

 20 0.200 9510.5 280.0 1882.6 

 21 0.120 11465.1 208.0 1954.6 

 22 0.052 13750.2 146.0 2285.1 

 23 0.000 16262.4 100.0 2512.1 

7 
Meteorological Modeling of 1996 for the United States with MM5

3.2 PHYSICAL OPTIONS 

The physical options selected for this configuration of MM5 include the following: 

1. One-way nested grids 

2. Nonhydrostatic dynamics 

3. Four-dimensional data assimilation (FDDA): 

• Analysis nudging of wind, temperature, and mixing ratios 

• Nudging coefficients range from 1.0 × 10

-5

 s

-1

 to 3.0 × 10

-4

 s

-1

4. Explicit moisture treatment: 

• 3-D predictions of cloud and precipitation fields 

• Simple ice microphysics 

• Cloud effects on surface radiation 

• Moist vertical diffusion in clouds 

• Normal evaporative cooling 

5. Boundary conditions: 

• Time and inflow/outflow relaxation 

6. Cumulus cloud parameterization schemes: 

• Anthes-Kuo (108-km grid) 

• Kain-Fritsch (36-km grid) 

7. No shallow convection 

8. Full 3-dimensional Coriolis force 

9. Drag coefficients vary with stability 

10. Vertical mixing of momentum in mixed layer 

11. Virtual temperature effects 

12. PBL process parameterization: MRF scheme

13. Surface layer parameterization:

• Fluxes of momentum, sensible and latent heat 

• Ground temperature prediction using energy balance equation 

• 24 land use categories 

14. Atmospheric radiation schemes: 

• Simple cooling 

• Long- and short-wave radiation scheme 

15. Sea ice treatment: 

• Forced Great Lakes/Hudson Bay to permanent ice under very cold conditions 

• 36-km treatment keyed by observations of sea ice over the Great Lakes 

16. Snow cover: 

• Assumed no snow cover for July and August 

• National Center for Environmental Prediction (NCEP) snow cover for January to 

June, and for September to December 

A few comments on the above list: The physical parameters and options listed above were 

chosen for their suitability for U.S. modeling during all four seasons. However, it is important to 

point out that the parameterization scheme used to represent atmospheric turbulent mixing in this 

study (MRF) was a local-closure scheme. In this simple scheme the coefficients of vertical eddy 

diffusivity are estimated (not calculated) using formulations suggested by Hong and Pan11. The 

8 
Meteorological Modeling of 1996 for the United States with MM5

depth of the boundary layer is a function of the bulk Richardson number. The primary benefits of 

using the MRF scheme are its speed of execution, its robustness, and its ability to readily output 

Kv’s. A concern is that its simplicity may not allow it to perform adequately. Also, the amount of 

longwave cooling of the land surface will not be restricted, a situation that may lead to strong 

negative turbulent sensible heat fluxes over some regions at night, thereby excessively cooling 

the surface. 

In test simulations we noticed spikes in the turbulent sensible heat fluxes as a result of 

oscillations in the ground/skin temperature calculations. Sometimes this error actually caused the 

model to fail to execute. We identified the source of error responsible for these oscillations and 

implemented a process time-step methodology (mitering) for time integration of ground/skin 

temperature calculations. This new procedure was found to be successful in eliminating spurious 

oscillations in boundary layer fields. Below is the code before modification, followed by the 

modified code. 

 IF(ISOIL.EQ.0)THEN SLAB.88 

 DTHGDT(I)=(RNET(I)-QS(I))/CAPG(I,J)-HM(I) SLAB.89 

 ELSEIF(ISLDIM.EQ.1.AND.ISOIL.EQ.1)THEN 24JUN97.355 

C DETERMINE SOIL TIMESTEPS PER DELTSM FOR I-SLICE, NSOIL SLAB.91 

 NSOILI=1+IFIX(4*DIFSL/(ZS(2)-ZS(1))*DELTSM/DZS(1)) 25MAY98.774 

 NSOIL=MAX0(NSOIL,NSOILI) SLAB.93 

C...Portions Copyright 1999, MCNC--North Carolina Supercomputing Center 

C... Kiran Alapaty has modified slab.F for use with MRF scheme. 

C 

 IF(ISOIL.EQ.0)THEN SLAB.88 

 DTHGDT(I)=(RNET(I)-QS(I))/CAPG(I,J)-HM(I) SLAB.89 

 ELSEIF(ISLDIM.EQ.1.AND.ISOIL.EQ.1)THEN 24JUN97.355 

C DETERMINE SOIL TIMESTEPS PER DELTSM FOR I-SLICE, NSOIL SLAB.91 

 NSOILI=1+IFIX(4*DIFSL/(ZS(2)-ZS(1))*DELTSM/DZS(1)) 25MAY98.774 

 NSOIL=MAX0(NSOIL,NSOILI) SLAB.93 

CAQM: Kiran: Ensure stability if large diffusion exists in soil layers AQMSOIL.01 

CAQM: particularly coarse horizontal resolution domain(s). AQMSOIL.01 

 IF(RNET(I).GT.0.0 .and. DELTSM.GE.29.) then AQMSOIL.02 

 DTKAY = AMIN1(30.,DELTSM) AQMSOIL.02 

 NSOIL = NINT(DELTSM/DTKAY) AQMSOIL.02 

 NSOIL = MAX(1,NSOIL) AQMSOIL.02 

 END IF AQMSOIL.02 

CAQM: end of stable time step AQMSOIL.02 

The wintertime simulations exposed a couple of shortcomings in MM5V2.12. The snow 

cover fields are set at model initialization, and the snow is not “allowed” to melt or to 

accumulate. This sometimes results in anomalously cold (or warm) areas, depending on the 

synoptic situation. Of more concern is the means by which MM5 treats water bodies that are 

subject to freezing over in the winter. The default mode is to keep all water bodies liquid 

regardless of the meteorological situation. Our testing revealed that this deficiency leads to water 

temperatures never falling below 0°C. At times this results in an artificial “heat” low over 

Hudson Bay. To combat this artificial feature, we implemented a code change in MM5. If the 

9 
Meteorological Modeling of 1996 for the United States with MM5

NMC analysis suggested snow cover over a particular water cell, and if the initial ground (skin) 

temperature was -5°C or less, we changed the land use category from water to snow/ice. For the 

36-km grid, this methodology was only applied if the National Ice Center (NIC) charts indicated 

significant ice coverage over the Great Lakes for the episode in question. (See 

http://www.natice.noaa.gov/greatlakes.htm.) The code that performs this modification is 

included below. 

 IF(IFSNOW(1).EQ.0)GOTO 190 INIT.314 

 DO 185 I=1,ILX INIT.315 

 DO 185 J=1,JLX INIT.316 

 IS=INT(SATBRT(I,J)+0.001) INIT.317 

CAQM IF((XLAND(I,J)-1.5).GE.0)THEN INIT.318 

 IF((XLAND(I,J)-1.5).GE.0 .and. SNOWC(I,J).LE.0.0)THEN INIT.318 

 SNOWC(I,J)=-0.01 INIT.319 

 ELSE INIT.320 

 SNOWC(I,J)=SNOWC(I,J) INIT.321 

 ENDIF INIT.322 

CAQM-Kiran: Begin Mods 

 IF((XLAND(I,J)-1.5).GE.0 .and. SNOWC(I,J).GT.0.0 .and. INITAQM1 

 & TGB(I,J).LT.268.15)THEN INITAQM1 

 XLAND(I,J) = 1.0 INITAQM2 

 SATBRT(I,J) = 24 INITAQM3 

 END IF INITAQM4 

CAQM-Kiran: End Mods 

10 
Meteorological Modeling of 1996 for the United States with MM5

4. MODEL INPUTS AND PREPROCESSING 

Various types of measured and analyzed meteorological fields are required to prepare 

model inputs: surface data, such as temperature, winds, and moisture; upper-air data (soundings); 

sea surface temperature; and global/regional analysis fields. The National Center for 

Environmental Prediction supplies data for the surface fields for every 3 and 6 hours, and upperair data at 12-h resolution. The National Meteorological Center provides global and regional 

analysis data along with the sea surface temperature data. Under the TOGA program, the 

ECMWF provides global analysis fields; analysis fields are usually available at 0000 and 1200 

UTC at a resolution of 2.5° on a latitude-longitude grid. 

In this section, we briefly describe preprocessors and their functionalities in preparing 

inputs to MM5. For detailed information, the reader is referred to the MM5 system

documentation available from NCAR (http://www.mmm.ucar.edu/mm5/mm5-home.html). 

4.1 PREPROCESSING SYSTEM 

There are four preprocessors that process various types of input data for use in MM5: 

Terrain, Datagrid/REGRID, Rawins, and Interp. 

4.1.1 Terrain 

The Terrain preprocessor allows the user to choose map projections, horizontal grid 

resolutions, the number of horizontal grid cells to be used in MM5 (and thus the domain 

coverage), and the number of land use categories to use in representing each of the grid cells in 

the domain. Inputs are available in a preprocessed format. For example, global terrain and land 

use data are available in the following five formats:

• 1o

 (~111 km) • 5’ (~9 km) 

• 30’ (~56 km) • 30” (~0.9 km) 

• 10’ (~19 km) 

In this study we used 108-, 36-, and 12-km horizontal grid resolutions to perform the 

model simulations. Thus, 56-, 19-, and 9-km input data were used. There are 24 categories in the 

U.S. Geological Survey (USGS) data to represent land use. Table 4.1 lists these land use 

categories. The dominant land use was used for each of the grid cells. Smoothing and 

desmoothing techniques were used to filter noise in the terrain data (such as 2-∆X modes). We

projected the input data to Lambert-Conformal coordinates. 

11 
Meteorological Modeling of 1996 for the United States with MM5

Table 4.1. USGS land use categories 

1. Urban 2. Dry Land Crops Pasture 3. Irrigated Crops Pasture 

4. Mixed Dry/Irrig. Crops Past. 5. Crops/Grassland Mosaic 6. Crops/Wood Mosaic 

7. Grassland 8. Shrubland 9. Mix Shrubland/Grassland 

10. Savanna 11. Deciduous Broadleaf 12. Deciduous Needleleaf 

13. Evergreen Broadleaf 14. Evergreen Needleleaf 15. Mixed Forest 

16. Water Bodies 17. Herbaceous Wetland 18. Wooden Wetland 

19. Barren Sparse Vegetation 20. Herbaceous Tundra 21. Wooden Tundra 

22. Mixed Tundra 23. Bare Ground Tundra 24. Snow or Ice 

4.1.2 Datagrid 

The Datagrid preprocessor is used to generate meteorological data at standard pressure 

levels and at all grid cells along the mapped coordinates. Typically, all the basic meteorological 

parameters (such as winds, temperature, relative humidity, and geopotential height) are 

interpolated from a latitude-longitude grid to a Lambert-Conformal grid using a two-dimensional 

16-grid overlapping parabolic fit method13. 

4.1.3 Rawins 

Datagrid output is used to improve the analysis fields, since the original analysis data 

(TOGA/NMC) are at a very coarse horizontal resolution. Using the upper-air data, Rawins 

performs objective analysis, and removes erroneous observations. 

4.1.4 Interp 

The Interp preprocessor handles the data transformation required to go from pressure 

levels to model sigma-based levels. Appropriate vertical interpolation schemes are used to 

facilitate the proper transfer of data from pressure to sigma coordinates. This preprocessor takes 

in data generated by Datagrid and Rawins to create final formats of initial and lateral boundary 

conditions for the outermost domain (108-km grid). For the inner 36-km domain, this 

preprocessor is used in “back-end” mode to generate initial and lateral boundary conditions. 

4.2 LATERAL BOUNDARY CONDITIONS 

For the outermost domain (108-km), the lateral boundary conditions are usually available 

at 0000 and 1200 UTC from the Interp preprocessor. For the inner 36-km domain, lateral 

boundary conditions are generated for every hour via the “back-end” mode of Interp, using the 

108-km results as input. During the MM5 model integration, these lateral boundary conditions 

are further interpolated in time to every model time-step. 

12 
Meteorological Modeling of 1996 for the United States with MM5

4.3 DATA ASSIMILATION 

We chose to use the continuous FDDA method, in which prognostic fields (winds, 

temperature, and water vapor mixing ratio) are continuously nudged towards respective 

observations. Basically, time tendencies from the analysis fields are merged with time tendencies 

from the model in a manner such that the model solution exhibits a smooth behavior. For each of 

the prognostic variables, there is an associated weighting function used in the assimilation 

technique. In this study, winds were nudged in the entire atmospheric column, while temperature 

and mixing ratio were nudged only above the PBL. The weighting coefficients for various 

domains used are given below: 

108-km domain: Temperature and winds: 3.0 × 10-4

s

-1 Mixing ratio: 1.0 × 10-5 s-1

36-km domain: Temperature and winds: 2.5 × 10-4s

-1 Mixing ratio: 1.0 × 10-5 s-1

13 
Meteorological Modeling of 1996 for the United States with MM5

5. MODEL PERFORMANCE EVALUATION 

METHODOLOGY 

5.1 DATA 

For this project we used the Techniques Development Laboratory (TDL) U.S. and Canada 

surface hourly observations (http://dss.ucar.edu/datasets/ds472.0/). These data contain most of 

the first- and second-order National Weather Service stations, as well as automated surface sites. 

While these data have been subjected to a measure of quality control, experience has shown that 

a few spurious reports typically remain. Time and budget constraints did not allow us to perform

additional quality control on these data, so the plots that follow may show evidence of these 

erroneous reports. Because the erroneous reports are limited, however, the essence of the plots is 

unaffected. The analyses that follow focus primarily on daily averages. We included only the 

reports that had nonmissing data for at least 21 hours in a day; our experience with the TDL data 

suggests that erroneous reports are less likely to occur at stations that report regularly. We also 

performed analysis based on daily maximum and minimum temperatures. Again, we included 

only regularly reporting stations, but we also excluded maximum/minimum that was

considerably higher/lower than the second highest/lowest value. For the upper-air wind plots, we 

modified Rawins to allow upper-air observations to be extracted. These data likewise were not 

quality controlled, so anomalous reports might appear on the upper-air plots that follow. It 

should be reiterated that MM5’s quality control procedures automatically remove erroneous 

observations before using them in its FDDA routine. 

5.2 PROCEDURES 

We performed qualitative and quantitative analyses of various prognostic and diagnostic 

parameters obtained from the MM5 simulations. First, we qualitatively visually analyzed the 

spatial distributions of boundary layer parameters such as simulated winds, temperature, water 

vapor mixing ratio, mean sea level pressure, skin temperature, rainfall, and turbulent heat fluxes. 

Second, we condensed the hourly data to daily-averaged quantities, as specified above. The daily 

data were used to produce spatial plots of model results with observations (or model-observation 

differences) overlaid. To reduce the number of plots, we produced spatial plots for one time each 

month, and the 15th day of each month was randomly selected. Aloft, we produced once-a-month 

“snap shots” of the wind field at three levels; we alternated between 1200 UTC and 0000 UTC, 

since the lower levels could show a diurnal variation. Time series of observations/model 

predictions were also made at a few selected sites. Finally, we produced statistical plots for daily 

(CST) averaged temperature, mixing ratio, wind speed, and wind direction, as well as daily 

maximum and minimum temperatures. These plots contain specified model-observation 

averages, mean bias, mean absolute error, index of agreement, and coefficient of determination. 

The plots were created for the entire domain (peripheral 2-3 cells excluded), and also for the 4 

subdomains shown in Figure 5.1. 

14 
Meteorological Modeling of 1996 for the United States with MM5

NW NE

SW SE 

Figure 5.1. Domains for statistical plots. The full domain matches the MM5 full 36-km 

domain with a 3-cell buffer to the west and south, and a 2-cell buffer to the east and 

north. The colored cells indicate those cells with valid observations for a randomly

selected day. The compass designations NW, NE, SW and SE are used to differentiate the 

four subdomains. 

The following methodologies were used to accomplish model performance evaluation. 

• PAVE—The Package for Analysis and Visualization of Environmental data is a graphical 

animation tool with interactive GUI capability. It is designed to assist modelers 

performing time series analysis, horizontal and vertical spatial analysis, and animations. 

Detailed information on using PAVE can be found at 

http://envpro.ncsc.org/EDSS/pave_doc/Pave.html 

We used PAVE to perform qualitative analysis of various diagnostic and prognostic 

parameters, as well as quality assurance. 

• GrADS—The Grid Analysis and Display System is used to visualize modeled data 

overlaid with observations. We selected three model vertical levels (L=6, 11, and 16), 

15 
Meteorological Modeling of 1996 for the United States with MM5

which approximately correspond to 400, 1400, and 3400 m above ground level (AGL). 

Spatial plots of wind, temperature, mixing ratio, and precipitation were generated with 

observations (or model-observation differences) overlaid. These plots are presented in 

Section 6. Further details on GrADS can be obtained at http://grads.iges.org/grads/. 

• XMgr—The XMgr is a simple and elegant graphical tool that we used to produce time

series plots. More information about XMgr can be found at 

http://plasmagate.weizmann.ac.il/Xmgr/. 

• Graph and NCAR graphics—Graph is an MM5 postprocessor that produces graphical 

images of modeled input and output data. It uses graphical software developed by NCAR. 

We used this postprocessor to produce sea-level pressure and 500-mb height spatial plots, 

and model sounding plots for preselected stations. These plots were generated daily at 

0000 and 1200 UTC. 

16 
Meteorological Modeling of 1996 for the United States with MM5

6. RESULTS 

In this section we analyze the model performance. First we give a qualitative overview; 

then we focus on spatial analyses of specified quantities at the surface and aloft. Next we 

examine statistical measures of surface variables for the entire analysis domain and for the four 

subregions. We then examine point-specific performance using time series. 

6.1 GENERAL OBSERVATIONS 

During the qualitative examination of MM5 output variables, three concerns surfaced. 

First, the PBL heights seemed to be capped at about 2000 m. This problem is most noticeable in 

the summer, since PBL heights seldom approach 2000 m at other times during the year. The 

second concern involves lake temperatures. A problem in MM5 version 2.12 exists whenever the 

“24 land use” option is selected, and this problem manifests itself in the temperatures assigned to 

the lakes. The resulting lake temperatures are far too high, often by as much as 10°C. We did not 

notice any anomalous flows resulting from this error, but we have seen high temperatures and 

mixing ratios in the areas near these lakes. The problem affects only the Great Salt Lake within 

the United States, while numerous Canadian lakes (e.g., Lake Winnepeg) are affected. The Great 

Lakes are immune to the problem. 

A third concern is the treatment of snow by MM5. Because MM5 version 2.12 does not 

have a land-surface module, the initial surface conditions remain throughout each individual 5.5- 

day simulation. These initial snowfields are based on the snow cover fields in the NCEP global 

analyses, which themselves are updated about once a week. So it is conceivable that the snow 

coverage used by MM5 near the end of a simulation is over ten days old from the “observed” 

snow coverage field. The fact that the NCEP analysis is at a 2.5° resolution (~270 km) suggests 

that snow could be artificially spread out over too many 36-km cells, especially in areas with 

significant topographical gradients. 

6.2 SPATIAL ANALYSES 

6.2.1 Upper-Level Winds 

We focused on three aloft levels for our upper-air analyses: 0.938, 0.808, and 0.582 

sigma, which approximately correspond to 400, 1400, and 3400 m AGL. These levels were 

chosen so that the lowest level is clearly in the middle of the boundary layer, the middle layer is 

towards the top of the boundary/residual layer, and the highest level is clearly above the 

boundary layer. Obviously there are too many days in our simulations to plot them all, so we 

focus only on a sampling. For the odd months (January, March, May, etc.) the plots are made at 

1200 UTC on the 15th of each month. The even months are plotted at 0000 UTC on the 15th of 

each month. Appendix B contains all these sample plots. Figures 6.1-6.3 show the plots for April 

15, 1996 (note that the images in Appendix B are larger and much easier to view). 

17 
Meteorological Modeling of 1996 for the United States with MM5

Figure 6.1. Modeled and observed horizontal wind vectors at 0000 UTC 15 April 1996 at 

~400 m AGL. Bold vectors are observations. Every fourth model vector is plotted. 

Figure 6.2 Modeled and observed horizontal wind vectors at 0000 UTC 15 April 1996 at 

~1400 m AGL. Bold vectors are observations. Every fourth model vector is plotted. 

18 
Meteorological Modeling of 1996 for the United States with MM5

Figure 6.3. Modeled and observed horizontal wind vectors at 0000 UTC 15 April 1996 at 

~3400 m AGL. Bold vectors are observations. Every fourth model vector is plotted. 

Since we nudge the model to the observed upper-level winds, we expect the model and 

observations to show much better agreement than what we might expect to see at the surface. At 

each level the modeled wind directions and magnitudes agree with the observations. As 

expected, the performance above the boundary layer (~3400 m altitude) is best, though at no 

level does one see poor model-observation agreement. As expected, MM5 fully captures the 

large-scale synoptic flow. 

6.2.2 Surface Winds 

Later in this report we concentrate on a statistical analysis of surface variables, and that is 

the primary means of model evaluation. Still, statistics alone are insufficient to paint a complete 

picture of model performance. We augment the statistical information by showing a sample 

spatial plot of each surface variable that we have statistically analyzed, starting with surface 

winds. Following the pattern we used for the upper-level winds, we have included dailyaveraged winds for the 15th of each month. Figure 6.4 shows the surface wind plot for April 15, 

1996. Appendix B includes the plots for the 15th of each month in 1996. Figure 6.4 shows a large 

low-pressure system over the Great Lakes region, which MM5 resolves quite well. The strong 

flow behind the trailing cold front is also resolved, though it appears that MM5 has stronger 

winds than what were actually observed. While model output and observations show excellent 

directional agreement in the east, the mountainous west proves to be a different story. The model 

resolution of 36 km is clearly too coarse to match the observations. The hope is that at least the 

19 
Meteorological Modeling of 1996 for the United States with MM5

synoptic flows over the mountains can be modeled, and that these flows can be hinted at in the 

observations. That appears to be the case for April 15. 

Figure 6.4. Near-surface daily-averaged modeled and observed horizontal wind vectors 

for April 15, 1996. Bold vectors are observations. Every third model vector is plotted. 

6.2.3 Surface-Level Temperatures 

We used linear interpolation between the ground and the lowest model layer to calculate 

temperatures at the observational height of 1.5 m. These interpolated near-surface temperatures 

were then averaged over each 24-hour period (0000-2300 CST). The daily maximum and 

minimum for each cell were also stored for later analysis. Attempting to produce modelobservation plots for scalar quantities such as temperature can be quite a challenge for a domain 

as large as the one we used for this modeling. Even the wind plots shown earlier became

cluttered at times, and we plotted only every third or fourth model vector. For smaller domains 

one can plot the observed value on top of model contours/tiles, but these numbers tend to cover 

each other up (and possibly even the model results too). It is a difficult problem. What we 

decided to do was to plot model-observation differences. Clearly the problem of the numbers 

obscuring each other exists, but we attempted to minimize this effect by colorizing these 

differences. Under this scheme, only absolute differences are plotted. The color scale is given in 

Table 6.1.

20 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.1. Difference color scale 

Difference Color scale

# < -5 Deep Blue 

-5 < # < -3 Medium Blue 

-3 < # < -1 Light Blue 

-1 < # < 1 White 

1 < # < 3 Yellow 

3 < # < 5 Orange 

# > 5 Red 

The difficulty with the approach given above is that it does not translate to black-andwhite displays. Also the numbers themselves are not easy to read. Nevertheless it seems to be as 

good a way as any to plot scalar overlays. One should also keep in mind that the numbers that 

appear on these plots are not the same as those used in the statistical plots. For these overlay 

plots, all observations within the domain are plotted. The model values are interpolated to the 

observational points by the standard GrADS Cressman-type scheme. The statistical plots that are 

shown later follow a slightly different methodology. For those plots, the observations are 

accumulated within model grid cells. Multiple observations within a single cell are averaged. All 

cells with observations are paired with the corresponding model cells to produce the raw 

statistical data. Either approach is acceptable, especially for qualitative assessments, which is our 

purpose in this section. 

Figure 6.5 shows the daily-averaged temperature plot for April 15. Figures 6.6-6.7 show 

corresponding plots for minimum and maximum temperatures. Appendix C is an accumulation 

of gif files containing the complete suite of figures for the 15th of each month in 1996, at a 33% 

larger size to facilitate viewing. Notice in the temperature plots the deleterious effects of the 

snowfields, especially on the maximum temperatures in Figure 6.7. Consider a west-east transect 

starting over the state of Washington. Modeled maximum temperatures are in the range 10-15o

C, 

and these values are close to what the observations indicated. Eastern Washington, Idaho, and 

western Montana show a clear snow signature, with daily maxima just over 0o

C. These low 

values lead to a tremendous underprediction in the model of more than 10o

C. Continuing 

eastward, MM5 places no snow over eastern Montana, so daily maximum temperatures are 15-

20o

C, agreeing within 1oC to the observations. This pattern of significant temperature 

underpredictions over snow and more reasonable predictions in the absence of snow is a 

common occurrence in our modeling. Part of the problem is due to the limitations of MM5 

regarding snow cover, as has already been discussed. But that does not explain everything we see 

in the modeling. Part of the problem is a known defect in MM5 whenever warm advection 

occurs over snow cover. The air quickly and sometimes anomalously cools to match the frozen 

surface. An additional concern is the methodology employed in our analysis. The linear 

interpolation technique we used means that the MM5 skin temperature is the dominant factor 

when we calculate the values we use to compare with observations. In the absence of snow this 

procedure works quite well. Over snow, however, there is likely to be a decoupling, to an extent, 

between the surface and the atmosphere. We have noticed that the layer 1 air temperatures in 

these cases could be over 5o

C higher than the skin temperature during the afternoon. The 

differences can be much greater at the edge of the snow pack. These snow effects should be kept 

in mind when examining the statistical plots that are presented later in this report. 

21 
Meteorological Modeling of 1996 for the United States with MM5

Figure 6.5. Modeled daily-averaged temperatures (oC) from MM5 at 1.5 m height for April 

15, 1996. The numbers are model-observation differences; blue colors represent model 

underpredictions, white colors indicate near 0 differences, and yellow/orange/red colors 

indicate model overpredictions. 

22 
Meteorological Modeling of 1996 for the United States with MM5

Figure 6.6. Modeled daily minimum temperatures (oC) from MM5 at 1.5 m height for April 

15, 1996. The numbers are model-observation differences; blue colors represent model 

underpredictions, white colors indicate near 0 differences, and yellow/orange/red colors 

indicate model overpredictions. 

23 
Meteorological Modeling of 1996 for the United States with MM5

Figure 6.7. Modeled daily maximum temperatures (oC) from MM5 at 1.5 m height for April 

15, 1996. The numbers are model-observation differences; blue colors represent model 

underpredictions, white colors indicate near 0 differences, and yellow/orange/red colors 

indicate model overpredictions. 

6.2.4 Surface-Level Water Vapor Mixing Ratio 

Our analysis of “surface”-level water vapor mixing ratio followed the same procedure we 

used for temperature, except we performed no vertical interpolation; we simply used the lowest 

model layer value. Figure 6.8 shows the daily-averaged plot for our sample day. As usual, plots 

for the 15th of each month can be found in Appendix C. No strong moisture bias is evident, and 

the modeled pattern seems to agree reasonably well with the observations. Overall the water 

vapor mixing ratios are modeled very well throughout the year. 

24 
Meteorological Modeling of 1996 for the United States with MM5

Figure 6.8. Modeled near-surface daily-averaged water vapor mixing ratios (g/kg) from 

MM5 for April 15, 1996. The numbers are model-observation differences; blue colors 

represent model underpredictions, white colors indicate near 0 differences, and 

yellow/orange/red colors indicate model overpredictions. 

25 
Meteorological Modeling of 1996 for the United States with MM5

6.2.5 Mixed-Layer Depths 

We have already stated our concern about the boundary layer heights output by MM5 during 

the simulation period (see Section 6.1). Unfortunately, routine observations of PBL heights are 

not directly made, so it is difficult to determine how well the MRF scheme is actually doing. To 

address this issue, we crudely estimated observed mixing heights from the 0000 UTC soundings. 

Our simple methodology assumed a potential temperature lapse rate of no more than 1.0°C/km in 

the mixed layer. Once the potential temperature increased at a higher rate, we assumed that it did 

so because it had encountered the mixed-layer capping inversion. We did try to incorporate into 

this methodology information from the dew point profile, but that actually seemed to cause the 

estimated PBL heights to deviate further from what an experienced meteorologist would estimate 

based on a visual examination of an observed sounding. Due to the weaknesses of this 

methodology, one should not place a lot of credence in the so-called PBL “observations,” 

especially if the value is low. Any number of factors (showers, fronts, etc.) could cause the 

methodology to fail. This methodology has very little chance of producing reasonable results in 

the winter half of the year. 

It is more difficult to imagine a scenario that would cause the “observed” PBL heights to 

exceed the actual heights, rather than the reverse. Therefore, if MM5 predicts a mixed-layer 

depth of 1800 m, while the “observed” depth is 500 m, this situation should not be considered 

evidence that the model is flawed. However, if a couple of the “observations” indicate PBL 

heights in excess of 3000 m, while the model predicts 1800 m in that area, there is justification in 

assuming a model low bias in mixing-height prediction. 

Figure 6.9 shows the maximum mixing heights for April 14, 1996 (based on 0000 UTC April 

15 soundings). As has already been noted, the PBL depths are capped in the 1750-2000 m range, 

which appears to be a problem even this early in the year, based on the “observations” in Texas 

and New Mexico. The snowfield dramatically inhibits the model mixing heights. In the 

mountainous northwestern United States, where we previously noted significant temperature 

underpredictions stemming from the underlying snowfield, we note observed mixing heights 

well over 1000 m, much higher than that output from MM5. Otherwise the modeled mixing 

heights seem to reasonably correlate with the “observations.” One should also note the paucity of 

plotted “observations” east of the Mississippi River. This occurs whenever the observational 

procedure fails to calculate a value at the sounding site, and this situation is the norm during the 

winter. The full set of once-a-month plots of modeled versus “observed” PBL heights can be 

found in Appendix B. 

26 
Meteorological Modeling of 1996 for the United States with MM5

 500 1000 1500 1750 2000

Figure 6.9. Modeled and “observed” daytime maximum depths (m) of the mixed layer for 

April 14, 1996. 

6.2.6 Rainfall 

From an air quality modeling point of view, it is important to correctly predict the 

geographical location of rainfall. In meteorological modeling, it is additionally important to 

accurately predict rainfall amounts. Figure 6.10 shows the 24-h (ending 1200 UTC April 15, 

1996) accumulated precipitation amounts output by MM5 with the corresponding surface 

measurements. For this day the observations indicate precipitation along the extreme 

northwestern U.S. coast, in New England, and especially in the Mississippi Valley. MM5 also 

produces precipitation over these areas, while not predicting precipitation over most of the areas 

where no observed precipitation was reported. In general the predicted precipitation matches the 

observed both in area coverage and, to a lesser extent, in amount. Daily (once a month) 

precipitation plots can be found in Appendix B. 

27 
Meteorological Modeling of 1996 for the United States with MM5

 0.01 0.1 0.2 0.5 1 2 4 

Figure 6.10. Modeled and observed accumulated rainfall for 24 hours ending 1200 UTC 15 

April 1996. The numbers indicate observed rainfall in tenths of a mm/day. A “0” denotes 

no observed rainfall and “1” denotes trace amount of observed rainfall. 

28 
Meteorological Modeling of 1996 for the United States with MM5

6.3 POINT-SPECIFIC ANALYSES 

6.3.1 Analyses of Meteorological Variables at Surface Observational Data Sites 

We selected six surface observational sites for comparison with modeled data (Table 6.2). 

The sites were chosen primarily for geographical balance, while also providing a sampling of the 

various geographical features encountered in the domain. For example, the northern sites provide 

information about model performance during the snow season and the transition from snow to no 

snow, and vice versa. The Los Angeles site gives a coastal perspective. The plots for Missoula, 

MT, show conditions in mountainous terrain. For each of these sites we produced time series 

plots (model vs. observed) for daily-averaged temperature, daily maximum temperature, daily 

minimum temperature, daily-averaged water vapor mixing ratio, daily-averaged wind direction, 

and daily-averaged wind speed. Each plot is broken into three segments of four months apiece. 

Also note that these observational time series represent the average of all valid observations in 

each cell containing a site from Table 6.2. Appendix B contains the entire suite of surface time

series plots. A sample plot of daily maximum temperature for Missoula is shown in Figure 6.11. 

MM5 almost always underpredicts the maximum temperature for this site, especially in the late 

spring when the model keeps the temperatures low due to snow coverage. After the snow goes 

away on April 23, the performance is much improved for a couple of months. By July and 

August the underpredictions become more significant, often being greater than 5o

C. Performance 

improves again for September and early October. This cell becomes snow-covered again on 

October 20, and the model underpredictions increase to 5-10o

C for the next 15 days or so. Then 

the cell becomes snow-free for 10 days before becoming snow-covered the rest of the year. The 

last month and a half show some of the best model performance of the entire year, indicating that 

snow cover in and of itself does not necessarily portend poor maximum temperature 

performance. 

The time series for the other variables and for other sites reveal more interesting 

information. In general, the wind direction plots show excellent agreement for the sites that are 

relatively geographically homogeneous (ATL, DFW, SYR, MSP). Los Angeles and Missoula 

show much worse performance, presumably because 36-km resolution cannot resolve the flow 

features adequately in coastal or mountainous environments. When viewing the wind direction 

plots, one should keep in mind the discontinuity at 360o

/0o that sometimes causes large apparent

differences in these plots. The wind speed plots show results similar to what was seen in the 

wind direction plots, though with slightly more error. The daily-averaged mixing ratio plots 

show excellent agreement for all plotted sites except Los Angeles, which again suggests a 

resolution problem near a large water source. Many of these sites show a tendency to slightly 

underpredict water vapor mixing ratio during the summer, while showing no bias the rest of the 

year. 

The snow cover effects already discussed reveal themselves in all of the temperature 

plots. In the absence of snow cover, the southern sites tend to underpredict temperatures more so 

than the northern sites, Missoula excluded. This underprediction of temperatures is most evident 

in the daily maximum plots during snow cover in the spring, but it shows itself in all the plots at 

one time or another. The transient synoptic features such as frontal passages appear to be picked 

up at approximately the right times in this modeling. Surprisingly, MM5 even tends to 

underpredict the daily minimum temperatures. 

29 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.2. Surface observational sites 

Station ID Site name

LAX Los Angeles, CA 

DFW Dallas/Ft Worth, TX 

ATL Atlanta, GA

MSO Missoula, MT

MSP Minneapolis/St Paul, MN

SYR Syracuse, NY

Figure 6.11. Temporal variation of modeled and observed daily maximum temperature for 

Missoula, MT, for the period January 2 to December 29, 1996. 

30 
Meteorological Modeling of 1996 for the United States with MM5

6.3.2 Statistical Analyses of Meteorological Variables 

To complete our model performance evaluation, we calculated and plotted a few 

statistical measures for the variables we have been tracking. The variables are daily-averaged 

wind direction and speed, daily-averaged temperature and water vapor mixing ratio, and daily 

maximum and minimum temperatures. The methodology is the same as what we used for the 

time series plots in that we are performing a cell-to-cell comparison. The observations are 

averaged into the cells demarcated by the model, and in the case of winds, rotated to the model 

coordinates. The resultant wind directions are therefore relative to the model grid, not to realearth coordinates. Figure 5.1 contained an example of the density of cells with valid 

observations. It also shows the subdomains over which the following statistics were calculated. 

The plots are broken into four seasons, each lasting three months, starting with January. The 

statistics shown are (1) mean of observations and model output, (2) mean absolute error, 

(3) mean bias, (4) index of agreement (IA), and (5) coefficient of determination (r2

); the latter 

four statistics are defined below. 

1. Mean Absolute Error = ∑| ( − ) | 1 M O

N

where N is the number of station pairs, M is the modeled value, and O is the observed value 

2. Mean Bias = ∑( − ) 1 M O

N

3. Index of Agreement = 1- ( ) ⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡

− + − 2

2

| | | |

( )

M O O O

N RMSE

where RMSE (root mean square error) = 

0.5

2 | | 1

⎥

⎦

⎤ ⎢

⎣

⎡ ∑ M − O

N

4. Coefficient of Determination (r2

) = [ ]

∑ ∑

∑ − −

− −

2 2

2

( ) ( )

( )( )

O O M M

O O M M

Table 6.3 shows the statistical summary for wind direction. We calculated wind direction 

only whenever the observed wind speed exceeded 0.5 m/s. Wind direction statistics can be 

slightly misleading in that we are dealing with a discontinuous quantity. Some measures, like 

index of agreement, depend on the actual error divided by the total possible error range. The 

maximum error range is assumed to be 360°, though in actuality the maximum error is only 

180°. This numerical artifact leads to higher than realistic index of agreement values. Generally 

speaking, model performance tends to be better in the east than in the west, presumably due to 

orographic effects. The summer period also tends to show slightly poorer performance than is 

31 
Meteorological Modeling of 1996 for the United States with MM5

seen for the other seasons. This is expected, since the synoptic forcing is weakest in the summer. 

Overall, MM5 does a credible job in modeling the daily-averaged wind direction. 

32 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.3. Surface statistical summary for wind direction 

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Wind direction Obs mean 203.8 190.9 187.1 196.0

(full domain) MM5 mean 202.2 190.0 186.9 194.7 

Bias 4.0 2.5 3.4 4.0

Absolute error 23.0 22.7 24.6 21.7

R2 0.46 0.48 0.43 0.48

Index of agreement 0.97 0.96 0.96 0.97 

RMSE 37.1 36.1 38.5 35.2

Wind direction Obs mean 198.1 196.1 179.6 193.8

(SW subdomain) MM5 mean 199.5 200.8 184.1 191.4 

Bias 6.8 6.5 6.7 7.3

Absolute error 30.9 27.4 30.3 30.5

R2 0.34 0.44 0.40 0.30

Index of agreement 0.95 0.94 0.92 0.95 

RMSE 47.8 43.2 46.6 47.1

Wind direction Obs mean 197.1 180.6 161.0 173.8

(SE subdomain) MM5 mean 195.6 176.6 159.7 171.7 

Bias 0.7 -0.3 0.4 0.1

Absolute error 17.4 17.9 20.3 16.9

R2 0.58 0.59 0.52 0.59

Index of agreement 0.98 0.97 0.97 0.98 

RMSE 27.0 27.7 31.0 25.8

Wind direction Obs mean 204.2 199.0 203.5 207.0

(NW subdomain) MM5 mean 197.6 198.5 204.2 204.4 

Bias 7.2 4.5 6.4 6.0

Absolute error 29.4 28.1 29.6 27.0

R2 0.36 0.34 0.27 0.37

Index of agreement 0.95 0.96 0.95 0.95 

RMSE 45.4 43.6 45.6 42.5

Wind direction Obs mean 212.7 188.8 195.4 205.0

(NE subdomain) MM5 mean 212.8 185.7 191.9 206.0 

Bias 2.3 0.2 0.9 3.0

Absolute error 17.0 18.7 19.4 15.3

R2 0.55 0.60 0.54 0.63

Index of agreement 0.98 0.98 0.98 0.98 

RMSE 27.2 28.9 29.7 23.6

Table 6.4 shows the statistical summary for wind speed. MM5 consistently overpredicts 

wind speed by 10-20%. This is not totally unexpected, since the MM5 value is based on a 38-m

layer and the observations are made around 10 m. As we saw with wind direction, performance 

is better in the east than in the west. Seasonal effects vary from region to region, with the SE 

quadrant showing the most noticeable effect, a performance dip in the summer. 

33 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.4. Surface statistical summary for wind speed 

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Wind speed Obs mean 3.41 3.09 2.52 3.15

(full domain) MM5 mean 3.93 3.46 2.95 3.77 

Bias 0.51 0.38 0.44 0.62

Absolute error 1.06 0.92 0.86 1.07

R2 0.63 0.64 0.61 0.62

Index of agreement 0.88 0.88 0.86 0.86 

RMSE 1.47 1.25 1.15 1.46

Wind speed Obs mean 3.19 3.63 2.85 3.11

(SW subdomain) MM5 mean 3.72 3.90 3.12 3.74 

Bias 0.54 0.27 0.27 0.63

Absolute error 1.18 1.11 0.94 1.22

R2 0.60 0.59 0.54 0.54

Index of agreement 0.86 0.87 0.85 0.84 

RMSE 1.63 1.51 1.27 1.69

Wind speed Obs mean 3.41 2.72 2.07 2.99

(SE subdomain) MM5 mean 3.78 3.12 2.56 3.58 

Bias 0.38 0.40 0.49 0.59

Absolute error 0.86 0.77 0.77 0.92

R2 0.69 0.69 0.62 0.66

Index of agreement 0.90 0.89 0.85 0.87 

RMSE 1.16 1.00 1.00 1.24

Wind speed Obs mean 3.26 2.98 2.58 3.14

(NW subdomain) MM5 mean 3.92 3.31 2.98 3.72 

Bias 0.66 0.33 0.40 0.58

Absolute error 1.28 0.97 0.90 1.17

R2 0.57 0.61 0.60 0.61

Index of agreement 0.85 0.87 0.86 0.86 

RMSE 1.73 1.33 1.21 1.58

Wind speed Obs mean 3.69 3.09 2.54 3.32

(NE subdomain) MM5 mean 4.18 3.56 3.09 3.99 

Bias 0.49 0.47 0.55 0.67

Absolute error 0.97 0.87 0.83 0.99

R2 0.70 0.67 0.68 0.68

Index of agreement 0.90 0.89 0.88 0.88 

RMSE 1.32 1.15 1.10 1.33

Table 6.5 shows the statistical summary for water vapor mixing ratio. At low mixing ratio 

values MM5 performs remarkably well. At higher values (>~10g/kg) MM5 tends to underpredict 

the observations. We believe this could be caused by the MRF PBL scheme being too efficient in 

its vertical mixing during hot, humid conditions. This tendency leads to seasonal and geographical patterns. The northern quadrants show better performance than the southern quadrants, and 

the summer period shows poorer performance than other seasons. Overall, though, MM5 does a 

better job modeling water vapor mixing ratio than it does any other base variable. 

34 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.5. Surface statistical summary for water vapor mixing ratio 

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Mixing ratio Obs mean 3.5 8.0 10.9 5.0 

(full domain) MM5 mean 3.6 7.6 10.1 5.0 

Bias 0.1 -0.4 -0.8 -0.0

Absolute error 0.5 1.0 1.3 0.6

R2 0.91 0.92 0.86 0.93

Index of agreement 0.98 0.97 0.95 0.98 

RMSE 0.8 1.4 1.7 0.9

Mixing ratio Obs mean 4.1 8.0 11.5 5.7 

(SW subdomain) MM5 mean 4.5 8.3 10.8 5.8 

Bias 0.4 0.3 -0.6 0.1

Absolute error 0.9 1.4 1.6 0.9

R2 0.81 0.87 0.79 0.89

Index of agreement 0.94 0.96 0.93 0.97 

RMSE 1.2 1.8 2.0 1.2

Mixing ratio Obs mean 5.3 11.8 14.9 7.6 

(SE subdomain) MM5 mean 5.5 10.8 13.3 7.4 

Bias 0.1 -0.9 -1.6 -0.2

Absolute error 0.6 1.3 1.7 0.7

R2 0.94 0.91 0.83 0.94

Index of agreement 0.98 0.96 0.90 0.98 

RMSE 0.9 1.6 2.0 1.0

Mixing ratio Obs mean 2.8 5.8 8.0 3.4

(NW subdomain) MM5 mean 2.7 5.5 7.8 3.5 

Bias -0.0 -0.4 -0.2 0.1

Absolute error 0.4 0.7 1.1 0.5

R2 0.89 0.88 0.73 0.90

Index of agreement 0.97 0.96 0.92 0.97 

RMSE 0.6 1.0 1.4 0.7

Mixing ratio Obs mean 2.2 6.4 9.4 3.9

(NE subdomain) MM5 mean 2.1 6.4 9.4 3.9 

Bias -0.1 -0.4 -0.8 -0.2

Absolute error 0.3 0.8 1.1 0.4

R2 0.90 0.93 0.83 0.93

Index of agreement 0.97 0.98 0.93 0.98 

RMSE 0.5 1.0 1.4 0.6

Table 6.6 provides the statistical summary for daily-averaged temperature. MM5 

consistently shows a negative temperature bias that is most pronounced in the colder seasons. 

The spring/summer seasons show less bias, but also less skill. The eastern quadrants show better 

performance than the western quadrants, matching what we saw in the wind statistics. 

35 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.6. Surface statistical summary for daily-averaged temperature 

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Temperature Obs mean 274.3 289.0 293.4 278.8

(full domain) MM5 mean 271.7 287.7 292.6 276.7 

Bias -2.6 -1.4 -0.8 -2.1

Absolute error 3.3 2.2 1.6 2.9

R2 0.91 0.89 0.86 0.92

Index of agreement 0.96 0.96 0.96 0.97 

RMSE 4.2 3.2 2.3 3.6

Temperature Obs mean 280.8 293.8 296.6 283.7

(SW subdomain) MM5 mean 278.4 291.8 295.3 281.4 

Bias -2.4 -1.9 -1.3 -2.2

Absolute error 3.1 2.7 2.2 2.8

R2 0.86 0.81 0.80 0.90

Index of agreement 0.94 0.93 0.93 0.96 

RMSE 4.0 3.7 2.9 3.6

Temperature Obs mean 281.1 294.2 297.2 285.4

(SE subdomain) MM5 mean 278.6 293.5 296.9 283.6 

Bias -2.5 -0.7 -0.3 -1.8

Absolute error 3.1 1.4 1.1 2.5

R2 0.89 0.92 0.86 0.92

Index of agreement 0.95 0.98 0.96 0.96 

RMSE 3.9 1.8 1.4 3.1

Temperature Obs mean 269.1 284.6 290.4 272.5

(NW subdomain) MM5 mean 266.6 282.8 289.2 270.1 

Bias -2.5 -1.8 -1.1 -2.4

Absolute error 3.5 2.6 2.0 3.1

R2 0.90 0.81 0.80 0.92

Index of agreement 0.96 0.93 0.94 0.97 

RMSE 4.4 3.8 2.8 3.8

Temperature Obs mean 268.3 285.2 291.2 275.8

(NE subdomain) MM5 mean 265.5 284.1 290.8 273.7 

Bias -2.9 -1.1 -0.4 -2.1

Absolute error 3.6 2.0 1.3 3.0

R2 0.85 0.89 0.85 0.86

Index of agreement 0.93 0.96 0.96 0.94 

RMSE 4.4 3.0 1.7 3.7

Table 6.7 shows the statistical summary for daily maximum temperature. The statistics 

follow the same general pattern seen in the daily-averaged temperature statistics. A model cold 

bias is evident for each region for each season. The bias is most severe during the cold seasons, 

and the northern subdomains show more bias than do the southern subdomains. This result 

probably stems from the snow effects already discussed. Even considering these effects, the r2

and IA indicate that the poorest performance occurs in the summer. The best performance for all 

regions is found in the October-December period. 

36 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.7. Surface statistical summary for daily maximum temperature 

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Max temp Obs mean 279.8 294.8 299.2 283.9

(full domain) MM5 mean 276.6 293.5 298.7 281.4 

Bias -3.3 -1.4 -0.5 -2.5

Absolute error 4.0 2.9 2.2 3.2

R2 0.87 0.82 0.81 0.92

Index of agreement 0.95 0.94 0.95 0.97 

RMSE 5.4 4.4 2.9 4.1

Max temp Obs mean 288.2 300.9 303.3 290.4

(SW subdomain) MM5 mean 285.3 299.6 303.1 288.2 

Bias -2.9 -1.3 -0.1 -2.3

Absolute error 3.9 3.1 2.6 3.2

R2 0.77 0.70 0.72 0.85

Index of agreement 0.91 0.90 0.92 0.94 

RMSE 5.3 4.6 3.3 4.3

Max temp Obs mean 286.8 299.8 302.5 290.8

(SE subdomain) MM5 mean 283.7 299.1 302.4 288.4 

Bias -3.1 -0.6 -0.1 -2.4

Absolute error 3.6 2.0 1.6 2.9

R2 0.85 0.84 0.71 0.90

Index of agreement 0.93 0.95 0.92 0.95 

RMSE 4.7 2.7 2.1 3.5

Max temp Obs mean 274.2 290.3 297.0 277.1

(NW subdomain) MM5 mean 270.5 288.2 295.8 274.2 

Bias -3.7 -2.0 -1.2 -2.9

Absolute error 4.6 3.5 2.5 3.5

R2 0.82 0.72 0.81 0.91

Index of agreement 0.92 0.90 0.94 0.96 

RMSE 6.1 5.4 3.3 4.5

Max temp Obs mean 272.9 290.3 296.2 279.7

(NE subdomain) MM5 mean 269.4 288.7 295.7 277.3 

Bias -3.5 -1.5 -0.5 -2.4

Absolute error 4.1 3.0 2.0 3.0

R2 0.78 0.79 0.75 0.88

Index of agreement 0.89 0.93 0.92 0.95 

RMSE 5.4 4.5 2.8 3.8

Table 6.8 shows the statistical summary for minimum temperature. A slight negative 1-

2o

C bias is seen for all regions for all of our summary periods. The snow issue is not as great a 

concern for minimum temperatures as it is for maximum temperatures, and the performance 

values have increased accordingly. Summer performance once again is poorer than the other 

seasons. 

37 
Meteorological Modeling of 1996 for the United States with MM5

Table 6.8. Surface statistical summary for daily minimum temperature 

Jan-Mar Apr-Jun Jul-Sep Oct-Dec

Min temp Obs mean 268.8 283.2 288.0 274.1

(full domain) MM5 mean 267.2 281.9 287.0 272.7 

Bias -1.6 -1.3 -1.0 -1.4

Absolute error 3.4 2.5 2.3 3.1

R2 0.87 0.87 0.77 0.87

Index of agreement 0.96 0.96 0.93 0.96 

RMSE 4.3 3.3 3.1 4.0

Min temp Obs mean 274.1 286.5 290.8 277.8

(SW subdomain) MM5 mean 272.8 284.7 288.6 276.4 

Bias -1.3 -1.9 -2.2 -1.4

Absolute error 3.1 3.0 3.0 2.9

R2 0.83 0.80 0.74 0.86

Index of agreement 0.95 0.93 0.89 0.96 

RMSE 3.9 3.8 3.8 3.6

Min temp Obs mean 275.5 288.7 292.8 280.5

(SE subdomain) MM5 mean 273.7 287.9 291.7 279.3 

Bias -1.8 -0.8 -1.1 -1.2

Absolute error 3.3 2.0 2.0 2.8

R2 0.85 0.88 0.75 0.85

Index of agreement 0.94 0.96 0.91 0.95 

RMSE 4.2 2.5 2.5 3.7

Min temp Obs mean 264.3 278.8 283.9 268.0

(NW subdomain) MM5 mean 263.0 277.1 283.2 266.8 

Bias -1.2 -1.7 -0.7 -1.3

Absolute error 3.3 2.8 2.5 3.1

R2 0.89 0.78 0.65 0.88

Index of agreement 0.96 0.92 0.89 0.96 

RMSE 4.2 3.7 3.3 3.9

Min temp Obs mean 263.4 280.0 286.3 271.9

(NE subdomain) MM5 mean 261.4 279.0 286.0 270.3 

Bias -2.0 -1.0 -0.4 -1.6

Absolute error 3.8 2.4 2.1 3.5

R2 0.78 0.86 0.66 0.74

Index of agreement 0.93 0.96 0.90 0.92 

RMSE 4.7 3.2 2.8 4.4

Appendix B contains a full suite of statistical time series plots. From these plots it is clear 

that MM5 does a good job in resolving the transient synoptic features (i.e. fronts) that existed in 

1996. 

38 
Meteorological Modeling of 1996 for the United States with MM5

7. CLOSING OBSERVATIONS 

In general, MM5 performed reasonably well. Major synoptic features were captured, and 

only a couple errors stood out. Below is our summary. 

1. Near-surface temperatures are generally underpredicted. 

The inability of MM5 to dynamically build/melt snowfields is the biggest concern we 

have with this modeling, along with the lack of proper air mass modification over these fields. 

The snow keeps the air mass above it from warming much above 0o

C, which leads to tremendous 

underpredictions of temperature in the late winter/early spring. We have seen no evidence that 

this problem significantly affects model precipitation or wind flow. Even when snow is not 

present, the model consistently underpredicts temperatures (mean, maximum, and minimum). 

2. PBL heights are capped at ~2000 m.

The PBL height problem seems to stem from the selection of the MRF scheme. We prefer 

a more sophisticated scheme, but the preferred schemes either do not output Kv’s, or they are too 

experimental for tasks such as this one, at least in MM5 version 2. This is primarily a 

summertime concern, and it affects the western states more so than the eastern states.

3. Wind fields problematic over the western states. 

The 36-km resolution used in this modeling is clearly insufficient to resolve the 

complicated orographically-induced flows near the surface over the western United States. 

Elsewhere the model does a really good job in replicating the mean flow on a cell-to-cell basis. 

The flows aloft are modeled well everywhere. 

4. Lake temperatures are too high. 

MM5 version 2.12 contains a bug that causes it to sometimes misdiagnose the 

temperatures of certain smaller lakes. The bug only shows itself whenever the 24-category land 

use option is requested, as we have employed in this modeling. The effect is to create warmerthan-reasonable lake temperatures, and error is largest in the spring/summer periods. The Great 

Salt Lake is most susceptible to this bug, though certain Canadian lakes may also exhibit the 

problem. The Great Lakes are unaffected by the error. 

5. Water vapor mixing ratio is modeled very well. 

In general, the surface moisture fields are modeled exceptionally well. The only concern 

with the mixing ratio predictions is that they tend to be negatively biased during the summer in 

the southern states. 

39 
Meteorological Modeling of 1996 for the United States with MM5

REFERENCES 

1. Dudhia, J. A nonhydrostatic version of the Penn State/NCAR mesoscale model: Validation 

tests and simulation of an Atlantic cyclone and cold front. Mon. Wea. Rev., 121, 1493-1513, 

1993. 

2. Grell, G.A.; Dudhia, J. and Stauffer, D.R. A Description of the Fifth Generation Penn 

State/NCAR Mesoscale Model (MM5). NCAR Tech. Note, NCAR TN-398-STR, 138 pp., 

1994. 

3. SAI. 1996. "User's Guide to the Variable-Grid Urban Airshed Model (UAM-V)" Systems 

Applications, Inc., San Rafael, California (SYSAPP-96-95/27r). 

4. ENVIRON (1997) User's Guide to the Comprehensive Air Quality Model with Extensions 

(CAMx), Version 1.10. Environ International Corporation, Novato, CA. 

5. Guthrie, P.G., C.A. Emery, M.P. Ligocki, G.E. Mansell, A.K. Kuklin and D. Gao. 1995. 

“Development and Preliminary Testing of the Regulatory Modeling System for Aerosols and 

Deposition (REMSAD).” Technical memorandum prepared for R. Scheffe, U.S. EPA Office 

of Policy, Planning, and Evaluation by Systems Applications International, San Rafael, 

California. 

6. Byun, D.W. and J.K.S. Ching, Eds, “Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System”, EPA/600/R-99/030, Office of Research 

and Development, U.S. EPA, Washington, D.C., March, 1999. 

7. MCNC, The Multiscale Air Quality Simulation Platform (MAQSIP): An Overview of Model 

Development and Initial Applications, in preparation, 2000. 

8. Zhang, D. L. and Anthes, R.A. A high-resolution model of the planetary boundary layer - 

Sensitivity tests and comparisons with SESAME-79 data. J. Appl. Meteor., 21, 1594-1609, 

1982. 

9. Gayno, G.A. Development of a Higher-Order, Fog-Producing Boundary Layer Model 

Suitable for Use in Numerical Weather Prediction. M.S. Thesis, The Pennsylvania State 

Univ., 104 pp. 

10. Mellor, G.L. and Yamada, T. A Hierarchy of Turbulence Closure Models for Planetary 

Boundary-Layers. J. Atmos. Sci., 31, 1791-1806, 1974. 

11. Hong S.Y. and H. L. Pan, H.L. Nonlocal boundary layer vertical diffusion in a medium-range 

forecast model. Mon. Wea. Rev., 124, 2322-2339, 1996. 

12. Dudhia, J. Numerical study of convection observed during the winter monsoon experiment 

using a mesoscale two-dimensional model. J. Atmos.Sci., 46, 3077-3107, 1989 

13. Chen, S., J. Dudhia, D. Gill, et al, 1995: PSU/NCAR Mesoscale Modeling System – Tutorial 

Class Notes. Available from NCAR (http://www.mmm.ucar.edu/mm5). 

40 
Meteorological Modeling of 1996 for the United States with MM5

APPENDICES 

APPENDIX A – MODEL CONFIGURATION FILE 

3 domains (1st number refers to 108 km domain, 2nd number to 36 km domain, 3rd

to the 12-km domain) 

Sigmas 

1.,0.995,.988,.98,.97,.956,.938,.916,.893,.868,.839,.808,.777,.744,.702,.648,

.582,.5,.4,.3,.2,.12,.052,.0, 

 KXS 23 # number of half-sigma layers 

 NESTIX "69,115,190," # domain heights in cells 

 NESTJX "89,169,166," # domain widths in cells 

 DIS "108.,36.,12.," # grid sizes (km) 

 MTHRD "1,1,2," # mother domain IDs 

 NESTI "1,15,35," # lower left I's of nests 

 NESTJ "1,18,21," # lower left J's of nests 

 NHYDRO = 1, #nonhydrostatic 

 LEVIDN "0,1,2," # level of nest for each domain 

 IMPHYS "5,5,5," # mixed phase ice processes 

 MPHYSTBL 0 # do not use look-up tables for moist physics 

 ICUPA "2,6,6," # Kuo,KF,KF 

 IBLTYP "5,5,5," # MRF 

 FRAD "1,1,1," # simple 

 ISOIL "1,1,1," # multi-layer soil temperature model (must use for MRF 

pbl) 

 ISHALLO "0,0,0," # No shallow convection 

 RADFRQ "30.,20.,10.," atmospheric radiation calculation freq (in minutes) 

 IMVDIF 1,1,1, # moist vertical diffusion in clouds 

 IVQADV 1,1,1, # linear vertical moisture advection 

 IVTADV 1,1,1, # linear vertical temperature advection 

 ITHADV 1,1,1, # advection of temperature uses potential temperature 

 ITPDIF 1,1,1, # diffusion using perturbation temperature 

 ICOR3D 1,1,1, # 3D Coriolis force 

 IFUPR 1,1,1, # upper radiative boundary condition 

 IFDRY 0,0,0, # Don't do fake-dry run (no latent heating) 

 ICUSTB 1,1,1, # stability check for Anthes-Kuo CPS only 

 IBOUDY 3,3,3, # time and inflow/outflow relaxation 

 IFSNOW 1,1,1, # SNOW COVER EFFECTS 

 ISFFLX 1,1,1, # surface fluxes 

 ITGFLG 1,1,1, # surface temperature prediction 

 ISFPAR 1,1,1, # surface characteristics 

 ICLOUD 1,1,1, # cloud effects on radiation 

 ICDCON 0,0,0, # Don't use constant drag coefficients 

 IVMIXM 1,1,1, # vertical mixing of momentum 

 HYDPRE 1,1,1, # HYDRO EFFECTS OF LIQ WATER (HY run only) 

 IEVAP 1,1,1, # EVAP OF CLOUD/RAINWATER 

 TISTEP 300.,100.,36., # Adv time steps (s) 

 

 I4D 1,1,1, # GRID 4DDA RUN 

41 
Meteorological Modeling of 1996 for the United States with MM5

 DIFTIM 720.,720.,720., # 3D ANALYSIS NUDGING 

 180.,180.,180., # SFC ANALYSIS NUDGING 

 IWIND 1,1,1, # 3D ANALYSIS NUDGING of winds 

 1,1,1, # SFC ANALYSIS NUDGING of winds 

 GV 3.0E-4,2.5E-4,1.0E-4, # 3D ANALYSIS NUDGING COEFFICIENT of 

winds 

 3.0E-4,2.5E-4,1.0E-4, # SFC ANALYSIS NUDGING COEFFICIENT of 

winds 

 ITEMP 1,1,1, # 3D ANALYSIS NUDGING of temperature 

 0,0,0, # SFC ANALYSIS NUDGING of temperature 

 GT 3.0E-4,2.5E-4,1.0E-4, # 3D ANALYSIS NUDGING COEFFICIENT of 

temps 

 0,0,0, # SFC NUDGING of temps 

 IMOIS 1,1,1, # 3D ANALYSIS NUDGING of moisture mixing ratio 

 0,0,0, # SFC ANALYSIS NUDGING of moisture mixing ratio 

 GQ 1.0E-5,1.0E-5,1.0E-5, # 3D ANALYSIS NUDGING COEFFICIENT of 

moisture 

 0,0,0, # SFC NUDGING of moisture 

 IROT 0,0,0, # GRID NUDGE THE ROTATIONAL WIND FIELD 

 GR 0,0,0, # NUDGING COEFFICIENT FOR THE ROTATIONAL COMPONENT 

 INONBL 0,0,0, # U WIND INCLUDE BOUNDARY LAYER NUDGING 

 0,0,0, # V WIND INCLUDE BOUNDARY LAYER NUDGING 

 1,1,1, # TEMPERATURE EXCLUDE BOUNDARY LAYER NUDGING 

 1,1,1, # MIXING RATIO EXCLUDE BOUNDARY LAYER NUDGING 

 RINBLW 350.,250.,150., # RADIUS OF INFLUENCE FOR SURFACE ANALYSIS (KM) 

??? 

 I4DI 0,0,0, # No obs nudging 

APPENDIX B – SUPPLEMENTAL PLOTS 

While only a small sample of plots is included in the main text, a more complete set is 

found here. These plots are also larger and more readable than those in the text. They do not have 

individual captions. Instead, a summary caption is included before each series of plots is 

presented. Dates and times are found in the plot labels. These plots follow:

1. Winds at ~400 m AGL on the 15th of each month (12 plots)

2. Winds at ~1400 m AGL on the 15th of each month (12 plots)

3. Winds at ~3400 m AGL on the 15th of each month (12 plots)

4. Daily-averaged surface winds on the 15th of each month (12 plots) 

5. Daily maximum PBL heights on the 14th of each month (12 plots) 

6. Daily accumulated rainfall on the 15th of each month (12 plots)

7. Time series for selected surface sites (36 plots)

8. Wind direction statistical time series (5 domains, 4 seasons) (20 plots) 

9. Wind speed statistical time series (5 domains, 4 seasons) (20 plots) 

10. Water vapor mixing ratio statistical time series (5 domains, 4 seasons) (20 plots) 

11. Daily-averaged temperature statistical time series (5 domains, 4 seasons) (20 plots) 

42 
Meteorological Modeling of 1996 for the United States with MM5

12. Daily maximum temperature statistical time series (5 domains, 4 seasons) (20 plots) 

13. Daily minimum temperature statistical time series (5 domains, 4 seasons) (20 plots) 

43 
Meteorological Modeling of 1996 for the United States with MM5

Modeled and observed horizontal wind vectors at ~400 m AGL (Sigma=0.938; Level=6). 

The plots are at 1200 UTC or 0000 UTC (alternating) for the 15th day of each month in 

1996. 

44 
Meteorological Modeling of 1996 for the United States with MM5

Modeled and observed horizontal wind vectors at ~1400 m AGL (Sigma=0.808; Level=11). 

The plots are at 1200 UTC or 0000 UTC (alternating) for the 15th day of each month in 

1996. 

45 
Meteorological Modeling of 1996 for the United States with MM5

Modeled and observed horizontal wind vectors at ~3400 m AGL (Sigma=0.582; Level=16). 

The plots are at 1200 UTC or 0000 UTC (alternating) for the 15th day of each month in 

1996. 

46 
Meteorological Modeling of 1996 for the United States with MM5

Modeled and observed daily-averaged horizontal wind vectors near the surface. The 

plots are made for the 15th day of each month in 1996. 

47 
Meteorological Modeling of 1996 for the United States with MM5

Modeled and “Observed” daytime maximum mixed-layer heights for the 14th of each 

month of 1996. 

48 
Meteorological Modeling of 1996 for the United States with MM5

Modeled and observed accumulated rainfall for the 24 hours ending at 12 UTC on the 15th

of each month in 1996. The numbers indicate observed rainfall in tenths of a mm/day. 

The “0” number denotes no observed rainfall and “1” denotes trace-amount of observed 

rainfall. 

49 
Meteorological Modeling of 1996 for the United States with MM5

Temporal variation of modeled and observed daily-averaged wind direction, wind speed, 

surface temperature, water vapor mixing ratio, daily maximum temperature and daily

minimum temperature. These sites, in alphabetical order by site name, are: 

Atlanta, GA (ATL) 

Dallas/Ft Worth, TX (DFW) 

Los Angeles (LAX) 

Minneapolis/St Paul, MN (MSP) 

Missoula, MT (MSO) 

Syracuse, NY (SYR) 

50 
Meteorological Modeling of 1996 for the United States with MM5

Daily-averaged wind direction statistical plots. The plots represent the temporal variation 

of (a) domain-averaged modeled and observed means, (b) absolute error and bias, and 

(c) index of agreement and coefficient of determination. The plots are in seasonal 

segments, and they are created for the domain as a whole and for four subdomains. 

51 
Meteorological Modeling of 1996 for the United States with MM5

Daily-averaged wind speed statistical plots. The plots represent the temporal variation of 

(a) domain-averaged modeled and observed means, (b) absolute error and bias, and (c) 

index of agreement and coefficient of determination. The plots are in seasonal segments, 

and they are created for the domain as a whole and for four subdomains. 

52 
Meteorological Modeling of 1996 for the United States with MM5

Daily-averaged water vapor mixing ratio statistical plots. The plots represent the 

temporal variation of (a) domain-averaged modeled and observed means, (b) absolute 

error and bias, and (c) index of agreement and coefficient of determination. The plots are 

in seasonal segments, and they are created for the domain as a whole and for four 

subdomains. 

53 
Meteorological Modeling of 1996 for the United States with MM5

Daily-averaged surface temperature statistical plots. The plots represent the temporal 

variation of (a) domain-averaged modeled and observed means, (b) absolute error and 

bias, and (c) index of agreement and coefficient of determination. The plots are in 

seasonal segments, and they are created for the domain as a whole and for four 

subdomains. 

54 
Meteorological Modeling of 1996 for the United States with MM5

Daily maximum temperature statistical plots. The plots represent the temporal variation 

of (a) domain-averaged modeled and observed means, (b) absolute error and bias, and 

(c) index of agreement and coefficient of determination. The plots are in seasonal 

segments, and they are created for the domain as a whole and for four subdomains. 

55 
Meteorological Modeling of 1996 for the United States with MM5

Daily minimum temperature statistical plots. The plots represent the temporal variation of 

(a) domain-averaged modeled and observed means, (b) absolute error and bias, and (c) 

index of agreement and coefficient of determination. The plots are in seasonal segments, 

and they are created for the domain as a whole and for four subdomains. 

56 
Meteorological Modeling of 1996 for the United States with MM5

APPENDIX C – SUPPLEMENTAL COLOR GRAPHIC PLOTS 

A file containing color gifs is available. It is a tar file named gifs96.tar which contain 48 gif files. 

These files show the model fields with model-observation differences overlaid. There are four 

categories of plots: 

1) Daily averaged temperature (tavg_01.gif, tavg_02.gif, ..., tavg_12.gif) 

2) Daily maximum temperature (tmax_01.gif, tmax_02.gif, ..., tmax_12.gif) 

3) Daily minimum temperature (tmin_01.gif, tmin_02.gif, ..., tmin_12.gif)

4) Daily averaged water vapor mixing ratio (qavg_01.gif, qavg_02.gif, ..., qavg_12.gif) 

The plots are made for the 15th of each month, with the number in each file representing a 

particular month. (01=Jan, 02=Feb, etc...) The differences follow a color scale represented in the 

following table: 

Difference color scale 

Difference Color scale

# < -5 Deep Blue 

-5 < # < -3 Medium Blue 

-3 < # < -1 Light Blue 

-1 < # < 1 White 

1 < # < 3 Yellow 

3 < # < 5 Orange 

# > 5 Red 

57