Appendix E 

Air Quality and Meteorological 

Preparations and Results 
 Appendix E - 1 
Appendix E – Air Quality and Meteorological Preparations and Results 

• Air Quality Data for 2002 

• Meteorological Data for 2002 

• E.1 – Review and Assessment of Available Ambient Air Quality Data to Support 

Modeling and Modeling Performance Evaluation for the Three VISTAS Phase I 

Episodes 

• E.2 – Protocol for Annual MM5 Modeling in Support of VISTAS 
 Appendix E - 2 

1. Air Quality Data for 2002 

As part of the VISTAS Phase I modeling study, the VISTAS emissions and air quality modeling 

team of ENVIRON International Corporation, Alpine Geophysics (AG) and the University of 

California at Riverside (UCR) compiled ambient monitoring data for both gas and particulate 

species to be used in the VISTAS regional haze model performance evaluation. UCR took the 

lead in this activity. The contractor’s report (found in Appendix E.1) describes the sources of the 

ambient data and the steps taken in the processing and quality assurance (QA) of the data. In 

addition, the ambient data is being processed and formatted in preparation for use in software 

packages designed for the model performance evaluation. 

The goal was to preprocess the ambient data and present plots of ambient data on the project 

website so model performance evaluations when the model simulations were completed. The 

ambient data are available from the following 9 monitoring networks or databases: 

• EPA’s AQS (Environmental Protection Agency’s Air Quality System) database 

• PAMS (Photochemical Assessment Monitoring Stations) 

• IMPROVE (The Interagency Monitoring of Protected Visual Environments) 

• SEARCH (Southeastern Aerosol Research and Characterization) 

• EPA’s STN (Speciation Trends Network) 

• NADP (National Atmospheric Deposition Program) 

• CASTNET (Clean Air Status and Trend Network) 

• PM Supersites 

• NARSTO SOS99 Aircraft data (for the July 1999 episode only) 

• TVA measurement network 

• Georgia Institute of Technology Assessment of Spatial Aerosol Composition in 

Atlanta (ASACA) monitors. 

Note that there is some overlap in the above monitoring networks. For example, some data from 

PAMS (such as ozone and nitrogen oxides) are included in the AQS database, and it appears that 

the STN network may also include speciated particulate matter data from IMPROVE and other 

monitoring networks. Appendix E.1 contains maps of many of the existing and planned (as of 

January 2002) urban and rural fine particulate matter speciation networks. The available data 

from the above monitoring networks for the three episodes of VISTAS regional haze modeling 

(July 9-22, 1999, July 7-28, 2001 and January 1- 21, 2002) was obtained. The attempt was made 

to reconcile data from the various networks and to perform a high level QA of the ambient data. 

The monitoring networks, the available data, and QA efforts are described in their entirety in the 

previously mentioned VISTAS report. 

Additional data used in the air quality modeling include the Total Ozone Mapping Spectrometer 

(TOMS). TOMS data is available for 24-hour average and is obtained from 

http://toms.gsfc.nasa.gov/eptoms/ep.html. The TOMS data is used in the CMAQ radiation 

model (JPROC) to calculate photolysis rates.
 Appendix E - 3 

1.1 Quality Assurance -- Overview 

The VISTAS Phase II emissions and air quality modeling team received emissions, 

meteorological and air quality data from other VISTAS contractors or other sources. As a first 

line of QA, a Gatekeeper function was defined to assure the data have been received correctly, 

evaluate the quality of the data, and document the data received. Separate air quality, 

meteorological and emissions Gatekeepers have been identified whose roles are defined below. 

In addition, a Data Management Gatekeeper has been defined who will post data, reports and 

results to the project website and archive all key data generated in the project. 

• Air Quality Data Gatekeeper. Obtain air quality data as appropriate for model input 

development and model performance evaluation and assure quality of all air quality 

data obtained, consistent with approved QA plan. This gatekeeper will also provide 

documentation of evaluation and generate IC/BC inputs for CMAQ for all modeling 

runs. 

• Meteorological Gatekeeper. Obtain meteorological data, as MM5 or MCIP files, as 

appropriate for annual 2002 modeling runs and other episode periods and perform 

data quality checks as approved in QA plan together with appropriate documentation 

of model performance evaluation activities.

1.2 Quality Assurance of Air Quality Data 

In gathering data from the monitoring networks it is assumed that the agency or researcher 

responsible for collecting the data performed quality assurance on the data. However, it is 

possible that the data sets may contain erroneous data (i.e., missing data, zeros during 

calibration, unrealistic values, etc). Due to poor documentation or poor formatting of some data 

sets it is also possible that mistakes may be made in our processing of the data. To guard against 

this possibility, a plan to perform a high level QA by visually inspecting time-series plots and 

scatter plots of the ambient data was developed and is outlined as follows: 

Plots of Time Series

Time series plots are generated for PAMS species. The plots should be inspected for the 

following: 

• Large "jumps" or "dips" in the concentrations 

• Periodicity of peaks, calibration carryover 

• Unexpected diurnal behavior (i.e., isoprene) 

• Unexpected relationships among species 

• High single-hour concentrations of less abundant species 
 Appendix E - 4 

Scatter Plots

Scatter plots may be prepared for the following: 

• Total NMOC vs. species group totals, vs. individual species 

• Benzene vs. Toluene, Acetylene, Ethane 

• Scatter plots comparing data for a single species measured with different sampling 

methods 

• Plots of reconstructed mass versus measured mass (to reveals if anything unusual is 

happening with the chemical measurements) 

• Plots of molal particulate ammonium versus the molal sum of sulfate and particulate 

nitrate (as a sanity check on the ion balance in the PM chemical measurements) 

If data is identified that appears flawed it will be flagged and either corrected if the error is in the 

processing step, or removed from the data set.

IMPROVE site Quality Assurance

The IMPROVE data have passed the “Level 0 and Level 1” quality assurance and control 

procedure conducted by the Crocker Nuclear Laboratory at UC Davis. In addition, the data are 

added to the online database after a 30 day period to allow the States, Tribes, Federal Land 

Managers’ or any other organization a chance to review and comment on the accuracy, 

credibility, and/or representativeness of aerosol speciation data collected and the reconciliation 

of any issues these organizations may find. In the past, additional QA was not performed on the 

IMPROVE data, but simply used the data obtained from the Cooperative Institute for Research 

in Atmosphere (CIRA) website: http://vista.cira.colostate.edu/improve.

Further detail on the quality assurance of air quality data modeling data can be found in the 

VISTAS document in Appendix E.1. Additional information on quality assurance procedures 

can be found in the QAPP, as referenced in Appendix C of the Regional Haze State 

Implementation Plan for the North Carolina Class I Areas. 

2. Meteorological Data for 2002 

The observations used for performance statistics come primarily from UCAR’s ds472.0 (TDL) 

archive (http://dss.ucar.edu/datasets/ds472.0/). These data are quality controlled and converted 

to NetCDF format, thus allowing the data to be visualized on the model fields via a graphical 

visualization tool such as PAVE. Unfortunately, the precipitation values in this dataset are not 

reliable, so precipitation statistics were calculated based on the 24-hour gridded accumulations 

available online from the Climate Prediction Center (CPC) (http://www.cpc.ncep.noaa.gov). 

These fields, originally at 0.25- degree resolution, undergo grid transformation to match the 

VISTAS 36-km and 12-km domains. Since the CPC analyses are derived primarily from rain 

gauges, the statistics are only calculated over cells that MM5 deems to be land. 
 Appendix E - 5 

For aloft analyses standard sounding observations from the NCEP ds353.4 archive 

(http://dss.ucar.edu/datasets/ds353.4/) were processed. These observations are quality controlled 

and used to produce model/observation skewT sounding plots for selected sites. Additionally, 

the observations were integrated into sigma levels that match the MM5 specifications, after 

which the performance can be statistically analyzed at sigma levels 9, 17, and 22 (~500m, 

~1600m, ~3400m, respectively). Qualitative profiler plots showing model/observed hourly 

winds are also created based upon the data stored at the Forecast Systems Lab 

(http://www.profiler.noaa.gov/jsp). For additional evaluation details and results, please refer to 

the Protocol for Annual MM5 Modeling in Support of VISTAS (DRAFT) Task 3a Deliverable 

found in Appendix E.2. 
Appendix E.1 

Review and Assessment of Available Ambient 

Air Quality Data to Support Modeling and 

Modeling Performance Evaluation for the 

Three VISTAS Phase I Episodes 
July 2003 

Draft VISTAS Emissions and 

Air Quality Modeling— 

Phase I Task 3 Report: 

Review and Assessment of Available Ambient 

Air Quality Data to Support Modeling and 

Modeling Performance Evaluation for the 

Three VISTAS Phase I Episodes 

Prepared by: 

ENVIRON International Corporation 

101 Rowland Way 

Novato, California 94945 

Alpine Geophysics LLC 

3479 Reeves Drive 

Ft. Wright, Kentucky 41017 

UC Riverside 

CE-CERT 

1084 Columbia Avenue 

Riverside, California 92507 

UC Davis 

Mechanical and Aeronautical Engineering 

Davis, California 95616 

Revised July 22, 2003 

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

Appendix E.1 - 1
July 2003 

i 

TABLE OF CONTENTS 

Page 

1.0. SUMMARY.......................................................................................1-1 

2.0. EPA’S AIR QUALITY SYSTEM (AQS)...................................................2-1 

3.0. PHOTOCHEMICAL ASSESSMENT MONITORING 

 STATIONS (PAMS) ............................................................................3-1 

4.0. INTERAGENCY MONITORING OF PROTECTED 

 VISUAL ENVIRONMENTS (IMPROVE) ................................................4-1 

5.0. SOUTHEASTERN AEROSOL RESEARCH AND 

 CHARACTERIZATION (SEARCH) .......................................................5-1 

6.0. SPECIATION TRENDS NETWORK (STN)..............................................6-1 

7.0. NATIONAL ATMOSPHERIC DEPOSITION PROGRAM (NADP)................7-1 

8.0. CLEAN AIR STATUS AND TREND NETWORK (CASTNet).......................8-1 

9.0. PM SUPERSITES...............................................................................9-1 

10.0. AIRCRAFT DATA............................................................................ 10-1 

11.0. DATA QUALITY ASSURANCE .......................................................... 11-1 

APPENDICES 

Appendix A: Locations of Speciated PM and IMPROVE Monitors 

Appendix E.1 - 2
July 2003 

ii 

TABLES 

Table 2-1. Currently available hourly concentration data 

 from AQS for three VISTAS episode selections....................................2-2 

Table 3-1. Species measured in PAMS network. ................................................3-2 

Table 4-1. Species of PM2.5 measured in IMPROVE network.................................4-2 

Table 4-2. Species mappings for IMPROVE species............................................4-4 

Table 4-3. Definitions of CMAQ species names used in Table 4-2...........................4-5 

Table 5-1. PM2.5 species measured in search network. .........................................5-1 

Table 5-2. Coarse PM species measured in search network. ..................................5-2 

Table 6-1. Species of PM2.5 measured in STN network.........................................6-1 

Table 8-1. Species mappings for CASTNET species............................................8-2 

Table 9-1. Locations of PM Supersites. ...........................................................9-1 

FIGURES 

Figure 2-1. Overlap among ambient data collected from 

 several monitoring networks...........................................................2-1 

Figure 2-2. Distribution of average ambient O3 

 concentrations (July 9-22, 1999). .....................................................2-4 

Figure 2-3. Distribution of average ambient PM2.5 

 Concentrations (July 9-22, 1999). ....................................................2-5 

Figure 2-4. Distribution of average ambient NO2 

 concentrations (July 9-22, 1999). .....................................................2-6 

Figure 2-5. Distribution of average ambient CO 

 concentrations (July 9-22, 1999). .....................................................2-7 

Figure 2-6. Distribution of average ambient SO2 

 concentrations (July 9-22, 1999). .....................................................2-8 

Figure 2-7. Distribution of average ambient O3 

 concentrations (July 7-28, 2001). .....................................................2-9 

Figure 2-8. Distribution of average ambient PM2.5 

 concentrations (July 7-28, 2001). ................................................... 2-10 

Figure 2-9. Distribution of average ambient O3 

 concentrations (January 1-21, 2002)................................................ 2-11 

Figure 2-10. Distribution of average ambient PM2.5 

 concentrations (January 1-21, 2002)................................................ 2-12 

Figure 3-1. Distribution of average ambient HCHO concentrations 

 (July 9-22, 1999) from the PAMS network .........................................3-4 

Appendix E.1 - 3
July 2003 

iii 

Figure 3-2. Distribution of average ambient acetone concentrations 

 (July 9-22, 1999) from the PAMS network .........................................3-5 

Figure 3-3. Distribution of average ambient HCHO concentrations 

 (January 1-21, 2002) from the PAMS network.....................................3-6 

Figure 3-4. Distribution of average ambient acetone concentrations 

 (January 1-21, 2002) from the PAMS network.....................................3-7 

Figure 5-1. Locations of the SEARCH monitoring sites (Source: Atmospheric 

 Research and Analysis, Inc., www.atmopheric-research.com). .................5-3 

Figure 7-1. Locations of NADP National Trends Network (NTN) monitors 

 (Figure obtained from the EPA NADP website). ..................................7-4 

Figure 7-2. Locations of the AIRMoN wet and dry deposition monitoring 

 sites (Figure obtained from the EPA AIRMoN website)..........................7-5 

Figure 7-3. Locations of the AIRMoN wet deposition monitoring sites 

 (Figure obtained from the EPA AIRMoN website). ...............................7-6 

Figure 8-1. Locations of CASTNet monitoring sites (Figure obtained 

 from EPA CASTNet website). ........................................................8-3 

Appendix E.1 - 4
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1.0 SUMMARY 

As part of the VISTAS Phase I modeling study, the VISTAS emissions and air quality 

modeling team of ENVIRON International Corporation, Alpine Geophysics (AG) and the 

University of California (UC) are compiling ambient monitoring data for both gas and 

particulate species to be used in the VISTAS regional haze model performance evaluation. 

The University of California at Riverside (UCR) is taking the lead in this activity. This report 

describes the sources of the ambient data and the steps that will be used in the processing and 

quality assurance (QA) of the data. In addition, the ambient data is being processed and 

formatted in preparation for use in software packages designed for the model performance 

evaluation. Our goal is to preprocess the ambient data and present plots of ambient data on the 

project website so that we can rapidly complete model performance evaluations when the 

model simulations are completed. As the processing of ambient data are completed, plots of 

the data are being made available at the project website: 

http://pah.cert.ucr.edu/vistas/ambient.shtml. A large amount of this data is currently available 

at the project website, and we are continuing to develop the website to facilitate the display of 

time series plots. 

The ambient data are available from the following 9 monitoring networks or databases: 

• EPA’s AQS (Air Quality System) database 

• PAMS (Photochemical Assessment Monitoring Stations) 

• IMPROVE (The Interagency Monitoring of Protected Visual Environments) 

• SEARCH (Southeastern Aerosol Research and Characterization) 

• EPA’s STN (Speciation Trends Network) 

• NADP (National Atmospheric Deposition Program) 

• CASTNET (Clean Air Status and Trend Network) 

• PM Supersites 

• NARSTO SOS99 Aircraft data (for the July 1999 episode only) 

• TVA measurement network 

• Georgia Institute of Technology Assessment of Spatial Aerosol Composition in Atlanta 

(ASACA) monitors. 

Note that there is some overlap in the above monitoring networks. For example, some data 

from PAMS (such as O3 and NOx) are included in the AQS database, and it appears that the 

STN network may also include speciated PM data from IMPROVE and other monitoring 

networks. Appendix A shows maps of many of the existing and planned (as of January, 2002) 

urban and rural PM2.5 speciation networks. 

We have obtained most of the available data from the above monitoring networks for the three 

episodes of VISTAS regional haze modeling (July 9-22, 1999, July 7-28, 2001 and January 1-

21, 2002). Although a comprehensive quality assurance and validation effort is beyond the 

scope of this effort, we are attempting to reconcile data from the various networks and to 

perform a high level QA of the ambient data. The monitoring networks, the available data, and 

QA efforts are described below. We expect that during the next two months we will continue 

Appendix E.1 - 5
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to gather and process ambient data, and will rely on expertise from other team members and 

VISTAS in identifying ambient data. We have not yet investigated the possibility of systematic 

bias among different analytical methods used in the various networks. However, this is an 

important subject to be addressed because of the possibility that networks employ different 

sampling methods. 

Measurement data from monitors operated by the Tennessee Valley Authority (TVA) and 

Georgia Technology’s ASACA monitors have not yet been acquired. ASACA has been 

operating three Particle Composition Monitors and three TEOMS in Atlanta since early 1999. 

When acquired, these data will be documented on the project website. 

Appendix E.1 - 6
July 2003 

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2.0 EPA’S AIR QUALITY SYSTEM (AQS) 

The Air Quality System (AQS) database is EPA's repository of “criteria air pollutant”

monitoring data since the 1970s. The criteria air pollutants are: 

• Carbon Monoxide (CO) 

• Nitrogen Dioxide (NO2) 

• Sulfur Dioxide (SO2) 

• Ozone (O3) 

• Particulate Matter (PM10 and PM2.5) 

• Lead (Pb) 

Ambient concentrations of these pollutants from more than 4000 monitoring stations are 

reported to AQS on weekly or monthly basis. While several other monitoring networks (e.g. 

PAMS, IMPROVE, CASTNet, and etc.) owned and operated by different agencies collect 

various air pollutants, only criteria air pollutants are reported to AQS (as shown in Figure 2-

1). 

Figure 2-1. Overlap among ambient data collected from several monitoring networks.

PM25, PM10 

O3, SO2 

PM25, PM10 

O3, NOx, CO, Pb, etc 

EPA PM Sites 

Other monitoring stations 

from state, local agencies 

VOCs O3, NOx 

Sp. PM25, 

Visibility

HNO3, NO3, 

SO4,

Appendix E.1 - 7
July 2003 

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Although direct access to full AQS raw data is currently not available, several archived data 

files can be downloaded from EPA’s website http://www.epa.gov/ttn/airs/airsaqs/. Table 2-1 

summarizes the data that are currently available and processed for hourly average 

concentration data from AQS for three VISTAS episodes. We have requested the remaining 

AQS data from EPA and will process them upon arrival. Note that the AQS includes O3 data 

from the State/Local/National Air Monitoring Stations (SLAMS). 

Table 2-1. Currently* available hourly concentration data from AQS for three VISTAS episode 

selections. 

Period O3 PM2.5 NO2 CO SO2

July 9-22, 1999 X X X X X 

July 7-28, 2001 X X 

Jan. 1-21, 2002 X X 

* Efforts are underway to acquire the NO2, CO and SO2 measurements for the July 2001 and January 2002 

episodes. 

These data are presented on the VISTAS project website, http://www.cert.ucr.edu/vistas

/ambient.shtml, under the title of “Ambient data for Model Evaluation”, as animated figures 

that show spatial variation of O3, PM2.5, NO2, CO and SO2 concentrations. These data will 

also be made available as time series plots for each monitor site. The spatial distributions for 

average species concentrations within each VISTAS episode are shown in Figures 2-2 through 

2-10. These plots are intended to be illustrative of the results on the project web page, and 

for more detailed evaluation please see the ambient data page: 

http://pah.cert.ucr.edu/vistas/ambient.shtml. 

Note that the web page also includes plots that zoom in on the southeastern US domain. 

July 9-22, 1999 

Hourly O3 concentrations were available at 1113 stations over USA for July 9-22, 1999. 

Figure 2-2 shows the distribution of average O3 concentrations for these stations. 

Hourly PM2.5 concentrations were available at 25 stations over USA for July 9-22, 1999. 

Figure 2-3 shows the distribution of average PM2.5 concentrations for these stations. 

Hourly NO2 concentrations were available at 170 stations over USA for July 9-22, 1999. 

Figure 2-4 shows the distribution of average NO2 concentrations for these stations. 

Hourly CO concentrations were available at 442 stations over USA for July 9-22, 1999. 

Figure 2-5 shows the distribution of average CO concentrations for these stations. 

Hourly SO2 concentrations were available at 563 stations over USA for July 9-22, 1999. 

Figure 2-6 shows the distribution of average SO2 concentrations for these stations. 

Appendix E.1 - 8
July 2003 

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July 7-28, 2001 

Hourly O3 concentrations were available at 1113 stations over USA for July 7-28, 2001. 

Figure 2-7 shows the distribution of average O3 concentrations for these stations. 

Hourly PM2.5 concentrations were available at 148 stations over USA for July 7-28, 2001. 

Figure 2-8 shows the distribution of average PM2.5 concentrations for these stations. 

January 1-21, 2002 

Hourly O3 concentrations were also available at 501 stations over USA for January 1-21, 

2002. Figure 2-9 shows the distribution of average O3 concentrations for these stations. 

Hourly PM2.5 concentrations were available at 180 stations over USA for January 1-21, 2002. 

Figure 2-10 shows the distribution of average PM2.5 concentrations for these stations. 

Appendix E.1 - 9
July 2003 

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Figure 2-2. Distribution of average ambient O3 concentrations (July 9-22, 1999).

Appendix E.1 - 10
July 2003 

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Figure 2-3. Distribution of average ambient PM2.5 concentrations (July 9-22, 1999). 

Appendix E.1 - 11
July 2003 

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Figure 2-4. Distribution of average ambient NO2 concentrations (July 9-22, 1999). 

Appendix E.1 - 12
July 2003 

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Figure 2-5. Distribution of average ambient CO concentrations (July 9-22, 1999).

Appendix E.1 - 13
July 2003 

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Figure 2-6. Distribution of average ambient SO2 concentrations (July 9-22, 1999).

Appendix E.1 - 14
July 2003 

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Figure 2-7. Distribution of average ambient O3 concentrations (July 7-28, 2001). 

Appendix E.1 - 15
July 2003 

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Figure 2-8. Distribution of average ambient PM2.5 concentrations (July 7-28, 2001). 

Appendix E.1 - 16
July 2003 

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Figure 2-9. Distribution of average ambient O3 concentrations (January 1-21, 2002).

Appendix E.1 - 17
July 2003 

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Figure 2-10. Distribution of average ambient PM2.5 concentrations (January 1-21, 2002). 

Appendix E.1 - 18
July 2003 

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3.0 PHOTOCHEMICAL ASSESSMENT MONITORING STATIONS (PAMS) 

In response to the 1990 Clean Air Act Amendments, EPA has required more extensive 

monitoring of ozone and its precursors in areas with persistently high ozone levels. 

Photochemical Assessment Monitoring Stations (PAMS) have been established by the States to 

collect and report detailed data for volatile organic compounds, nitrogen oxides, ozone and 

meteorological parameters. The EPA lists five objectives for the PAMS program: 

1. Provide a speciated ambient air database which is both representative and useful for 

ascertaining ambient profiles and distinguishing among various individual VOC. 

2. Provide local, current meteorological and ambient data to serve as initial and boundary 

condition information for photochemical grid models, a method that simulates 

meteorological and physical processes that affect air pollution emissions in the 

atmosphere. 

3. Provide a representative, speciated ambient air database which is characteristic of 

source emission impacts. 

4. Provide ambient data measurements which would allow later preparation of unadjusted 

and adjusted pollutant trends reports. 

5. Provide additional measurements of selected criteria pollutants. 

Because different types of ambient monitoring data are required to characterize regional 

background concentration, emissions sources, and peak pollutant levels, four different types of 

PAMS sites are used: 

• Type I: Upwind and background characterization 

• Type II: Maximum ozone precursor emissions impact 

• Type III: Maximum ozone concentration 

• Type IV: Extreme downwind monitoring 

Hourly average concentrations of O3, NO, NO2, NOx and about 60 species of VOC (volatile 

organic compounds) are measured at each PAMS station. Archived data files can be 

downloaded from the website: http://www.epa.gov/ttn/airs/airsaqs/archived%

20data/archivedaqsdata.htm. Table 3-1 shows the species names, carbon numbers and 

molecule weights for 60 species of VOC measured in PAMS. The PAMS data are currently 

available for VISTAS’ two modeling periods of July 9-22, 1999 and Jan. 1-21, 2002. Since 

some data, such as O3, NO, NO2 and NOx may also be included in the AQS database, we are 

still considering the best approach to treat these data (i.e., present them with the AQS data 

only, or with the PAMS data only, or both). 

For the data from selected PAMS sites, hourly average concentrations are available for 64 

species (Table 2-1) for July 9-22, 1999 and Jan. 1-21, 2002. The animated graphics that show 

five species (TNMOC, THC, HCHO, acetaldehyde and acetone) concentrations have been put 

on the VISTAS project website (http://pah.cert.ucr.edu/vistas/ambient.shtml, under the title of 

“Ambient data for Model Evaluation”. Example plots of average concentrations for two 

important species (HCHO and acetone) are shown in Figures 3-1 through 3-4. 

Appendix E.1 - 19
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec3.gst.doc 3-2 

Table 3-1. Species measured in PAMS network. 

Name Description 

Carbon 

number MWt 

NO NITRIC OXIDE 

NO2 NITROGEN DIOXIDE 

NOX OXIDES OF NITROGEN 

PAMHC SUM OF PAMS TARGET COMPOUNDS 

TNMOC TOTAL NMOC 

ETHAN ETHANE AKA-METHYLMETHANE 2 30.07 

ETHYL ETHYLENE AKA-ETHENE 2 62.07 

PROPA PROPANE AKA-DIMETHYLMETHANE 3 44.10 

PRPYL PROPYLENE AKA-PROPENE 4 102.09 

ACETE ACETYLENE AKA-ETHYNE 2 26.04 

NBUTA N-BUTANE AKA-BUTANE 4 58.12 

ISBTA ISOBUTANE AKA-2-METHYLPROPANE 4 58.12 

T2BTE TRANS-2-BUTENE 4 56.11 

C2BTE CIS-2-BUTENE 4 56.11 

NPNTA N-PENTANE AKA-AMYL HYDRIDE 5 72.15 

ISPNA ISOPENTANE AKA-2-METHYLBUTANE 5 72.15 

1PNTE 1-PENTENE AKA-PROPYLETHYLENE 5 70.14 

T2PNE TRANS-2-PENTENE 5 70.14 

C2PNE CIS-2-PENTENE AKA-CIS-B-N-AMYLENE 5 70.14 

3MPNA 

3-METHYLPENTANE AKADIETHYLMETHYLMETHANE 16 226.45 

NHEXA N-HEXANE 6 86.18 

NHEPT N-HEPTANE AKA-DIPROPYLMETHANE 7 100.21 

NOCT N-OCTANE 8 114.23 

NNON N-NONANE AKA-NONYL HYDRIDE 9 128.26 

NDEC N-DECANE 10 142.29 

CYPNA CYCLOPENTANE AKA-PENTAMETHYLENE 5 70.14 

ISPRE ISOPRENE AKA-3-METHYL-1,3-BUTADIENE 5 68.12 

22DMB 2,2-DIMETHYLBUTANE AKA-NEOHEXANE 6 86.18 

2M1PE 

2-METHYL-1-PENTENE AKA-1-METHYL-1-

PROPYLETHYLENE 6 84.16 

24DMP 2,4-DIMETHYLPENTANE 7 100.21 

CYHXA CYCLOHEXANE AKA-HEXAMETHYLENE 6 84.16 

3MHXA 3-METHYLHEXANE 7 100.21 

224TM 2,2,4-TRIMETHYLPENTANE AKA-ISOOCTANE 8 114.23 

234TM 2,3,4-TRIMETHYLPENTANE 8 114.23 

3MHEP 3-METHYLHEPTANE 8 114.23 

MCYHX 

METHYLCYCLOHEXANE AKAHEXAHYDROTOLUENE 7 98.19 

MCPNA METHYLCYCLOPENTANE 6 84.16 

2MHXA 2-METHYLHEXANE AKA-ISOHEPTANE 8 

1BUTE 1-BUTENE AKA-ETHYLETHYLENE 4 56.11 

23DMB 2,3-DIMETHYLBUTANE AKA-DIISOPROPYL 6 86.18 

2MPNA 2-METHYLPENTANE AKA-ISOHEXANE 7 

23DMP 2,3-DIMETHYLPENTANE 7 100.21 

FORM FORMALDEHYDE AKA-OXYMETHYLENE 1 30.03 

Appendix E.1 - 20
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec3.gst.doc 3-3 

Name Description 

Carbon 

number MWt 

ACETA ACETALDEHYDE AKA ACETIC ALDEHYDE 2 44.05 

ACET ACETONE AKA-DIMETHYLKETONE 3 58.08 

NUNDC N-UNDECANE 11 156.31 

2MHEP 2-METHYLHEPTANE 8 

O3 OZONE 

M/PXY M/P XYLENE 8 106.17 

BZ BENZENE 6 78.11 

TOLU TOLUENE AKA METHYLBENZENE 7 92.14 

EBENZ ETHYLBENZENE AKA-PHENYLETHANE 8 106.17 

OXYL O-XYLENE AKA-1,2-DIMETHYLBENZENE 8 106.17 

135TB 1,3,5-TRIMETHYLBENZENE AKA-MESITYLENE 9 120.20 

124TB 1,2,4-TRIMETHYLBENZENE AKA-PSEUDOCUMENE 9 120.20 

NPBZ N-PROPYLBENZENE AKA-1-PHENYLPROPANE 9 120.20 

ISPBZ ISOPROPYLBENZENE AKA CUMENE 9 120.20 

OETOL O-ETHYLTOLUENE 8 

METOL M-ETHYLTOLUENE 8 

PETOL P-ETHYLTOLUENE (AKA 4-ETHYLTOLUENE) 8 

MDEB M-DIETHYLBENZENE 8 

PDEB P-DIETHYLBENZENE AKA-1,4-DIETHYLBENZENE 6 147.01 

STYR STYRENE AKA ETHENYLBENZENE 8 104.15 

123TB 1,2,3-TRIMETHYLBENZENE 9 120.20 

July 9-22, 1999 

Hourly HCHO concentrations are available at 92 stations over USA for July 9-22, 1999. 

Figure 2-11 shows the distribution of average HCHO concentrations during that period of time 

for these stations. 

Hourly Acetone concentrations are available at 130 stations over USA for July 9-22, 1999. 

Figure 2-12 shows the distribution of average acetone concentrations for these stations. 

Jan. 1-21, 2002 

Hourly HCHO concentrations are available at 57 stations over USA for Jan. 1-21, 2002. 

Figure 2-13 shows the distribution of average HCHO concentrations for these stations. 

Hourly Acetone concentrations are available at 67 stations over USA for Jan. 1-21, 2002. 

Figure 2-14 shows the distribution of average Acetone concentrations for these stations. 

We have recently obtained more speciated VOC data for additional PAMS sites and are 

currently evaluating these data. 

Appendix E.1 - 21
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Figure 3-1. Distribution of average ambient HCHO concentrations (July 9-22, 1999) from the 

PAMS network. 

Appendix E.1 - 22
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Figure 3-2. Distribution of average ambient acetone concentrations (July 9-22, 1999) from the 

PAMS network. 

Appendix E.1 - 23
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Figure 3-3. Distribution of average ambient HCHO concentrations (January 1-21, 2002) from 

the PAMS network. 

Appendix E.1 - 24
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Figure 3-4. Distribution of average ambient acetone concentrations (January 1-21, 2002) from 

the PAMS network. 

Appendix E.1 - 25
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec4.gst.doc 4-1 

4.0 INTERAGENCY MONITORING OF PROTECTED VISUAL ENVIRONMENTS 

(IMPROVE) 

The Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring 

network consists of air quality data from Class I areas that include national parks and 

wilderness areas where visibility is deemed an important attribute. There are also IMPROVE 

protocol monitoring sites that are not located in Class I areas (see Appendix A for locations). 

This monitoring program is an interagency effort with the U.S. Environmental Protection 

Agency (USEPA) and the U.S. Department of the Interior (USDOI), including the U.S. Forest 

Service, U.S. Fish and Wildlife Service, and the Bureau of Land Management. The 

IMPROVE fine particle network collects PM2.5 and PM10 samples over a twenty four hour 

using IMPROVE samplers. The current network consists of over 160 monitoring sites, mainly 

located in Class I ("Clean Air") areas. Some of the earliest sites have been in operation since 

March 1988, although most sites were installed in the mid 1990’s or later. The PM samples 

are analyzed for PM2.5 mass and its elemental constituents, organics, ions, light absorption and 

PM10 mass. 

The objectives of IMPROVE are: (1) to establish current visibility and aerosol conditions in 

mandatory class I areas; (2) to identify chemical species and emission sources responsible for 

existing man-made visibility impairment; (3) to document long-term trends for assessing 

progress towards the national visibility goal; (4) and to provide regional haze monitoring 

representing all visibility-protected federal class I areas where practical. In 1999 there were 70 

IMPROVE sites including 30 sites in Class I areas and an additional 40 sites using the 

IMPROVE protocol. By 2002 there were approximately 110 IMPROVE sites and 53 

IMPROVE Protocol sites. 

The standard IMPROVE sampler has four sampling modules, listed below, although some 

sites only include the Module A: 

Module A: PM2.5 particles on Teflon are analyzed at UC Davis using the following methods: 

• gravimetric mass for PM2.5

• hybrid integrating plate/sphere method for optical absorption 

• Proton Elastic Scattering Analysis (PESA) for hydrogen 

• Proton Induced X-ray Emission (PIXE) for Na-Mn 

• X-Ray Fluorescence (XRF) for Fe-Pb 

Module B: PM2.5 particles on nylon. A denuder before the nylon filter removes nitric acid 

vapors. These are analyzed by ion chromatography (IC) at Research Triangle Institute for 

nitrate (NO3

-

), chloride (Cl-), sulfate (SO4

2-), and nitrite (NO2-

). 

Module C: PM2.5 particles on quartz. These are analyzed at Desert Research Institute for 

carbon using the Thermal Optical Reflectance (TOR) combustion method. A secondary filter 

at selected sites is used to determine artifacts. These are reported in 8 temperature categories. 

Appendix E.1 - 26
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec4.gst.doc 4-2 

Module D: PM10 particles on Teflon. All are measured for PM10 mass. Approximately 4% are 

analyzed by the other four methods listed for Module A. 

For selected IMPROVE sites, there also are transmissometer data to directly measure light 

extinction and nephelometer data to provide direct measurements of light scattering. 

The IMPROVE data have passed the “Level 0 and Level 1” quality assurance and control 

procedure conducted by the Crocker Nuclear Laboratory at UC Davis, and are added to the 

online database after a 30 day period to allow the States, Tribes, FLM’s or any other 

organization a chance to review and comment on the accuracy, credibility, and/or 

representativeness of aerosol speciation data collected and the reconciliation of any issues these 

organizations may find. In the past, we have not performed any additional QA of the 

IMPROVE data, and have simply used the data obtained from the Cooperative Institute for 

Research in Atmosphere (CIRA) website: http://vista.cira.colostate.edu/improve. 

A 24 hour averaging period is used for IMPROVE data. Prior to 2000, two 24 hour samples 

were collected twice a week, on Wednesday and Saturday. After 2000, 24 hour samples were 

collected every three days. 

Table 4-1 lists the 41 species measured at IMPROVE sites. The IMPROVE data are available 

for all three VISTAS modeling episodes. The downloaded data have been formatted for 

evaluation software, and time series plots are also available for download on the project 

website. Additional QA of these data can be performed, as discussed in Section 10, using time 

series and scatter plots of these data. 

Table 4-1. Species of PM2.5 measured in IMPROVE network. 

CODE NAME CODE NAME 

AL Aluminum: Fine NH4 Ammonium ion: Fine 

AS Arsenic: Fine NI Nickel: Fine 

BR Bromine: Fine NO3 Nitrate: Fine 

CA Calcium: Fine OC1 Carbon: Fine organic (OC1) 

CHL Chloride: Fine OC2 Carbon: Fine organic (OC2) 

CL Chlorine: Fine OC3 Carbon: Fine organic (OC3) 

CR Chromium: Fine OC4 Carbon: Fine organic (OC4) 

CU Copper: Fine OP Carbon: Fine organic (OP) 

EC1 Carbon: Fine elemental (EC1) P Phosphorus: Fine 

EC2 Carbon: Fine elemental (EC2) PB Lead: Fine 

EC3 Carbon: Fine elemental (EC3) RB Rubidium: Fine 

FE Iron: Fine S Sulfur: Fine 

H Hydrogen: Fine SE Selenium: Fine 

K Potassium: Fine SI Silicon: Fine 

MASS PM2.5: mass SO4 Sulfate: Fine 

MASS PM10: mass SR Strontium: Fine 

MG Magnesium: Fine TI Titanium: Fine 

MN Manganese: Fine V Vanadium: Fine 

MO Molybdenum: Fine ZN Zinc: Fine 

N2 Nitrite: Fine ZR Zirconium: Fine 

Appendix E.1 - 27
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CODE NAME CODE NAME 

NA Sodium: Fine 

For most species, the IMPROVE data cannot be matched directly to the model gas and PM 

species because of lumping schemes in the model chemistry. Therefore, it is necessary to 

convert the ambient data and model species into forms that can be directly compared. Table 4-

2 shows the mapping scheme used for the IMPROVE comparison to the model species. Table 

4-3 list definitions for CMAQ model species. Definitions for compounds in Table 4-2 include 

the following: OC is organic carbon; EC is elemental carbon or soot; CM is coarse mass; 

RCFM is reconstructed fine mass; and Bext_Recon is reconstructed extinction coefficient. 

We note that in previous model evaluation for WRAP the sulfate and nitrate were assumed to 

be full neutralized and that ammonium sulfates and nitrate were represented in CMAQ as 

1.375*(ASO4J + ASO4I) +1.29*(ANO3J + ANO3I), respectively. This was used for 

consistency with the formula used to calculate the model reconstructed extinction coefficient. 

For VISTAS we propose to use the CMAQ model ammonium mass (ANH4J+ANH4J) 

explicitly because this provides a more accurate estimate of modeled fine mass. 

In previous modeling studies we have not had access to direct measurements of ammonium ion 

(NH4

+). VISTAS is funding the Research Triangle Institute (RTI) to measure NH4+ at 10 sites 

beginning in September, 2002. Prior to September, NH4

+ data will be available for only 3 

sites: Great Smokey Mountains (GRSM), Shenandoah (SHEN), Class I areas, and Dolly Sods 

(DOSO). 

Concerns have been raised regarding the accuracy of the IMPROVE HNO3 data, and we have 

found that it tends to be lower than HNO3 data from the CASTNET network. However, we 

do not have a basis for adjusting or rejecting the IMPROVE HNO3 data, and further 

evaluation of HNO3 data from all monitoring networks is required. In September 2003 EPA 

will conduct a field study intercomparison of HNO3 methods using a Chemical Ionization 

Mass Spectrometer as a reference method. This may provide insight into the accuracy of 

HNO3 methods employed at the different monitoring networks, however, it is unlikely that 

results from this study will be available in 2003 to affect the VISTAS Phase I modeling. 

There are concerns that the coarse mass (CM) IMPROVE measurements may also include 

some sulfate, nitrate and other species (e.g., sea salt and organics) that occur in the coarse 

mode. There are two different mechanism by which NO3 can be transferred to the coarse 

mode: formation of coarse mode sodium nitrate or calcium nitrate, and the ammonium nitrate 

distributions that can extend in to the coarse mode. In previous CMAQ performance 

evaluations, all nitrate has been assumed to be in the fine mode. If substantial fractions of 

nitrate are in the coarse mode, CMAQ would be expected to over predict the mass of fine 

nitrate. Speciation of the CM fraction is ongoing at several IMPROVE sites to investigate this 

issue. A coarse mode speciation measurement program was begun in spring, 2002, and it is 

possible that initial results will be available in fall, 2002. 

Appendix E.1 - 28
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec4.gst.doc 4-4 

We are also investigating the modal distributions of the Aitken and accumulation modes in 

CMAQ. Although these modes are typically assumed to be entirely PM2.5, we have found that 

some of the mass does extend in to the coarse mode. 

Table 4-2. Species mappings for IMPROVE species. Note that CMAQ represents fine PM 

species in two size modes: Aitken nuclei (0.03 to 0.5 μm) and accumulation mode (0.5 to 

2.5 μm) and in CMAQ by represented by J and I, respectively. Compounds listed include coarse 

mass (CM); 

Compound IMPROVE Species CMAQ Mapping 

SO4 SO4 ASO4J + ASO4I 

NO3 NO3 ANO3J + ANO3I 

NH4+ NH4+ ANH4J + ANH4I 

OC 1.4*(OC1+OC2+OC3+OC4+OP) AORGAJ + AORGAI + AORGPAJ + 

AORGPAI + AORGBJ + AORGBI 

EC EC1+EC2+EC3-OP AECJ + AECI 

SOIL 2.2*Al + 2.49*Si + 1.63*Ca + 

2.42*Fe + 1.94*Ti A25I +A25J 

CM MT – FM ACORS + ASEAS + ASOIL 

PM25a FM 

ASO4J + ASO4I + ANO3J + ANO3I + ANH4J 

+ ANH4I + AORGAJ + AORGAI + AORGPAJ 

+ AORGPAI + AORGBJ + AORGBI + AECJ + 

AECI + A25J + A25I 

RCFM 1.375*SO4 + 1.29*NO3 + EC + OC 

+ SOIL Same for PM25 

PM10 MT 

ASO4J + ASO4I + ANO3J + ANO3I + ANH4J 

+ ANH4I + AORGAJ + AORGAI + AORGPAJ 

+ AORGPAI + AORGBJ + AORGBI + AECJ + 

AECI + A25J + A25I + ACORS + ASEAS + 

ASOIL 

Bext_Recon 

(1/Mm) 

10b

 + 3*f(RH)c(1.375*SO4 + 

1.29*NO3) + 4*OC + 10*EC + SOIL

+ 0.6*CM 

10b

 + 3*f(RH)c[1.375*(ASO4J + ASO4I) + 

1.29*(ANO3J + ANO3I)] + 4*1.4*(AORGAJ + 

AORGAI + AORGPAJ + AORGPAI + AORGBJ 

+ AORGBI) + 10*(AECJ + AECI) + 1*(A25J + 

A25I) + 0.6*(ACORS + ASEAS + ASOIL) 

a Measured; b

 Rayleigh scattering correction; c f(RH) site and day specific relative humidity adjustment 

factor. 

Appendix E.1 - 29
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Table 4-3. Definitions of CMAQ species names used in Table 4-2. 

CMAQ Mapping 

ASO4 Aerosol sulfate 

ANO3 Aerosol nitrate 

ANH4 Ammonium ion 

AORGPA Organic aerosols from primary organic emissions 

AORGA, AORGB Secondary organic aerosols from anthropogenic sources 

(aromatics, paraffin) and biogenic source, respectively. 

AEC Elemental Carbon 

A25 Unspecified fine mass including fine crustal material (fine soil)

ASOIL Soil-derived (crustal) coarse materials 

ASEAS Sea salt (only in coarse mode) 

ACORS Unspecified anthropogenic coarse mass 

RCFM Reconstructed fine mass 

MT Mass total 

CM Coarse mass 

Bext_recon Reconstructed extinction coefficient 

Appendix E.1 - 30
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec5.gst.doc 5-1

5.0 SOUTHEASTERN AEROSOL RESEARCH AND CHARACTERIZATION 

(SEARCH) 

SEARCH is a monitoring network for Southeastern Aerosol Research and Characterization. 

There are 8 monitoring sites located in four states in the SEARCH network whose locations 

are shown in Figure 5-1. Daily PM2.5 data are measured for 46 species, while daily coarse PM 

data are measured for 18 species in SEARCH network. Archived data file can be downloaded 

from the website (http://www.atmospheric-research.com/public/index.html). Tables 5-1 and 5-

2 show the names for 46 PM2.5 species and 18 coarse PM species. The SEARCH data are 

currently available for two of the VISTAS Phase I modeling episodes (July 9-22, 1999 and 

July 7-28, 2001). 

The frequency of the PM2.5 measurements of SEARCH has varied from 1998 to the present. 

Measurements were made daily at all sites for a little over one year. Subsequently, the 

measurements frequency varies from daily to every third or sixth day depending on the 

specific site of interest. Semi-continuous TEOM PM2.5 mass measurements have been 

available since the onset of the program and are reported as hourly averages. Semi-continuous 

measurements of PM chemical components have been phased in over the years, initially at 

Jefferson Street (Atlanta) and later at other sites. However, the ARS Data Gap report 

indicates that such hourly data have not yet been made generally available on the SEARCH 

web site. We need to determine whether the modeling team has this data or how best to get 

access to this data. 

Table 5-1. PM2.5 species measured in SEARCH network. 

Name Name Name Name 

FRM Mass PM25 Major 

Metal Oxides XRF Pb Al2O3 

FRM SO4 PCM2 SO4 XRF Sb SiO2 

FRM NO3 PCM2 NO3 XRF Se K2O 

FRM NH4 PCM2 NH4 XRF Sn CaO 

PCM1 Mass PCM2 CL XRF Ti TiO2 

PCM1 SO4 PCM3 EC XRF Zn Fe2O3 

PCM1 NO3 PCM3 OC WS Chromium XRF S 

PCM1 Vol NO3 XRF As WS Copper TEOM Mass 

PCM1 Teflon NO3 XRF Ba WS Iron BackupPCM3 EC 

PCM1 NH4 XRF Br WS Manganese BackupPCM3 OC 

PCM1 Vol NH4 XRF Cu WS Nickel 

PCM1 Teflon NH4 XRF Mn WS Vanadium 

Appendix E.1 - 31
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Table 5-2. Coarse PM species measured in SEARCH network. 

Name Name Name Name 

Coarse Mass Chromium Vanadium TiO2 

Coarse SO4 Copper Al2O3 Fe2O3 

Coarse NO3 Iron SiO2 Coarse S 

Coarse NH4 Manganese K2O 

CoarseMajorMetalOxides Nickel CaO 

For the data from SEARCH network, daily average PM2.5 concentrations for 46 species (Table 

5-1) and daily coarse PM concentration for 18 species (Table 5-2) are available for two 

modeling episodes (July 9-22, 1999 and July 7-28, 2001). 

We are working with the SEARCH sponsors and scientists to obtain data suitable for model 

performance evaluation and obtain data for the January 2002 episode. Locations of the 

SEARCH monitors are shown in Figure 5-1. 

Appendix E.1 - 32
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Figure 5-1. Locations of the SEARCH monitoring sites (Source: Atmospheric Research and 

Analysis, Inc., www.atmopheric-research.com).

Appendix E.1 - 33
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec6.gst.doc 6-1 

6.0 SPECIATION TRENDS NETWORK (STN) 

EPA’s Speciation Trends Network (STN) includes about 215 monitoring stations nationwide. 

It appears that among these 215 sites may include IMPROVE sites or other data from other 

networks. This, however, needs to be verified. Daily PM2.5 data are measured for 64 species 

in the STN network. Some archived STN data files were obtained from the 

website:http://www.epa.gov/ttn/airs/airsaqs/archived%20data/archivedaqsdata.htm. 

Additional documentation of the STN and descriptions of the data are still needed. Table 6-1 

shows the codes and names for 64 species measured in STN. Locations of the STN monitors, 

as well as other specified PM monitors, are shown in Appendix A. 

Table 6-1. Species of PM2.5 measured in STN network. 

Code Description Code Description 

88101 PM2.5 - Local Conditions 88160 Tin PM2.5 LC 

88102 Antimony PM2.5 LC 88161 Titanium PM2.5 LC 

88103 Arsenic PM2.5 LC 88162 Samarium PM2.5 LC 

88104 Aluminum PM2.5 LC 88163 Scandium PM2.5 LC 

88105 Beryllium PM2.5 LC 88164 Vanadium PM2.5 LC 

88107 Barium PM2.5 LC 88165 Silicon PM2.5 LC 

88109 Bromine PM2.5 LC 88166 Silver PM2.5 LC 

88110 Cadmium PM2.5 LC 88167 Zinc PM2.5 LC 

88111 Calcium PM2.5 LC 88168 Strontium PM2.5 LC 

88112 Chromium PM2.5 LC 88169 Sulfur PM2.5 LC 

88113 Cobalt PM2.5 LC 88170 Tantalum PM2.5 LC 

88114 Copper PM2.5 LC 88172 Terbium PM2.5 LC 

88115 Chlorine PM2.5 LC 88176 Rubidium PM2.5 LC 

88117 Cerium PM2.5 LC 88179 Uranium PM2.5 LC 

88118 Cesium PM2.5 LC 88180 Potassium PM2.5 LC 

88121 Europium PM2.5 LC 88183 Yttrium PM2.5 LC 

88124 Gallium PM2.5 LC 88184 Sodium PM2.5 LC 

88126 Iron PM2.5 Lc 88185 Zirconium PM2.5 LC 

88127 Hafnium PM2.5 LC 88186 Tungsten PM2.5 LC 

88128 Lead PM2.5 LC 88301 Ammonium Ion PM2.5 LC 

88131 Indium PM2.5 LC 88302 Sodium Ion Pm2.5 LC 

88132 Manganese PM2.5 LC 88303 Potassium Ion PM2.5 LC 

88133 Iridium PM2.5 LC 88304 OCX Carbon 

88134 Molybdenum PM2.5 LC 88305 Organic Carbon PM2.5 LC 

88136 Nickel PM2.5 LC 88306 Total Nitrate PM2.5 LC 

88140 Magnesium PM2.5 LC 88307 Elemental Carbon PM2.5 LC 

88142 Mercury PM2.5 LC 88308 Carbonate Carbon PM2.5 LC 

88143 Gold PM2.5 LC 88309 Volatile Nitrate PM2.5 LC 

88146 Lanthanum PM2.5 LC 88310 Non-volatile Nitrate PM2.5 LC 

88147 Niobium PM2.5 LC 88311 OCX2 Carbon 

88152 Phosphorus PM2.5 LC 88312 Total Carbon PM2.5 LC 

88154 Selenium PM2.5 LC 88403 Sulfate PM2.5 LC 

Appendix E.1 - 34
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-1 

7.0 NATIONAL ATMOSPHERIC DEPOSITION PROGRAM (NADP) 

The National Atmospheric Deposition Program/National Trends Network (NADP/NTN) is 

designed to measure wet deposition. The network is a cooperative effort between State 

Agricultural Experiment Stations, the U.S. Geological Survey, U.S. Department of 

Agriculture, and other governmental and private entities. It includes over 200 sites in the 

continental United States, Alaska, and Puerto Rico, and the Virgin Islands whose locations are 

shown in Figure 7-1. The purpose of the network is to collect data on the chemistry of 

precipitation for monitoring of geographical and temporal long-term trends. The precipitation 

at each station is collected weekly is analyzed for hydrogen (acidity as pH), sulfate, nitrate, 

ammonium, chloride, and base cations (such as calcium, magnesium, potassium and sodium). 

The NADP network includes a quality assurance program, so we expect to use this data 

without any additional QA. 

The major wet deposition network -- the National Atmospheric Deposition Program (NADP) -

- has a large array of about 200 stations. Weekly samples of precipitation are collected at these 

stations and then sent to a single central laboratory for chemical analysis. The weekly 

sampling network has three severe drawbacks: 

Because the NADP program uses a weekly sampling period the data has poor temporal 

resolution and the sample chemistry can be affected by chemical and biological activity. The 

NADP also includes the Atmospheric Integrated Research Monitoring Network (AIRMoN) 

which was designed to study precipitation chemistry trends with greater temporal resolution. 

Precipitation samples are collected daily from a network of nine wet deposition sites and 

analyzed for the same constituents as the NADP/NTN samples. AIRMoN also includes a dry 

deposition network and these are described next. NADP measures weekly deposition of 

compounds in units of mg/l as well as precipitation in units of l. The PM models will output 

deposition in terms of hourly mass flux per grid cell (e.g., 12 km x 12 km). The modeled 

results will be accumulated to weekly deposition fluxes per unit area (e.g., gm/km2

/week). 

Using the NADP precipitation and information on the NADP Sampler, the NADP deposition 

measurements will be converted to the same units as the modeled values. 

AIRMoN Dry Deposition Data 

The AIRMoN program includes both dry and wet deposition components. Figure 7-2 shows 

the locations of monitoring sites for both programs. The dry deposition data is described at 

their web page: http://www.arl.noaa.gov/research/projects/airmon_data.html. The description 

of the AIRMoN dry deposition data from the website is included verbatim here for 

convenience: 

Dry deposition rates are computed in the AIRMoN network by combining estimates of sitespecific and time-evolving deposition velocities with measurements of air concentrations 

obtained using a weekly sampling protocol. The intent has been to mirror the 0900 Tuesday 

sample change standards adopted by the National Atmospheric Deposition Program. At times, 

the AIRMoN dry samples are obtained over substantially different periods, because of 

Appendix E.1 - 35
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-2 

operator absence or problems with instrumentation. The data summaries have been arranged 

so that each sequential week is represented, even though some of the initial data represent 

periods longer than a single week. 

The AIRMoN concentration sampler is a three-element filterpack, with a leading teflon filter 

to remove particles, a middle nylon filter to extract nitric acid vapor, and a final doped 

cellulose filter intended primarily to sample sulfur dioxide. An inlet tube is used to impose a 

small amount of heat on the incoming air stream, to protect against liquid formation on the 

filters in periods of high humidities. There is no doubt that this influences the measurement of 

ammonium nitrate. In practice, any temperature change imposed on collected ammonium 

nitrate particles will cause some change in the sample, so that any long-term accumulative 

measurement of related species (such as that reported here) will be susceptible to error 

because of the effects of the diurnal cycle in air temperature. Tests of the AIRMoN sampling 

system indicate that particulate ammonium nitrate deposited on the teflon filter is 

incompletely disassociated with minor consequences on the measurement of nitric acid vapor 

and of sulfur dioxide, but with major influence on the measurement of nitrate on the doped 

cellulose filter. For this reason, measurements of nitrate reported here are considered to be 

unreliable. 

It should also be noted that tests indicate that the values associated with nitric acid vapor are 

underestimates, on the average by 25%. The values listed should be increased accordingly, to 

correct for this error (due to deposition on the walls of the inlet tube). 

Deposition velocities tabulated here are derived using a multi-layer numerical model, driven 

by field observations of selected key variables (such as wind speed, the standard deviation of 

the wind direction, surface wetness, incident solar radiation, temperature, humidity, plant 

species distribution, etc.) It is estimated that these deposition velocities might be in error by 

as much as 30%. 

Weekly average deposition rates are computed as the product of the weekly average 

deposition velocities and the weekly average concentration, thus omitting consideration of a 

correlation term that can be significant when air concentrations display a consistent and 

significant diurnal cycle. 

Regarding the last paragraph above, there are in fact large diurnal variations in several trace 

species of interest, and the errors introduced by this approach should be further investigated. 

Omitting consideration of a correlation term is likely to introduce additional errors greater than 

the 30% error mentioned above. 

As noted on the AIRMoN web page, there is very large uncertainty in dry deposition 

estimates, and it is possible that the data on the website will be modified as more is learned 

about the processes that control dry deposition. 

UCR is currently investigating the availability of the NADP and AIRMoN data. We expect to 

obtain the AIRMoN data from the website, but we still need to determine if other NADP data 

is available. 

Appendix E.1 - 36
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-3 

AIRMoN Wet Deposition Data: 

The AIRMoN wet deposition program is described at the web page: 

http://www.arl.noaa.gov/research/projects/airmon_wet.html 

The AIRMoN wet deposition monitoring employs a daily sample collection protocol, thus 

differentiating itself from the weekly operations of the mainstream NADP stations. In practice, 

daily sampling provides a greatly improved quantification of ammonium deposition. At the 

National Atmospheric Deposition Program Technical Committee Meeting in 1994 (October 

24-27) final decisions were made regarding the AIRMoN-wet quality assurance plan; a system 

of flags will be used to alert data users to specific problems. 

UCR is currently downloading data from both wet and dry AIRMoN networks.

Appendix E.1 - 37
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-4 

Figure 7-1. Locations of NADP National Trends Network (NTN) monitors (Figure obtained from 

the EPA NADP website).

Appendix E.1 - 38
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-5 

Figure 7-2. Locations of the AIRMoN wet and dry deposition monitoring sites (Figure obtained 

from the EPA AIRMoN website).

Appendix E.1 - 39
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec7.gst.doc 7-6 

Figure 7-3. Locations of the AIRMoN wet deposition monitoring sites (Figure obtained from the 

EPA AIRMoN website).

Appendix E.1 - 40
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G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec8.gst.doc 8-1 

8.0 CLEAN AIR STATUS AND TREND NETWORK (CASTNET) 

CASTNET is designed to measure dry deposition and it is comprised of 123 sites across the 

United States as shown in Figure 8-1. It includes measurements of ambient concentration and 

meteorology and land use which are then used to calculate dry deposition rates. For the 

model performance evaluation we will use the ambient concentration measurements to 

compare with the model. Dry deposition data are measured for 13 species including: TOTAL 

SO4, TOTAL NO3, TOTAL NH4, CA, MG, NA, K, NSO4, NHNO3, WSO2, WNO3, 

TOTAL SO2, and TOTAL NO3. 

Detailed data collection procedures are described at the EPA CASTNET website: 

http://www.epa.gov/castnet. In short, atmospheric concentration data are collected at each 

site with open-faced, 3-stage filter packs. The filter pack contains a Teflon filter for collection 

of particulate species, a nylon filter for nitric acid and a base-impregnated cellulose 

(Whatman) filter for sulfur dioxide. Filter packs are exposed for 1-week intervals (i.e., 

Tuesday to Tuesday) at a flow rate of 1.5 liters per minute (3.0 liters per minute for western 

sites), and sent to the Harding ESE, Gainesville, FL laboratory for chemical analysis. 

All three filters are extracted and analyzed for certain species: 

Teflon filter: SO4

2-, NO3-

, NH4

+

Nylon filter: SO4

2-, NO3-

Cellulose filter (Whatman): SO4

2-, NO3-

The sulfate, nitrate and ammonium in the teflon filter extract are interpreted as particulate 

species (listed above as TSO4, TNO3 and TNH4). The nitrate in the nylon filter extract is 

interpreted as nitric acid. The sum of sulfate in the nylon and cellulose filter extracts is 

interpreted as sulfur dioxide (SO2). Any nitrate detected in the cellulose filter extract is not 

interpreted, since it likely represents a host of oxidized nitrogen species. 

Because aerosol nitrate on the teflon filter can revolatilize, the nitrate on the teflon filter 

represent those particles that have not volatilized during the sampling period. The nitrate on 

the nylon back filter represents nitric acid that was originally in the atmosphere plus any that 

was produced through revolatilization of the nitrate particles on the teflon filter. Thus, 

because of the volatilization losses, the CASTNet particulate nitrate is less than the IMPROVE 

nitrate. Also, because IMPROVE uses a nitrate denuder to remove ambient nitric acid, the 

CASTNet total nitrate (the sum of the teflon and nylon filter nitrates) is greater than the 

IMPROVE nitrate. Details are provided in Appendix G of the 2000 IMPROVE Report and by 

Ames and Malm [Ames R.B and W.C. Malm, Comparison of sulfate and nitrate particle mass 

concentrations measured by IMPROVE and the CDN, Atmospheric Environment, 2001, 905-

916.] Thus, the IMPROVE and CASTNet nitrate values cannot be directly compared. Moreover, 

the CASTNet NO3 and HNO3 data cannot be directly compared to the modeled species. In 

previous applications we have compared the CASTNet data to the model for the sum 

NO3+HNO3 and we propose to use the same approach for the VISTAS model evaluation.

The CASTNet data have been downloaded from: http://www.epa.gov/castnet/data.html. 

The CASTNET data are available for all three modeling episodes. The actual start time and 

Appendix E.1 - 41
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec8.gst.doc 8-2 

collection period varies among sites, so this makes it difficult to automate the model 

performance evaluation, and additional effort is required to match the sample period with the 

model output period. 

As in the case of the IMPROVE data, measured species can not be compared directly to the 

models species, and Table 8-1 shows the mapping scheme to be used for comparing the 

CASTNET data to model species. 

Table 8-1. Species mappings for CASTNet species. 

Compound CASTNet Species CMAQ Mapping

Gaseous HNO3 NHNO3 (nylon filter) 2176.9*DENSa*HNO3 

Particulate NO3 TNO3 (Teflon filter) ANO3J + ANO3I 

Total HNO3 + NO3

NHNO3 (nylon filter) + TNO3 

(Teflon filter) 

2176.9*DENSa

*HNO3 + 

ANO3J + ANO3I 

Particulate NH4 TNH4 (Teflon filter) ANH4I + ANH4J 

Gaseous SO2 TOTAL_SO2 2211.5*DENS*SO2 

Particulate SO4 TSO4 (Teflon filter) ASO4I + ASO4J 

a Air density obtained from MCIP outputs

Appendix E.1 - 42
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec8.gst.doc 8-3 

Figure 8-1. Locations of CASTNet monitoring sites (Figure obtained from EPA CASTNet 

website). 

Appendix E.1 - 43
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec9.gst.doc 9-1

9.0 PM SUPERSITES 

There are 8 PM Supersites. The locations of these sites are listed in Table 9-1. The data 

includes PM size distribution, ions, metal elements, EC, OC, radical, and gaseous pollutants 

is measured in Supersites. Available data varies with the site. The archived data files can be 

downloaded from the website (ftp://ftp.supersitesdata.umd.edu). 

Eastern Supersites Program (ESP01) 

The ESP01 was initially planned as an effort to coordinate an intensive monitoring in July 

2001 among the three PM Supersites projects (New York, Pittsburgh, and Baltimore). As 

other researchers expressed interest in the program it grew in scope and eventually over 30 

groups were involved in ESP01. The intensive monitoring program was planned for June 30, 

2001 at 0000 hrs to July 29, 2001 at 2400 hrs. The program included an extensive set of gas, 

PM, PM precursor, photochemical, visibility, and meteorological measurements. The study 

also included an extensive network of Radar Profilers (NOAA FSL) which will be useful for 

evaluating transport within and above the boundary layer. EPA plans to centralize all air 

quality and meteorological data in one location within several relational databases to allow for 

easy access to the data and to help ensure that modelers and data analysts will be working with 

uniform databases. Details on data collected at the PM Supersites can be found at 

www.epa.gov/ttn/amtic/supersites.html. We have contacted Paul Solomon and Marc 

Pitchford to determine the schedule for releasing this database. 

Table 9-1. Locations of PM Supersites. 

Location Institution 

Atlanta GA Georgia Institute of Technology 

Baltimore, MD University of Maryland at College Park 

Fresno, CA Desert Research Institute 

Houston, TX University of Texas at Austin 

Los Angeles, CA University of California, Los Angeles 

New York, NY University at Albany, State University of New York 

Pittsburgh, PA Carnegie Mellon University 

St. Louis, MO Washington University 

Appendix E.1 - 44
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec10.gst.doc 10-1

10.0 AIRCRAFT DATA 

Of the three episodes selected for Phase I, only during episode 3 (13-21 July 1999) were there 

research aircraft flights performed that provide aloft meteorological and air quality data 

suitable for model performance testing. These data were collected during the Southern 

Oxidants Study (SOS) intensive field program performed principally in Nashville, TN during 

the summer of 1999. An overview of the SOS/Nashville 1999 aircraft data has been submitted 

to VISTAS as Appendix A to the Task 2 report (ENVIRON, 2003) and is not repeated here. 

Aircraft data are available through the NARSTO Archive and are available online at: 

http://eosweb.larc.nasa.gov/project/narsto/table_narsto.html. We have downloaded the 

NOAA WP3 data, but have not yet attempted to process this data for model evaluation. The 

Brookhaven/DOE data does not appear to be accessible through the NARSTO archive, and we 

will contact Brookhaven directly to access this data. 

Appendix E.1 - 45
July 2003 

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec11.gst.doc 11-1 

11.0 DATA QUALITY ASSURANCE 

In gathering data from the monitoring networks we have assumed that QA was performed by 

the agency or researcher responsible for collecting the data. However, it is possible that the 

data sets may contain erroneous data (i.e., missing data, zeros during calibration, unrealistic 

values, etc). Due to poor documentation or poor formatting of some data sets it is also 

possible that we will make mistakes in our processing of the data. To guard against this 

possibility, we plan to perform a high level QA by visually inspecting time-series plots and 

scatter plots of the ambient data as follows: 

Plots of Time Series 

Time series plots are generated for PAMS species. The plots should be inspected for the 

following: 

• Large "jumps" or "dips" in the concentrations 

• Periodicity of peaks, calibration carryover 

• Unexpected diurnal behavior (i.e., isoprene) 

• Unexpected relationships among species 

• High single-hour concentrations of less abundant species 

Scatter Plots 

Scatter plots may be prepared for the following: 

• Total NMOC vs. species group totals, vs. individual species 

• Benzene vs. Toluene, Acetylene, Ethane 

• Scatter plots comparing data for a single species measured with different sampling 

methods 

• Plots of reconstructed mass versus measured mass (to reveals if anything unusual is 

happening with the chemical measurements) 

• Plots of molal particulate ammonium versus the molal sum of sulfate and particulate 

nitrate (as a sanity check on the ion balance in the PM chemical measurements)

If we identify data that appears flawed it will be flagged and either corrected if the error is in 

the processing step, or removed from the data set. 

Appendix E.1 - 46
Appendix A 

Locations of Speciated PM and IMPROVE Monitors 

Appendix E.1 - 47
Trends (54)

Supplemental (~215 sites currently known)

Supersites

Daily Sites 

IMPROVE

IMPROVE Protocol Castnet conversion Deploy in 2002 Deploy in 2003

Current/Planned

 Urban & Rural PM2.5Speciation Networks 

SS 

SS

SS

SS

SS

SS

Appendix E.1 - 48
IMPROVE or IMPROVE Protocol sites

IMPROVE Protocol Sites t/b deployed 2002 2003

From Discussions with RPOs and Tribal Agencies

Castnet sites converted to IMPROVE

Supplemental Speciation Sites

Class I Areas

Tribal Lands

Current/Planned IMPROVE, IMPROVE Protoc

Rural Supplemental PM2.5 Speciation Site

Appendix E.1 - 49
Appendix E.2 

Protocol for Annual MM5 Modeling 

in Support of VISTAS 
Draft

Protocol for Annual MM5 Modeling in Support of VISTAS (Visibility 

Improvement – State and Tribal Association) 

(DRAFT)

Task 3a Deliverable

Prepared for:

Mr. Mike Abraczinskas

VISTAS Technical Analysis Workgroup

NC Division of Air Quality

1641 Mail Service Center

Raleigh, NC 276990-1641

Prepared by:

Mr. Don Olerud

Mr. Aaron Sims

Baron Advanced Meteorological Systems, LLC

P.O. Box 12889, 3021 Cornwallis Road

Research Triangle Park, North Carolina 27709

February 9, 2004

Appendix E.2 - 1
Draft

ii

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

2 Description of the Meteorological Modeling Approach................................................................................. 1 

3 Evaluation Approach....................................................................................................................................... 3 

4 Results............................................................................................................................................................. 5 

5 Auxiliary Results........................................................................................................................................... 52 

6 Conclusions from Sensitivity Modeling ..................................................................................................... 109 

7 Methodology for Annual Modeling Effort ................................................................................................. 110 

A. Modeling Approach................................................................................................................................ 110 

B. Model Execution..................................................................................................................................... 112 

Terrain......................................................................................................................................................... 112 

REGRID (pregrid)....................................................................................................................................... 112 

REGRID (regridder) ................................................................................................................................... 112 

FETCH (adp_sfc)........................................................................................................................................ 113 

FETCH (adp_upa)....................................................................................................................................... 113 

LITTLE_R (Objective Analysis)................................................................................................................ 113 

INTERPF .................................................................................................................................................... 113 

MM5............................................................................................................................................................ 113 

C. Model Evaluation Procedures................................................................................................................. 114 

D. Computing Resources............................................................................................................................. 124 

E. Project Schedule...................................................................................................................................... 124 

8 References................................................................................................................................................... 124 

9 Appendices...................................................................................................................................................... 1 

A. Terrain namelist ...................................................................................................................................... 1 

B. Pregrid script........................................................................................................................................... 2 

C. Regridder 36-km sample namelist ........................................................................................................ 10 

D. FETCH (adp_sfc) sample namelist....................................................................................................... 11 

E. FETCH (adp_upa) sample namelist...................................................................................................... 11 

F. LITTLE_R sample 36-km namelist ...................................................................................................... 11 

G. INTERPF sample 36-km namelist ........................................................................................................ 14 

H. MM5 sample mmlif .............................................................................................................................. 15 

Appendix E.2 - 2
Draft

1

1 Introduction

In recent years visibility concerns have come to the forefront in the air quality community. Millions of 

visitors to national parks in the United States have their views obstructed by pollution-induced haze. The 

USEPA reports that average visibility in the east has been reduced from 90 miles to 15-25 miles 

(http://www.epa.gov/oar/visibility/what.html). To address this issue, the USEPA in 1999 instituted policies to 

improve visibility in the national parks. As part of this initiative, five multi-state regional planning 

organizations (RPO) were formed. The RPO governing visibility issues in the southeastern US is the Visibility 

Improvement State and Tribal Association of the Southeast (VISTAS) (http://www.vistas-sesarm.org/). 

VISTAS recognizes the regional nature of haze, and has therefore set up a modeling approach to address the 

problem in the southeast US. Ultimately pollution controls will be enacted based upon chemical modeling 

results over the region of interest. To support this modeling effort, Baron Advanced Meteorological Systems 

(BAMS) is tasked with conducting the meteorological modeling. A 12-month modeling period is deemed 

necessary to cover an adequate range of visibility impairment. Prior to investing the resources to produce 

meteorological results at 36-km and 12-km resolution for the full 12-month period, BAMS executed a series of 

sensitivity tests to determine the optimal meteorological setup for the annual modeling. This report details the 

results from this sensitivity testing, leading directly to the protocol we will use for this project. 

2 Description of the Meteorological Modeling Approach

The meteorological model used in this study is the PSU/NCAR Mesoscale Model (MM5 version 3.6, Grell 

et al., 1994, MPP version). In order to build on prior relevant MM5 modeling results funded by the EPA and 

other RPOs, those studies serve to establish the initial model configuration for this effort, as best can be 

determined from available reports. Those findings are summarized in Olerud, 2003a. The modeling results 

indicate that MM5 is most sensitive to the selection of planetary boundary layer (PBL) and soil schemes. 

Therefore a series of sensitivity tests are recommended in Olerud, 2003b. Given limited time and budget 

resources, a series of five sensitivity tests are laid out testing primarily the model response to the selection of 

PBL scheme and soil model. These are the tests:

• px_acm: Pleim-Xiu land surface model, asymmetric convective mixing PBL (Xiu and 

Pleim, 2000), interppx to link runs.

• px_acm2: Like px_acm, except no interppx linkage.

• noah_mrf: Noah land-surface scheme (Chen and Dudhi, 2001) with the medium range 

forecast (mrf) PBL (Hong and Pan, 1996). 

• multi_blkdr: Multi-layer soil scheme with Blackadar PBL and Zilitinkevich thermal 

roughness length.

• noah_eta-my: Noah land-surface scheme with the ETA Mellor-Yamada PBL (IMVDIF=0).

Appendix E.2 - 3
Draft

2

The common options for all sensitivity tests include Kain-Fritsch 2 cumulus parameterization (Kain and 

Fritsch, 1993; Kain, 2002), mixed phase (Reisner 1) microphysics (Reisner et al, 1998), and Rapid Radiative 

Transfer Model (RRTM) radiation (Mlawer et al, 1997). Snow effects are turned on (IFSNOW=1). Note that the 

use of the ETA M-Y pbl scheme necessitates moist vertical diffusion being turned off. The runs are made with 

analysis nudging coefficients set as follows (36-km and 12-km resolutions):

Winds (aloft): 2.5E-4,1.0E-4, 

Winds (surface): 2.5E-4,1.0E-4, 

Temp (aloft): 2.5E-4,1.0E-4, 

Temp (surface): Not employed

Moisture (aloft): 1.0E-5,1.0E-5 

Moisture (surface): Not employed

In the cross-sensitivity plots that follow we compare the px_acm2 sensitivity run to the noah_mrf, 

multi_blkdr, and noah_eta-my sensitivities for episode 1. Episodes 2 and 3 are similar except px_acm will serve 

as the base configuration. 

The runs are executed in 2-way mode with feedback turned off. The five sensitivity runs are executed for 

three separate episodes listed below:

• Episode 1: January 2-21, 2002 

• Episode 2: July 13-28, 2001 

• Episode 3: July 13-22, 1999. 

Each episode is preceded by a spin-up period (7, 7, and 4 days, respectively) that will not be discussed in 

this report. The runs are made in 5.5-day segments, each starting at 00 UTC, with the first 12 hours of each 

segment serving as spin-up. Figure 1 shows the modeling domains used in this study.

Figure 1. VISTAS 36-km/12-km MM5 modeling domains are shown.

Appendix E.2 - 4
Draft

3

3 Evaluation Approach

It is common in the air quality community to use surface statistics of the base meteorological variables as 

the dominant metrics to determine acceptable model performance. Often statistics for only temperature, mixing 

ratio and wind speed are calculated. Obviously it is important for the model to accurately represent these 

variables, but there are additional variables that also become important when one considers that the results will 

be used to improve visibility. As such we have added cloud cover, relative humidity, and precipitation to the 

performance suite. While we calculate metrics separately for wind direction and wind speed, we also calculate 

the mean error vector as perhaps the single best metric to quantify overall wind performance. 

Recognizing that qualitative analyses of the model output are as important as standard quantitative analyses, 

we enable the systematic visualization of model fields with observations overlaid whenever possible. To do this 

we process the MM5 output through EPA’s MCIP2 program. MCIP2 transforms the data into NetCDF format 

while also calculating a few fields (e.g. low, middle, and high CFRAC) that are not readily available in the raw 

MM5 output. MCIP2 also interpolates temperature and wind speed to observation height (1.5m and 10m, 

respectively) for more accurate evaluation. Even though MCIP2 outputs a total cloud fraction, CMAQ uses this 

quantity to estimate optical depth. Accordingly its value can be markedly different than what meteorologists 

typically regard as cloud fraction. To make things as consistent as possible between the model and observations, 

the cloud fractions presented in this report represent the maximum of the low, middle, and high cloud fractions. 

We also use MCIP2 to cull a minimum of six cells about the domain periphery to minimize edge effects. The 

reduced domain precisely matches the domain used in the air quality modeling. The 36-km analysis domain 

thus contains 148 columns, 112 rows, and 34 layers. The 12-km analysis domain covers 168 columns, 177 rows, 

and 34 layers.

The observations used for statistics come primarily from UCAR’s ds472.0 (TDL) archive 

(http://dss.ucar.edu/datasets/ds472.0/). These data are quality controlled and converted to NetCDF format, thus 

allowing the data to be visualized on the model fields via PAVE 

(http://www.cep.unc.edu/empd/EDSS/pave_doc/index.shtml). Unfortunately the precipitation values in this 

dataset are not reliable, so we calculate precipitation statistics based on the 24-h gridded accumulations 

available from the Climate Prediction Center (CPC) 

(http://www.cpc.ncep.noaa.gov/products/precip/realtime/retro.html). These fields, originally at 0.25- degree 

resolution, undergo grid transformation to match our 36-km and 12-km domains. Since the CPC analyses are 

derived primarily from rain gauges, the statistics are only calculated over cells that MM5 deems to be land. 

For aloft analyses we process standard sounding observations from the NCEP ds353.4 archive 

(http://dss.ucar.edu/datasets/ds353.4/). These observations are quality controlled and used to produce 

model/observation skewT sounding plots for selected sites. Additionally we integrate the observations into 

sigma levels that match the MM5 specifications, after which we can statistically analyze performance at sigma 

levels 9, 17, and 22 (~500m, ~1600m, ~3400m, respectively). Qualitative profiler plots showing 

model/observed hourly winds are also created based upon the data stored at the Forecast Systems Lab 

(http://www.profiler.noaa.gov/jsp/). These results, along with much more, will not be presented here. The reader 

is referred to the VISTAS meteorological website (http://www.baronams.com/projects/VISTAS/) for additional 

evaluation details and results. 

Appendix E.2 - 5
Draft

4

The number of analysis plots available on the above website is truly daunting. To keep this report at a 

somewhat manageable level, we will focus primarily on cross-sensitivity plots and surface statistics. Except for 

precipitation (as mentioned above), these statistics are calculated at the sites (color-coded by RPO) shown in 

Figure 2. Statistics are calculated and stored at each observing site, and we routinely aggregate these results to 

produce statistical time series plots and tables for every appropriate RPO region. This approach also enables us 

to produce station-specific statistical quantities that can be plotted in a similar manner to Figure 2. The VISTAS 

web page even shows an animation of how these quantities change throughout an episode-composite day. The 

results shown in this document focus on statistics aggregated only over the VISTAS portion of the 12-km 

domain, and the US portion of the 36-km domain. The cross-sensitivity plots are shown only for the 12-km 

VISTAS domain.

Figure 2. Surface observing network color-coded to represent Regional Planning Organization areas. Dark blue diamonds 

are in the VISTAS RPO, green diamonds are in the MANE-VU RPO, light blue diamonds are in the MIDWEST RPO, 

yellow diamonds are in the CENRAP RPO, and red diamonds are in the WRAP RPO.

 

Appendix E.2 - 6
Draft

5

4 Results

The initial results for the px_acm run for episode 1 are quite discouraging. The run showa a significant cold 

bias over much of the eastern US, including the VISTAS region as illustrated in figure 3. While mixing ratio, 

clouds, precipitation, and winds are modeled reasonably well, the large cold bias is unexpected based on prior 

findings from other RPO’s and the EPA. Note in figure 3 that the first couple of days show very little 

temperature bias, but the bias increases as the mean temperature rises. After much investigation it becomes 

apparent that the deep soil temperature is initiated during an extreme cold event in the eastern US. Since the 

model soil temperatures and moisture are passed from one model segment to another via the interppx 

preprocessor, the cold soil acts as a continuous drag on the atmosphere that the model physics can never quite 

overcome. 

The bias problem is significantly reduced by simply running each model segment independently, thus 

limiting the cold drag to actual cold conditions. Figure 4 shows the statistical time series for this new case, 

px_acm2. This px_acm2 configuration will henceforth be considered the base case episode 1 for crosssensitivity purposes, while px_acm will be used for episodes 2 and 3. For the statistical difference tables that 

make up the bulk of the table portion of this document, we employ px_acm2 as the de facto base case. 

Figure 3. Episode 1 temperature (1.5 m) statistical time series plot for the 12-km VISTAS region, px_acm sensitivity. The 

top panel shows the mean of the observations (blue) and the model (red), the middle plot shows the model bias (blue) and 

the absolute error (red), while the bottom plot shows the index of agreement (blue) and coefficient of determination (red).

Appendix E.2 - 7
Draft

6

Figure 4. Episode 1 temperature (1.5 m) statistical time series plot for the 12-km VISTAS region, px_acm2 sensitivity. 

The top panel shows the mean of the observations (blue) and the model (red), the middle plot shows the model bias (blue) 

and the absolute error (red), while the bottom plot shows the index of agreement (blue) and coefficient of determination 

(red).

Figure 5 shows the daytime PBL heights for January 10, 2002 at 12-km resolution over the VISTAS region. 

It should be noted that the noah_eta-my PBL heights can erroneously become negative over small spatial areas; 

we set all such negative values to zero before averaging. Due to the small areal extent of these negative PBL 

values, we do not anticipate any qualitative assessments to be affected by those artifacts. The January 10, 2002 

PBL heights are rather typical of winter PBL heights. The noah_mrf heights are significantly higher and 

smoother than those in the other sensitivities. Generally speaking, the noah_eta-my daytime PBL heights are 

lower than they are in the other sensitivity runs. The px_acm2 heights tend to be more in the middle of those 

extremes, though they also “bottom out” more than the other runs. 

Figure 6 shows the cross-sensitivity daytime precipitation plot for this same day. The low PBL heights in 

the px_acm2 run are closely correlated to precipitation in the Ohio Valley, while melting snow and clouds 

(figure 7) might inhibit mixing over the northern Mid-Atlantic States. 

Appendix E.2 - 8
Draft

7

Figure 5. Daytime (18-21 UTC) average PBL heights for the 12-km VISTAS region for January 10, 2002 are displayed. 

The px_acm2 sensitivity is shown in the upper left, the noah_mrf in the upper right, the multi_blkdr in the lower left, and 

the noah_eta-my in the lower right. Note that the time value (0:00:00) is only a placeholder and has no physical meaning.

Appendix E.2 - 9
Draft

8

Figure 6. Like figure 5, except for precipitation. 

Appendix E.2 - 10
Draft

9

Figure 7. Like figure 5, except for cloud coverage. 

Figure 8 shows the daily average temperature for this same day. Note that the px_acm2 case is generally 

warmer than the other cases, while the noah_eta-my run is the coldest. Figure 9 shows that for daily averaged 

mixing ratio the patterns are very similar for all runs. The combination of warmer daytime temperatures and 

similar mixing ratios results in lower daytime relative humidity for the px_acm2 case compared with the other 

sensitivity runs (figure 10). Finally, figure 11 shows that the daytime wind speeds are similar in all cases. 

Appendix E.2 - 11
Draft

10

Figure 8. Like figure 5, except for daily average temperature.

Appendix E.2 - 12
Draft

11

Figure 9. Like figure 5, except for daily average temperature.

Appendix E.2 - 13
Draft

12

Figure 10. Like figure 5, except for relative humidity.

Appendix E.2 - 14
Draft

13

Figure 11. Like figure 5,except for wind speed.

Traditionally nighttime PBL heights have not been considered very important for air quality modeling, but 

they certainly may be for visibility/particulate modeling. Figure 12 shows the nighttime (07-10 UTC) PBL 

heights for January 10, 2002. Notice that the px_acm2 produces lower PBL heights than the rest of the 

sensitivity cases do, thus trapping surface-based emissions in a smaller volume of air than would occur in 

another MM5 configuration. The noah_mrf run again produces the highest PBL heights at night, while the 

multi_blkdr and noah_eta-my runs are somewhere in the middle. 

Appendix E.2 - 15
Draft

14

Figure 12. Nighttime (07-10 UTC) average PBL heights for the 12-km VISTAS region for January 10, 2002 are 

displayed. The px_acm2 sensitivity is shown in the upper left, the noah_mrf in the upper right, the multi_blkdr in the 

lower left, and the noah_eta-my in the lower right. Note that the time value (0:00:00) is only a placeholder and has no 

physical meaning.

Appendix E.2 - 16
Draft

15

Nighttime cloud cover is shown in figure 13. The most striking observation from this figure is the cloud 

deck over Tennessee in the noah_eta-my run that does not exist to the same extent in the other sensitivity runs. 

Figure 13. Like figure 12, except for cloud cover.

Appendix E.2 - 17
Draft

16

Figure 14 shows the nighttime relative humidity plot. One might expect that the px_acm2 case would show 

the highest relative humidity, given the low PBL heights as indicated by figure 12. The opposite is actually the 

case. The warmer temperatures in this run counteract the increased stability such that the relative humidity 

values are the lowest of all the runs. The noah_eta-my run easily exhibits the highest relative humidity. 

Figure 14. Like figure 12, except for relative humidity.

Appendix E.2 - 18
Draft

17

Figure 15 shows the nighttime wind speed. Speeds are lowest in the noah_eta-my run, followed by 

px_acm2, noah_mrf, and multi_blkdr. In fact, the latter two cases seem to show an inappropriate diurnal pattern 

in that their nighttime wind speeds are higher than their daytime wind speeds (figure 12). 

Figure 15. Like figure 12, except for wind speed.

Appendix E.2 - 19
Draft

18

Many of the same observations reported above are also valid for the summer episodes. To save time we will 

only show spatial 4-panel plots for PBL heights for a sample summer day, July 19, 2001. Figure 16 shows the 

daytime average, while figure 17 shows the nighttime average.

Figure 16. Daytime (18-21 UTC) average PBL heights for the 12-km VISTAS region for July 19, 2001 are displayed. The 

px_acm sensitivity is shown in the upper left, the noah_mrf in the upper right, the multi_blkdr in the lower left, and the 

noah_eta-my in the lower right. Note that the time value (0:00:00) is only a placeholder and has no physical meaning.

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Figure 17. Nighttime (07-10 UTC) average PBL heights for the 12-km VISTAS region for July 19, 2001 are displayed. 

The px_acm sensitivity is shown in the upper left, the noah_mrf in the upper right, the multi_blkdr in the lower left, and 

the noah_eta-my in the lower right. Note that the time value (0:00:00) is only a placeholder and has no physical meaning.

Now that a qualitative understanding of these sensitivity runs has been established, the remainder of this 

report will focus on quantitative comparisons between the sensitivity cases. Figure 18 shows the temperature 

statistical time series plot for episode 1. While the general performance of the model is very similar across the 

sensitivity runs, close examination reveals that the px_acm2 case performs the best. Figure 19 shows the 

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corresponding plot for episode 2. The noah_eta-my run clearly performs the worst, while the other sensitivity 

runs show similar performance. The episode 3 plot (not shown) reveals similar responses. 

Figure 20 shows the 12-km mixing ratio statistical time series plot for episode 1 over the VISTAS region. 

Overall the noah_mrf case performs the best, followed by px_acm2. The multi_blkdr case is clearly the poorest 

performing. The corresponding episode 2 plot (figure 21) reveals a different result in that the noah_mrf case is 

negatively biased in mixing ratio. This weakness presumably stems from dry air being mixed down from aloft 

as the PBL becomes too high. The other cases are relatively similar. The negative mixing ratio bias is also 

evident for noah_mrf in episode 3 (not shown). 

The wind direction plot for episode 1 is shown in figure 22. The direction bias and error plots show similar 

performance among the sensitivity cases, but the magnitude of the error vector plot (bottom panel) shows that 

the noah_eta-my is the best performing run, especially at night. The px_acm2 run is generally second best. 

Figure 23 shows that similar results are seen in episode 2, as well as in episode 3 (not shown). 

The cloud cover statistical plots (figures 24-25) show very little difference in performance among the 

sensitivity runs. Figure 26 reveals that relative humidity for episode 1 is best modeled by either px_acm2 or 

noah-mrf, with the multi_blkdr case performing the worst. The episode 2 plot (figure 27) shows a strong diurnal 

signature with the sensitivity runs generally being negatively biased at night and positively biased during the 

day. The diurnal signature is interestingly the weakest for the noah_eta-my run, leading to that sensitivity 

possibly performing the best for this quantity. The episode 3 plot (not shown) has the noah_eta-my run 

displaying the poorest performance, no doubt due to its negative temperature bias. 

Figure 28 shows the precipitation statistics for the full 12-km grid for episode 1. The bias blip on January 10 

resulted from there being very few grid cells that actually observed measurable precipitation on that day. 

Sensitivity px_acm2 clearly outperforms the other cases for this episode. Figure 29 shows the corresponding 

plot for episode 2. Sensitivity noah_eta-my seems to be relatively unbiased, while the other sensitivity runs 

show a slight low bias. Nevertheless the skill plots show little difference in performance among the runs. The 

px_acm case appears to show slightly better results than do the other runs. Similar results are found for episode 

3 (not shown). 

Figure 30 is designed to show which sensitivity case statistically performs the best at each valid observation 

site. This particular image represents a composite of 1.5m temperatures for all hours, with absolute error being 

the defining metric. Note that the px_acm2 run performs best for a majority of the sites. The noah_eta-my run 

appears to do quite well over Florida. Figure 31 shows the corresponding plot for episode 2. Again the px_acm 

case seems to perform best overall, though noah_eta-my again performs best in Florida and along the 

southeastern coastline. The results for episode 3 (not shown) reveal no best performing sensitivity. 

Figure 32 shows a similar type of plot for mixing ratio. The noah_mrf case appears to perform best for the 

largest number of sites, followed by the px_acm2 case. The episode 2 results (figure 33) indicate just the 

opposite, as the noah_eta-my or multi_blkdr cases seem to perform best for most of the sites. The episode 3 plot 

(not shown) is a mixed bag with the noah_eta-my and px_acm cases seemingly performing best.

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Figure 18. Episode 1 (Jan 2-21, 2002) cross-sensitivity statistical time series plot for temperature is shown. The top panel 

shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm2 case is shown in 

blue, noah_mrf in red, multi_blkdr in black, and noah_eta-my in purple.

Figure 19. Episode 2 (Jul 13-28, 2001) cross-sensitivity statistical time series plot for temperature is shown. The top panel 

shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm case is shown in blue, 

noah_mrf in red, multi_blkdr in black, and noah_eta-my in purple.

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Figure 20. Episode 1 (Jan 2-21, 2002) cross-sensitivity statistical time series plot for mixing ratio is shown. The top panel 

shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm2 case is blue,

noah_mrf red, multi_blkdr black, and noah_eta-my purple.

Figure 21. Episode 2 (Jul 13-28, 2001) cross-sensitivity statistical time series plot for mixing ratio is shown. The top panel 

shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm case is blue, noah_mrf 

red, multi_blkdr black, and noah_eta-my purple.

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Figure 22. Episode 1 (Jan 2-21, 2002) cross-sensitivity statistical time series plot for winds is shown. The top panel shows 

wind direction bias, the second panel absolute wind direction error, and the bottom panel the magnitude of the error wind 

vector. The px_acm2 case is shown in blue, noah_mrf in red, multi_blkdr in black, and noah_eta-my in purple.

Figure 23. Episode 2 (Jul 13-28, 2001) cross-sensitivity statistical time series plot for winds is shown. The top panel 

shows wind direction bias, the second panel absolute wind direction error, and the bottom panel the magnitude of the error 

wind vector. The px_acm case is shown in blue, noah_mrf in red, multi_blkdr in black, and noah_eta-my in purple.

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Figure 24. Episode 1 (Jan 2-21, 2002) cross-sensitivity statistical time series plot for cloud coverage is shown. The top 

panel shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm2 case is blue, 

noah_mrf red, multi_blkdr black, and noah_eta-my purple.

Figure 25. Episode 2 (Jul 13-28, 2001) cross-sensitivity statistical time series plot for cloud coverage is shown. The top 

panel shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm case is blue, 

noah_mrf red, multi_blkdr black, and noah_eta-my purple.

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Figure 26. Episode 1 (Jan 2-21, 2002) cross-sensitivity statistical time series plot for relative humidity is shown. The top 

panel shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm2 case is blue, 

noah_mrf red, multi_blkdr black, and noah_eta-my purple.

Figure 27. Episode 2 (Jul 13-28, 2001) cross-sensitivity statistical time series plot for relative humidity is shown. The top 

panel shows bias, the second panel absolute error, and the bottom panel index of agreement. The px_acm case is blue, 

noah_mrf red, multi_blkdr black, and noah_eta-my purple.

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Figure 28. Episode 1 (Jan 2-21, 2002) cross-sensitivity statistical time series plot for 24-h measurable precip is shown. 

The top panel shows bias, the second panel accuracy, and the bottom panel equitable threat score. The px_acm2 case is 

blue, noah_mrf red, multi_blkdr black, and noah_eta-my purple.

Figure 29. Episode 2 (Jul 13-28, 2001) cross-sensitivity statistical time series plot for 24-h measurable precip is shown. 

The top panel shows bias, the second panel accuracy, and the bottom panel equitable threat score. The px_acm case is 

blue, noah_mrf red, multi_blkdr black, and noah_eta-my purple.

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Figure 30. Episode 1 (Jan 2-21, 2002) cross-sensitivity 1.5m temperature absolute error comparison plot is shown. 

Stations for which px_acm2 show the smallest composite error are plotted in blue, noah_mrf in green, multi_blkdr in 

yellow, and noah_eta-my in red. The date/time/max/min information at the bottom of the plot serves only as placeholders 

and should be ignored.

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Figure 31. Episode 2 (Jul 13-28, 2001) cross-sensitivity 1.5m temperature absolute error comparison plot is shown. 

Stations for which px_acm2 show the smallest composite error are plotted in blue, noah_mrf in green, multi_blkdr in 

yellow, and noah_eta-my in red. The date/time/max/min information at the bottom of the plot serves only as placeholders 

and should be ignored.

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Figure 32. Episode 1 (Jan 2-21, 2002) cross-sensitivity mixing ratio absolute error comparison plot is shown. Stations for 

which px_acm2 show the smallest composite error are plotted in blue, noah_mrf in green, multi_blkdr in yellow, and 

noah_eta-my in red. The date/time/max/min information at the bottom of the plot serves only as placeholders and should 

be ignored.

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Figure 33. Episode 2 (Jul 13-28, 2001) cross-sensitivity mixing ratio absolute error comparison plot is shown. Stations for 

which px_acm2 show the smallest composite error are plotted in blue, noah_mrf in green, multi_blkdr in yellow, and 

noah_eta-my in red. The date/time/max/min information at the bottom of the plot serves only as placeholders and should 

be ignored.

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The corresponding series of plots for the magnitude of the error wind vector (figures 34-35) show the clear 

superiority of the noah_eta-my runs. The px_acm(2) runs perform a distant second best for all sensitivities. The 

main reason why noah_eta-my performs so well is its ability to calm its wind speeds at night (figure 15) relative 

to the other model configurations. 

Figure 34. Episode 1 (Jan 2-21, 2002) cross-sensitivity error vector magnitude comparison plot is shown. Stations for 

which px_acm2 show the smallest composite error are plotted in blue, noah_mrf in green, multi_blkdr in yellow, and 

noah_eta-my in red. The date/time/max/min information at the bottom of the plot serves only as placeholders and should 

be ignored.

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Figure 35. Episode 2 (Jul 13-28, 2001) cross-sensitivity error vector magnitude comparison plot is shown. Stations for 

which px_acm2 show the smallest composite error are plotted in blue, noah_mrf in green, multi_blkdr in yellow, and 

noah_eta-my in red. The date/time/max/min information at the bottom of the plot serves only as placeholders and should 

be ignored.

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The remainder of this report will focus primarily on statistical tables. These tables are episode-composites 

and are divided into three sections. The top section contains bias, absolute error, coefficient of determination 

(r2), index of agreement, and root mean square error for temperature (1.5 m), water vapor mixing ratio, relative 

humidity, wind speed (10 m), and cloud coverage. The second section contains additional statistics for winds, 

including wind direction bias, absolute wind direction error, U-wind component error, V-wind component error, 

and the magnitude of the average error wind vector. The latter metric is perhaps the single best means of 

assessing model wind performance. The final section contains precipitation statistics at various thresholds (0.01, 

0.05, 0.10, 0.25, 0.5, and 1.0 inches) for daily precipitation accumulations. The metrics presented are accuracy, 

bias, false alarm ratio (FAR), probability of detection (POD), critical success index (CSI, i.e. threat score), 

equitable threat score (ETS), “true skill score” (TSS, i.e. Hanssen and Kuipers score), and Heidke skill score 

(HSS). These precipitation statistics are only generated for approximately the US portion of the grids; there are 

no RPO-specific statistics for this variable. Since an RPO-specific statistical summary is incomplete without 

some sort of precipitation metric, we have decided to use the precipitation statistics described above for every 

statistical composite we produce. The reader should understand the inconsistencies that this approach entails. 

Note also that the precipitation observational grids are obtained, in part, by objectively analyzing rain gauge 

data. This tends to “spread out” the measurable precipitation beyond where it actually occurs. Since a model 

does not do that, a perfect precipitation forecast might appear to be slightly low biased at the 0.01-inch 

threshold. At higher thresholds the number of valid occurrences may not reach a robust level.

 Sensitivity px_acm2 is the base case for all of the statistical tables that follow. Table 1 shows the base case 

statistics for the US portion of the 36-km domain for episode 1, while table 2 reports statistics for the VISTAS 

12-km domain for episode 1. Recall the meteorological statistical benchmarks reported by Emery (2001):

Wind speed: RSME <= 2 m/s, Bias <= +/- 0.5 m/s, IA >= 0.6

Wind direction: Gross Error <= 30 deg, Bias <= +/- 10 deg.

Temperature: Gross Error <= 2 K, Bias <= +/- 0.5 K, IA >= 0.8

Humidity: Gross Error <= 2 g/kg, Bias <= +/- 1 g/kg, IA >= 0.6

Note that the only metrics that fail to meet these benchmarks are temperature bias and error. It should be

understood that the above benchmarks are based primarily on meteorological modeling of summertime 

episodes. While ideally we want less temperature bias and error, the results seen here are not unusually bad. 

Note that the index of agreement temperature statistic easily betters the benchmark value for both domains. No 

benchmarks exist for relative humidity, cloud cover, and precipitation. It is encouraging that these quantities are 

all relatively unbiased and appear to show reasonable skill.

Tables 3-4 show the episode 2 statistical tables for px_acm2 for the selected domains. The only base 

variable that fails to meet the above benchmark is wind direction gross error at 36-km resolution, and that value 

(32.27o) is certainly in the ballpark. Given the weak synoptic forcing in this episode, it is likely that subsynoptic forcing (e.g. terrain flows, thunderstorm outflows, etc...) unresolved adequately at this resolution 

degrades the wind direction performance. The other variables are modeled reasonably well, with a couple of 

possible exceptions. Note the positive cloud coverage bias (>7%) in table 4, and the positive precipitation bias 

at higher thresholds in both table 3 and table 4. The model appears to overestimate the extent and intensity of 

summertime convection. Similar results are seen for episode 3 (tables 5-6). The cloud biases are less than in 

episode 2, though the positive precipitation biases at higher thresholds are accentuated at 36-km resolution. 

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Total stats bias abserr r2 ia rmse 

Temperature (K) -1.14 2.30 0.879 0.963 3.0613 

Mixing Ratio (g/kg) 0.07 0.53 0.876 0.967 0.7673 

Rel. Humidity (%) 3.54 12.62 0.451 0.814 16.4208 

Wind Speed (m/s) -0.02 1.33 0.510 0.808 1.7307 

Clouds (%) -4.00 26.12 0.340 0.763 37.6462 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 1.913 25.11 1.2520 1.2552 1.7729 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.8360 1.0061 0.2760 0.7284 0.5701 0.4379 0.6102 0.6091 

0.05 0.9038 1.1156 0.3204 0.7581 0.5585 0.4914 0.6898 0.6590 

0.10 0.9352 1.1405 0.3308 0.7632 0.5541 0.5115 0.7187 0.6768 

0.25 0.9703 1.0628 0.2959 0.7484 0.5693 0.5502 0.7309 0.7099 

0.50 0.9845 1.0006 0.3133 0.6872 0.5232 0.5140 0.6792 0.6790 

1.00 0.9940 0.8873 0.3833 0.5472 0.4083 0.4054 0.5446 0.5769 

Table 1. Episode 1 composite statistical summary for base case px_acm2 for the US portion of the 36-km domain is 

shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -1.03 2.25 0.858 0.957 2.9596 

Mixing Ratio (g/kg) 0.12 0.62 0.914 0.977 0.8708 

Rel. Humidity (%) 3.36 12.79 0.422 0.802 16.7355 

Wind Speed (m/s) 0.43 1.35 0.407 0.764 1.7320 

Clouds (%) 0.95 22.80 0.419 0.809 34.9631 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 2.216 22.74 1.1604 1.2419 1.6996 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.8735 1.0329 0.2086 0.8174 0.6725 0.5513 0.7170 0.7108 

0.05 0.9148 1.0648 0.2238 0.8265 0.6675 0.5955 0.7643 0.7465 

0.10 0.9363 1.0381 0.2145 0.8154 0.6669 0.6159 0.7741 0.7623 

0.25 0.9635 0.9532 0.1842 0.7776 0.6615 0.6343 0.7599 0.7763 

0.50 0.9756 0.9480 0.2568 0.7045 0.5665 0.5511 0.6929 0.7106 

1.00 0.9907 0.7899 0.3092 0.5457 0.4385 0.4337 0.5423 0.6050 

Table 2. Episode 1 composite statistical summary for base case px_acm2 for the VISTAS portion of the 12-km domain is 

shown.

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Total stats bias abserr r2 ia rmse 

Temperature (K) -0.16 1.62 0.877 0.964 2.1443 

Mixing Ratio (g/kg) -0.17 1.47 0.809 0.947 1.9714 

Rel. Humidity (%) -0.28 9.78 0.672 0.904 13.0680 

Wind Speed (m/s) -0.20 1.24 0.429 0.772 1.6149 

Clouds (%) 1.38 27.52 0.134 0.635 37.9526 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 2.010 32.37 1.2469 1.3037 1.8040 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.7375 0.8822 0.2501 0.6616 0.5420 0.3067 0.4663 0.4694 

0.05 0.7578 1.0815 0.4105 0.6376 0.4415 0.2794 0.4470 0.4368 

0.10 0.7807 1.2392 0.5089 0.6085 0.3732 0.2511 0.4363 0.4014 

0.25 0.8387 1.5406 0.6509 0.5378 0.2685 0.2008 0.4137 0.3345 

0.50 0.9052 1.6829 0.7565 0.4098 0.1803 0.1482 0.3416 0.2582 

1.00 0.9715 1.5577 0.8696 0.2030 0.0862 0.0782 0.1849 0.1450 

Table 3. Episode 2 composite statistical summary for base case px_acm2 for the US portion of the 36-km domain is 

shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.05 1.45 0.815 0.938 1.8473 

Mixing Ratio (g/kg) -0.17 1.38 0.749 0.924 1.7851 

Rel. Humidity (%) -0.72 9.44 0.541 0.858 12.4555 

Wind Speed (m/s) 0.41 1.24 0.415 0.776 1.5632 

Clouds (%) 7.27 30.11 0.170 0.660 39.2499 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -1.254 29.29 1.1294 1.2068 1.6529 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.7787 0.8714 0.1843 0.7108 0.6125 0.3855 0.5553 0.5564 

0.05 0.7785 1.0044 0.3065 0.6966 0.5326 0.3524 0.5216 0.5211 

0.10 0.7803 1.0860 0.3857 0.6672 0.4702 0.3176 0.4942 0.4821 

0.25 0.8045 1.2707 0.5345 0.5915 0.3522 0.2503 0.4426 0.4003 

0.50 0.8585 1.4779 0.6821 0.4699 0.2340 0.1784 0.3678 0.3028 

1.00 0.9398 1.7059 0.8631 0.2336 0.0945 0.0774 0.1929 0.1436 

Table 4. Episode 2 composite statistical summary for base case px_acm2 for the VISTAS portion of the 12-km domain is 

shown.

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Total stats bias abserr r2 ia rmse 

Temperature (K) -0.19 1.64 0.862 0.957 2.1492 

Mixing Ratio (g/kg) -0.25 1.49 0.771 0.934 1.9660 

Rel. Humidity (%) -0.22 9.77 0.640 0.893 12.9109 

Wind Speed (m/s) -0.34 1.24 0.490 0.801 1.6120 

Clouds (%) -0.59 26.66 0.179 0.672 36.8433 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 2.458 32.13 1.2014 1.2878 1.7612 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.7199 0.8128 0.2526 0.6075 0.5040 0.2749 0.4265 0.4313 

0.05 0.7517 1.0015 0.4218 0.5791 0.4071 0.2520 0.4028 0.4026 

0.10 0.7820 1.1542 0.5201 0.5539 0.3461 0.2305 0.3959 0.3746 

0.25 0.8447 1.4487 0.6681 0.4808 0.2443 0.1813 0.3679 0.3070 

0.50 0.9114 1.7663 0.7772 0.3936 0.1659 0.1371 0.3292 0.2412 

1.00 0.9687 2.0344 0.9079 0.1874 0.0658 0.0579 0.1654 0.1095 

Table 5. Episode 3 composite statistical summary for base case px_acm2 for the US portion of the 36-km domain is 

shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.18 1.52 0.821 0.933 1.9103 

Mixing Ratio (g/kg) 0.01 1.35 0.488 0.833 1.7674 

Rel. Humidity (%) 1.35 8.58 0.524 0.842 11.1546 

Wind Speed (m/s) 0.13 1.16 0.282 0.699 1.4752 

Clouds (%) 3.91 29.83 0.169 0.664 38.7202 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 1.595 34.59 1.0827 1.1372 1.5702 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.7015 0.8579 0.2708 0.6256 0.5077 0.2512 0.4006 0.4015 

0.05 0.7219 0.9406 0.3852 0.5783 0.4245 0.2378 0.3791 0.3842 

0.10 0.7461 0.9913 0.4560 0.5393 0.3714 0.2241 0.3651 0.3661 

0.25 0.8068 1.0958 0.5801 0.4602 0.2813 0.1924 0.3351 0.3227 

0.50 0.8806 1.2216 0.6944 0.3734 0.2020 0.1569 0.2986 0.2712 

1.00 0.9536 1.5517 0.8494 0.2337 0.1008 0.0872 0.2037 0.1605 

Table 6. Episode 3 composite statistical summary for base case px_acm2 for the VISTAS portion of the 12-km domain is 

shown.

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To augment the statistical tables described above, we have also produced tables of statistical differences 

from the base case px_acm2. The table setup is precisely the same as above, but the differences reported are not 

simple differences. Instead we present differences designed to indicate whether the sensitivity produces better 

results than the base case. For some metrics (e.g. r2, IA, CSI) larger numbers indicate improved performance. 

For these metrics we calculate differences: SENS – BASE. Positive results are color-coded green, indicating

that the sensitivity improved performance. Negative results are color-coded red, indicating that the sensitivity 

degraded performance. For some other metrics (e.g. abserr, uverr, FAR) smaller numbers indicate improved 

performance. Thus the difference table shows BASE – SENS, and the color-coding described above still 

applies. Similarly the differences for the bias statistics are calculated relative to optimum performance (0 

normally, 1 for precipitation). In this manner one can readily ascertain statistical benefits/disbenefits for all 

sensitivity runs. One should keep in mind that very small non-zero statistical changes are color-coded the same 

as significant changes, so the actual numerical change should be considered more important than the colorcoding. 

Tables 7-8 show the statistical difference results for sensitivity px_acm for episode 1. The most significant 

differences are found in the temperature metrics, revealing that the px_acm configuration is significantly more 

cold-biased than is px_acm2 for this wintertime episode. Most of the other surface variables also show degraded 

performance. The px_acm run does seem to very slightly improve cloud coverage and precipitation, but the 

improvements are very slight and are probably less meaningful than the temperature degradation. These 

performance patterns hold for both the US 36-km results and the VISTAS 12-km results. Tables 9-10 indicate 

very little performance difference between the two configurations for episode 2. Precipitation performance is 

slightly enhanced for this summertime period, while interestingly it actually slightly degrades in the other 

summertime episode (tables 11-12). Overall px_acm seems to be a less desirable configuration for annual 

modeling.

Tables 13-14 show the statistical difference results for the noah_mrf sensitivity for episode 1. The 

performance of mixing ratio and relative humidity for both grids seem to be improved slightly in this 

configuration. The remaining variables are degraded almost universally. Perhaps the most striking result seen in 

the episode 2 tables (tables 15-16) is the degraded mixing ratio performance, countermanding the wintertime 

result. The noah_mrf sensitivity is significantly more low-biased for this variable than is the base case px_acm2, 

especially for the 12-km VISTAS domain. At 36-km this seems to help the run actually improve its cloud and 

precipitation performance relative to the base. The 12-km results are more ambiguous for these variables. 

Generally similar results are found for episode 3 (tables 17-18). These findings, combined with the likelihood 

that the PBL heights are overestimated under this configuration, lead one to reject this configuration for annual 

modeling. 

The multi_blkdr results for episode 1 are displayed in tables 19-20. With few exceptions this sensitivity 

shows degraded performance for all variables for both grid resolutions. The low temperature bias is perhaps the 

most significant issue with this configuration. The summertime tables for multi_blkdr (tables 21-24) show 

results that are generally degraded from px_acm2. This configuration clearly degrades performance compared 

with the base case px_acm2. 

The noah_eta-my results are found in tables 25-30. As mentioned earlier this configuration tends to improve 

performance for winds, but the other variables tend to be degraded. Temperature especially is biased low for all 

grids/resolutions. Still, the superior surface wind performance of the noah_eta-my run deserves additional 

attention. To compare the performance of this run versus px_acm(2), we computed composite statistics at 

various sounding locations. These statistics considered only the 00 UTC (or 12 UTC) data in the composite. A 

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plot of this type is shown in figure 36 for Greensboro, NC, episode 1, 00 UTC. Note that at the surface the wind 

errors are less in the noah_eta-my case than they are in the px_acm2 case, but for the majority of the lower 

portions of the atmosphere the opposite occurs. The large temperature/dew point biases/errors in the noah_etamy are expected and corroborate what is seen in the surface statistics. Figure 37 shows that for this site similar 

results are found in the wind profile for a summer episode. Performance at other upper air stations vary, and for 

Florida stations the noah_eta-my results appear to be slightly improved over the px_acm results. Generally 

speaking, though, the improved wind performance for noah_eta-my appears to be relegated to the lowest model 

layer. For a multitude of reasons, therefore, this configuration is not recommended for the annual modeling. 

Figure 36. Episode 1 (Jan 2-21, 2002) composite vertical statistics for all 00 UTC times for Greensboro, NC are shown. 

The magnitude of the error vector is plotted in the leftmost panel, followed by temperature bias and error, then dew point 

bias and error. Blue represents the px_acm2 case, while dashed red shows the noah_eta-my results.

Appendix E.2 - 40
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39

Figure 37. Episode 2 (Jul 13-28, 2001) composite vertical statistics for all 00 UTC times for Greensboro, NC are shown. 

The magnitude of the error vector is plotted in the leftmost panel, followed by temperature bias and error, then dew point 

bias and error. Blue represents the px_acm2 case, while dashed red shows the noah_eta-my results.

Appendix E.2 - 41
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40

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.92 -0.60 -0.021 -0.017 -0.6456 

Mixing Ratio (g/kg) -0.01 0.00 -0.002 -0.001 0.0023 

Rel. Humidity (%) 0.26 -0.08 -0.017 -0.006 -0.1239 

Wind Speed (m/s) -0.02 -0.03 -0.022 -0.015 -0.0392 

Clouds (%) -0.23 0.12 0.002 0.001 0.0373 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.424 -0.06 -0.0205-0.0178 -0.0270 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0035 -0.0140 0.0120 -0.0073 0.0028 0.0057 0.0008 0.0054 

0.05 0.0023 0.0235 0.0104 -0.0045 0.0045 0.0057 -0.0010 0.0051 

0.10 0.0014 0.0238 0.0097 -0.0051 0.0039 0.0045 -0.0029 0.0040 

0.25 0.0006 0.0221 0.0103 -0.0049 0.0039 0.0042 -0.0038 0.0034 

0.50 0.0000 -0.0111 0.0030 -0.0056 -0.0016 -0.0015 -0.0054 -0.0013 

1.00 0.0000 -0.0051 -0.0010-0.0041 -0.0027 -0.0028 -0.0040 -0.0028 

Table 7. Episode 1 composite statistical comparison of sensitivity px_acm with base case px_acm2 for the US portion of 

the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -1.05 -0.57 -0.015 -0.019 -0.6223 

Mixing Ratio (g/kg) 0.04 -0.01 -0.006 -0.001 -0.0164 

Rel. Humidity (%) -0.28 -0.39 -0.026 -0.013 -0.5072 

Wind Speed (m/s) 0.00 -0.04 -0.031 -0.019 -0.0431 

Clouds (%) 0.16 0.07 0.001 0.001 0.0341 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.222 -0.24 -0.0291-0.0208 -0.0351 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0059 0.0231 0.0219 -0.0121 0.0072 0.0125 0.0022 0.0103 

0.05 0.0049 0.0370 0.0215 -0.0067 0.0112 0.0145 0.0013 0.0113 

0.10 0.0024 0.0299 0.0160 -0.0073 0.0065 0.0080 -0.0031 0.0061 

0.25 0.0006 -0.0251 0.0118 -0.0095 0.0007 0.0011 -0.0079 0.0008 

0.50 0.0000 -0.0165 0.0041 -0.0084 -0.0030 -0.0030 -0.0079 -0.0025 

1.00 -0.0001 -0.0202 0.0006 -0.0136 -0.0085 -0.0085 -0.0134 -0.0083 

Table 8. Like table 7, except for 12-km VISTAS domain.

Appendix E.2 - 42
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41

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.04 0.05 0.004 0.002 0.0393 

Mixing Ratio (g/kg) -0.24 -0.04 0.003 -0.002 -0.0335 

Rel. Humidity (%) -1.56 -0.13 -0.003 -0.002 -0.2838 

Wind Speed (m/s) 0.02 0.00 -0.001 0.000 -0.0004 

Clouds (%) 0.06 -0.07 -0.002 -0.002 -0.0969 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.074 0.07 -0.0015-0.0012 -0.0019 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0026 -0.0124 0.0068 -0.0034 0.0012 0.0043 0.0045 0.0050 

0.05 0.0031 0.0170 0.0063 -0.0034 0.0019 0.0038 0.0025 0.0046 

0.10 0.0032 0.0169 0.0059 -0.0010 0.0030 0.0042 0.0033 0.0053 

0.25 0.0032 0.0281 0.0066 0.0002 0.0040 0.0047 0.0038 0.0064 

0.50 0.0014 0.0371 0.0027 -0.0047 0.0005 0.0008 -0.0030 0.0012 

1.00 0.0004 0.0356 0.0006 -0.0036 -0.0003 -0.0003 -0.0032 -0.0005 

Table 9. Episode 2 composite statistical comparison of sensitivity px_acm with base case px_acm2 for the US portion of 

the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.01 0.05 0.007 0.004 0.0413 

Mixing Ratio (g/kg) -0.20 -0.05 0.003 -0.002 -0.0506 

Rel. Humidity (%) -1.33 -0.13 0.004 -0.001 -0.3367 

Wind Speed (m/s) -0.02 0.00 -0.001 -0.001 -0.0069 

Clouds (%) 0.07 -0.01 0.000 0.000 0.0241 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.092 0.04 -0.0035-0.0038 -0.0051 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0029 -0.0150 0.0090 -0.0046 0.0015 0.0054 0.0055 0.0057 

0.05 0.0030 -0.0130 0.0085 -0.0068 0.0010 0.0039 0.0019 0.0043 

0.10 0.0040 0.0275 0.0093 -0.0071 0.0018 0.0045 0.0014 0.0052 

0.25 0.0049 0.0391 0.0099 -0.0059 0.0035 0.0053 0.0014 0.0069 

0.50 0.0048 0.0489 0.0119 0.0013 0.0067 0.0077 0.0064 0.0109 

1.00 0.0011 0.0331 0.0047 0.0032 0.0027 0.0028 0.0042 0.0049 

Table 10. Like table 9, except for 12-km VISTAS domain.

Appendix E.2 - 43
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42

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.14 -0.01 0.002 0.001 0.0048 

Mixing Ratio (g/kg) -0.25 -0.02 0.001 -0.002 -0.0184 

Rel. Humidity (%) -0.99 0.09 0.010 0.003 0.0700 

Wind Speed (m/s) 0.01 0.00 -0.001 -0.001 0.0011 

Clouds (%) 0.18 -0.15 -0.004 -0.002 -0.1736 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.009 0.08 -0.0012-0.0004 -0.0012 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0166 0.0050 -0.0061-0.0013 -0.0037 -0.0216 -0.0237 -0.0270 

0.05 -0.0168 -0.0082 -0.0071-0.0136 -0.0102 -0.0197 -0.0268 -0.0256 

0.10 -0.0126 0.0113 -0.0130-0.0203 -0.0145 -0.0190 -0.0286 -0.0254 

0.25 -0.0067 -0.0385 -0.0312-0.0335 -0.0250 -0.0246 -0.0384 -0.0360 

0.50 0.0003 -0.1315 -0.0330-0.0335 -0.0240 -0.0220 -0.0341 -0.0348 

1.00 0.0027 0.0539 -0.0273-0.0592 -0.0208 -0.0200 -0.0568 -0.0364 

Table 11. Episode 3 composite statistical comparison of sensitivity px_acm with base case px_acm2 for the US portion of 

the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.09 0.06 0.005 0.008 0.0655 

Mixing Ratio (g/kg) -0.22 0.00 -0.007 -0.004 -0.0056 

Rel. Humidity (%) 1.01 0.17 0.022 0.013 0.2309 

Wind Speed (m/s) -0.03 -0.01 -0.004 -0.002 -0.0088 

Clouds (%) -0.11 -0.12 -0.002 -0.001 -0.0867 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.083 0.22 -0.0043-0.0073 -0.0082 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0320 -0.0381 -0.0245-0.0478 -0.0425 -0.0475 -0.0625 -0.0631 

0.05 -0.0218 -0.0299 -0.0461-0.0604 -0.0526 -0.0474 -0.0661 -0.0643 

0.10 -0.0139 -0.0286 -0.0642-0.0774 -0.0636 -0.0535 -0.0771 -0.0746 

0.25 0.0057 -0.0350 -0.0865-0.0832 -0.0664 -0.0530 -0.0770 -0.0781 

0.50 0.0193 -0.0746 -0.0733-0.0723 -0.0511 -0.0399 -0.0600 -0.0617 

1.00 0.0152 0.1611 -0.0075-0.0347 -0.0100 -0.0055 -0.0237 -0.0095 

Table 12. Like table 11, except for 12-km VISTAS domain.

Appendix E.2 - 44
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43

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.14 -0.17 -0.012 -0.005 -0.1357 

Mixing Ratio (g/kg) 0.01 0.04 0.009 0.002 0.0382 

Rel. Humidity (%) -0.40 0.22 0.006 0.000 0.3528 

Wind Speed (m/s) -0.13 -0.02 0.001 0.025 -0.0283 

Clouds (%) -2.68 -1.58 -0.042 -0.029 -1.4727 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -2.704 -0.27 -0.0108-0.0156 -0.0187 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0153 -0.0554 -0.0359 0.0020 -0.0213 -0.0302 -0.0207 -0.0298 

0.05 -0.0042 -0.0243 -0.0154-0.0009 -0.0109 -0.0128 -0.0058 -0.0116 

0.10 -0.0015 -0.0225 -0.0093 0.0042 -0.0042 -0.0050 0.0021 -0.0044 

0.25 -0.0027 -0.0400 -0.0299-0.0049 -0.0223 -0.0234 -0.0073 -0.0198 

0.50 -0.0011 -0.0105 -0.0224-0.0155 -0.0217 -0.0220 -0.0161 -0.0195 

1.00 -0.0006 0.0437 -0.0464-0.0162 -0.0290 -0.0293 -0.0167 -0.0303 

Table 13. Episode 1 composite statistical comparison of sensitivity noah_mrf with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.29 -0.08 0.009 0.000 -0.0457 

Mixing Ratio (g/kg) 0.00 0.04 0.007 0.002 0.0375 

Rel. Humidity (%) 1.54 0.98 0.035 0.015 1.1957 

Wind Speed (m/s) 0.18 -0.11 -0.060 -0.005 -0.1359 

Clouds (%) -1.84 -0.72 -0.027 -0.016 -0.5780 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -1.433 -1.71 -0.0155-0.0637 -0.0575 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0325 -0.0745 -0.0657-0.0137 -0.0561 -0.0767 -0.0549 -0.0671 

0.05 -0.0071 -0.0054 -0.0174-0.0144 -0.0221 -0.0258 -0.0196 -0.0207 

0.10 -0.0041 0.0143 -0.0089-0.0203 -0.0198 -0.0217 -0.0214 -0.0168 

0.25 -0.0015 0.0054 -0.0103-0.0054 -0.0107 -0.0114 -0.0066 -0.0087 

0.50 0.0002 -0.0472 0.0152 -0.0214 -0.0055 -0.0053 -0.0201 -0.0044 

1.00 -0.0003 0.0821 -0.0289 0.0315 0.0073 0.0070 0.0309 0.0068 

Table 14. Like table 13, except for 12-km VISTAS domain.

Appendix E.2 - 45
Draft

44

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.37 -0.27 -0.021 -0.005 -0.3041 

Mixing Ratio (g/kg) -0.67 -0.05 0.017 -0.005 -0.0018 

Rel. Humidity (%) -2.50 -0.02 0.016 -0.002 0.3326 

Wind Speed (m/s) -0.31 -0.20 -0.104 -0.047 -0.2402 

Clouds (%) 0.83 0.50 0.005 0.001 0.6075 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -1.086 -0.53 -0.0358-0.0394 -0.0531 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0003 -0.0157 0.0042 -0.0082 -0.0034 -0.0009 -0.0015 -0.0010 

0.05 0.0058 0.0206 0.0109 -0.0007 0.0058 0.0085 0.0079 0.0103 

0.10 0.0076 0.0410 0.0145 -0.0027 0.0072 0.0100 0.0077 0.0127 

0.25 0.0150 0.1450 0.0331 -0.0045 0.0179 0.0214 0.0130 0.0292 

0.50 0.0151 0.3081 0.0500 -0.0064 0.0243 0.0272 0.0098 0.0403 

1.00 0.0047 0.2763 0.0575 0.0378 0.0318 0.0324 0.0419 0.0542 

Table 15. Episode 2 composite statistical comparison of sensitivity noah_mrf with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.08 -0.18 -0.026 0.001 -0.2689 

Mixing Ratio (g/kg) -0.90 -0.18 -0.008 -0.024 -0.2066 

Rel. Humidity (%) -4.76 -0.32 0.041 -0.015 -0.0816 

Wind Speed (m/s) 0.29 -0.27 -0.195 -0.084 -0.3222 

Clouds (%) 4.64 0.49 -0.013 -0.007 0.5665 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.317 -1.61 -0.0994-0.0984 -0.1397 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0069 -0.0472 0.0095 -0.0306 -0.0180 -0.0136 -0.0147 -0.0143 

0.05 0.0031 -0.0423 0.0149 -0.0213 -0.0042 0.0013 -0.0043 0.0015 

0.10 0.0049 0.0566 0.0143 -0.0201 -0.0021 0.0026 -0.0049 0.0030 

0.25 0.0085 0.0674 0.0179 -0.0098 0.0065 0.0097 0.0028 0.0124 

0.50 0.0094 0.0540 0.0288 0.0238 0.0217 0.0232 0.0316 0.0327 

1.00 0.0023 -0.0311 0.0312 0.0583 0.0249 0.0248 0.0591 0.0419 

Table 16. Like table 15, except for 12-km VISTAS domain.

Appendix E.2 - 46
Draft

45

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.25 -0.13 -0.017 0.000 -0.1497 

Mixing Ratio (g/kg) -0.74 -0.08 0.020 -0.009 -0.0576 

Rel. Humidity (%) -3.06 0.00 0.018 -0.001 0.1712 

Wind Speed (m/s) -0.32 -0.20 -0.084 -0.038 -0.2410 

Clouds (%) -1.72 -0.03 -0.008 -0.007 -0.0666 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.471 -0.27 -0.0195-0.0240 -0.0309 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0017 -0.0352 0.0089 -0.0194 -0.0096 -0.0037 -0.0055 -0.0047 

0.05 0.0086 -0.0425 0.0190 -0.0082 0.0051 0.0103 0.0075 0.0130 

0.10 0.0110 0.0771 0.0229 -0.0123 0.0066 0.0115 0.0048 0.0150 

0.25 0.0154 0.1750 0.0351 -0.0134 0.0145 0.0185 0.0055 0.0261 

0.50 0.0144 0.3705 0.0420 -0.0240 0.0165 0.0193 -0.0077 0.0293 

1.00 0.0073 0.6216 0.0406 0.0000 0.0184 0.0194 0.0074 0.0341 

Table 17. Episode 3 composite statistical comparison of sensitivity noah_mrf with base case px_acm2 for the US portion

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.13 0.01 -0.032 0.008 -0.0685 

Mixing Ratio (g/kg) -0.95 -0.11 0.010 -0.036 -0.1291 

Rel. Humidity (%) -2.84 -0.35 0.028 -0.003 -0.3691 

Wind Speed (m/s) -0.17 -0.30 -0.159 -0.085 -0.3600 

Clouds (%) 2.88 0.55 0.001 -0.004 1.1355 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 1.036 -1.65 -0.0814-0.0266 -0.0759 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0044 -0.0586 0.0225 -0.0248 -0.0064 0.0065 0.0080 0.0083 

0.05 0.0116 -0.0506 0.0250 -0.0089 0.0067 0.0147 0.0141 0.0190 

0.10 0.0112 -0.0575 0.0244 -0.0085 0.0069 0.0129 0.0104 0.0171 

0.25 0.0111 0.0901 0.0257 -0.0171 0.0043 0.0091 -0.0004 0.0128 

0.50 0.0100 0.1478 0.0306 -0.0124 0.0088 0.0117 -0.0004 0.0173 

1.00 0.0047 0.1971 0.0281 0.0083 0.0138 0.0145 0.0130 0.0242 

Table 18. Like table 17, except for 12-km VISTAS domain.

Appendix E.2 - 47
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46

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.36 -0.22 -0.023 -0.010 -0.3301 

Mixing Ratio (g/kg) -0.31 -0.14 -0.033 -0.017 -0.1974 

Rel. Humidity (%) -11.03 -4.38 -0.080 -0.147 -5.0237 

Wind Speed (m/s) -0.26 -0.05 -0.002 0.024 -0.0604 

Clouds (%) 2.38 -1.57 -0.042 -0.020 -1.3545 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -1.938 -0.38 -0.0476-0.0389 -0.0611 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0282 -0.1382 0.0682 0.0220 -0.0318 -0.0495 -0.0275 -0.0496 

0.05 -0.0058 -0.0490 0.0231 0.0065 -0.0124 -0.0152 -0.0017 -0.0138 

0.10 -0.0028 -0.0385 0.0168 0.0060 -0.0085 -0.0099 0.0021 -0.0087 

0.25 -0.0012 -0.0291 0.0154 0.0036 -0.0080 -0.0086 0.0023 -0.0072 

0.50 -0.0004 -0.0099 0.0091 -0.0025 -0.0068 -0.0069 -0.0028 -0.0060 

1.00 -0.0001 -0.0284 0.0009 -0.0183 -0.0106 -0.0106 -0.0182 -0.0108 

Table 19. Episode 1 composite statistical comparison of sensitivity multi_blkdr with base case px_acm2 for the US

portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.75 -0.38 -0.025 -0.017 -0.5501 

Mixing Ratio (g/kg) -0.30 -0.12 -0.008 -0.007 -0.1244 

Rel. Humidity (%) -11.86 -4.44 -0.072 -0.159 -5.5613 

Wind Speed (m/s) -0.04 -0.09 -0.003 0.019 -0.1052 

Clouds (%) 0.45 -0.03 -0.003 -0.002 -0.0372 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.167 -1.15 -0.0333-0.0397 -0.0518 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0496 -0.1959 0.1099 0.0200 -0.0707 -0.1048 -0.0620 -0.0935 

0.05 -0.0072 -0.0412 0.0260 0.0032 -0.0174 -0.0219 -0.0067 -0.0175 

0.10 -0.0035 -0.0233 0.0168 0.0005 -0.0119 -0.0139 -0.0038 -0.0108 

0.25 -0.0017 0.0177 0.0156 -0.0007 -0.0108 -0.0117 -0.0026 -0.0089 

0.50 -0.0010 0.0061 0.0137 -0.0084 -0.0132 -0.0137 -0.0091 -0.0115 

1.00 -0.0003 0.0150 0.0154 -0.0021 -0.0075 -0.0076 -0.0022 -0.0074 

Table 20. Like table 19, except for 12-km VISTAS domain.

Appendix E.2 - 48
Draft

47

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.02 -0.14 -0.019 -0.006 -0.1609 

Mixing Ratio (g/kg) 0.15 -0.02 -0.030 -0.010 -0.0399 

Rel. Humidity (%) -0.65 -0.48 -0.032 -0.021 -0.2737 

Wind Speed (m/s) 0.10 -0.12 -0.073 -0.018 -0.1302 

Clouds (%) 0.04 0.91 0.011 0.005 0.9705 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.841 -0.24 -0.0488-0.0678 -0.0828 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0254 0.0395 0.0400 -0.0073 -0.0257 -0.0413 -0.0487 -0.0499 

0.05 -0.0254 -0.0682 0.0421 -0.0083 -0.0276 -0.0377 -0.0411 -0.0475 

0.10 -0.0229 -0.0816 0.0399 -0.0125 -0.0276 -0.0342 -0.0382 -0.0449 

0.25 -0.0107 -0.0272 0.0284 -0.0350 -0.0250 -0.0263 -0.0426 -0.0373 

0.50 0.0007 0.1146 0.0144 -0.0505 -0.0176 -0.0169 -0.0471 -0.0261 

1.00 0.0023 0.1937 -0.0100-0.0116 0.0019 0.0023 -0.0092 0.0040 

Table 21. Episode 2 composite statistical comparison of sensitivity multi_blkdr with base case px_acm2 for the US 

portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.22 -0.02 -0.019 0.002 -0.0627 

Mixing Ratio (g/kg) -0.23 0.05 -0.009 -0.002 0.0662 

Rel. Humidity (%) -1.53 0.68 0.060 0.014 1.1438 

Wind Speed (m/s) -0.11 -0.23 -0.108 -0.044 -0.2627 

Clouds (%) 4.25 0.65 -0.007 -0.004 0.8315 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.872 -0.25 -0.0930-0.1375 -0.1640 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0136 -0.0485 -0.0018-0.0381 -0.0276 -0.0261 -0.0280 -0.0276 

0.05 -0.0051 -0.0353 -0.0016-0.0291 -0.0162 -0.0142 -0.0204 -0.0156 

0.10 -0.0035 0.0398 0.0015 -0.0260 -0.0139 -0.0125 -0.0203 -0.0145 

0.25 -0.0005 0.0463 0.0024 -0.0245 -0.0101 -0.0089 -0.0196 -0.0114 

0.50 0.0020 0.0806 0.0027 -0.0294 -0.0089 -0.0078 -0.0242 -0.0113 

1.00 0.0059 0.2326 -0.0171-0.0067 0.0065 0.0074 -0.0005 0.0128 

Table 22. Like table 21, except for 12-km VISTAS domain.

Appendix E.2 - 49
Draft

48

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.16 -0.03 -0.011 -0.001 -0.0470 

Mixing Ratio (g/kg) -0.12 0.01 -0.016 -0.008 0.0215 

Rel. Humidity (%) -0.31 -0.16 -0.026 -0.015 -0.0298 

Wind Speed (m/s) 0.10 -0.13 -0.081 -0.017 -0.1462 

Clouds (%) -2.60 0.38 0.001 -0.002 0.2603 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -1.000 -0.37 -0.0601-0.0617 -0.0861 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0193 0.0011 0.0256 -0.0200 -0.0250 -0.0310 -0.0386 -0.0391 

0.05 -0.0119 -0.0047 0.0197 -0.0240 -0.0213 -0.0230 -0.0308 -0.0299 

0.10 -0.0080 0.0283 0.0177 -0.0335 -0.0220 -0.0220 -0.0348 -0.0296 

0.25 0.0034 0.1115 0.0018 -0.0394 -0.0115 -0.0095 -0.0310 -0.0138 

0.50 0.0079 0.2509 -0.0122-0.0374 -0.0009 0.0008 -0.0274 0.0011 

1.00 0.0052 0.5231 -0.0052-0.0403 -0.0036 -0.0028 -0.0345 -0.0050 

Table 23. Episode 3 composite statistical comparison of sensitivity multi_blkdr with base case px_acm2 for the US

portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.14 0.07 -0.009 0.007 0.0185 

Mixing Ratio (g/kg) -0.40 -0.02 -0.004 -0.006 -0.0280 

Rel. Humidity (%) -0.41 -0.12 0.014 0.008 -0.0297 

Wind Speed (m/s) 0.02 -0.22 -0.119 -0.050 -0.2555 

Clouds (%) 2.84 0.87 0.013 0.006 1.2969 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.528 0.09 -0.0884-0.0628 -0.1066 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0069 -0.1108 -0.0247-0.0624 -0.0319 -0.0115 -0.0155 -0.0148 

0.05 0.0041 -0.1003 -0.0205-0.0445 -0.0159 -0.0040 -0.0137 -0.0052 

0.10 0.0012 -0.0871 -0.0066-0.0415 -0.0174 -0.0108 -0.0239 -0.0145 

0.25 0.0001 0.0839 0.0063 -0.0417 -0.0187 -0.0157 -0.0333 -0.0223 

0.50 0.0033 0.1390 0.0062 -0.0492 -0.0176 -0.0153 -0.0412 -0.0232 

1.00 0.0033 0.2018 -0.0040-0.0250 -0.0033 -0.0026 -0.0210 -0.0045 

Table 24. Like table 23, except for 12-km VISTAS domain.

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Total stats bias abserr r2 ia rmse 

Temperature (K) -1.30 -0.90 -0.046 -0.030 -1.1408 

Mixing Ratio (g/kg) -0.06 -0.08 -0.022 -0.007 -0.0803 

Rel. Humidity (%) -10.88 -4.71 -0.077 -0.125 -5.2197 

Wind Speed (m/s) -0.06 0.03 0.026 0.028 0.0442 

Clouds (%) 1.19 -0.98 -0.016 -0.011 -0.2897 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.668 0.35 0.0371 0.0318 0.0487 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0434 -0.2155 -0.0990 0.0351 -0.0465 -0.0732 -0.0418 -0.0747 

0.05 -0.0015 -0.0123 -0.0063 0.0013 -0.0036 -0.0043 -0.0008 -0.0039 

0.10 0.0003 0.0009 0.0015 0.0011 0.0017 0.0017 0.0013 0.0015 

0.25 -0.0001 0.0093 0.0016 -0.0050 -0.0019 -0.0018 -0.0047 -0.0016 

0.50 -0.0002 -0.0047 -0.0015-0.0056 -0.0041 -0.0042 -0.0056 -0.0036 

1.00 -0.0001 -0.0142 -0.0051-0.0132 -0.0095 -0.0096 -0.0132 -0.0098 

Table 25. Episode 1 composite statistical comparison of sensitivity noah_eta-my with base case px_acm2 for the US 

portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -1.08 -0.56 -0.012 -0.018 -0.7253 

Mixing Ratio (g/kg) -0.07 -0.06 -0.010 -0.003 -0.0629 

Rel. Humidity (%) -8.65 -2.57 -0.041 -0.092 -3.3834 

Wind Speed (m/s) 0.29 0.09 0.050 0.042 0.1122 

Clouds (%) -0.05 0.00 0.001 0.001 -0.0017 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.568 -0.15 0.0837 0.0833 0.1179 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0759 -0.3018 -0.1553 0.0315 -0.1011 -0.1512 -0.0944 -0.1393 

0.05 -0.0080 -0.0418 -0.0277 0.0018 -0.0196 -0.0244 -0.0087 -0.0195 

0.10 -0.0023 -0.0065 -0.0088-0.0040 -0.0090 -0.0103 -0.0060 -0.0079 

0.25 -0.0009 0.0020 -0.0058-0.0039 -0.0067 -0.0071 -0.0045 -0.0054 

0.50 -0.0007 0.0135 -0.0115-0.0010 -0.0073 -0.0077 -0.0017 -0.0064 

1.00 -0.0002 0.0539 -0.0179 0.0221 0.0065 0.0063 0.0217 0.0061 

Table 26. Like table 25, except for 12-km VISTAS domain.

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Total stats bias abserr r2 ia rmse 

Temperature (K) -1.15 -0.71 -0.043 -0.022 -0.8789 

Mixing Ratio (g/kg) 0.00 0.05 0.009 0.003 0.0895 

Rel. Humidity (%) -5.67 -0.74 0.032 -0.008 -0.5429 

Wind Speed (m/s) -0.24 0.00 0.026 0.002 -0.0108 

Clouds (%) -0.54 0.45 -0.006 -0.008 0.2731 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 1.138 0.31 0.0335 0.0337 0.0475 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0176 0.1064 -0.0459 0.0344 -0.0036 -0.0269 -0.0293 -0.0321 

0.05 -0.0149 -0.1029 -0.0288 0.0265 -0.0046 -0.0146 -0.0062 -0.0180 

0.10 -0.0181 -0.1142 -0.0304 0.0150 -0.0128 -0.0197 -0.0122 -0.0255 

0.25 -0.0085 -0.0389 -0.0208-0.0193 -0.0169 -0.0182 -0.0264 -0.0256 

0.50 0.0045 0.1814 -0.0016-0.0465 -0.0104 -0.0091 -0.0393 -0.0139 

1.00 0.0040 0.3590 0.0130 -0.0311 -0.0014 -0.0006 -0.0268 -0.0010 

Table 27. Episode 2 composite statistical comparison of sensitivity noah_eta-my with base case px_acm2 for the US 

portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.81 -0.45 -0.025 -0.014 -0.6594 

Mixing Ratio (g/kg) 0.04 0.05 -0.001 0.003 0.0776 

Rel. Humidity (%) -1.75 1.04 0.105 0.029 1.7577 

Wind Speed (m/s) 0.29 0.09 0.023 0.019 0.0956 

Clouds (%) 4.90 0.36 -0.013 -0.007 0.4730 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -1.641 0.26 0.0688 0.0883 0.1115 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0024 0.0915 -0.0481 0.0853 0.0290 0.0056 0.0073 0.0059 

0.05 -0.0126 -0.1823 -0.0444 0.0737 0.0113 -0.0074 0.0121 -0.0081 

0.10 -0.0169 -0.1763 -0.0389 0.0592 0.0027 -0.0113 0.0109 -0.0131 

0.25 -0.0111 -0.0996 -0.0201 0.0189 -0.0054 -0.0098 0.0012 -0.0125 

0.50 0.0045 0.1230 0.0012 -0.0375 -0.0091 -0.0072 -0.0288 -0.0105 

1.00 0.0101 0.4790 0.0112 -0.0519 -0.0057 -0.0035 -0.0401 -0.0060 

Table 28. Like table 27, except for 12-km VISTAS domain.

Appendix E.2 - 52
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Total stats bias abserr r2 ia rmse 

Temperature (K) -1.08 -0.65 -0.052 -0.022 -0.7857 

Mixing Ratio (g/kg) 0.24 0.07 0.013 0.006 0.1111 

Rel. Humidity (%) -5.41 -0.81 0.018 -0.011 -0.8111 

Wind Speed (m/s) -0.23 -0.02 0.032 0.000 -0.0192 

Clouds (%) -3.24 0.25 -0.004 -0.005 0.0162 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 1.051 0.49 0.0283 0.0293 0.0407 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0211 0.1203 -0.0560 0.0376 -0.0031 -0.0305 -0.0353 -0.0385 

0.05 -0.0115 -0.0834 -0.0240 0.0222 -0.0018 -0.0099 -0.0033 -0.0128 

0.10 -0.0108 -0.0680 -0.0201 0.0081 -0.0076 -0.0118 -0.0076 -0.0157 

0.25 -0.0009 0.0351 -0.0070-0.0215 -0.0093 -0.0088 -0.0200 -0.0128 

0.50 0.0058 0.2353 -0.0001-0.0527 -0.0102 -0.0087 -0.0441 -0.0137 

1.00 0.0061 0.5801 0.0139 -0.0332 0.0012 0.0022 -0.0267 0.0038 

Table 29. Episode 3 composite statistical comparison of sensitivity noah_eta-my with base case px_acm2 for

the US portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.52 -0.27 -0.052 -0.005 -0.4031 

Mixing Ratio (g/kg) -0.12 0.00 -0.003 0.000 -0.0028 

Rel. Humidity (%) -1.43 -0.02 0.022 0.006 -0.1437 

Wind Speed (m/s) -0.03 0.03 0.033 0.014 0.0470 

Clouds (%) 0.01 1.11 0.020 0.010 1.3794 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.358 0.67 0.0526 0.0891 0.1007 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0031 0.0764 -0.0417 0.1071 0.0419 0.0064 0.0095 0.0082 

0.05 -0.0133 -0.1067 -0.0382 0.0940 0.0256 0.0017 0.0218 0.0022 

0.10 -0.0267 -0.1942 -0.0475 0.0579 0.0005 -0.0175 -0.0013 -0.0237 

0.25 -0.0168 -0.0750 -0.0384-0.0136 -0.0223 -0.0264 -0.0309 -0.0380 

0.50 0.0068 0.1725 0.0080 -0.0444 -0.0108 -0.0077 -0.0331 -0.0115 

1.00 0.0093 0.5063 0.0305 -0.0444 0.0012 0.0034 -0.0339 0.0056 

Table 30. Like table 29, except for 12-km VISTAS domain.

Appendix E.2 - 53
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5 Auxiliary Results

The results from the five main sensitivity runs indicate that the px_acm2 configuration is overall the 

preferred option for annual modeling. Still there are numerous model options that are not tested in the above 

configurations. Accordingly we have conducted a variety of auxiliary sensitivity tests with limited analyses. 

These include:

• px_acm3: Like px_acm2, except with soil moisture/temperature initialization for P-X soil 

layer 2 from EDAS soil layer 3 (40-100 cm).

• px_acm4: Like px_acm2, except it uses the SFCFDDA file from RAWINS and 

MMINPUT files processed using RAWINS data rather than REGRID data.

• px_acm5: Like px_acm2, except it uses Reisner 2 microphysics rather than Reisner 1. 

• px_acm6: Like px_acm2, except analysis nudging strengths aloft for wind/temps are 

increased from 2.5E-4, 1.0E-4 (36/12 km) to 3.0E-4, 3.0E-4. 

• px_acm7: Like px_acm2, except the SFCFDDA files from RAWINS are used instead of 

those from LITTLE_R.

• px_acm8: Like px_acm2, except the MMINPUT files are processed using data from 

LITTLE_R rather than from REGRID.

• px_acm9: Like px_acm3, except the initialization for soil temperature/moisture is based 

on a 25% weighting of EDAS soil layer 1 (0-10 cm) values and a 75% 

weighting from EDAS soil layer 3 (40-100 cm). Code supplied by Aijun Xiu; 

executed for episode 3 only. 

• px_acm_n: Like px_acm2, except surface analysis nudging is turned on for temp/moisture 

at a strength of 2.5E-4, 1.0E-4 (36, 12 km temps) and 1.0E-4, 1.0E-4 (36, 12 

km moisture).

• px_acm_n2: Like px_acm_n, except surface analysis nudging for moisture ss reduced to 

1.0E-5, 1.0E-5 (36, 12 km). Run only for episode 2.

• s0px_acm2: Like px_acm2, except snow cover effects are not considered (IFSNOW=0) as 

opposed to IFSNOW=1 in px_acm2. Run only for episode 1.

• s2px_acm2: Like px_acm2, except the simple snow model is employed (IFSNOW=2) as 

opposed to IFSNOW=1 in px_acm2. Run only for episode 1.

• n0_px_acm2: Like px_acm2, except soil moisture nudging is turned off. 

For the most part the analyses of these runs will be statistical and will follow the statistical table format 

detailed in the prior section. The goal is to quickly determine whether the option being tested proves beneficial. 

The analyses are limited to the wintertime episode 1 and the longer of the two summertime episodes, episode 2. 

Recall that the px_acm2 run, while performing the best of the original five sensitivities, failed to meet the 

statistical “benchmark” for temperature for episode 1. The px_acm3 configuration is an attempt to rectify this 

bias by initializing the model from the single most appropriate EDAS soil layer (40-100 cm). This run included 

an error correction pass that reduced the effect of “missing” values in the EDAS data set. Tables 31-32 show the

results of this sensitivity for episode 1 for both grid resolutions modeled. Note the vast improvement in 

temperature bias in both tables. Most of the other variables are relatively unaffected.

Appendix E.2 - 54
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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.65 0.17 -0.002 0.003 0.2029 

Mixing Ratio (g/kg) 0.05 0.00 -0.002 0.000 -0.0035 

Rel. Humidity (%) 2.53 0.18 0.025 0.015 0.1032 

Wind Speed (m/s) -0.03 0.02 0.010 0.009 0.0192 

Clouds (%) -0.02 -0.01 0.001 0.000 0.0497 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.643 -0.02 0.0072 0.0026 0.0070 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0028 -0.0257 -0.0101 0.0082 -0.0014 -0.0038 0.0007 -0.0037 

0.05 -0.0016 -0.0193 -0.0075 0.0047 -0.0026 -0.0034 0.0019 -0.0031 

0.10 -0.0012 -0.0252 -0.0086 0.0069 -0.0023 -0.0030 0.0048 -0.0026 

0.25 -0.0007 -0.0199 -0.0092 0.0039 -0.0038 -0.0041 0.0031 -0.0035 

0.50 -0.0002 -0.0251 -0.0083 0.0086 0.0000 -0.0001 0.0082 -0.0001 

1.00 0.0000 0.0152 -0.0014 0.0081 0.0039 0.0038 0.0081 0.0039 

Table 31. Episode 1 composite statistical comparison of sensitivity px_acm3 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.96 0.13 -0.004 0.002 0.1753 

Mixing Ratio (g/kg) 0.03 0.00 0.003 0.001 0.0160 

Rel. Humidity (%) 3.05 0.39 0.043 0.025 0.2853 

Wind Speed (m/s) -0.09 0.00 0.022 0.011 0.0014 

Clouds (%) 0.19 -0.09 -0.002 -0.001 -0.0258 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.913 0.13 0.0089 -0.0115 -0.0025 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0028 -0.0285 -0.0119 0.0100 -0.0021 -0.0049 0.0013 -0.0041 

0.05 -0.0013 -0.0182 -0.0076 0.0059 -0.0019 -0.0029 0.0027 -0.0023 

0.10 -0.0013 -0.0242 -0.0105 0.0079 -0.0024 -0.0034 0.0049 -0.0026 

0.25 -0.0009 0.0187 -0.0113 0.0043 -0.0044 -0.0050 0.0029 -0.0038 

0.50 -0.0004 0.0186 -0.0093 0.0049 -0.0022 -0.0026 0.0043 -0.0021 

1.00 -0.0002 0.0146 -0.0105 0.0016 -0.0032 -0.0032 0.0015 -0.0031 

Table 32. Like table 31, except for 12-km VISTAS domain.

Appendix E.2 - 55
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Unfortunately the improvements noted in the wintertime episode do not translate to the summer episode 

(tables 33-34). In fact the results indicate a degraded bias result, with the temperatures more significantly coldbiased for both resolutions modeled. The remaining variables tend to be relatively unaffected, generally 

showing a very slight degradation in performance. If this sensitivity had produced superior results for all 

episodes, we would recommend further efforts to possibly improve the initialization of the P-X module in 

MM5. Given the ad hoc nature of this sensitivity test that only yields mixed-bag results at best, we do not 

recommend applying this approach for the annual modeling.

An alternative approach to using LITTLE_R in the MM5 preprocessing stream is to use RAWINS. 

Functionally they serve the same purpose, which is to use observations to “improve” the first-guess and analysis 

fields that drive MM5. NCAR recommends that users employ LITTLE_R, which is a newer, more flexible 

program, but also somewhat more difficult to implement. (Unfortunately when this project started the 

documentation for LITTLE_R was less than stellar. The updated documentation indicates that upper air data 

“probably should” be added to the sfc_obs files; we plan to do just that for the annual modeling, but these tests 

will not have that “benefit”.) Prior modeling results found little difference between using these programs and 

not using them at all. To use P-X in the recommended manner (i.e. with indirect soil nudging) requires that the 

SFCFDDA file be available, which necessitates running one of those objective analysis preprocessors. The pxacm2 configuration used LITTLE_R only for that purpose. The px_acm4 configuration is actually a two-fold 

sensitivity in that it tests the effects of 1) running RAWINS rather than LITTLE_R to generate the SFCFDDA 

files, and 2) using objective analysis (RAWINS, in this case) output as input to INTERPF, as opposed to using 

REGRID output. Tables 35-36 show the results for the wintertime episode. In general we see improvement for 

most of the variables, especially for the VISTAS 12-km domain. Precipitation results are very slightly degraded, 

but not significantly so. The summertime results for this sensitivity are shown in tables 37-38, and they indicate 

no clear overall improvement/degradation.

The Reisner 2 microphysics package is sophisticated and computationally expensive, but is believed to 

incorporate the appropriate science necessary to properly model precipitation over a variety of conditions. 

Tables 39-40 show the statistical differences for the wintertime episode. Interestingly enough this px_acm5 

sensitivity yields improved temperature and cloud coverage performance, but the precipitation skill scores show 

a very slight degradation. Summertime performance for this sensitivity (tables 41-42) show generally degraded 

performance. These results do not justify the increased computational/storage cost of implementing Reisner 2 

microphysics.

Sensitivity test px_acm6 is designed to show the effects of nudging strength. The very slight increase in 

nudging strength at 36-km is not expected to have a large effect on the results, and the episode 1 statistical 

difference table (table 43) bears this out. The 12-km results (table 44) are a little more interesting since the 

nudging strength aloft is increased more dramatically. The idea is that above the planetary boundary layer 

synoptic conditions prevail, thus making analysis nudging at a reasonably strong level appropriate. In the 

boundary layer itself the wind nudging is kept at a relatively weak level. We do note an improvement in the 

temperature and precipitation metrics, but a slight degradation in mixing ratio and winds. The summer case 

expectedly reveals little change at 36-km resolution (table 45), while the 12-km results (table 46) show 

decreased skill for almost all variables, with temperature and winds being the notable exceptions. Overall it is 

hard to justify this configuration as an improvement over the base case.

Appendix E.2 - 56
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Total stats bias abserr r2 ia rmse 

Temperature (K) -0.62 -0.23 -0.018 -0.007 -0.2704 

Mixing Ratio (g/kg) -0.25 -0.03 -0.006 -0.004 -0.0304 

Rel. Humidity (%) -0.09 -0.18 -0.009 -0.003 -0.2014 

Wind Speed (m/s) 0.00 -0.01 -0.008 -0.005 -0.0114 

Clouds (%) 0.15 0.06 0.001 0.001 -0.0039 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.147 0.07 -0.0061-0.0036 -0.0068 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0068 -0.0401 0.0033 -0.0273 -0.0168 -0.0126 -0.0159 -0.0149 

0.05 0.0005 0.0599 0.0060 -0.0292 -0.0110 -0.0069 -0.0160 -0.0085 

0.10 0.0043 0.0794 0.0079 -0.0297 -0.0071 -0.0034 -0.0162 -0.0044 

0.25 0.0075 0.1285 0.0101 -0.0305 -0.0022 0.0003 -0.0184 0.0004 

0.50 0.0061 0.1842 0.0082 -0.0326 -0.0025 -0.0010 -0.0245 -0.0016 

1.00 0.0031 0.2807 0.0104 -0.0232 -0.0004 0.0001 -0.0198 0.0003 

Table 33. Episode 2 composite statistical comparison of sensitivity px_acm3 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.37 -0.05 -0.006 -0.001 -0.0514 

Mixing Ratio (g/kg) -0.08 0.01 0.001 0.001 0.0144 

Rel. Humidity (%) 0.60 -0.11 -0.005 -0.002 -0.0932 

Wind Speed (m/s) 0.01 0.00 -0.011 -0.006 -0.0085 

Clouds (%) -0.25 0.09 0.003 0.003 0.0358 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.273 -0.07 -0.0054-0.0047 -0.0071 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0050 -0.0409 0.0094 -0.0255 -0.0141 -0.0099 -0.0107 -0.0103 

0.05 0.0019 -0.0455 0.0138 -0.0246 -0.0068 -0.0014 -0.0076 -0.0015 

0.10 0.0050 0.0672 0.0159 -0.0252 -0.0039 0.0015 -0.0077 0.0017 

0.25 0.0080 0.0981 0.0162 -0.0267 -0.0009 0.0031 -0.0110 0.0040 

0.50 0.0071 0.1388 0.0097 -0.0312 -0.0031 -0.0008 -0.0203 -0.0012 

1.00 0.0051 0.2605 -0.0003-0.0361 -0.0067 -0.0056 -0.0299 -0.0096 

Table 34. Like table 33, except for 12-km VISTAS domain.

Appendix E.2 - 57
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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.08 0.07 0.004 0.002 0.0950 

Mixing Ratio (g/kg) -0.02 0.01 0.003 0.000 0.0093 

Rel. Humidity (%) -0.18 0.09 0.009 0.003 0.1488 

Wind Speed (m/s) 0.02 -0.03 -0.024 -0.010 -0.0397 

Clouds (%) -0.22 -0.15 -0.006 -0.003 -0.2251 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.255 -0.62 -0.0339-0.0300 -0.0452 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0033 -0.0153 -0.0087 0.0022 -0.0041 -0.0062 -0.0035 -0.0061 

0.05 -0.0010 -0.0074 -0.0042 0.0004 -0.0026 -0.0031 -0.0009 -0.0028 

0.10 -0.0010 -0.0073 -0.0050-0.0009 -0.0039 -0.0044 -0.0018 -0.0038 

0.25 -0.0001 0.0203 0.0032 -0.0110 -0.0043 -0.0042 -0.0104 -0.0036 

0.50 0.0000 -0.0346 0.0079 -0.0170 -0.0055 -0.0054 -0.0165 -0.0048 

1.00 0.0001 -0.0264 0.0130 -0.0051 0.0028 0.0028 -0.0049 0.0028 

Table 35. Episode 1 composite statistical comparison of sensitivity px_acm4 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.05 0.05 0.009 0.002 0.0709 

Mixing Ratio (g/kg) 0.03 0.01 0.002 0.001 0.0147 

Rel. Humidity (%) 0.12 0.11 0.008 0.003 0.1409 

Wind Speed (m/s) -0.01 0.00 0.003 0.001 0.0005 

Clouds (%) 0.34 0.25 0.006 0.003 0.2262 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.039 0.04 -0.0010 0.0036 0.0019 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0035 -0.0029 -0.0061-0.0040 -0.0071 -0.0092 -0.0072 -0.0077 

0.05 -0.0002 0.0202 0.0049 -0.0106 -0.0034 -0.0032 -0.0081 -0.0025 

0.10 -0.0019 0.0205 -0.0001-0.0162 -0.0110 -0.0117 -0.0155 -0.0091 

0.25 -0.0018 -0.0322 0.0004 -0.0259 -0.0186 -0.0192 -0.0253 -0.0147 

0.50 0.0001 -0.0513 0.0145 -0.0250 -0.0083 -0.0081 -0.0237 -0.0068 

1.00 -0.0004 0.0103 -0.0209-0.0097 -0.0145 -0.0146 -0.0098 -0.0143 

Table 36. Like table 35, except for 12-km VISTAS domain.

Appendix E.2 - 58
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57

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.10 0.01 0.005 0.001 0.0265 

Mixing Ratio (g/kg) 0.02 0.01 0.003 0.001 0.0200 

Rel. Humidity (%) 0.18 0.22 0.016 0.005 0.3339 

Wind Speed (m/s) -0.01 -0.03 -0.030 -0.016 -0.0438 

Clouds (%) 0.05 -0.13 -0.004 -0.003 -0.1747 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.087 -0.91 -0.0344-0.0341 -0.0484 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0027 -0.0096 0.0060 -0.0020 0.0018 0.0044 0.0048 0.0051 

0.05 0.0030 0.0180 0.0063 -0.0040 0.0016 0.0036 0.0021 0.0043 

0.10 0.0002 0.0281 0.0000 -0.0137 -0.0052 -0.0041 -0.0098 -0.0053 

0.25 0.0033 0.0683 0.0031 -0.0193 -0.0031 -0.0019 -0.0132 -0.0026 

0.50 0.0018 0.0780 -0.0014-0.0213 -0.0050 -0.0044 -0.0182 -0.0068 

1.00 0.0012 0.0465 0.0179 0.0211 0.0118 0.0118 0.0219 0.0202 

Table 37. Episode 2 composite statistical comparison of sensitivity px_acm4 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.18 0.01 0.003 0.002 0.0145 

Mixing Ratio (g/kg) -0.06 0.02 0.004 0.002 0.0246 

Rel. Humidity (%) 0.37 0.14 0.009 0.003 0.2255 

Wind Speed (m/s) 0.01 0.00 -0.008 -0.005 -0.0036 

Clouds (%) 0.50 0.01 -0.001 -0.001 0.0901 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.021 -0.16 -0.0117-0.0020 -0.0094 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0002 -0.0212 0.0081 -0.0104 -0.0033 0.0002 0.0000 0.0003 

0.05 0.0014 -0.0212 0.0079 -0.0131 -0.0031 -0.0001 -0.0034 0.0000 

0.10 0.0010 0.0379 0.0057 -0.0174 -0.0055 -0.0030 -0.0088 -0.0035 

0.25 0.0060 0.0729 0.0120 -0.0196 -0.0005 0.0025 -0.0079 0.0033 

0.50 0.0076 0.1079 0.0156 -0.0131 0.0048 0.0067 -0.0034 0.0096 

1.00 0.0050 0.1819 0.0174 0.0015 0.0082 0.0090 0.0066 0.0154 

Table 38. Like table 37, except for 12-km VISTAS domain.

Appendix E.2 - 59
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58

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.12 0.07 0.003 0.002 0.0972 

Mixing Ratio (g/kg) -0.01 0.00 0.000 0.000 0.0004 

Rel. Humidity (%) -0.01 -0.06 -0.001 0.000 -0.0488 

Wind Speed (m/s) 0.00 0.01 0.001 0.001 0.0018 

Clouds (%) 2.28 0.95 0.024 0.017 0.6766 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.046 -0.01 0.0017 0.0008 0.0018 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0031 -0.0799 0.0283 -0.0408 -0.0094 -0.0030 -0.0190 -0.0029 

0.05 0.0033 0.0860 0.0247 -0.0329 -0.0025 0.0001 -0.0228 0.0001 

0.10 0.0018 0.0700 0.0190 -0.0265 -0.0017 -0.0006 -0.0213 -0.0005 

0.25 0.0007 0.0505 0.0169 -0.0186 -0.0002 0.0002 -0.0168 0.0001 

0.50 -0.0003 -0.0442 0.0046 -0.0269 -0.0133 -0.0132 -0.0264 -0.0117 

1.00 -0.0001 -0.0325 -0.0015-0.0213 -0.0126 -0.0126 -0.0212 -0.0129 

Table 39. Episode 1 composite statistical comparison of sensitivity px_acm5 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.07 0.05 0.003 0.001 0.0578 

Mixing Ratio (g/kg) -0.01 0.00 0.000 0.000 -0.0024 

Rel. Humidity (%) 0.01 -0.02 0.006 0.002 -0.0119 

Wind Speed (m/s) 0.00 0.00 0.001 0.000 0.0018 

Clouds (%) -1.42 0.26 0.011 0.005 0.1818 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.055 -0.02 0.0006 0.0017 0.0016 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0059 -0.0605 0.0509 -0.0538 -0.0044 0.0049 -0.0200 0.0040 

0.05 0.0037 0.0531 0.0305 -0.0292 0.0019 0.0053 -0.0169 0.0041 

0.10 0.0014 0.0130 0.0232 -0.0271 -0.0025 -0.0010 -0.0204 -0.0008 

0.25 -0.0006 -0.0400 0.0103 -0.0232 -0.0104 -0.0104 -0.0215 -0.0079 

0.50 0.0001 -0.0454 0.0133 -0.0217 -0.0067 -0.0065 -0.0205 -0.0055 

1.00 0.0001 -0.0020 0.0067 0.0038 0.0053 0.0053 0.0040 0.0052 

Table 40. Like table 39, except for 12-km VISTAS domain.

Appendix E.2 - 60
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59

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.07 -0.03 -0.002 -0.001 -0.0285 

Mixing Ratio (g/kg) 0.01 -0.02 -0.004 -0.002 -0.0248 

Rel. Humidity (%) -0.20 -0.12 -0.009 -0.003 -0.1517 

Wind Speed (m/s) -0.02 0.00 -0.001 -0.002 -0.0034 

Clouds (%) -0.30 -0.11 -0.001 0.000 -0.2593 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.122 -0.02 0.0000 -0.0005 -0.0003 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0066 -0.0324 0.0013 -0.0232 -0.0150 -0.0121 -0.0151 -0.0143 

0.05 -0.0023 0.0426 0.0000 -0.0252 -0.0122 -0.0099 -0.0177 -0.0122 

0.10 0.0006 0.0525 0.0006 -0.0250 -0.0092 -0.0072 -0.0175 -0.0092 

0.25 0.0046 0.1108 0.0029 -0.0344 -0.0072 -0.0052 -0.0249 -0.0074 

0.50 0.0060 0.1887 0.0071 -0.0354 -0.0037 -0.0022 -0.0272 -0.0034 

1.00 0.0024 0.2219 0.0059 -0.0210 -0.0017 -0.0013 -0.0184 -0.0021 

Table 41. Episode 2 composite statistical comparison of sensitivity px_acm5 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.04 0.00 0.001 -0.001 -0.0038 

Mixing Ratio (g/kg) 0.00 -0.02 -0.003 -0.001 -0.0145 

Rel. Humidity (%) -0.21 -0.06 -0.006 -0.003 -0.0767 

Wind Speed (m/s) 0.01 0.00 -0.004 -0.001 -0.0015 

Clouds (%) -0.90 -0.13 0.001 0.001 -0.3080 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.189 -0.06 -0.0011-0.0002 -0.0008 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0087 -0.0369 0.0034 -0.0273 -0.0186 -0.0169 -0.0180 -0.0177 

0.05 -0.0034 -0.0169 0.0002 -0.0176 -0.0102 -0.0093 -0.0130 -0.0101 

0.10 -0.0022 0.0294 -0.0004-0.0185 -0.0095 -0.0084 -0.0140 -0.0097 

0.25 0.0020 0.0616 0.0029 -0.0252 -0.0075 -0.0055 -0.0172 -0.0070 

0.50 0.0073 0.1220 0.0128 -0.0214 0.0011 0.0032 -0.0112 0.0046 

1.00 0.0038 0.1704 0.0055 -0.0149 -0.0001 0.0006 -0.0106 0.0011 

Table 42. Like table 41, except for 12-km VISTAS domain.

Appendix E.2 - 61
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60

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.01 0.00 0.000 0.000 0.0053 

Mixing Ratio (g/kg) 0.00 0.00 0.000 0.000 -0.0009 

Rel. Humidity (%) 0.07 0.07 0.003 0.002 0.0799 

Wind Speed (m/s) -0.02 0.00 -0.002 -0.002 -0.0041 

Clouds (%) -0.03 0.02 0.001 0.000 0.0167 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.145 -0.04 -0.0006-0.0011 -0.0011 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0001 0.0034 0.0010 -0.0014 -0.0003 0.0000 -0.0006 0.0000 

0.05 0.0003 0.0041 0.0013 -0.0012 0.0002 0.0004 -0.0007 0.0003 

0.10 0.0004 0.0025 0.0023 0.0009 0.0021 0.0021 0.0013 0.0019 

0.25 0.0002 0.0000 0.0018 0.0019 0.0023 0.0024 0.0020 0.0019 

0.50 -0.0001 -0.0105 -0.0025 0.0046 0.0012 0.0011 0.0045 0.0010 

1.00 0.0000 0.0031 -0.0021 0.0000 -0.0009 -0.0010 0.0000 -0.0010 

Table 43. Episode 1 composite statistical comparison of sensitivity px_acm6 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.16 0.11 0.008 0.004 0.1259 

Mixing Ratio (g/kg) -0.03 -0.03 -0.010 -0.003 -0.0530 

Rel. Humidity (%) 0.33 0.38 0.023 0.011 0.4391 

Wind Speed (m/s) 0.11 -0.01 -0.026 -0.017 -0.0069 

Clouds (%) 0.28 -0.19 -0.006 -0.003 -0.1165 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 1.201 -0.22 -0.0119 0.0096 -0.0012 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0051 0.0104 0.0108 0.0028 0.0097 0.0132 0.0090 0.0108 

0.05 0.0041 0.0060 0.0110 0.0070 0.0128 0.0151 0.0104 0.0118 

0.10 0.0041 -0.0031 0.0119 0.0149 0.0187 0.0208 0.0170 0.0158 

0.25 0.0012 0.0277 -0.0022 0.0204 0.0132 0.0136 0.0197 0.0100 

0.50 0.0006 0.0197 -0.0140 0.0483 0.0218 0.0216 0.0466 0.0177 

1.00 -0.0001 0.0324 -0.0090 0.0149 0.0058 0.0057 0.0148 0.0056 

Table 44. Like table 43, except for 12-km VISTAS domain.

Appendix E.2 - 62
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61

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.00 0.00 0.001 0.001 0.0059 

Mixing Ratio (g/kg) -0.01 -0.01 -0.002 -0.001 -0.0132 

Rel. Humidity (%) -0.06 0.01 0.001 0.001 0.0190 

Wind Speed (m/s) 0.00 0.00 -0.001 -0.001 -0.0023 

Clouds (%) 0.01 -0.14 -0.003 -0.003 -0.2124 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.026 -0.12 -0.0029-0.0027 -0.0039 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0005 0.0045 -0.0019 0.0017 0.0002 -0.0007 -0.0008 -0.0008 

0.05 -0.0023 -0.0094 -0.0042 0.0009 -0.0019 -0.0031 -0.0028 -0.0038 

0.10 -0.0031 -0.0116 -0.0057-0.0014 -0.0038 -0.0048 -0.0050 -0.0061 

0.25 -0.0018 -0.0141 -0.0041-0.0014 -0.0027 -0.0031 -0.0033 -0.0043 

0.50 -0.0015 -0.0280 -0.0046-0.0011 -0.0028 -0.0030 -0.0027 -0.0046 

1.00 -0.0005 -0.0485 -0.0017 0.0037 -0.0001 -0.0002 0.0030 -0.0003 

Table 45. Episode 2 composite statistical comparison of sensitivity px_acm6 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.07 0.04 0.007 0.004 0.0450 

Mixing Ratio (g/kg) -0.53 -0.26 -0.081 -0.034 -0.3480 

Rel. Humidity (%) -2.23 -0.75 -0.035 -0.020 -1.0033 

Wind Speed (m/s) 0.13 0.01 -0.015 -0.004 0.0137 

Clouds (%) -2.24 -2.06 -0.039 -0.025 -2.9210 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.618 -1.08 -0.0026 0.0070 0.0034 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0258 0.0287 -0.0392-0.0118 -0.0306 -0.0478 -0.0512 -0.0515 

0.05 -0.0290 -0.0430 -0.0461-0.0185 -0.0374 -0.0508 -0.0534 -0.0577 

0.10 -0.0311 -0.0461 -0.0518-0.0304 -0.0443 -0.0548 -0.0618 -0.0659 

0.25 -0.0251 -0.0470 -0.0517-0.0462 -0.0445 -0.0494 -0.0666 -0.0658 

0.50 -0.0170 -0.0812 -0.0497-0.0518 -0.0387 -0.0409 -0.0653 -0.0610 

1.00 -0.0069 -0.2206 -0.0253-0.0186 -0.0152 -0.0160 -0.0252 -0.0280 

Table 46. Like table 45, except for 12-km VISTAS domain.

Appendix E.2 - 63
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62

Recall from the discussion of sensitivity px_acm4 that a slight improvement was found by using RAWINS 

output to drive both INTERPF and MM5. Since that sensitivity involved changing two variables at once, we do 

not know which change (or both) led to the improvement. Sensitivities px_acm7 and px_acm8 are designed to 

answer that question. In px_acm7 we change only the SFCFDDA file that MM5 reads in, using the RAWINS 

output rather than LITTLE_R output. Tables 47-50 show the results for both grids and for both episodes. Very 

slight performance degradation is seen in all tables. Tables 51-54 show the results for px_acm8, a sensitivity in 

which LITTLE_R outputs are used as inputs both to MM5 and INTERPF. While the wintertime tables show 

very slight performance degradation for precipitation, the remaining variables show universal improvement. 

Similar improvements are seen in the summertime cases with no performance degradation in the precipitation 

statistics. This configuration appears to be an improvement over the base case and should be incorporated into 

the annual modeling protocol.

The MM5 (http://www.mmm.ucar.edu/mm5/documents/MM5_tut_Web_notes/MM5/mm5.html) 

documentation states that the IFSNOW option applies when the LSM is not used. So this flag was not deemed 

to be important for P-X applications. Of course when MM5 documentation refers to the LSM it normally means 

the NOAH LSM, not the P-X LSM. So we performed a couple of wintertime only sensitivities to determine the 

effect the IFSNOW flag may have on a P-X run. Table 55 shows the results for the 36-km domain if the 

IFSNOW flag is set to 0. Note the bias improvement of 0.07 oC for temperature for this sensitivity s0px_acm2. 

Perhaps snow effects are being double-counted in P-X if this flag is turned on? The other variables are relatively 

unaffected, though it is interesting to note the almost universal slight degradation of precipitation skill. Since 

snow is a relatively rare occurrence in the VISTAS domain, little change is seen at 12-km resolution for the 

VISTAS domain, as illustrated in table 56. 

Tables 57-58 show the statistical difference tables for the s2px_acm2 configuration. This sensitivity turns 

the simple snow model on. These runs are very cold-biased, even for the 12-km VISTAS domain. Clearly this 

configuration is not recommended for annual modeling. 

Tables 59-60 show the shorter summertime episode (episode 3) results for the px_acm9 sensitivity. The idea 

for this test is to see if an integration of EDAS soil temperature/moisture might yield similar wintertime benefits 

that we saw in the px_acm3 sensitivity, while not showing the degraded summertime performance that prior 

sensitivity showed. Admittedly more work could be done along these lines, but the results for this px_acm9 

sensitivity show the summertime performance degradation we had hoped would not exist. This configuration is 

not recommended for annual modeling. 

The next few sensitivity runs quantify the effects of nudging temperature/moisture near the surface. One 

advantage of the P-X module is that it allows indirect soil moisture nudging to be applied, thereby in essence 

“nudging” the lowest layers of the atmosphere without the instabilities that might result from directly applying 

surface analysis nudging of temperature/moisture. To quantify the effects this indirect soil nudging has on the 

results, we turned it off in sensitivity n0px_acm2 and applied the model only for episode 2 at 36-km resolution 

(table 61). Not surprisingly we see general performance degradation, though the character of the simulation is 

not changed dramatically. For example, the temperature error metric still shows better performance than what 

we saw in the noah_mrf, multi_blkdr, and noah_eta-my runs (tables 15, 21, and 27, respectively). 

Appendix E.2 - 64
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63

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.00 -0.01 0.000 0.000 -0.0029 

Mixing Ratio (g/kg) 0.00 0.00 -0.001 0.000 -0.0009 

Rel. Humidity (%) -0.05 0.01 0.000 0.000 0.0095 

Wind Speed (m/s) 0.00 -0.04 -0.028 -0.013 -0.0470 

Clouds (%) 0.05 0.01 0.001 0.000 0.0226 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.017 -0.77 -0.0373-0.0370 -0.0525 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0002 0.0014 0.0000 -0.0010 -0.0006 -0.0006 -0.0008 -0.0006 

0.05 -0.0001 -0.0008 -0.0005 0.0000 -0.0003 -0.0003 -0.0001 -0.0003 

0.10 0.0005 0.0020 0.0023 0.0013 0.0023 0.0024 0.0017 0.0021 

0.25 0.0001 0.0022 0.0015 0.0000 0.0010 0.0010 0.0001 0.0008 

0.50 -0.0001 -0.0000 -0.0003-0.0013 -0.0010 -0.0010 -0.0012 -0.0008 

1.00 0.0000 0.0152 -0.0025 0.0071 0.0028 0.0027 0.0071 0.0028 

Table 47. Episode 1 composite statistical comparison of sensitivity px_acm7 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.00 0.00 0.000 0.000 0.0019 

Mixing Ratio (g/kg) -0.01 0.00 0.000 0.000 -0.0027 

Rel. Humidity (%) -0.16 0.01 0.003 0.000 0.0066 

Wind Speed (m/s) -0.01 -0.01 -0.004 -0.004 -0.0074 

Clouds (%) -0.19 0.05 0.002 0.001 0.0657 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.013 -0.17 -0.0064-0.0081 -0.0104 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0006 -0.0033 -0.0019 0.0007 -0.0009 -0.0014 -0.0005 -0.0012 

0.05 -0.0004 -0.0021 -0.0013 0.0002 -0.0009 -0.0011 -0.0002 -0.0009 

0.10 -0.0001 0.0020 0.0004 -0.0011 -0.0005 -0.0005 -0.0010 -0.0004 

0.25 -0.0005 -0.0074 -0.0006-0.0066 -0.0052 -0.0054 -0.0065 -0.0041 

0.50 0.0003 0.0029 0.0023 0.0044 0.0043 0.0043 0.0045 0.0035 

1.00 -0.0001 0.0189 -0.0088 0.0059 0.0003 0.0002 0.0058 0.0002 

Table 48. Like table 47, except for 12-km VISTAS domain.

Appendix E.2 - 65
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64

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.00 -0.01 -0.001 0.000 -0.0153 

Mixing Ratio (g/kg) 0.06 0.01 0.003 0.000 0.0100 

Rel. Humidity (%) 0.26 0.04 0.003 0.001 0.0765 

Wind Speed (m/s) -0.01 -0.04 -0.033 -0.018 -0.0484 

Clouds (%) -0.18 -0.13 -0.002 -0.002 -0.1188 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.023 -1.18 -0.0404-0.0433 -0.0592 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0025 -0.0088 -0.0005-0.0071 -0.0050 -0.0045 -0.0055 -0.0053 

0.05 -0.0026 0.0117 -0.0031-0.0103 -0.0066 -0.0065 -0.0097 -0.0080 

0.10 -0.0043 0.0104 -0.0083-0.0152 -0.0104 -0.0108 -0.0165 -0.0139 

0.25 -0.0018 0.0062 -0.0060-0.0114 -0.0063 -0.0064 -0.0119 -0.0090 

0.50 -0.0012 -0.0166 -0.0047-0.0039 -0.0033 -0.0035 -0.0050 -0.0053 

1.00 -0.0002 -0.0326 0.0014 0.0066 0.0018 0.0017 0.0062 0.0030 

Table 49. Episode 2 composite statistical comparison of sensitivity px_acm7 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.01 -0.01 -0.003 -0.002 -0.0171 

Mixing Ratio (g/kg) 0.04 0.01 0.000 0.001 0.0203 

Rel. Humidity (%) 0.30 0.06 0.002 0.000 0.1136 

Wind Speed (m/s) -0.01 0.00 -0.007 -0.004 -0.0082 

Clouds (%) -0.15 -0.14 -0.004 -0.003 -0.1314 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.086 -0.39 -0.0154-0.0095 -0.0174 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0013 0.0000 -0.0015-0.0012 -0.0018 -0.0025 -0.0025 -0.0025 

0.05 -0.0011 0.0012 -0.0013-0.0021 -0.0020 -0.0023 -0.0026 -0.0024 

0.10 -0.0008 0.0025 -0.0011-0.0028 -0.0020 -0.0021 -0.0028 -0.0025 

0.25 -0.0005 0.0070 -0.0013-0.0049 -0.0024 -0.0024 -0.0044 -0.0030 

0.50 -0.0010 -0.0050 -0.0033-0.0034 -0.0027 -0.0028 -0.0043 -0.0041 

1.00 0.0001 -0.0088 0.0031 0.0065 0.0025 0.0025 0.0065 0.0043 

Table 50. Like table 49, except for 12-km VISTAS domain.

Appendix E.2 - 66
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65

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.22 0.15 0.008 0.004 0.1800 

Mixing Ratio (g/kg) -0.02 0.01 0.002 0.000 0.0076 

Rel. Humidity (%) 0.35 0.32 0.021 0.010 0.4144 

Wind Speed (m/s) 0.02 0.02 0.010 0.007 0.0180 

Clouds (%) 0.03 0.05 0.003 0.001 0.1037 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.237 0.19 0.0098 0.0129 0.0161 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0043 -0.0168 -0.0107 0.0013 -0.0059 -0.0085 -0.0054 -0.0082 

0.05 -0.0015 -0.0107 -0.0059 0.0007 -0.0036 -0.0043 -0.0012 -0.0039 

0.10 -0.0009 -0.0110 -0.0055 0.0010 -0.0032 -0.0037 -0.0002 -0.0032 

0.25 0.0003 0.0225 0.0078 -0.0078 0.0005 0.0008 -0.0069 0.0006 

0.50 -0.0003 -0.0312 0.0004 -0.0220 -0.0126 -0.0126 -0.0217 -0.0111 

1.00 0.0000 -0.0233 0.0014 -0.0132 -0.0068 -0.0068 -0.0131 -0.0069 

Table 51. Episode 1 composite statistical comparison of sensitivity px_acm8 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.08 0.11 0.011 0.003 0.1407 

Mixing Ratio (g/kg) 0.01 0.01 0.002 0.001 0.0141 

Rel. Humidity (%) 0.49 0.27 0.016 0.008 0.3229 

Wind Speed (m/s) -0.01 0.01 0.009 0.004 0.0087 

Clouds (%) 0.52 0.29 0.006 0.003 0.2435 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.108 0.24 0.0045 0.0133 0.0127 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0021 -0.0001 -0.0031-0.0032 -0.0044 -0.0056 -0.0047 -0.0047 

0.05 -0.0003 0.0141 0.0030 -0.0077 -0.0029 -0.0028 -0.0061 -0.0022 

0.10 -0.0016 0.0194 0.0005 -0.0147 -0.0095 -0.0100 -0.0138 -0.0077 

0.25 -0.0015 -0.0226 -0.0015-0.0198 -0.0153 -0.0159 -0.0195 -0.0121 

0.50 0.0002 -0.0344 0.0115 -0.0150 -0.0032 -0.0031 -0.0140 -0.0026 

1.00 -0.0005 -0.0239 -0.0158-0.0287 -0.0246 -0.0246 -0.0287 -0.0244 

Table 52. Like table 51, except for 12-km VISTAS domain.

Appendix E.2 - 67
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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.06 0.07 0.011 0.003 0.0879 

Mixing Ratio (g/kg) 0.00 0.00 0.002 0.000 0.0121 

Rel. Humidity (%) -0.13 0.23 0.014 0.005 0.3027 

Wind Speed (m/s) 0.00 0.01 0.008 0.005 0.0113 

Clouds (%) 0.33 0.19 0.004 0.002 0.2725 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.250 0.26 0.0091 0.0118 0.0148 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0040 0.0122 0.0014 0.0104 0.0077 0.0074 0.0088 0.0086 

0.05 0.0011 -0.0235 -0.0002 0.0136 0.0064 0.0052 0.0094 0.0063 

0.10 -0.0018 -0.0272 -0.0032 0.0094 0.0016 0.0004 0.0045 0.0005 

0.25 -0.0019 -0.0089 -0.0049-0.0044 -0.0039 -0.0042 -0.0060 -0.0059 

0.50 -0.0023 -0.0344 -0.0080-0.0054 -0.0055 -0.0058 -0.0076 -0.0088 

1.00 0.0002 -0.0058 0.0064 0.0109 0.0048 0.0048 0.0109 0.0082 

Table 53. Episode 2 composite statistical comparison of sensitivity px_acm8 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.04 0.04 0.008 0.003 0.0389 

Mixing Ratio (g/kg) -0.02 0.02 0.002 0.002 0.0233 

Rel. Humidity (%) 0.07 0.17 0.012 0.004 0.2162 

Wind Speed (m/s) 0.01 0.01 0.004 0.002 0.0100 

Clouds (%) 0.49 0.02 -0.002 -0.002 0.1619 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.079 0.17 0.0072 0.0065 0.0098 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0027 -0.0030 0.0043 0.0012 0.0033 0.0051 0.0053 0.0054 

0.05 0.0013 -0.0017 0.0038 -0.0036 0.0001 0.0015 0.0005 0.0017 

0.10 0.0012 0.0188 0.0039 -0.0074 -0.0014 0.0000 -0.0026 -0.0001 

0.25 0.0033 0.0476 0.0063 -0.0144 -0.0016 0.0002 -0.0071 0.0003 

0.50 0.0051 0.0777 0.0098 -0.0111 0.0023 0.0037 -0.0043 0.0053 

1.00 0.0055 0.2039 0.0188 0.0003 0.0086 0.0094 0.0059 0.0162 

Table 54. Like table 53, except for 12-km VISTAS domain.

Appendix E.2 - 68
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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.07 0.03 0.000 0.001 0.0466 

Mixing Ratio (g/kg) -0.01 0.00 -0.001 0.000 -0.0007 

Rel. Humidity (%) -0.08 -0.02 0.000 0.000 -0.0138 

Wind Speed (m/s) 0.01 0.01 0.001 0.001 0.0017 

Clouds (%) 0.20 0.03 0.001 0.001 0.0607 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.040 -0.01 0.0007 0.0002 0.0007 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0014 -0.0095 -0.0044 0.0025 -0.0012 -0.0022 -0.0005 -0.0022 

0.05 -0.0009 -0.0076 -0.0038 0.0010 -0.0021 -0.0025 -0.0004 -0.0023 

0.10 -0.0003 -0.0057 -0.0020 0.0015 -0.0005 -0.0007 0.0010 -0.0006 

0.25 -0.0001 -0.0003 -0.0006-0.0005 -0.0006 -0.0006 -0.0005 -0.0006 

0.50 -0.0001 -0.0013 0.0002 -0.0016 -0.0008 -0.0008 -0.0015 -0.0007 

1.00 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 

Table 55. Episode 1 composite statistical comparison of sensitivity s0px_acm with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.01 0.01 0.001 0.000 0.0150 

Mixing Ratio (g/kg) 0.00 0.00 0.000 0.000 -0.0005 

Rel. Humidity (%) -0.01 -0.01 0.000 -0.001 -0.0059 

Wind Speed (m/s) 0.00 0.00 0.001 0.000 -0.0002 

Clouds (%) -0.05 0.04 0.001 0.001 0.0520 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.000 0.01 0.0001 -0.0001 -0.0001 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0008 -0.0032 -0.0021 0.0004 -0.0012 -0.0018 -0.0009 -0.0016 

0.05 -0.0001 -0.0013 -0.0004 0.0005 0.0000 -0.0001 0.0004 -0.0001 

0.10 -0.0001 -0.0007 -0.0004 0.0002 -0.0001 -0.0002 0.0001 -0.0001 

0.25 -0.0002 -0.0001 -0.0010-0.0010 -0.0014 -0.0015 -0.0011 -0.0012 

0.50 0.0001 -0.0030 0.0014 -0.0009 0.0003 0.0002 -0.0008 0.0002 

1.00 -0.0001 0.0021 -0.0013 0.0004 -0.0002 -0.0002 0.0004 -0.0002 

Table 56. Like table 55, except for 12-km VISTAS domain.

Appendix E.2 - 69
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Total stats bias abserr r2 ia rmse 

Temperature (K) -0.91 -0.70 -0.046 -0.025 -0.9534 

Mixing Ratio (g/kg) 0.02 -0.02 -0.016 -0.005 -0.0415 

Rel. Humidity (%) -0.03 -0.33 -0.048 -0.021 -0.5583 

Wind Speed (m/s) 0.00 -0.02 -0.019 -0.011 -0.0332 

Clouds (%) -0.13 0.17 0.004 0.002 0.1250 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.189 -0.09 -0.0204-0.0149 -0.0249 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0035 -0.0093 0.0109 -0.0048 0.0037 0.0064 0.0023 0.0061 

0.05 0.0017 0.0188 0.0078 -0.0042 0.0029 0.0038 -0.0014 0.0034 

0.10 0.0011 0.0164 0.0069 -0.0032 0.0031 0.0035 -0.0016 0.0031 

0.25 0.0004 0.0180 0.0077 -0.0047 0.0023 0.0025 -0.0039 0.0020 

0.50 -0.0001 -0.0105 0.0016 -0.0065 -0.0029 -0.0029 -0.0064 -0.0025 

1.00 0.0000 -0.0091 0.0006 -0.0051 -0.0025 -0.0026 -0.0050 -0.0026 

Table 57. Episode 1 composite statistical comparison of sensitivity s2px_acm with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.92 -0.79 -0.108 -0.043 -1.3572 

Mixing Ratio (g/kg) 0.11 -0.04 -0.015 -0.003 -0.0642 

Rel. Humidity (%) -1.17 -1.13 -0.085 -0.045 -1.5513 

Wind Speed (m/s) 0.01 -0.03 -0.030 -0.017 -0.0378 

Clouds (%) -0.05 0.10 0.002 0.001 0.0779 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.138 -0.33 -0.0272-0.0155 -0.0299 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 0.0044 0.0137 0.0107 0.0001 0.0078 0.0110 0.0065 0.0090 

0.05 0.0029 0.0156 0.0109 -0.0007 0.0075 0.0094 0.0032 0.0073 

0.10 0.0011 0.0073 0.0056 0.0000 0.0040 0.0047 0.0014 0.0036 

0.25 0.0002 -0.0050 0.0029 -0.0013 0.0010 0.0011 -0.0009 0.0008 

0.50 0.0000 -0.0050 0.0012 -0.0025 -0.0008 -0.0009 -0.0023 -0.0007 

1.00 -0.0001 -0.0041 -0.0014-0.0039 -0.0030 -0.0030 -0.0038 -0.0029 

Table 58. Like table 57, except for 12-km VISTAS domain.

Appendix E.2 - 70
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Total stats bias abserr r2 ia rmse 

Temperature (K) -0.42 -0.12 -0.009 -0.004 -0.1230 

Mixing Ratio (g/kg) -0.08 0.01 0.000 0.000 0.0113 

Rel. Humidity (%) 0.00 -0.08 -0.006 -0.002 -0.0670 

Wind Speed (m/s) -0.01 -0.01 -0.005 -0.005 -0.0098 

Clouds (%) -0.09 -0.03 -0.002 0.000 -0.0983 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.139 0.05 -0.0036-0.0039 -0.0053 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0049 -0.0160 -0.0015-0.0132 -0.0098 -0.0084 -0.0108 -0.0105 

0.05 0.0000 -0.0220 0.0020 -0.0125 -0.0052 -0.0033 -0.0072 -0.0043 

0.10 0.0011 0.0376 0.0016 -0.0163 -0.0056 -0.0040 -0.0107 -0.0052 

0.25 0.0034 0.0449 0.0062 -0.0062 0.0017 0.0027 -0.0017 0.0038 

0.50 0.0019 0.0502 0.0047 -0.0031 0.0020 0.0024 -0.0009 0.0036 

1.00 0.0013 0.1032 0.0068 0.0036 0.0039 0.0040 0.0049 0.0071 

Table 59. Episode 3 composite statistical comparison of sensitivity px_acm9 with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.57 -0.23 -0.063 -0.021 -0.3328 

Mixing Ratio (g/kg) -0.07 0.02 0.005 0.002 0.0325 

Rel. Humidity (%) -0.70 -0.25 -0.014 -0.008 -0.2777 

Wind Speed (m/s) -0.01 -0.03 -0.021 -0.013 -0.0265 

Clouds (%) -0.06 0.11 0.003 0.002 0.1224 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.142 0.12 -0.0143-0.0077 -0.0154 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0054 -0.0321 0.0023 -0.0216 -0.0133 -0.0087 -0.0114 -0.0112 

0.05 -0.0005 -0.0424 0.0047 -0.0219 -0.0098 -0.0054 -0.0105 -0.0070 

0.10 0.0014 -0.0461 0.0048 -0.0206 -0.0078 -0.0043 -0.0108 -0.0057 

0.25 0.0046 0.0522 0.0093 -0.0123 -0.0006 0.0017 -0.0044 0.0023 

0.50 0.0040 0.0653 0.0105 -0.0079 0.0021 0.0033 -0.0028 0.0050 

1.00 0.0022 0.1121 0.0077 -0.0058 0.0023 0.0027 -0.0034 0.0044 

Table 60. Like table 59, except for 12-km VISTAS domain.

Appendix E.2 - 71
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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.09 -0.07 -0.010 -0.002 -0.0789 

Mixing Ratio (g/kg) -0.08 -0.09 -0.021 -0.007 -0.1060 

Rel. Humidity (%) -0.51 -0.45 -0.018 -0.006 -0.4458 

Wind Speed (m/s) 0.00 0.00 -0.002 -0.001 -0.0039 

Clouds (%) 0.04 0.00 0.000 -0.001 0.0099 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.048 -0.12 -0.0029-0.0032 -0.0043 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0030 -0.0048 -0.0022-0.0056 -0.0049 -0.0052 -0.0063 -0.0061 

0.05 -0.0013 0.0071 -0.0014-0.0057 -0.0035 -0.0033 -0.0051 -0.0041 

0.10 -0.0013 0.0051 -0.0026-0.0056 -0.0036 -0.0036 -0.0058 -0.0046 

0.25 0.0002 0.0279 -0.0022-0.0130 -0.0045 -0.0041 -0.0111 -0.0058 

0.50 0.0014 0.0497 0.0006 -0.0111 -0.0019 -0.0015 -0.0090 -0.0023 

1.00 0.0009 0.0776 0.0014 -0.0079 -0.0008 -0.0007 -0.0071 -0.0012 

Table 61. Episode 2 composite statistical comparison of sensitivity n0px_acm2 with base case px_acm2 for the US 

portion of the 36-km domain is shown.

As mentioned above instabilities can sometimes result when the thermodynamic variables (t, q) are nudged 

in the boundary layer. In a test performed years ago using Blackadar mixing we saw checkerboard daytime PBL 

height patterns that were obviously wrong. Heights varied in adjacent cells from ~2500 m to ~40 m to ~2500 m 

and so on. The warning against nudging temperature/moisture in the boundary layer appears to be only passed 

down by word of mouth; no warning appears to exist in the MM5 documentation. In fact, the sample deck in the 

documentation has FDDA turned off completely, but if it were turned on the default would be for surface 

analysis nudging of temperature/moisture to be enabled. Might the warnings against this be outdated? To test 

this we have conducted two sensitivities. Both tests have temperature nudging at the same strength as wind 

nudging in the boundary layer. The first test (px_acm_n) has moisture nudged at 1.0E-4 for both 36-km and 12-

km resolutions, while the second test (px_acm_n2) reduces the moisture nudging strength to the default value of 

1.0E-5 for both grids. Aloft we keep the nudging strengths the same as in the base case. Note that the low values 

for moisture nudging aloft make sense given the discontinuous nature of moisture aloft. There is no guarantee 

that the moisture from an observed sounding is actually representative of the synoptic conditions. At the 

surface, however, mixing ratio is a much more continuous quantity and theoretically could be nudged at a 

higher strength. 

With the daytime checkerboard PBL pattern in mind, we checked the PBL heights from px_acm_n to see if 

similar instabilities exist. We could find not find them. Tables 62-63 show the statistical difference results for 

this sensitivity for the wintertime episode 1. Note the general improvement in the surface statistics, especially 

Appendix E.2 - 72
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71

the temperature bias for both grids. Precipitation skill is relatively unaffected. The episode 2 results (tables 64-

65) show a different story. This sensitivity improves the error statistics for temperature, mixing ratio, and 

relative humidity, and the winds are only slightly affected. But look at the precipitation statistics! The runs are 

significantly biased high at all thresholds, especially in the 36-km grid. Accordingly all of the precipitation skill 

metrics, with the exception of course of probability of detection, show degraded performance. Apparently the 

surface nudging is causing some sort of convective instabilities that are not evident to a significant extent in the 

winter.

Perhaps the convective instabilities might go away if the moisture nudging coefficients are reduced to the 

default values. Tables 66-67 show the results for the px_acm_n2 sensitivity for episode 2. The bias 

overestimation improves over px_acm_n, but it still shows a significant degradation compared with our original 

base case. It should be noted that if we did not consider precipitation statistics we would probably consider 

these last two sensitivities to be the most appealing of all the sensitivities tested. Fortunately that is not the case 

here. We should also note that the precipitation instabilities are relatively subtle and might not be found by 

simply looking at a PAVE/RIP animation of model precipitation.

To further investigate the precipitation patterns of all the sensitivities tested, we have developed a 4-panel 

episode-composite approach. The upper left panel contains the total observed precipitation for the episode. The 

upper right panel shows the total modeled precipitation for the sensitivity being examined, while the lower left 

panel shows the difference between the total modeled precipitation and the observed. Finally, for all non-base 

case runs, the lower right panel shows the difference between the sensitivity and the base case px_acm2. Figure 

38 shows the episode 1 36-km precipitation composite plot for the base case. The general precipitation patterns 

are captured nicely by the px_acm2 configuration, though there are places where the model underestimates total 

precipitation (e.g. northwest coastline) and places where the model overestimates total precipitation (e.g. eastern 

South Carolina). Figure 39 shows the corresponding plot for the 12-km grid. Again the model does a decent job 

in the general placement and magnitude of the precipitation, though certain precipitation bands are slightly 

displaced. 

Figures 40-41 show the px_acm total precipitation plots for the px_acm case. The results are similar to the 

px_acm2 case, though less precipitation falls in this sensitivity, presumably due to the cold bias seen in this run. 

Figures 42-43 show the episode 1 plots for noah_mrf. The results are different from the base case but not 

particularly striking. Figures 44-45 show that the corresponding plots for the multi_blkdr runs exhibit large 

areas of slightly increased precipitation compared with the base case. This is very similar to the noah_eta-my 

results shown in figures 46-47. 

The base case results for episode 2 (figures 48-49) are less encouraging. The px_acm2 run shows a clear 

positive accumulation bias at both grid resolutions. The bias is probably manageable, but it is certainly 

something to consider when analyzing air quality runs driven by this meteorology. The px_acm (figures 50-51), 

noah_mrf (figures 52-53), multi_blkdr (figures 54-55), and noah_eta-my (figures 56-57) results all are 

qualitatively similar. Perhaps the model triggers convection a little too easily.

The Reisner 2 (px_acm5) plots are shown in figures 58-61. This sensitivity tends to produce slightly less 

total precipitation than the base case, though the character of the run is essentially unchanged. The same can be 

said for the px_acm8 results shown in figures 62-65. The wintertime plots for px_acm_n (figures 66-67) are 

also very similar to the base case. The summertime px_acm_n plots (figures 68-69) are another matter entirely! 

Appendix E.2 - 73
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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.27 0.47 0.039 0.012 0.5621 

Mixing Ratio (g/kg) -0.07 0.17 0.065 0.017 0.2255 

Rel. Humidity (%) 0.38 4.66 0.293 0.105 5.9568 

Wind Speed (m/s) -0.05 0.04 0.025 0.014 0.0433 

Clouds (%) 0.26 -0.04 -0.002 0.000 -0.0581 

Wind stats bias abserr Uerr verr uverr

Wind (deg, m/s) -0.586 0.07 0.0133 0.0093 0.0160 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0018 -0.0310 -0.0095 0.0126 0.0016 -0.0007 0.0047 -0.0007 

0.05 -0.0049 -0.0512 -0.0211 0.0102 -0.0091 -0.0117 0.0023 -0.0106 

0.10 -0.0027 -0.0503 -0.0180 0.0122 -0.0062 -0.0077 0.0077 -0.0068 

0.25 -0.0008 -0.0434 -0.0148 0.0141 -0.0018 -0.0023 0.0126 -0.0019 

0.50 -0.0003 -0.0328 -0.0101 0.0120 0.0009 0.0008 0.0115 0.0007 

1.00 -0.0002 0.0406 -0.0171 0.0091 -0.0027 -0.0028 0.0089 -0.0029 

Table 62. Episode 1 composite statistical comparison of sensitivity px_acm_n with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.20 0.39 0.040 0.013 0.4709 

Mixing Ratio (g/kg) -0.04 0.14 0.033 0.009 0.1789 

Rel. Humidity (%) -0.06 3.25 0.225 0.080 4.2115 

Wind Speed (m/s) -0.03 0.03 0.031 0.014 0.0386 

Clouds (%) -0.55 -0.21 -0.005 -0.002 -0.1870 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.313 0.17 0.0263 0.0129 0.0273 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0012 -0.0229 -0.0081 0.0095 0.0005 -0.0012 0.0033 -0.0010 

0.05 -0.0020 -0.0258 -0.0109 0.0081 -0.0030 -0.0045 0.0035 -0.0036 

0.10 -0.0028 -0.0303 -0.0163 0.0064 -0.0077 -0.0095 0.0019 -0.0073 

0.25 -0.0026 0.0294 -0.0237 0.0007 -0.0152 -0.0166 -0.0022 -0.0127 

0.50 -0.0001 0.0099 -0.0034 0.0042 0.0008 0.0006 0.0040 0.0005 

1.00 0.0002 0.0750 -0.0040 0.0483 0.0289 0.0288 0.0480 0.0275 

Table 63. Like table 62, except for 12-km VISTAS domain.

Appendix E.2 - 74
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Total stats bias abserr r2 ia rmse 

Temperature (K) -0.02 0.21 0.029 0.009 0.2498 

Mixing Ratio (g/kg) -0.45 0.34 0.080 0.018 0.4189 

Rel. Humidity (%) -3.58 2.54 0.174 0.042 3.5250 

Wind Speed (m/s) 0.06 0.01 0.002 0.009 0.0041 

Clouds (%) -0.56 -0.79 -0.020 -0.021 -0.4014 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.106 -0.41 -0.0204-0.0090 -0.0206 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0874 -0.3281 -0.1617 0.1888 -0.0090 -0.1196 -0.1430 -0.1542 

0.05 -0.1698 -0.9034 -0.1832 0.1689 -0.0712 -0.1464 -0.1463 -0.2021 

0.10 -0.1863 -1.1667 -0.1762 0.1491 -0.0871 -0.1379 -0.1289 -0.1980 

0.25 -0.1372 -1.4409 -0.1362 0.0971 -0.0788 -0.0992 -0.0691 -0.1501 

0.50 -0.0651 -1.3678 -0.0949 0.0436 -0.0543 -0.0616 -0.0274 -0.0987 

1.00 -0.0131 -1.0558 -0.0411 0.0305 -0.0171 -0.0187 0.0167 -0.0327 

Table 64. Episode 2 composite statistical comparison of sensitivity px_acm_n with base case px_acm2 for the US portion 

of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) 0.02 0.23 0.053 0.018 0.2624 

Mixing Ratio (g/kg) -0.35 0.28 0.068 0.019 0.3339 

Rel. Humidity (%) -2.14 1.89 0.167 0.044 2.6796 

Wind Speed (m/s) 0.06 0.04 0.023 0.017 0.0440 

Clouds (%) -1.97 -2.41 -0.056 -0.050 -1.9282 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.675 0.35 0.0223 0.0377 0.0428 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0568 -0.1110 -0.1403 0.1264 -0.0155 -0.0987 -0.1078 -0.1106 

0.05 -0.1082 -0.4847 -0.1632 0.0930 -0.0680 -0.1368 -0.1294 -0.1664 

0.10 -0.1177 -0.5577 -0.1613 0.0774 -0.0781 -0.1313 -0.1208 -0.1681 

0.25 -0.0976 -0.6405 -0.1305 0.0488 -0.0702 -0.0963 -0.0808 -0.1335 

0.50 -0.0508 -0.6025 -0.0801 0.0248 -0.0427 -0.0517 -0.0337 -0.0779 

1.00 -0.0127 -0.5768 -0.0120 0.0516 0.0006 -0.0012 0.0371 -0.0020 

Table 65. Like table 64, except for 12-km VISTAS domain.

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Total stats bias abserr r2 ia rmse 

Temperature (K) 0.11 0.26 0.034 0.011 0.3233 

Mixing Ratio (g/kg) 0.05 0.09 0.016 0.005 0.1248 

Rel. Humidity (%) -0.94 1.85 0.114 0.036 2.6266 

Wind Speed (m/s) 0.08 0.01 0.004 0.012 0.0078 

Clouds (%) 0.83 -0.44 -0.017 -0.015 -0.4841 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) -0.162 -0.51 -0.0240-0.0119 -0.0252 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0431 -0.0421 -0.0993 0.0930 -0.0051 -0.0628 -0.0705 -0.0772 

0.05 -0.0849 -0.4172 -0.1192 0.0673 -0.0485 -0.0898 -0.0829 -0.1180 

0.10 -0.0867 -0.4984 -0.1138 0.0470 -0.0584 -0.0857 -0.0763 -0.1175 

0.25 -0.0545 -0.5351 -0.0804 0.0199 -0.0470 -0.0570 -0.0437 -0.0831 

0.50 -0.0236 -0.4618 -0.0528-0.0009 -0.0308 -0.0341 -0.0257 -0.0533 

1.00 -0.0040 -0.3357 -0.0159 0.0138 -0.0052 -0.0059 0.0094 -0.0101 

Table 66. Episode 2 composite statistical comparison of sensitivity px_acm_n2 with base case px_acm2 for the US 

portion of the 36-km domain is shown.

Total stats bias abserr r2 ia rmse 

Temperature (K) -0.04 0.25 0.055 0.020 0.2906 

Mixing Ratio (g/kg) 0.10 0.09 0.007 0.006 0.1162 

Rel. Humidity (%) 0.17 1.70 0.138 0.048 2.3239 

Wind Speed (m/s) 0.04 0.03 0.024 0.016 0.0392 

Clouds (%) -0.16 -1.09 -0.029 -0.022 -0.9229 

Wind stats bias abserr uerr verr uverr

Wind (deg, m/s) 0.727 0.46 0.0204 0.0353 0.0398 

Pcp Threshold (in) Acc Bias FAR POD CSI ETS TSS HSS

0.01 -0.0338 0.1227 -0.0763 0.0330 -0.0232 -0.0612 -0.0654 -0.0666 

0.05 -0.0554 -0.1760 -0.0937 0.0115 -0.0517 -0.0840 -0.0820 -0.0979 

0.10 -0.0559 -0.1935 -0.0921 0.0010 -0.0555 -0.0798 -0.0784 -0.0978 

0.25 -0.0365 -0.1856 -0.0653-0.0087 -0.0411 -0.0511 -0.0512 -0.0681 

0.50 -0.0143 -0.1517 -0.0309-0.0022 -0.0177 -0.0204 -0.0178 -0.0300 

1.00 -0.0019 -0.0956 -0.0007 0.0117 0.0015 0.0011 0.0094 0.0020 

Table 67. Like table 66, except for 12-km VISTAS domain.

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Figure 38. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the base case px_acm2 model configuration. The lower left 

plot shows the difference plot of the top panels. 

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Figure 39. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the base case px_acm2 model configuration, and the lower 

left plot shows the difference plot between the model and the observations. 

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Figure 40. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm sensitivity and the px_acm2 base case. 

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Figure 41. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm sensitivity and the px_acm2 base case. 

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Figure 42. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

noah_mrf sensitivity and the px_acm2 base case. 

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Figure 43. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

noah_mrf sensitivity and the px_acm2 base case. 

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Figure 44. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

multi_blkdr sensitivity and the px_acm2 base case. 

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Figure 45. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

multi_blkdr sensitivity and the px_acm2 base case. 

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Figure 46. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the noah_eta-my model configuration, and the lower left 

plot shows the difference plot between the model and the observations. The bottom right plot shows the difference 

between the noah_eta-my sensitivity and the px_acm2 base case. 

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Figure 47. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the noah_eta-my model configuration, and the lower left 

plot shows the difference plot between the model and the observations. The bottom right plot shows the difference 

between the noah_eta-my sensitivity and the px_acm2 base case. 

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Figure 48. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the base case px_acm2 model configuration. 

The lower left plot shows the difference plot of the top panels. 

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Figure 49. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the base case px_acm2 model configuration. 

The lower left plot shows the difference plot of the top panels. 

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Figure 50. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm sensitivity and the px_acm2 base case. 

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Figure 51. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm sensitivity and the px_acm2 base case. 

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Figure 52. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumula ted precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the noah_mrf sensitivity and the px_acm2 base case. 

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Figure 53. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the noah_mrf model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the noah_mrf sensitivity and the px_acm2 base case. 

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Figure 54. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the multi_blkdr sensitivity and the px_acm2 base case. 

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Figure 55. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the multi_blkdr model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the multi_blkdr sensitivity and the px_acm2 base case. 

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Figure 56. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the noah_eta-my model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the noah_eta-my sensitivity and the px_acm2 base case. 

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Figure 57. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the noah_eta-my model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the noah_eta-my sensitivity and the px_acm2 base case. 

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Figure 58. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm5 sensitivity and the px_acm2 base case. 

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Figure 59. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm5 sensitivity and the px_acm2 base case. 

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Figure 60. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm5 sensitivity and the px_acm2 base case. 

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Figure 61. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm5 model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm5 sensitivity and the px_acm2 base case. 

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Figure 62. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm8 sensitivity and the px_acm2 base case. 

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Figure 63. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm8 sensitivity and the px_acm2 base case. 

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Figure 64. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm8 sensitivity and the px_acm2 base case. 

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Figure 65. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm8 model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm8 sensitivity and the px_acm2 base case. 

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Figure 66. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 36-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm_n sensitivity and the px_acm2 base case. 

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Figure 67. Episode 1 (Jan 2-21, 2002) composite precipitation for the US land-portion of the 12-km domain is shown. The 

top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded precipitation 

datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the lower left plot 

shows the difference plot between the model and the observations. The bottom right plot shows the difference between the 

px_acm_n sensitivity and the px_acm2 base case. 

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Note the explosion of precipitation that nudging temperature/moisture in the boundary layer produces in 

figures 68-69. The problem is lessoned somewhat by reducing the nudging strength of moisture (px_acm_n2, 

figures 70-71), but the accumulation bias already evident in the base case is exacerbated nonetheless. We might 

be able to get away with a px_acm_n or px_acm_n2 configuration in the winter, but definitely not in the 

summer. To avoid these potential precipitation artifacts altogether, we recommend not nudging temperature or 

moisture in the boundary layer at all. 

Figure 68. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm_n sensitivity and the px_acm2 base case. 

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Figure 69. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm_n sensitivity and the px_acm2 base case. 

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Figure 70. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 36-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n2 model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm_n2 sensitivity and the px_acm2 base case. 

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Figure 71. Episode 2 (Jul 13-28, 2001) composite precipitation for the US land-portion of the 12-km domain is shown. 

The top left panel displays the accumulated precipitation generated from the Climate Prediction Center gridded

precipitation datasets. The top right panel shows the corresponding plot for the px_acm_n2 model configuration, and the 

lower left plot shows the difference plot between the model and the observations. The bottom right plot shows the 

difference between the px_acm_n2 sensitivity and the px_acm2 base case. 

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6 Conclusions from Sensitivity Modeling

Considering only the original four (five, including px_acm2) sensitivity runs mandated under VISTAS 

Met task 2d, the px_acm2 run performs best overall. This conclusion is the result of a rather exhaustive 

quantitative and qualitative assessment of these sensitivity runs over three episodes. While the statistical 

performance for px_acm2 for certain quantities might be surpassed by other model configurations (e.g. 

noah_eta-my winds), it seldom performs the poorest of the five original sensitivities tested. 

A few general conclusions can be drawn about these initial sensitivity tests. They are:

• PBL heights are consistently highest in the noah_mrf simulations, while the noah_eta-my runs 

consistently produce the lowest daytime PBL heights. At night the px_acm(2) PBL heights are 

usually lowest.

• Surface winds are consistently best represented by the noah_eta-my runs, in large part because that 

configuration appropriately produces the calmest winds at night. 

• The px_acm2 temperatures are modeled better than all other configurations for the winter episode, 

including px_acm. The noah_eta-my runs consistently show the most extreme low temperature 

bias of all configurations for all episodes. 

• All configurations other than noah_eta-my exhibit similar performance in modeling measurable 

24-h precipitation. The noah_eta-my runs produce more precipitation coverage and are generally 

slightly less skilled than the other sensitivities, especially for the winter episode. The runs all seem 

to produce too much accumulated precipitation in the summer.

The auxiliary tests show that using LITTLE_R output to drive INTERPF (px_acm8) produces slightly 

beneficial results. In essence px_acm8 is the “best and final” configuration. Our recommendation for the annual 

modeling effort, therefore, is to implement the px_acm8 configuration. These tests also reveal that MM5 seems 

to suffer from a wintertime cold bias. All of our configurations yielded that signature. While the px_acm8 setup produces the best results of the credible configurations, the cold bias still remains. The problem seems to be 

most acute whenever the temperature changes significantly over the course of a 5-5 day segment. More effort 

from the MM5 community at large needs to be made to correct this bias, but at present it appears that these 

transient cold bias signatures are unavoidable. 

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7 Methodology for Annual Modeling Effort

With the sensitivity runs completed, we can establish the procedures we will use to execute/analyze MM5 

for the annual modeling study period. 

A. Modeling Approach

For the sensitivity modeling we used the MPP implementation of MM5 version 3.6.1. It is important to 

note that considerable effort was expended to port EPA’s MPP version of the Pleim-Xiu LSM into the 3.6.1 

MM5 code. A few other modifications were made to the code to allow MPP, most notably modifications to the 

Kain-Fritsch 2 cumulus parameterization module. This version was kept frozen throughout the course of the 

sensitivity testing. Since then a new minor-release version of MM5 (v3.6.2) has been released. According to 

NCAR’s release notes, the only changes in the actual MM5 code that could possibly affect our modeling results 

involve seasonal vegetative adjustments and sea ice treatments. The vegetative adjustments have already been 

implemented in our version of the code, and the sea ice issue is likely to have negligible effects on our results. 

Considering the resources required to test the v3.6.2 code with all of our modifications added, and considering 

the expected negligible effects of these modifications, for the annual modeling effort we will implement the 

v3.6.1 code used in the sensitivity modeling. The preprocessor code, however, could affect results positively. 

Therefore we will use the latest v3.6.2 releases of those codes. 

In the sensitivity modeling we generally executed MM5 in 5.5-day segments starting at 00Z, and we will 

continue this approach for the annual modeling. Though our modeling period is all of 2002, we will begin our 

simulations at 00Z December 17, 2001. This allows the air quality team to “spin-up” CMAQ (or another air 

quality model) for up to two weeks before our period of interest. It requires 76 5.5-day segments to process the 

entire year, assuming a 12-hr overlap between runs. Realizing that new runs may at some point be needed, we 

will assign a “case” tag to each file associated with a particular segment. This tag includes the start date/year of 

the segment, as well as “v02_aaa” (“v” for VISTAS, “02” for 2002, and “aaa” indicating the first 

meteorological run). The segments align as follows:

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Date tag Duration Date Tag Duration

dec17_01 00Z Dec 17, 2001 – 12Z Dec 22, 2001 jun25_02 00Z Jun 25, 2002 – 12Z Jun 30, 2002

dec22_01 00Z Dec 22, 2001 – 12Z Dec 27, 2001 jun30_02 00Z Jun 30, 2002 – 12Z Jul 5, 2002

dec27_01 00Z Dec 27, 2001 – 12Z Jan 1, 2002 jul05_02 00Z Jul 5, 2002 – 12Z Jul 10, 2002

jan01_02 00Z Jan 1, 2002 – 12Z Jan 6, 2002 jul10_02 00Z Jul 10, 2002 – 12Z Jul 15, 2002

jan06_02 00Z Jan 6, 2002 – 12Z Jan 11, 2002 jul15_02 00Z Jul 15, 2002 – 12Z Jul 20, 2002

jan11_02 00Z Jan 11, 2002 – 12Z Jan 16, 2002 jul20_02 00Z Jul 20, 2002 – 12Z Jul 25, 2002

jan16_02 00Z Jan 16, 2002 – 12Z Jan 21, 2002 jul25_02 00Z Jul 25, 2002 – 12Z Jul 30, 2002

jan21_02 00Z Jan 21, 2002 – 12Z Jan 26, 2002 jul30_02 00Z Jul 30, 2002 – 12Z Aug 4, 2002

jan26_02 00Z Jan 26, 2002 – 12Z Jan 31, 2002 aul04_02 00Z Aug 4, 2002 – 12Z Aug 9, 2002

jan31_02 00Z Jan 31, 2002 – 12Z Feb 5, 2002 aug09_02 00Z Aug 9, 2002 – 12Z Aug 14, 2002

feb05_02 00Z Feb 5, 2002 – 12Z Feb 10, 2002 aug14_02 00Z Aug 14, 2002 – 12Z Aug 19, 2002

feb10_02 00Z Feb 10, 2002 – 12Z Feb 15, 2002 aug19_02 00Z Aug 19, 2002 – 12Z Aug 24, 2002

feb15_02 00Z Feb 15, 2002 – 12Z Feb 20, 2002 aug24_02 00Z Aug 24, 2002 – 12Z Aug 29, 2002

feb20_02 00Z Feb 20, 2002 – 12Z Feb 25, 2002 aug29_02 00Z Aug 29, 2002 – 12Z Sep 3, 2002

feb25_02 00Z Feb 25, 2002 – 12Z Mar 2, 2002 sep03_02 00Z Sep 3, 2002 – 12Z Sep 8, 2002

mar02_02 00Z Mar 2, 2002 – 12Z Mar 7, 2002 sep08_02 00Z Sep 8, 2002 – 12Z Sep 13, 2002

mar07_02 00Z Mar 7, 2002 – 12Z Mar 12, 2002 sep13_02 00Z Sep 13, 2002 – 12Z Sep 18, 2002

mar12_02 00Z Mar 12, 2002 – 12Z Mar 17, 2002 sep18_02 00Z Sep 18, 2002 – 12Z Sep 23, 2002

mar17_02 00Z Mar 17, 2002 – 12Z Mar 22, 2002 sep23_02 00Z Sep 23, 2002 – 12Z Sep 28, 2002

mar22_02 00Z Mar 22, 2002 – 12Z Mar 27, 2002 sep28_02 00Z Sep 28, 2002 – 12Z Oct 2, 2002

mar27_02 00Z Mar 27, 2002 – 12Z Apr 1, 2002 oct03_02 00Z Oct 3, 2002 – 12Z Oct 8, 2002

apr01_02 00Z Apr 1, 2002 – 12Z Apr 6, 2002 oct08_02 00Z Oct 8, 2002 – 12Z Oct 13, 2002

apr06_02 00Z Apr 6, 2002 – 12Z Apr 11, 2002 oct13_02 00Z Oct 13, 2002 – 12Z Oct 18, 2002

apr11_02 00Z Apr 11, 2002 – 12Z Apr 16, 2002 oct18_02 00Z Oct 18, 2002 – 12Z Oct 23, 2002

apr16_02 00Z Apr 16, 2002 – 12Z Apr 21, 2002 oct23_02 00Z Oct 23, 2002 – 12Z Oct 27, 2002

apr21_02 00Z Apr 21, 2002 – 12Z Apr 26, 2002 oct28_02 00Z Oct 28, 2002 – 12Z Nov 2, 2002

apr26_02 00Z Apr 26, 2002 – 12Z May 1, 2002 nov02_02 00Z Nov 2, 2002 – 12Z Nov 7, 2002

may01_02 00Z May 1, 2002 – 12Z May 6, 2002 nov07_02 00Z Nov 7, 2002 – 12Z Nov 12, 2002

may06_02 00Z May 6, 2002 – 12Z May 11, 2002 nov12_02 00Z Nov 12, 2002 – 12Z Nov 17, 2002

may11_02 00Z May 11, 2002 – 12Z May 16, 2002 nov17_02 00Z Nov 17, 2002 – 12Z Nov 22, 2002

may16_02 00Z May 16, 2002 – 12Z May 21, 2002 nov22_02 00Z Nov 22, 2002 – 12Z Nov 27, 2002

may21_02 00Z May 21, 2002 – 12Z May 26, 2002 nov27_02 00Z Nov 27, 2002 – 12Z Dec 2, 2002

may26_02 00Z May 26, 2002 – 12Z May 31, 2002 dec02_02 00Z Dec 2, 2002 – 12Z Dec 7, 2002

may31_02 00Z May 31, 2002 – 12Z Jun 5, 2002 dec07_02 00Z Dec 7, 2002 – 12Z Dec 12, 2002

jun05_02 00Z Jun 5, 2002 – 12Z Jun 10, 2002 dec12_02 00Z Dec 12, 2002 – 12Z Dec 17, 2002

jun10_02 00Z Jun 10, 2002 – 12Z Jun 15, 2002 dec17_02 00Z Dec 17, 2002 – 12Z Dec 22, 2002

jun15_02 00Z Jun 15, 2002 – 12Z Jun 20, 2002 dec22_02 00Z Dec 22, 2002 – 12Z Dec 27, 2002

jun20_02 00Z Jun 20, 2002 – 12Z Jun 25, 2002 dec27_02 00Z Dec 27, 2002 – 12Z Jan 1, 2003

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B. Model Execution

To prepare the MM5 input files we execute the MM5v3.6.2 preprocessor system. The general flow stream 

is as follows:

Processor Function Execution Schedule

TERRAIN Prepare time-independent grid information Once

REGRID (Pregrid) Extract files from EDAS analyses at 3-hr (sfc) 

and 12-hr (3D) temporal resolution

Monthly

REGRID (Regridder) Translates pregrid output onto TERRAIN grids 6 days

FETCH (adp_sfc) Creates surface/ship data at 3-hr resolution Monthly (nominal 3-day chunks)

FETCH (adp_upa) Creates upper air data (cat with sfc data) at 3-hr 

resolution

Monthly (nominal 15-day chunks)

LITTLE_R Uses FETCH output to “improve” REGRID 

output

6 days

INTERPF Translates LITTLE_R output to MM5 vertical 

coordinate system

6 days

MM5 Creates hourly gridded meteorological files 5.5 days

Terrain

Terrain is processed using the 2 min (~4 km) global land-use data set for both the 36-km and 12-km 

domains. The 36-km grid is the national RPO domain, and the 12-km domain is identical to what was used in 

the sensitivity modeling. Note that we have selected the “BotSoil” option per EPA’s example. The exact details 

are found in the terrain.namelist file that is included in the Appendix 9A.

REGRID (pregrid)

Since the EDAS files (ds609.2 from NCAR) are archived in month chunks (half month 3D analyses files), 

we process these files through pregrid at monthly intervals. We use the standard GRID vtables to process four 

types of files: 1) FILE*, 2) SNOW*, 3) SOIL*, and 4) SST*, each at 3-hourly resolution. An example pregrid 

namelist file is shown in Appendix 9B.

REGRID (regridder)

The output of pregrid is interpolated to the MM5 grids for each modeled segment by regridder. Note that 

these input grids cover 6 days rather than 5.5 days due to an INTERPF requirement. Separate regridder runs are 

made for both the 36-km and 12-km grids, and the temporal resolution is 3 hours. An example regridder 

namelist file is shown in Appendix 9C. We have implemented one minor change from the methodology used in 

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the sensitivity tests, that being that the climatological albedo file (used by the NOAH LSM) is not processed for 

the annual run. 

FETCH (adp_sfc)

The adp_sfc preprocessor converts surface and ship data files (ds464.0) into a format that can be read by 

LITTLE_R (Objective Analysis). The surface data are stored in five or six day chunks so that a month of data is 

always included in six data sets, while the ship data are in monthly chunks. We have discovered that adp_sfc 

might not work correctly if we try to process too long a time span, so we limit our processing to 3 or 4 day 

periods. So we execute adp_sfc ten times for both the surface data and the ship data. Hourly output files are 

produced, though we only use the 3-hourly files. The surface and ship data are concatenated, and eventually the 

upper air data are also concatenated at the end of the data file. The preprocessing for the sensitivity runs did not 

include upper air data in the surface_obs files. An example namelist for adp_sfc is shown in Appendix 9D. 

FETCH (adp_upa)

The adp_upa preprocessor converts upper air data files (ds353.4) into a format that can be read by 

LITTLE_R. The upper air data are stored in monthly segments, and we process the data in half-month intervals 

one after the other. The output interval is typically 6-hourly. We concatenate the surface/ship data into these 

data sets, so the output upper-air_obs files are actually at 3-hour intervals. An example namelist for adp_upa is 

shown in Appendix 9E.

LITTLE_R (Objective Analysis)

LITTLE_R performs internal quality control on the observational data produced by FETCH and uses these 

data to “improve” the REGRID output fields. Our auxiliary sensitivity tests indicate that this provides a 

discernable improvement in the MM5 predictions, so unlike the base sensitivity runs we will use the LITTLE_R 

output as input to INTERPF. LITTLE_R needs to be executed for both the 36-km and 12-km grids. This 

processor produces SFCFDDA files that directly feed into MM5. An example LITTLE_R namelist file is shown 

in Appendix 9F.

INTERPF

INTERPF converts the LITTLE_R output into the 34-sigma layers that have become the RPO standard. 

Because the LOWBDY files require a full day (00Z-21Z) to produce output, this preprocessor, as well as 

REGRID and LITTLE_R, needs to be run for a full six days to allow MM5 to execute the desired 5.5 days. An 

example INTERPF namelist is included in Appendix 9G.

MM5

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The sensitivity tests described earlier in this document enables us to define the likely optimal model 

configuration we will implement for the annual run. One expected minor change from px_acm8 is that the 

RADFRQ interval is decreased from 30 minutes to 15. In summary here are the key parameters:

36-km domain: 129x165x34 (N-S dot cells, W-E dot cells, vertical levels)

12-km domain: 190x181x34 (N-S dot cells, W-E dot cells, vertical levels)

2-way nesting, no feedback, 90-second outer grid time step, 7920 minutes total, hourly output

LSM: Pleim-Xiu (independent segments initiated from EDAS soil quantities)

PBL: Asymmetric Convective Model (ACM)

Radiation scheme: Rapid Radiative Transfer Model (RRTM)

Cumulus scheme: Kain-Fritsch 2

Snow: Snow effects on (IFSNOW=1)

SST: Varying (ISSTVAR=1)

Analysis nudging: 2.5E-4 (36-km sfc/aloft winds, temps aloft only)

 1.0E-4 (12-km sfc/aloft winds, temps aloft only)

 1.0E-5 (36-km/12-km mixing ratio, aloft only)

Appendix 9H shows an example mmlif file.

The MM5 output files are broken down into daily chunks to keep the output files from becoming too 

large. The default is for MM5 to name the outer 36-km grid as domain “1”, while the 12-km grid is referred to 

as domain “2”. This is what the 36-km files are named by default:

MMOUT_DOMAIN1_00 (initial hour 0 only)

MMOUT_DOMAIN1_01 (hours 1-24, inclusive)

MMOUT_DOMAIN1_02 (hours 25-48, inclusive)

MMOUT_DOMAIN1_03 (hours 49-72, inclusive)

MMOUT_DOMAIN1_04 (hours 73-96, inclusive)

MMOUT_DOMAIN1_05 (hours 97-120, inclusive)

MMOUT_DOMAIN1_06 (hours 121-132, inclusive)

When the model files are sent to the air quality team they will have the date/case tag added as previously 

described. So for the model run that initiates on March 17, 2002, a file might be named 

MMOUT_DOMAIN2_04.mar17_02.v02_aaa. This file contains 12-km MM5 output data for the time period 

01Z March 20, 2002 through 00Z March 21, 2002 from the run that started at 00Z March 17, 2002. 

C. Model Evaluation Procedures

A variety of evaluation procedures have been established for this project (see 

http://www.baronams.com/projects/VISTAS/reports/VISTAS_TASK1.pdf). The results will be transmitted to 

the TAWG and other interested parties via the VISTAS meteorological web page 

(http://www.baronams.com/projects/VISTAS/select_annual_product.html). Here is a summary of the evaluation 

products that will be available on that web site:

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1) Surface Products

a. Spatial Plots

b. Timeseries Plots

c. Combination Plots

2) Aloft Products

a. Spatial Plots

b. Sounding Plots

c. Profiler Plots

3) Statistical Products

a. Surface Statistical Timeseries

b. Aloft Statistical Timeseries

c. Statistical Tables

4) Monthly Products

a. Spatial Statistical Plots

b. Spatial Summary Plots

c. Monthly “Bakergram” Plots

d. Annual “Bakergram” Plots

e. Monthly Summary Statistics

In many of the web forms the user is asked to select options from a few drop-down menus. Here is a summary 

of the most common menus:

1) Region, or areal extent of results

a. VISTAS (VISTAS RPO, valid for both 36-km and 12-km grids)

b. Full (Entire grid, valid for both grids)

c. US (All US stations, 36-km stats only)

d. MANE-VU (MANE-VU RPO, 36-km only)

e. MIDWEST (MIDWEST RPO, 36-km only)

f. CENRAP (CENRAP RPO, 36-km only)

i. CENRAP_N (Northern portion of CENRAP)

ii. CENRAP_S (Southern portion of CENRAP)

g. WRAP (WRAP RPO, 36-km only)

i. WRAP_N (Northern portion of WRAP)

ii. WRAP_S (Southern portion of WRAP)

2) Sensitivity

a. v02_aaa (for Vistas ’02, initial met/emis/air_quality)

3) Scale

a. 12km (12-km grid)

b. 36km (36-km grid)

4) 5-day segment (Segment identifier, MonDD_YY)

a. dec17_01 (Segment from Dec 17, 2001, 12Z to Dec 22, 2001, 12Z)

b. dec22_01 (Segment from Dec 22, 2001, 12Z to Dec 27, 2001, 12Z)

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c. ... 

d. dec27_02 (Last segment, Dec 27, 2002, 12Z to Jan 1, 2003, 12Z)

 

 

A brief summary of each product type is given below.

Surface Products (Spatial Plots)

Spatial plots of key surface variables are available at 6-hrly intervals. Variables include:

1) Temperature (1.5 m), with TDL obs overlaid.

2) Mixing Ratio, with TDL obs overlaid.

3) Winds, vectors (with a very light wind speed background) at an MCIP2-derived 10 meter 

(observational) height. Not available for 36-km Full region (too busy).

4) Cloud fraction (CFRAC in MCIP2.1). The overlaid obs come from the TDL surface reports and 

represent the maximum of the low-, middle-, and high- observed cloud coverage. The black and 

white color scale is designed to mimic (sort of) satellite imagery.

5) Cloud fraction (alt). Like the above cloud fraction, except we take the maximum of the MCIP2.1 

low, middle, and high clouds to represent the total model cloud fraction. This is the preferred 

variable from a meteorological perspective.

6) 6-hrly accumulated precipitation, with TDL obs overlaid. Be warned that in the TDL obs it is 

impossible to differentiate “missing” precipitation values from “No precip” reports. So use these 

images qualitatively with that realization. 

7) Relative humidity, with TDL obs overlaid. 

8) Temperature (layer 1), with TDL obs overlaid.

9) PBL heights. 

Surface Products (Timeseries Plots)

Time series plots of key meteorological variables are available for a number of important sites. Note 

that no 12-km plots are available for stations outside of the 12-km grid. Plots are available for the 

following scales:

1) 12KM: 12-km grid (includes aloft trace where applicable)

2) 36KM: 36-km grid (includes aloft trace where applicable)

3) 36_12KM: 36-km/12-km results co-plotted. (no aloft traces)

Surface Products (Combination Plots)

Model/observed (or analyzed) fields are plotted next to each other. These animations are region 

independent. Here are the available fields:

1) pcp24: 24-hr accumulated precipitation ending at 12Z daily. Observed values derive from the 

Climate Prediction Center.

2) ir_sat: Model cfrac (alt) is compared with GOES 8 infrared imagery at 00Z and 12Z.

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3) slp: The Unisys surface analyses are compared with a RIP-generated analyses. Note that the 

plotted precipitation is qpr, the total precipitation hydrometeor mixing ratio. The scale for 

this variable does not match the analyzed precipitation, and in fact there are times, 

especially in the summer, in which actual modeled precipitation falls below the lower 

range of the qpr scale. 

4) vis_sat: Model cfrac (alt) is compared with GOES 8 visible imagery daily at 18Z.

 

Aloft Products (Spatial Plots)

This product shows spatial plots for the basic meteorological variables at layers 9 (sigma 0.94-0.93, 

midpoint ~500m), 17 (sigma 0.82-0.80, midpoint ~1600m), and 22 (sigma 0.65-0.60, midpoint 

~3400m). The observations are integrated through the depth of the sigma layers. Note that only full 

grid images are produced. Here are the available variables:

1) Temperature

2) Mixing ratio

3) Winds

Aloft Products (Sounding Plots)

This product shows sounding plot animations for selected upper air observing sites. Observations are 

co-plotted, and soundings are available from the surface to 100 mb as well as from the surface to 500 

mb. 

Aloft Products (Profiler Plots)

This product shows profiler plot wind animations for selected profiler sites in the southeastern US up 

to a height of 2500 AGL. Individual images show 12 hours of data, from 00-11Z or 12-23Z.

Statistical Products (Surface Statistical Timeseries)

Statistical metrics are plotted on a three-panel image. For most variables the top panel shows the 

mean obs (blue) and the mean model (red), the second panel shows the bias (blue) and absolute error 

(red), while the third panel shows the Index of Agreement (IA, in blue), the coefficient of 

determination (r2, in red), and the number of valid obs/model pairs (#, thin black, right axis). Wind 

direction plots differ from the above paradigm in that the number of valid obs/model pairs appears in 

the top panel, and more importantly in that the third panel shows the U/V wind component absolute 

error and bias. The three-panel precipitation plots are vastly different in description from the other 

variable. The top panel shows precipitation accuracy (“Acc”, in blue), false alarm ratio (“FAR”, in 

red), probability of detection (“POD”, in black), and bias (“Bias”, right axis, in magenta). A thin 

green line marks the 1.0 level (right axis), the “perfect” precipitation bias level. The second panel 

shows the following skill measures: Threat score (“Threat”, in blue), the Equitable Threat Score 

(“ETS”, in red), the True Skill Score (or Hanssen and Kuipers score, “TSS”, in black), and Heidke 

Skill Score (“HSS”, in magenta). Before describing the third panel, we need to define a few terms. 

Measurable precipitation, by definition, matches or exceeds 0.01 inches. A “Hit” means that the 

model predicted measurable precipitation and measurable precipitation actually occurred. A “Miss” 

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means the model failed to predict measurable precipitation when measurable precipitation occurred. 

A “False” means that the model predicted measurable precipitation when none actually occurred. 

Finally, a “Zero” means that the model and observations both indicated no measurable precipitation. 

The third panel thus shows the number of “Hits” (blue), “Misses” (red), and “Falses” (black) plotted 

using the left axis, and the number of “Zeroes” (magenta) plotted using the right axis. All of the 

metrics plotted in the first two panels are calculated based on the numbers plotted in this third panel. 

A final note about precipitation metrics needs to be made. It is possible to calculate these metrics 

using a threshold other than 0.01, which we indeed will do. A higher threshold can yield insight into 

the ability of the model to accurately handle more significant precipitation events. The downside of 

using higher thresholds is that some of the metrics (especially “Bias”) may end up being calculated 

with smallish numbers in its denominator, thus enabling the metric to at times reach very high 

numbers, essentially blowing the scale for other time periods. The third panel plot then becomes a 

useful surrogate to gauge model performance. 

Here are the available statistical time series variables:

1) Temperature (1.5m)

2) Mixing Ratio

3) Wind Speed (10m)

4) Wind Direction

5) Cloud Fraction (orig): (From MCIP2.1 CFRAC)

6) Cloud Fraction (alt): (preferred)

7) Relative Humidity

8) Temperature (layer 1)

9) Wind Speed (layer 1)

10) 24-hr precipitation (0.01 in threshold): 

i. These stats are calculated using the Climate Prediction Center (CPC) gridded 

precipitation fields (0.25x0.25 degree resolution) regridded to match our 36km and 12km 

grids. The stats are then calculated on a cell by cell basis. Since the CPC fields are mainly 

for the US, we applied a mask (Figure 72) to only consider grid points near the US, and 

another mask to only consider land points in the calculations.

11) 24-hr precipitation (0.05 in threshold)

12) 24-hr precipitation (0.10 in threshold)

13) 24-hr precipitation (0.25 in threshold)

14) 24-hr precipitation (0.50 in threshold)

15) 24-hr precipitation (1.00 in threshold)

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Figure 72. Mask for precipitation stats (36km).

Statistical Products (Aloft Statistical Timeseries)

This product shows the 3-panel statistical time series plots for our three aloft sigma layers (09, 17, 

and 22). Here are the available variables:

1) Temperature (K)

2) Mixing Ratio (g/kg)

3) Wind Speed (m/s)

4) Wind Direction (degrees)

Statistical Products (Statistical Tables)

This product shows region-specific statistical tables for all applicable surface variables other than 

precipitation. The data are parsed temporally such that: 

1) All hours are considered

2) Only 00Z-11Z (“nighttime”) hours are considered

3) Only 12Z-23Z (“daytime”) hours are considered

The variable/stat labels are very cryptic. Here’s how to interpret them:

Variables in first part of table:

1) TMP-1.5m: Temperature at 1.5 m (deg K)

2) QV: Mixing ratio (g/kg)

3) RH: Relative humidity (%)

4) WSPD-10m: Wind speed reduced by MCIP2 to 10m observational height (m/s)

5) SPD-lyr1: Layer 1 wind speed (m/s)

6) CLD: Cloud cover – original (%) (use CLD2 instead)

7) CLD2: Cloud cover – alternate (%) 

8) TMP-lyr1: Layer 1 temperature (deg K) (use TMP-1.5m instead)

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Labels in first part of table:

1) obsmean: Average of observations

2) modmean: Average of model predictions

3) bias: Simple bias

4) abserr: Absolute error

5) r2: Coefficient of determination

6) ia: Index of Agreement

7) rmse: Root Mean Square Error

8) nbias: Normalized bias

9) jtot: Number of valid obs/model pairs for stats

The WDIR (wind direction, degrees) metric has its own label structure:

1) obsmean: Average of observations

2) modmean: Average of model predictions

3) bias: Simple bias (Should be ignored for this discontinuous measure)

4) abserr: Absolute error

5) ubias: Simple bias for U-wind component (m/s)

6) vbias: Simple bias for V-wind component (m/s)

7) uerr: Absolute error of U-wind component (m/s)

8) verr: Absolute error of V-wind component (m/s)

9) newtot: Number of valid obs/model pairs for wind direction

10) dbias: Correct direction bias (Use instead of bias!)

Monthly Products (Spatial Statistical Plots)

This page shows basic statistical metrics (bias, absolute error) plotted on a per station basis. The idea 

is to graphically display these metrics so that the analyst can see the spatial pattern of model 

bias/error. There are two forms of these plots. The string “TOTAL” in the variable name designates 

the first of the forms. The resultant plot shows the metric calculated over all valid times. If the string 

“TOTAL” does not appear in the variable name, then a 24-hr animation appears. The first plot (hour 

0) in the animation shows the metric calculated over all valid 00Z times, the second plot (hour 1) 

shows the metric calculated over all valid 01Z times, and so on through 23Z. This enables one to see 

the average diurnal/spatial variation of the metrics. Since only the hour field has meaning in these 

plots (and not even the hour has meaning in the “TOTAL” plots), we’ve arbitrarily set the Julian date 

for display to be January 1, 0000, and the “TOTAL” plots have an arbitrarily defined hour of 0. 

These are only placeholders so that we can use PAVE to visualize the results. 

The naming convention for the variables that can be displayed has already partially been described. 

In review, variable names that have “BIAS” in them display the bias metric, while variable names 

that have “ERR” in them display the absolute error metric. If “TOTAL” appears in the variable 

name, a single plot showing the metric for all valid hours will be displayed at the arbitrarily defined 

time of Jan 1, 0000, 00Z. If “TOTAL” does not appear in the variable name, then an animation of 

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the metric will appear cycling from 00Z-23Z, Jan 1, 0000. The actual variable to be plotted is listed 

first in the variable name string. They are:

1) CLD: Cloud cover (orig), as defined in spatial plots (%).

2) CLD2: Cloud cover (alt)

3) DIR: Wind direction (degrees)

4) Q: Water vapor mixing ratio (g/kg)

5) RH: Relative humidity (%)

6) SPD...10M: Wind speed reduced by MCIP2 to 10m obs height (m/s)

7) SPD...LYR1: Layer 1 wind speed (m/s)

8) T...1P5M: Temperature reduced by MCIP2 to 1.5m obs height (K)

9) T...LYR1: Layer 1 temperature (K)

10) UV: This is a unique variable in that only “ERR” is plotted. UV_ERR can be interpreted as the 

average magnitude of the error vector, defined as the square root of (U_ERR^2+V_ERR^2). 

This metric essentially combines wind speed error and wind direction error into one quantifiable 

metric.

Monthly Products (Spatial Summary Plots)

This product is designed to show daily summary information for a few of the key meteorological 

variables. Whenever possible the observations are overlaid onto the MM5 predicted fields. Generally 

speaking for each variable a 24-hr average (01Z-00Z), a “daytime” average (18Z-21Z) and a 

“nighttime” average (07Z-10Z) are generated. For temperature the “daytime” average is replaced by 

a daily maximum, and the “nighttime” average is replaced with the daily minimum. For precipitation 

a 24-hr accumulation ending at 00Z is created, as well as a “daytime” accumulation that we define to 

be 18Z-00Z. Since observations may include missing data, we require at least 3 (out of 4) valid 

observations per “daytime”/”nighttime” block, and 22 valid observations for the daily averages. 

These fields are generated for all regions. Here are the variables available for the three output 

averages:

1) Temperature (deg K)

2) Mixing Ratio (g/kg)

3) Precipitation (in)

4) Relative Humidity (%)

5) PBL height (m), with no obs overlay.

6) Wind Vectors with a wind speed (m/s) background 

7) Cloud cover (alt)

Monthly Products (Monthly “Bakergram” Plots)

This product shows key statistical quantities summarized in a tile plot following the paradigm Kirk 

Baker developed for his “Mosaic” plots. This particular product has hours (00Z-23Z, top to bottom) 

as the y-axis and day of month (1-31, left to right) as the x-axis, as illustrated in figure 73. 

We produce bias, error, and index of agreement plots for the following variables:

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1) Temperature (deg K)

2) Mixing Ratio (g/kg)

3) Relative Humidity (%)

4) Cloud cover – alt (%)

5) Wind Speed (m/s)

6) Wind direction (degrees), bias and error only

Additionally we produce a plot showing the magnitude of error vector (m/s).

Figure 73. “Bakergram” prototype plot for February 2002 is shown. Hourly temperature biases (K) are plotted for 

the 12-km VISTAS region. The upper left tile represents the average bias in the selected region for 

00Z, February 1, 2002. Hours increase from 00Z at the top to 23Z at the bottom, while the day of the 

month increases from 1 on the left to 31 on the right. Missing periods (e.g. Feb 29-31) are set to the 

default white background.

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Monthly Products (Annual “Bakergram” Plots)

This page shows key statistical quantities summarized in a tile plot following the paradigm Kirk 

Baker developed for his “Mosaic” plots. This particular product has days (1-31, top to bottom) as the 

y-axis and month (1-12, left to right) as the x-axis, creating a calendar effect as illustrated in figure 

3. (This particular example only shows the first six months of the year.) The available variables 

exactly match the variable list from the Monthly “Bakergram” Plot list.

Figure 74. “Bakergram” prototype plot for February 2002 is shown. Daily temperature biases (K) are plotted 

for the 12-km VISTAS region. The upper left tile represents the average bias in the selected 

region for January 1, 2002. Days of month increase from 1 at the top to 31 at the bottom, while 

the month increases from 1 (January) on the left to 12 (December) on the right. Missing periods 

(e.g. Feb 29-31, Apr 31) are set to the default white background.

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Monthly Products (Monthly Summary Statistical Tables)

This product is analogous to the 5-day segment statistical tables, except it covers a full month. These 

also include precipitation performance metrics when applicable. 

Monthly Products (Monthly Accumulated Precipitation)

This product is analogous to the precipitation combination plots, except precipitation will be 

accumulated over monthly intervals. 

D. Computing Resources

Except for the TERRAIN preprocessor and a few miscellaneous analysis routines, we anticipate using 

MCNC-GTEC’s Linux cluster, which is a 64-node IBM X335's with dual 2.8Ghz Xeon processors, 4GB of 

memory, 40GB online storage. The actual MM5 runs will be run in MPP mode using 32 processors. Since 

TERRAIN did not readily work on the cluster, it was easier to run that processor on a 300 MHZ SGI machine. 

Once runs are completed, we will archive the model inputs/outputs onto 100/200 GB LTO tapes. 

Additionally we will make triplicate copies of those files onto 250 GB FireWire/USB 2.0 external drives. 

E. Project Schedule

Here are some key deliverable dates in the project:

Draft Protocol Approved: Dec 12, 2003

Annual Modeling Begins: Dec 15, 2003

First 6 Months MM5 Completed: Jan 30, 2004

Website for Annual Products: Feb 5, 2004 

Revised Protocol: Feb 11, 2004 

First 6 Months MM5 Results to Air Quality Team: Feb 19, 2004 

MM5 Runs Completed: Mar 1, 2004

MM5 Evaluation Completed: Mar 19, 2004

Delivery of Second 6 Months to Air Quality Team: Mar 19, 2004

Documentation of Annual Run: Jun 30, 2004

8 References

Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model with the Penn State/NCAR 

MM5 modeling system. Part I: Model implementation and sensitivity. Mon. Wea. Rev., 129, 569-585. 

Chen, F., and J. Dudhia, 2001: Coupling an advanced land-surface/hydrology model with the Penn State/NCAR 

MM5 modeling system. Part II: Preliminary Model validation. Mon. Wea. Rev., 129, 587-604. 

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Emery, C., E. Tai, and G. Yarwood, 2001. “Enhanced Meteorological Modeling and Performance Evaluation 

for Two Texas Episodes”, report to the Texas Natural Resources Conservation Commission, prepared by 

ENVIRON, International Corp, Novato, CA.

Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR 

Mesoscale Model (MM5). NCAR Tech. Note, NCAR/TN-398+STR, 122 pp.

Hong, S.-Y., and H.-L. Pan, 1996: Nocturnal boundary layer vertical diffusion in a medium range forecast 

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

Kain, J. S. and J. M. Fritsch, 1993: Convective parameterization for mesoscale models: The Kain-Fritsch 

scheme, The Representation of Cumulus Convection in Mesoscale Models, K. A. Emanuel and D. J. 

Raymond, Eds., American Meteorological Society, Boston, 246 pp.

Kain, John S., 2002: The Kain-Fristch convective parameterization: An update. To be submitted to J. of Appl. 

Meteoro.

Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for 

inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. 

Res., 102(D14), 16,663-16,682. 

Olerud, D. T., 2003: Summation of Relevant MM5 Sensitivity Modeling in Support of VISTAS (Visibility 

Improvement - State and Tribal Association), VISTAS task 2a deliverable. Available from Mike 

Abraczinskas, Meteorologist, NC Division of Air Quality, 1641 Mail Service Center, Raleigh, NC 

27699-1641 

Olerud, D. T., 2003: Recommended MM5 Sensitivity Modeling in Support of VISTAS (Visibility Improvement 

- State and Tribal Association)] VISTAS Task 2b deliverable. Available from Mike Abraczinskas, 

Meteorologist, NC Division of Air Quality, 1641 Mail Service Center, Raleigh, NC 27699-1641 

Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in 

winter storms using the MM5 mesoscale model. Quart. J. Roy. Metero. Soc., 124 B, 1071-1107. 

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

meteorology model. Journal of Applied Meteorology, 40, 192-209. 

Appendix E.2 - 127
Draft

1

9 Appendices

A. Terrain namelist

&MAPBG

 PHIC = 40.0, 

 XLONC = -97.0, 

 IEXP = .F., 

 AEXP = 144., 

 IPROJ = 'LAMCON', 

 &END

 &DOMAINS

 MAXNES = 2, 

 NESTIX = 129, 190, 136, 181, 211, 221, 

 NESTJX = 165, 181, 181, 196, 211, 221, 

 DIS = 36., 12., 9., 3.0, 1.0, 1.0, 

 NUMNC = 1, 1, 2, 3, 4, 5, 

 NESTI = 1, 18, 28, 35, 45, 50, 

 NESTJ = 1, 84, 25, 65, 55, 50, 

 RID = 1.5, 1.5, 1.5, 3.1, 2.3, 2.3, 

 NTYPE = 5, 5, 4, 6, 6, 6, 

 NSTTYP= 1, 2, 2, 2, 2, 2, 

 &END

 &OPTN

 IFTER = .TRUE., 

 IFANAL = .F., 

 ISMTHTR = 2 , 

 IFEZFUG = .F., 

 IFTFUG = .F., 

IFFUDG = .F., 

 IPRNTD = .F., 

 IPRTHT = .F., 

 IPRINT = 0, 

 FIN = 200., 100., 100., 100., 100., 100., 

 TRUELAT1=33., 

 TRUELAT2=45., 

 IFILL = .TRUE., 

 LSMDATA = .TRUE., 

 VEGTYPE = 1, 

 VSPLOT = .TRUE., 

 IEXTRA = .TRUE., 

 &END

 &FUDGE

 IFFUG = .F.,.F., 

 NDFUG = 0,0,

 IFUG(1,1)= 200*0, 

 IFUG(1,2)= 200*0, 

 JFUG(1,1)= 200*0, 

 JFUG(1,2)= 200*0, 

 LNDFUG(1,1)= 200*0, 

 LNDFUG(1,2)= 200*0, 

 &END

 &FUDGET

 NFUGBOX = 2 

STARTLAT=45.0,44.0, 

 ENDLAT =46.5,45.0, 

 STARTLON=-95.0,-79.8, 

 ENDLON =-92.6,-78.5, 

 &END

 &EZFUDGE

 HTPS(441) = -.001 

 HTPS(550) = 183. 

 HTPS(587) = 177. 

 HTPS(618) = 176. 

 HTPS(613) = 174. 

 HTPS(645) = 75. 

Appendix E.2 - 128
Draft

2

 HTPS(480) = 1897. 

 HTPS(500) = 1281. 

 &END

 &DATANAME

 TERNAME = 'Data/DEM_60M_GLOBAL ',

 'Data/DEM_30M_GLOBAL ',

 'Data/DEM_10M_GLOBAL ',

 'Data/DEM_05M_GLOBAL ',

 'Data/DEM_02M_GLOBAL ',

 'Data/DEM_30S_GLOBAL ',

 LNDNAME = 'Data/LANDUSE.60 ',

 'Data/LANDUSE.30 ',

 'Data/LANDUSE.10 ',

 ' ',

 ' ',

 ' ',

 LWNAME = 'Data/LWMASK-USGS.60 ',

 'Data/LWMASK-USGS.30 ',

 'Data/LWMASK-USGS.10 ',

 'Data/LWMASK-USGS.05 ',

 'Data/LWMASK-USGS.02 ',

 'Data/LWMASK-USGS.30s',

 VGNAME = 'Data/VEG-USGS.60 ',

 'Data/VEG-USGS.30 ',

 'Data/VEG-USGS.10 ',

 'Data/VEG-USGS.05 ',

 'Data/VEG-USGS.02 ',

 'Data/VEG-USGS.30s ',

 SONAME = 'Data/SOILCATB.60 ',

 'Data/SOILCATB.30 ',

 'Data/SOILCATB.10 ',

 'Data/SOILCATB.05 ',

 'Data/SOILCATB.02 ',

 'Data/SOILCATB.30s ',

 VFNAME = 'Data/VEG-FRACTION.10',

 TSNAME = 'Data/SOILTEMP.60 ',

 &END

B. Pregrid script

#!/bin/csh -f

 set echo

 set MON_PROC = jan02

 set OLD_PROC = dec01

 set OYR = 2001

 set OMM_PROC = 12 

#

# Put your input files for pregrid into the directory you specify as DataDir:

#

#set DataDir = /usr/tmp/username/REGRID

set DataDir = /scratch/olerud/pregrid/${MON_PROC}

if (! -e $DataDir) mkdir -p $DataDir

rm -rf $DataDir/*AWIP*

cp /scratch/olerud/ds609.2/${MON_PROC}/*_3Danal/*AWIP* $DataDir/

cp /scratch/olerud/ds609.2/${MON_PROC}/*_SFanal/*AWIP* $DataDir/

#

# Specify the source of 3-d analyses

#

# set SRC3D = ON84 # Old ON84-formatted NCEP GDAS analyses

# set SRC3D = NCEP # Newer GRIB-formatted NCEP GDAS analyses

Appendix E.2 - 129
Draft

3

 set SRC3D = GRIB # Many GRIB-format datasets

# InFiles: Tell the program where you have put the analysis files, 

# and what you have called them. If SRC3D has the value "GRIB", 

# then the Vtables you specify below in the script variable VT3D will 

# be used to interpret the files you specify in the ${InFiles} variable.

# set InFiles = ( ${DataDir}/NCEP* )

 set InFiles = ( ${DataDir}/*AWIP* )

#

# Specify the source of SST analyses

#

# set SRCSST = ON84

# set SRCSST = NCEP

# set SRCSST = NAVY

 set SRCSST = $SRC3D

#

# InSST: Tell the program where the files with SST analyses are. Do 

# this only if SST analyses are coming from files not named above in

# InFiles. If SRCSST has the value "GRIB", then the Vtables you 

# specify below in the script variable VTSST will be used to interpret 

# the files you specify in the ${InSST} variable.

#

 set InSST = ()

#

# Select the source of snow-cover analyses (entirely optional)

#

 

 set SRCSNOW = $SRC3D

# set SRCSNOW = ON84

# set SRCSNOW = GRIB

# InSnow: Set InSnow only if the snow-cover analyses are from files 

# not listed in InFiles. If SRCSNOW has the value "GRIB", then the 

# Vtables you specify below in the script variable VTSNOW will be used 

# to interpret the files you specify in the ${InSnow} variable.

 set InSnow = ()

#

# Select the source of soil model analyses (entirely optional)

#

 

 set SRCSOIL = $SRC3D

# InSoil: Set InSoil only if the soil analyses are from files 

# not listed in InFiles. If SRCSOIL has the value "GRIB", then the 

# Vtables you specify below in the script variable VTSOIL will be

# used to interpret the files you specify in the ${InSoil} variable.

 set InSoil = ()

#

# Build the Namelist

#

if ( -e ./pregrid.namelist ) then

 rm ./pregrid.namelist

endif

cat << End_Of_Namelist | sed -e 's/#.*//; s/ *$//' > ./pregrid.namelist

&record1

#

# Set the starting date of the time period you want to process:

#

 START_YEAR = 2001 # Year (Four digits)

 START_MONTH = 12 # Month ( 01 - 12 )

 START_DAY = 31 # Day ( 01 - 31 )

Appendix E.2 - 130
Draft

4

 START_HOUR = 15 # Hour ( 00 - 23 )

 END_YEAR = 2002 # Year (Four digits)

 END_MONTH = 01 # Month ( 01 - 12 )

 END_DAY = 31 # Day ( 01 - 31 )

 END_HOUR = 21 # Hour ( 00 - 23 )

#

# Define the time interval to process.

#

 INTERVAL = 10800 # Time interval (seconds) to process.

 # This is most sanely the same as the time interval for

 # which the analyses were archived, but you can really

 # set this to just about anything, and pregrid will

 # interpolate in time and/or skip over time periods for

 # your regridding pleasure.

/

End_Of_Namelist

#

# Tell the pregrid programs which Vtables to use. Do this only 

# if you have selected GRIB-formatted input using SRC___ = GRIB above.

# The directories referenced here are relative to REGRID/pregrid/. 

#

# The Vtable files specified in VT3D will be applied to the files

# specified in the InFiles variable. Similarly, the Vtable files 

# specified in VTSST, VTSNOW, and VTSOIL will be applied to the files 

# listed above in InSST, InSNOW, and InSoil, respectively.

# 

# set VT3D = ( grib.misc/Vtable.NNRP3D )

# set VTSST = ( grib.misc/Vtable.NNRPSST )

# set VTSNOW = ( grib.misc/Vtable.xxxxSNOW )

# set VTSOIL = ( grib.misc/Vtable.xxxxSOIL )

 set VT3D = ( grib.misc/Vtable.AWIP3D )

 set VTSST = ( grib.misc/Vtable.AWIPSST )

 set VTSNOW = ( grib.misc/Vtable.AWIPSNOW )

 set VTSOIL = ( grib.misc/Vtable.AWIPSOIL )

########################################################################

########################################################################

###### ######

###### END USER MODIFICATION ######

###### ######

########################################################################

########################################################################

if ( ! $?SRC3D ) then

 set SRC3D

endif

if ( ! $?SRCSST ) then

 set SRCSST

endif

if ( ! $?SRCSNOW ) then

 set SRCSNOW

endif

if ( ! $?SRCSOIL ) then

 set SRCSOIL

endif

if ( ! $?VTSOIL ) then

 set VTSOIL

endif

if ( ! $?VTSNOW ) then

 set VTSNOW

endif

if ( ! $?VTSST ) then

 set VTSST

endif

if ( ! $?VT3D ) then

 set VT3D

endif

Appendix E.2 - 131
Draft

5

if ( ! $?InFiles ) then

 set InFiles = ()

endif

if ( ! $?InSST ) then

 set InSST = ()

endif

if ( ! $?InSnow ) then

 set InSnow = ()

endif

if ( ! $?InSoil ) then

 set InSoil = ()

endif

if ( $SRCSST == $SRC3D) then

 if ( $#InSST == 0 ) then

 set InSST = ( ${InFiles} )

 endif

endif

if ( $SRCSNOW == $SRC3D) then

 if ( $#InSnow == 0 ) then

 set InSnow = ( ${InFiles} )

 endif

endif

if ( $SRCSOIL == $SRC3D) then

 if ( $#InSoil == 0 ) then

 set InSoil = ( ${InFiles} )

 endif

endif

set LETTERS = ( A B C D E F G H I J K L M N O P Q R S T U V W X Y Z )

foreach SourceType ( 3D SST SNOW SOIL) 

 printf "\nProcessing for SourceType = %s\n\n" $SourceType

 if ( ( $SourceType == SOIL ) && ( $SRCSOIL == ON84) ) then

 printf "\n\nSoil fields not available in ON84 Dataset.\n"

 printf "Do not request soil fields or select another source for soil fields.\n\n"

 exit (1)

 endif

 if ( ( $SourceType == SOIL ) && ( $SRCSOIL == NCEP) ) then

 printf "\n\nSoil fields not available in NCEP GDAS Dataset."

 printf "Do not request soil fields or select another source for soil fields.\n\n"

 exit (1)

 endif

###############################################################################

 if ( ( ( $SourceType == 3D ) && ( $SRC3D == ON84) ) || \

 ( ( $SourceType == SST ) && ( $SRCSST == ON84) ) || \

 ( ( $SourceType == SNOW ) && ( $SRCSNOW == ON84) ) ) then

 printf "\n\nStarting ON84 processing for type %s\n\n" $SourceType 

#

# Go down to the "on84" directory.

#

 printf "cd %s\n\n" `pwd`/on84

 cd on84

#

# Remove whatever files may be leftover from a prior job. Redirect 

# printout to supress warnings if there is nothing to remove.

#

 rm ON84FILE* >&! /dev/null

 rm PSST:* >&! /dev/null

 rm PSNOW:* >&! /dev/null

 rm -f pregrid.namelist

 rm -f Vtable

#

Appendix E.2 - 132
Draft

6

# Build the Vtable:

#

 touch Vtable

 if ($SourceType == 3D) then

 cat Vtable.ON84 >> Vtable

 else if ($SourceType == SST) then

 cat Vtable.SST >> Vtable

 else if ($SourceType == SNOW) then

 cat Vtable.SNOW >> Vtable

 endif

#

# Link the requested input files to "ON84FILE.A", "ON84FILE.B", etc.

#

 set Num = 0

 if ($SourceType == 3D) then

 foreach file ( $InFiles )

 @ Num ++

 printf " ln -s %s %s\n" $file ON84FILE${LETTERS[$Num]}

 ln -s $file ON84FILE${LETTERS[$Num]}

 end

 endif

 if ($SourceType == SST) then

 foreach file ( $InSST )

 @ Num ++

 printf " ln -s %s %s\n" $file ON84FILE${LETTERS[$Num]}

 ln -s $file ON84FILE${LETTERS[$Num]}

 end

 endif

 if ($SourceType == SNOW) then

 foreach file ( $InSnow )

 @ Num ++

 printf " ln -s %s %s\n" $file ON84FILE${LETTERS[$Num]}

 ln -s $file ON84FILE${LETTERS[$Num]}

 end

 endif

#

# Link the pregrid.namelist file and run the program.

#

 ln -s ../pregrid.namelist pregrid.namelist

 ./pregrid_on84.exe 

#

# Move the output up to the "pregrid" directory.

#

 if ($SourceType == 3D) then

 foreach file ( ON84:* )

 printf " mv %s ../%s\n" $file $file

 mv $file ..

 end

 else if ($SourceType == SST) then

 foreach file ( SST:* )

 printf " mv %s ../ON84_%s\n" $file $file

 mv $file ../ON84_$file

 end

 else if ($SourceType == SNOW) then

 foreach file ( SNOW:* )

 printf " mv %s ../ON84_%s\n" $file $file

 mv $file ../ON84_$file

 end

 endif

#

# Go back up to the "pregrid" directory.

#

 printf "\ncd %s\n" `pwd`/..

 cd ..

 printf "\nDone with ON84 processing for type %s\n\n" $SourceType 

 endif

###############################################################################

Appendix E.2 - 133
Draft

7

 if ( ( ( $SourceType == 3D ) && ( $SRC3D == NCEP) ) || \

 ( ( $SourceType == SST ) && ( $SRCSST == NCEP) ) || \

 ( ( $SourceType == SNOW ) && ( $SRCSNOW == NCEP) ) ) then

#

# Go down to the "ncep.grib" directory.

#

 printf "\ncd %s\n" `pwd`/ncep.grib

 cd ncep.grib

#

# Remove whatever files may be leftover from a prior job. Redirect 

# printout to supress warnings if there is nothing to remove.

#

 rm GRIBFILE* >&! /dev/null

 rm -f pregrid.namelist

 rm -f Vtable

#

# Build the Vtable:

#

 touch Vtable

 if ($SourceType == 3D) then

 cat Vtable.NCEP >> Vtable

 else if ($SourceType == SST) then

 cat Vtable.SST >> Vtable

 else if ($SourceType == SNOW) then

 cat Vtable.SNOW >> Vtable

 endif

#

# Link the requested input files to "GRIBFILE.A", "GRIBFILE.B", etc.

#

 set Num = 0

 if ( $SourceType == 3D ) then

 foreach file ( $InFiles )

 @ Num ++

 ln -s $file GRIBFILE${LETTERS[$Num]}

 end

 endif

 if ($SourceType == SST) then

 foreach file ( $InSST )

 @ Num ++

 ln -s $file GRIBFILE${LETTERS[$Num]}

 end

 endif

 if ($SourceType == SNOW) then

 foreach file ( $InSnow )

 @ Num ++

 ln -s $file GRIBFILE${LETTERS[$Num]}

 end

 endif

#

# Link the pregrid.namelist file and run the program.

#

 ln -s ../pregrid.namelist pregrid.namelist

 ./pregrid_ncep.exe 

#

# Move the output up to the "pregrid" directory.

#

 if ($SourceType == 3D) then

 mv NCEP:* ..

 else if ($SourceType == SST) then

 foreach file ( SST:* )

 mv $file ../NCEP_$file

 end

 else if ($SourceType == SNOW) then 

 foreach file ( SNOW:* )

 mv $file ../NCEP_$file

 end

 endif

#

# Go back up to the "pregrid" directory.

#

Appendix E.2 - 134
Draft

8

 echo "cd `pwd`/.."

 cd ..

 endif

###############################################################################

 if ( ($SourceType == SST) && ( $SRCSST == NAVY ) ) then

 printf "\n\nStarting NAVYSST processing.\n\n"

#

# Go down to the "navysst" directory.

#

 echo "cd `pwd`/navysst"

 cd navysst

#

# Remove whatever files may be leftover from a prior job. Redirect 

# printout to supress warnings if there is nothing to remove.

#

 rm -f pregrid.namelist

 rm NAVYFILE* >&! /dev/null

#

# Link the requested files to "NAVYFILE.A", "NAVYFILE.B", etc.

#

 set Num = 0

 foreach file ( $InSST ) 

 @ Num ++

 ln -s ${file} NAVYFILE${LETTERS[$Num]}

 end

#

# Link the pregrid.namelist file and run the program.

#

 ln -s ../pregrid.namelist pregrid.namelist

 ./pregrid_navy.exe

#

# Move the output files up to the "pregrid" directory.

#

 foreach file ( SST:* )

 mv $file ../NAVY_$file

 end

#

# Go back up to the "pregrid" directory.

#

 echo "cd `pwd`/.."

 cd ..

 printf "\n\nDone with NAVYSST processing.\n\n"

 endif

###############################################################################

 if ( ( ( $SourceType == 3D ) && ( $SRC3D == GRIB) ) || \

 ( ( $SourceType == SST ) && ( $SRCSST == GRIB) ) || \

 ( ( $SourceType == SNOW ) && ( $SRCSNOW == GRIB) ) || \

 ( ( $SourceType == SOIL ) && ( $SRCSOIL == GRIB) ) ) then

 printf "\n\nStarting GRIB processing for type %s\n\n" $SourceType 

#

# Go down to the "grib.misc" directory.

#

 echo "cd `pwd`/grib.misc"

 cd grib.misc

#

# Remove whatever files may be leftover from a prior job. Redirect 

# printout to supress warnings if there is nothing to remove.

#

 rm FILE:* >&! /dev/null

 rm GRIBFILE* >&! /dev/null

Appendix E.2 - 135
Draft

9

 rm -f Vtable

 rm -f pregrid.namelist

#

# Build the Vtable:

#

 touch Vtable

 if ( $SourceType == 3D ) then

 foreach file ( $VT3D )

 cat ../$file >> Vtable

 end

 else if ( $SourceType == SST ) then

 foreach file ( $VTSST )

 cat ../$file >> Vtable

 end

 else if ( $SourceType == SNOW ) then

 foreach file ( $VTSNOW )

 cat ../$file >> Vtable

 end

 else if ( $SourceType == SOIL ) then

 foreach file ( $VTSOIL ) 

 cat ../$file >> Vtable

 end

 endif

#

# Link the requested files to "GRIBFILE.AA", "GRIBFILE.AB", etc.

#

 set NUM = 0

 set num = 1

 if ( $SourceType == 3D ) then

 foreach file ( $InFiles ) 

 @ NUM ++

 if ( $NUM == 27 ) then

 set NUM = 1

 @ num ++

 endif

 printf " ln -s %s %s\n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 ln -s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 end

 else if ( $SourceType == SST ) then

 foreach file ( $InSST ) 

 @ NUM ++

 if ( $NUM == 27 ) then

 set NUM = 1

 @ num ++

 endif

 printf " ln -s %s %s\n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 ln -s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 end

 else if ( $SourceType == SNOW ) then

 foreach file ( $InSnow ) 

 @ NUM ++

 if ( $NUM == 27 ) then

 set NUM = 1

 @ num ++

 endif

 printf " ln -s %s %s\n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 ln -s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 end

 else if ( $SourceType == SOIL ) then

 foreach file ( $InSoil ) 

 @ NUM ++

 if ( $NUM == 27 ) then

 set NUM = 1

 @ num ++

 endif

 printf " ln -s %s %s\n" $file GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 ln -s ${file} GRIBFILE.${LETTERS[$num]}${LETTERS[$NUM]}

 end

 endif

#

Appendix E.2 - 136
Draft

10

# Link the pregrid.namelist file and run the program.

#

 ln -s ../pregrid.namelist pregrid.namelist

 ./pregrid_grib.exe

#

# Move the output files up to the "pregrid" directory.

#

 if ( $SourceType == 3D ) then

 mv FILE:* ..

 else

 foreach file ( FILE:* )

 printf "mv %s %s\n" $file ../${SourceType}_${file}

 mv $file ../${SourceType}_${file}

 end

 endif

#

# Go back to the "pregrid" directory.

#

 echo "cd `pwd`/.."

 cd ..

 printf "\n\nDone with GRIB processing for type %s\n\n" $SourceType 

 endif

#

# Print out five lines of # as a delimiter between ${SourceType}s

#

 repeat 5 printf \

"################################################################################\n"

end

printf "\n"

mv *FILE:${OYR}-${OMM_PROC}-* $DataDir/../${OLD_PROC}

mv *FILE* $DataDir/

C. Regridder 36-km sample namelist

&record1

 start_year = 2002

 start_month = 01

 start_day = 01

 start_hour = 00

 end_year = 2002

 end_month = 01

 end_day = 07

 end_hour = 00

 interval = 10800 /

&record2

 ptop_in_Pa = 10000

 new_levels_in_Pa = 95000 , 92500 , 90000 ,

 80000 ,

 75000 ,

 65000 , 60000 ,

 55000 ,

 45000 ,

 35000

 sst_to_ice_threshold = -9999

 linear_interpolation = .FALSE. /

&record3

 root = '../pregrid/FILE' '../pregrid/SOIL_FILE' '../pregrid/SST_FILE' '../pregrid/SNOW_FILE'

 terrain_file_name = '../../TERRAIN/TERRAIN_DOMAIN1' /

 constants_full_name = './ALMX_FILE' /

&record4

 print_echo = .FALSE. ,

Appendix E.2 - 137
Draft

11

 print_debug = .FALSE. ,

 print_mask = .FALSE. ,

 print_interp = .FALSE. ,

 print_link_list_store = .FALSE. ,

 print_array_store = .FALSE. ,

 print_header = .FALSE. ,

 print_output = .FALSE. ,

 print_file = .FALSE. ,

 print_tc = .FALSE. ,

 print_f77_info = .TRUE. /

&record5

 insert_bogus_storm = .FALSE.

 num_storm = 1

 latc_loc = 36.0

 lonc_loc = -35.0

 vmax_meters_per_second = 35.0

 rmax = 90000.0

 vmax_ratio = 0.75 /

D. FETCH (adp_sfc) sample namelist

&LATLON

 XLONE = -40

 XLONW = -140

 XLATS = 15

 XLATN = 60

/

 &DATE

 ISTARTYR = 2002

 ISTARTMO = 01

 ISTARTDY = 01

 ISTARTHR = 00

 IENDYR = 2002

 IENDMO = 01

 IENDDY = 04

 IENDHR = 00

/

E. FETCH (adp_upa) sample namelist

&LATLON 

 XLONE = -50

 XLONW = -150

 XLATS = 15

 XLATN = 62

/

 &DATE 

 ISTARTYR = 2002

 ISTARTMO = 01

 ISTARTDY = 01

 ISTARTHR = 00

 IENDYR = 2002

 IENDMO = 01

 IENDDY = 15

 IENDHR = 23

/

F. LITTLE_R sample 36-km namelist

&record1

Appendix E.2 - 138
Draft

12

 start_year = 2002

 start_month = 01

 start_day = 01

 start_hour = 00

 end_year = 2002

 end_month = 01

 end_day = 07

 end_hour = 00

 interval = 10800/

&record2

 fg_filename = '../regridder/REGRID_DOMAIN1'

 obs_filename = '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_00' 

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_03'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_06'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_09'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_12'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_15'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_18'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-01_21'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_00'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_03'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_06'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_09'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_12'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_15'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_18'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-02_21'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_00'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_03'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_06'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_09'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_12'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_15'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_18'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-03_21'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_00'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_03'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_06'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_09'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_12'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_15'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_18'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-04_21'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_00'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_03'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_06'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_09'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_12'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_15'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_18'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-05_21'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_00'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_03'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_06'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_09'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_12'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_15'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_18'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-06_21'

 '../../FETCH/adp_upa/jan02/upper-air_obs_r:2002-01-07_00'

 sfc_obs_filename = '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_00'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_03'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_06'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_09'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_12'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_15'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_18'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-01_21'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_00'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_03'

Appendix E.2 - 139
Draft

13

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_06'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_09'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_12'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_15'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_18'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-02_21'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_00'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_03'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_06'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_09'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_12'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_15'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_18'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-03_21'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_00'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_03'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_06'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_09'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_12'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_15'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_18'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-04_21'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_00'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_03'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_06'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_09'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_12'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_15'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_18'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-05_21'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_00'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_03'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_06'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_09'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_12'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_15'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_18'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-06_21'

 '../../FETCH/adp_sfc/jan02/surface_obs_r:2002-01-07_00' /

&record3

 max_number_of_obs = 10000

 fatal_if_exceed_max_obs = .TRUE./

&record4

 qc_test_error_max = .TRUE.

 qc_test_buddy = .TRUE.

 qc_test_vert_consistency = .FALSE.

 qc_test_convective_adj = .FALSE.

 max_error_t = 10

 max_error_uv = 13

 max_error_z = 8 

 max_error_rh = 50

 max_error_p = 600

 max_buddy_t = 8

 max_buddy_uv = 8

 max_buddy_z = 8

 max_buddy_rh = 40

 max_buddy_p = 800

 buddy_weight = 1.0

 max_p_extend_t = 1300

 max_p_extend_w = 1300/

&record5

 print_obs_files = .FALSE.

 print_found_obs = .FALSE.

 print_header = .FALSE.

 print_analysis = .FALSE.

 print_qc_vert = .FALSE.

 print_qc_dry = .FALSE.

 print_error_max = .FALSE.

 print_buddy = .FALSE.

Appendix E.2 - 140
Draft

14

 print_oa = .FALSE./

&record7

 use_first_guess = .TRUE.

 f4d = .TRUE.

 intf4d = 10800

 lagtem = .FALSE. /

&record8

 smooth_type = 1

 smooth_sfc_wind = 0

 smooth_sfc_temp = 0

 smooth_sfc_rh = 0

 smooth_sfc_slp = 0

 smooth_upper_wind = 0

 smooth_upper_temp = 0

 smooth_upper_rh = 0/

&record9

 oa_type = 'MQD'

 mqd_minimum_num_obs = 30

 mqd_maximum_num_obs = 1000

 radius_influence = 5,4,3,2

 oa_min_switch = .TRUE.

 oa_max_switch = .TRUE./

G. INTERPF sample 36-km namelist

&record0

 input_file = '../LITTLE_R/LITTLE_R_DOMAIN1' / ! pressure-level data file name

&record1

 start_year = 2002 ! The starting and

 start_month = 01 ! ending dates to

 start_day = 01 ! process

 start_hour = 00

 end_year = 2002

 end_month = 01

 end_day = 07

 end_hour = 00

 interval = 10800 ! time difference (s)

 less_than_24h = .FALSE. / ! if input is less than 24 h

&record2

 sigma_f_bu = 1.000,0.995,0.990,0.985,0.980,0.970,0.960, ! full sigma, bottom-up,

 0.950,0.940,0.930,0.920,0.910,0.900,0.880, ! end

 0.860,0.840,0.820,0.800,0.770,0.740,0.700, ! with 0.0

 0.650,0.600,0.550,0.500,0.450,0.400,0.350,

 0.300,0.250,0.200,0.150,0.100,0.050,0.000

 ptop = 10000 ! top pressure if need to be redefined

 isfc = 0 / ! # sigma levels to spread

 ! surface information

&record3

 p0 = 1.e5 ! base state sea-level pres (Pa)

 tlp = 50. ! base state lapse rate d(T)/d(ln P)

 ts0 = 275. ! base state sea-level temp (K)

 tiso = 0./ ! base state isothermal stratospheric temp (K)

&record4

 removediv = .TRUE. ! T/F remove integrated mean divergence

 usesfc = .TRUE. ! T/F use surface data

 wrth2o = .TRUE. ! T/F specific humidity wrt H2O

 psfc_method = 0 / ! T/F sfc temperature from diurnal avg

&record5

 ifdatim = -1 / ! # of IC time periods to output

Appendix E.2 - 141
Draft

15

H. MM5 sample mmlif

 &OPARAM

 TIMAX = 7920., 

 TISTEP = 90., 

 IFREST = .FALSE., 

 IXTIMR = 0, 

 IFSAVE = .TRUE., 

 SVLAST = .TRUE., 

 SAVFRQ = 360., 

 IFTAPE = 1, 

 TAPFRQ = 60., 

 BUFFRQ = 1440., 

 INCTAP = 1,1,1,1,1,1,1,1,1,1, 

 IFSKIP = .FALSE., 

 CDATEST = '2002-10-28_00:00:00', 

 IFPRT = 0, 

 PRTFRQ = 720., 

 MASCHK = 99999, 

 IFTSOUT = .FALSE., 

 TSLAT = 0.0,0.0,0.0,0.0,0.0, 

 TSLON = 0.0,0.0,0.0,0.0,0.0, 

 &END

 &LPARAM

 RADFRQ = 15., 

 IMVDIF = 1, 

 IVQADV = 1, 

 IVTADV = 1, 

 ITHADV = 1, 

 ITPDIF = 1, 

 ICOR3D = 1, 

 IEXSI = 0, 

 IFUPR = 1, 

 IBOUDY = 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 

 IFDRY = 0, 

 ISSTVAR= 1, 

 IMOIAV = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 IZ0TOPT= 0,

 IFSNOW = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 

 ISFFLX = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 

 ITGFLG = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 

 ISFPAR = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 

 ICLOUD = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 

 IEVAP = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 

 ISMRD = 2,

 NUDGE = 1, 

 IFGROW = 2, 

 RDMAXALB=.FALSE.

 RDBRDALB=.FALSE. 

 IFRAD = 4,

 ICUPA = 8,8,8,8,8,8,8,8,8,8,

 IMPHYS = 5,5,4,4,4,4,4,4,4,4 ,

 IBLTYP = 7,7,7,7,7,7,7,7,7,7,

 ISHALLO = 0,0,0,0,0,0,0,0,0,0,

 IPOLAR = 0,

 ISOIL = 3,

 &END 

 &NPARAM

 LEVIDN = 0,1,2,1,1,1,1,1,1,1, 

 NUMNC = 1,1,2,1,1,1,1,1,1,1, 

 NESTIX = 129, 190, 31, 46, 46, 46, 46, 46, 46, 46, 

 NESTJX = 165, 181, 31, 61, 61, 61, 61, 61, 61, 61, 

 NESTI = 1, 18, 8, 1, 1, 1, 1, 1, 1, 1, 

 NESTJ = 1, 84, 9, 1, 1, 1, 1, 1, 1, 1, 

 XSTNES = 0., 0.,900., 0., 0., 0., 0., 0., 0., 0., 

 XENNES =7920.,7920.,1440.,720.,720.,720.,720.,720.,720.,720.

 IOVERW = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 

 IACTIV = 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 

Appendix E.2 - 142
Draft

16

 IMOVE = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 IMOVCO = 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 

 IMOVEI = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 IMOVEJ = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 IMOVET = 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 

 IFEED = 0,

 &END

 &PPARAM

 ZZLND = 0.1, 

 ZZWTR = 0.0001, 

 ALBLND = 0.15, 

 THINLD = 0.04, 

 XMAVA = 0.3, 

 CONF = 1.0, 

 &END 

 &FPARAM

 FDASTA=0.,0.,0.,0.,0.,0.,0.,0.,0.,0.

 FDAEND=7920.,7920.,0.,0.,0.,0.,0.,0.,0.,0.,

 I4D= 1,1,0,0,0,0,0,0,0,0,

 1,1,0,0,0,0,0,0,0,0,

 DIFTIM=180.,180.,0.,0.,0.,0.,0.,0.,0.,0., 

 180.,180.,0.,0.,0.,0.,0.,0.,0.,0., 

 IWIND=1,1,0,0,0,0,0,0,0,0, 

 1,1,0,0,0,0,0,0,0,0, 

 GV=2.5E-4,1.0E-4,0.,0.,0.,0.,0.,0.,0.,0., 

 2.5E-4,1.0E-4,0.,0.,0.,0.,0.,0.,0.,0., 

 ITEMP=1,1,0,0,0,0,0,0,0,0, 

 0,0,0,0,0,0,0,0,0,0, 

 GT=2.5E-4,1.0E-4,0.,0.,0.,0.,0.,0.,0.,0., 

 2.5E-4,1.0E-4,0.,0.,0.,0.,0.,0.,0.,0., 

 IMOIS=1,1,0,0,0,0,0,0,0,0, 

 0,0,0,0,0,0,0,0,0,0, 

 GQ=1.E-5,1.E-5,0.,0.,0.,0.,0.,0.,0.,0., 

 1.E-5,1.E-5,0.,0.,0.,0.,0.,0.,0.,0., 

 IROT=0,0,0,0,0,0,0,0,0,0, 

 GR=5.E6,5.E6,0.,0.,0.,0.,0.,0.,0.,0., 

 INONBL =0,0,0,0,0,0,0,0,0,0, 

 0,0,0,0,0,0,0,0,0,0, 

 1,1,1,1,1,1,1,1,1,1, 

 1,1,1,1,1,1,1,1,1,1, 

 RINBLW=250.,

 NPFG=50,

 I4DI =0,0,0,0,0,0,0,0,0,0,

 ISWIND =1,0,0,0,0,0,0,0,0,0,

 GIV =4.E-4,4.E-4,0.,0.,0.,0.,0.,0.,0.,0.,

 ISTEMP=1,0,0,0,0,0,0,0,0,0,

 GIT =4.E-4,4.E-4,0.,0.,0.,0.,0.,0.,0.,0.,

 ISMOIS=1,0,0,0,0,0,0,0,0,0,

 GIQ =4.E-4,4.E-4,0.,0.,0.,0.,0.,0.,0.,0.,

 RINXY=240.,

 RINSIG=0.001,

 TWINDO=40.0,

 NPFI=20,

 IONF=2,

 IDYNIN=0, 

 DTRAMP=60.,

 &END

Appendix E.2 - 143