# Appendix E

Air Quality and Meteorological

Preparations and ResultsAppendix E - 1Appendix E – Air Quality and Meteor ological 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 Eval uation for the Three VISTAS Phase I

Episodes

• E.2 – Protocol for Annual MM5 Modeling in Support of VISTASAppendix 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, Al pine 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 pa rticulate 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 VIST AS 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 pl an. 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 speciesAppendix 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 Laborator y 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 CP C 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 EpisodesJuly 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 415.899.0700July 2003

i TABLE OF CONTENTS

Page

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

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

3.0. PHOTOCHEMICAL ASSESSMENT MONITORING

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

4.0. INTERAGENCY MONITORING OF PROTECTED

VISUAL ENVIRONMEN TS (IMPROVE) ................................................4-1

5.0. SOUTHEASTERN AEROSOL RESEARCH AND

CHARACTERIZATIO N (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 TR END NETWORK ( CASTNet).......................8-1

9.0. PM SUPE RSITES...............................................................................9-1

10.0. AIRCRAF T DATA............................................................................ 10-1

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

APPENDICES

Appendix A: Locations of Speciated PM and IMPROVE MonitorsJuly 2003

ii TABLES

Table 2-1. Currently available hourly concentration data

from AQS for three VIST AS episode selections....................................2-2

Table 3-1. Species measur ed in PAMS network. ................................................3-2

Table 4-1. Species of PM 2.5 measured in IMPR OVE network.................................4-2

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

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

Table 5-1. PM 2.5 species measured in search network. .........................................5-1

Table 5-2. Coarse PM species me asured in search network. ..................................5-2

Table 6-1. Species of PM 2.5 measured in ST N network.........................................6-1

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

Table 9-1. Locations of PM Supe rsites. ...........................................................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 PM 2.5

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 PM 2.5

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

Figure 2-9. Distribution of average ambient O3

concentrations (J anuary 1-21, 2002)................................................ 2-11

Figure 2-10. Distribution of average ambient PM 2.5 concentrations (J anuary 1-21, 2002)................................................ 2-12

Figure 3-1. Distribution of average ambient HCHO concentrations

(July 9-22, 1999) from the PAMS network .........................................3-4July 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) fr om the PAMS network.....................................3-6

Figure 3-4. Distribution of average ambient acetone concentrations

(January 1-21, 2002) fr om 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 AIRM oN 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). ........................................................8-3July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec1.gst.doc 1 -1 1.0 SUMMARY

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

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

University of California (UC) are compil ing 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 th e steps that will be used in the processing and

quality assurance (QA) of the data. In additi on, 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 comp lete model performance evaluations when the

model simulations are completed. As the proce ssing 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 de velop the website to facilitate the display of

time series plots.

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

• EPA’s AQS (Air Quality System) database

• PAMS (Photochemical Asse ssment Monitoring Stations)

• IMPROVE (The Interagency Monitoring of Protected Visual Environments)

• SEARCH (Southeastern Aerosol Research and Characterization)

• EPA’s STN (Speciation Trends Network)

• NADP (National Atmosphe ric Deposition Program)

• CASTNET (Clean Air Status and Trend Network)

• PM Supersites

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

• TVA measurement network

• Georgia Institute of Technology Assessment of Spatial Aerosol Com position in Atlanta

(ASACA) monitors.

Note that there is some overlap in the above monitoring networks. Fo r 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 th e existing and planned (as of January, 2002)

urban and rural PM 2.5 speciation networks.

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

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

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

scope of this effort, we are attempting to rec oncile 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 continueJuly 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec1.gst.doc 1 -2 to gather and process ambient data, and will rely on expertise from other team members and

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

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

important subject to be addre ssed 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 no t yet been acquired. ASACA has been

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

When acquired, these data will be documented on the project website.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-1 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 (PM 10 and PM 2.5 )

• 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 O3, NOx VOCs

Sp. PM25,

Visibilit y

HNO3, NO3,

SO4

,July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-2 Although direct access to full AQS raw data is curre ntly 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 av ailable 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 a rrival. Note that the AQS includes O3 data

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

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

selections

.

Period O 3 PM 2.5 NO 2 CO SO 2 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 NO 2 , CO and SO 2 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 O 3 , PM 2.5 , NO 2 , CO and SO 2 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 O 3 concentrations were available at 1113 stations over USA for July 9-22, 1999.

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

Hourly PM 2.5 concentrations were available at 25 stations over USA for July 9-22, 1999.

Figure 2-3 shows the distribution of average PM 2.5 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 averag e 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 averag e 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 averag e SO2 concentrations for these stations.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-3 July 7-28, 2001

Hourly O 3 concentrations were available at 1113 stations over USA for July 7-28, 2001.

Figure 2-7 shows the distribution of average O 3 concentrations for these stations.

Hourly PM 2.5 concentrations were available at 1 48 stations over USA for July 7-28, 2001.

Figure 2-8 shows the distribution of average PM 2.5 concentrations for these stations.

January 1-21, 2002

Hourly O 3 concentrations were also available at 501 stations over USA for January 1-21,

2002. Figure 2-9 shows the distribution of average O 3 concentrations for these stations.

Hourly PM 2.5 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.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-4 Figure 2-2. Distribution of average ambient O3 concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-5 Figure 2-3 . Distribution of average ambient PM 2.5 concentrations (July 9-22, 1999)

.July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-6 Figure 2-4. Distribution of average ambient NO2 concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-7 Figure 2-5. Distribution of average ambient CO concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-8 Figure 2-6. Distribution of average ambient SO2 concentrations (July 9-22, 1999).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-9 Figure 2-7. Distribution of average ambient O3 concentrations (July 7-28, 2001).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-10 Figure 2-8. Distribution of average ambient PM2.5 concentrations (July 7-28, 2001).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-11 Figure 2-9. Distribution of average ambient O3 concentrations (January 1-21, 2002).July 2003

G:\VISTAS Modeling\Task3a Air Quality Data\Revised_Draft\Sec2.gst.doc 2-12 Figure 2-10. Distribution of average ambient PM2.5 concentrations (January 1-21, 2002).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-1 3.0 PHOTOCHEMICAL ASSESSMENT MONITORING STATIONS (PAMS)

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

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

Photochemical Assessment Monitoring Stations (P AMS) 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 disti nguishing among various individual VOC.

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

condition information for photochemical gr id models, a meth od that simulates

meteorological and physical processes that affect air pollution emissions in the

atmosphere.

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

source emission impacts.

4. Provide ambient data meas urements which would allow la ter preparation of unadjusted

and adjusted pollutant trends reports.

5. Provide additional me asurements of selected criteria pollutants.

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

background concentration, emissions sources, and peak pollutant le vels, 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 O 3 , 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 th ese 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 Ja n. 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”. Exampl e plots of average concentrations for two

important species (HCHO and acetone) are shown in Figures 3-1 through 3-4.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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-NO NYL 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 30.03July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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 distributio n of average HCHO concentratio ns 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-4 Figure 3-1. Distribution of average ambient HCHO concentrations (July 9-22, 1999) from the

PAMS network

.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-5 Figure 3-2. Distribution of average ambient acetone concentrations (July 9-22, 1999) from the

PAMS network

.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-6 Figure 3-3. Distribution of average ambient HCHO concentrations (January 1-21, 2002) from

the PAMS network.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec3.gst.doc 3-7 Figure 3-4. Distribution of average ambient acetone concentrations (January 1-21, 2002) from

the PAMS network

.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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 Cl ass 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 PM 2.5 and PM 10 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 PM 2.5 mass and its elemental constituents, organics, ions, light absorption and

PM 10 mass.

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

mandatory class I areas; (2) to identify chemi cal 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 cl ass 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 a pproximately 110 IMPROVE sites and 53

IMPROVE Protocol sites.

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

sites only include the Module A:

Module A: PM 2.5 particles on Teflon are analyzed at UC Davis using the following methods:

• gravimetric mass for PM 2.5

• 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: PM 2.5 particles on nylon. A denuder before the nylon filter removes nitric acid

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

nitrate (NO 3 - ), chloride (Cl

- ), sulfate (SO 4 2- ), and nitrite (NO 2 - ).

Module C: PM 2.5 particles on quartz. These are analyzed at Desert Research Institute for

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

at selected sites is used to determine artifacts . These are reported in 8 temperature categories.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-2 Module D: PM 10 particles on Teflon. All are measured for PM 10 mass. Approximately 4% are

analyzed by the other four methods listed for Module A.

For selected IMPROVE sites, there also are tr ansmissometer 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 Labor atory 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 coll ected and the reconciliation of any issues these

organizations may find. In the past, we ha ve 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.col ostate.edu/improve

.

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

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

collected every three days.

Table 4-1 lists the 41 species measured at IM PROVE 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 ar e also available for download on the project

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

series and scatter plots of these data.

Table 4-1. Species of PM 2.5 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 PM 10 : 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: FineJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-3 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 sche mes 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. Defi nitions 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_R econ is reconstructed extinction coefficient.

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

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

1.375*(ASO4J + ASO4I) +1.29*(ANO3J + ANO3 I), respectively. This was used for

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

For VISTAS we propose to use the C MAQ 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

(NH 4 + ). VISTAS is funding the Research Triangle Institute (RTI) to measure NH4 + at 10 sites

beginning in September, 2002. Prior to September, NH 4 + data will be available for only 3

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

(DOSO).

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

found that it tends to be lower than HNO3 da ta 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 ne tworks 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 moni toring 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 (C M) 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 wh ich NO3 can be transferred to the coarse

mode: formation of coarse mode sodium nitrate or calcium nitrate, a nd 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 s ubstantial 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 site s to investigate this

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

possible that initial results wi ll be available in fall, 2002.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-4 We are also investigating the modal distributio ns of the Aitken and accumulation modes in

CMAQ. Although these modes are typi cally assumed to be entirely PM 2.5 , 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

PM25 a 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) 10 b + 3*f(RH) c (1.375*SO4 +

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

+ 0.6* CM 10 b + 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec4.gst.doc 4-5 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 coefficientJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec5.gst.doc 5-1 5.0 SOUTHEASTERN AEROSOL RE SEARCH AND CHARACTERIZATION

(SEARCH)

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

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

are shown in Figure 5-1. Daily PM 2.5 data are measured for 46 sp ecies, while daily coarse PM

data are measured for 18 species in SEARCH netw ork. 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 PM 2.5 species and 18 coarse PM species. The SEARCH data are

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

July 7-28, 2001).

The frequency of the PM 2.5 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 PM 2.5 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 . PM 2.5 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 VanadiumJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec5.gst.doc 5-2 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 PM 2.5 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec5.gst.doc 5-3 Figure 5-1. Locations of the SEARCH monitoring sites (Source: Atmospheric Research and

Analysis, Inc., www.atmopheric-research.com).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec6.gst.doc 6-1 6.0 SPECIATION TR ENDS NETWORK (STN)

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

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

networks. This, however, need s to be verified. Daily PM 2.5 data are measured for 64 species

in the STN network. Some archived ST N 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 PM 2.5 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 881 70 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 883 02 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 LCJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-1 7.0 NATIONAL ATMOSPHERIC DE POSITION PROGRAM (NADP)

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

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

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

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

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

shown in Figure 7-1. The purpose of the netw ork 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 Nation al 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 sa mpling period the data has poor temporal

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

NADP also includes the Atmospheric Integrat ed Research Monitoring Network (AIRMoN)

which was designed to study precipitation chemistr y 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 ne xt. NADP measures weekly deposition of

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

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

results will be accumulated to weekly de position fluxes per unit area (e.g., gm/km 2 /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 we t deposition components. Figure 7-2 shows

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

their web page: http://www.arl.noaa.gov/resear ch/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 AIRM oN network by combining estimates of sitespecific and time-evolving depos ition velocities with measuremen ts of air concentrations

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

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

the AIRMoN dry samples are obtained over s ubstantially different periods, because ofJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec7.gst.doc 7-2 operator absence or problems with instrumenta tion. 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 sulf ur 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 th e 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 ammoni um nitrate deposited on the teflon filter is

incompletely disassociated with minor conseque nces 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 th e 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 he re 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 rate s are computed as the product of the weekly average

deposition velocities and the weekly average con centration, 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.

O mitting consideration of a correlation term is like ly 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 th e 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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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/resear ch/projects/airmon_wet.html

The AIRMoN wet deposition moni toring 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 quan tification of ammonium deposition. At the

National Atmospheric Deposition Program Te chnical 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 da ta users to specific problems.

UCR is currently downloading data from both wet and dry AIRMoN networks.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec8.gst.doc 8-1 8.0 CLEAN AIR STATUS A ND TREND NETWORK (CASTNET)

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

United States as shown in Figure 8-1. It incl udes 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 th e ambient concentration measurements to

compare with the model. Dry deposition data ar e 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 ar e 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 f ilter 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 p acks are exposed for 1-week intervals (i.e.,

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

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

All three filters are extracted a nd analyzed for certain species:

Teflon filter: SO 4 2- , NO 3 - , NH 4 +

Nylon filter: SO 4 2- , NO 3 -

Cellulose filter (Whatman): SO 4 2- , NO 3 -

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

species (listed above as TSO4, TNO3 and T NH4). 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 volatil ized 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 nitr ate 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 direct ly 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 andJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y 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 mapp ing 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 HNO 3 NHNO3 (nylon filter) 2176.9*DENS a *HNO3

Particulate NO 3 TNO3 (Teflon filter) ANO3J + ANO3I

Total HNO 3 + NO 3 NHNO3 (nylon filter) + TNO3

(Teflon filter) 2176.9*DENS a *HNO3 +

ANO3J + ANO3I

Particulate NH 4 TNH4 (Teflon filter) ANH4I + ANH4J

Gaseous SO 2 TOTAL_SO2 2211.5*DENS*SO2

Particulate SO 4 TSO4 (Teflon filter) ASO4I + ASO4J

a Air density obtained from MCIP outputsJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec8.gst.doc 8-3 Figure 8-1. Locations of CASTNet monitoring sites (Figure obtained from EPA CASTNet

website).July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec9.gst.doc 9-1 9.0 PM SUPERSITES

There are 8 PM Supersites. The locations of th ese 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 (N ew York, Pittsburgh, and Baltimore). As

other researchers expressed interest in the pr ogram 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 bound ary layer. EPA plans to centralize all air

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

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

uniform databases. Details on data collect ed 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 UniversityJuly 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec10.gst.doc 10-1 10.0 AIRCRAFT DATA

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

research aircraft flights performed that provi de aloft meteorological and air quality data

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

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

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

to VISTAS as Appendix A to the Task 2 repor t (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/pr oject/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.July 2003

G:\VISTAS Modeling\Task3a Air Qualit y Data\Revised_Draft\Sec11.gst.doc 11-1 11.0 DATA QUALITY ASSURANCE

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

the agency or researcher responsible for collect ing 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 pr ocessing 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 sp ecies. The plots should be inspected for the

following:

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

• Periodicity of peaks, calibration carryover

• Unexpected diurnal beha vior (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 to tals, 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 measur ed 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 bala nce 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 A

Locations of Speciated PM and IMPROVE Monitors

### 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 PM

2.5 

# Speciation Networks

SS SS

SS



### 01/02 SS

SS SS SS

SS

### 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 Protocol &

Rural Supplemental PM

2.5 

# Speciation Sites

2/02

# Appendix E.2

Protocol for Annual MM5 Modeling

in Support of VISTASDraft



## 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 Ma il 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, 2004Draft

ii 1 Introduction

................................................................................................ ..................................................... 1

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

3 Evaluation Approac h................................................................................................ ....................................... 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

R EGRID (regridder)

................................................................................................ ................................... 112

FETCH (adp_sfc)

................................................................................................ ........................................ 113

FETCH (adp_upa)

................................................................................................ ....................................... 113

LITTLE_R

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

INTERPF

................................................................................................ .................................................... 113

MM5

................................................................................................ ............................................................ 113

C. Model Evaluation Proc edures ................................................................................................................. 114

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

E. Project Schedule

................................................................................................ ...................................... 124

8 Refere nces

................................................................................................ ................................................... 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 .............................................................................................................................. 15Draft

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 Unit ed 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/visibil ity/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 s et 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

(BA MS) 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 interp px 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).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 coe fficients 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 cr oss

- 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.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 re sults 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 ov erlaid 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 ou tput. 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 v alue 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 co lumns, 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.e du/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 stor ed 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.Draft

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

somewhat manag

eable 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.Draft

5 

## 4 Results

The initial results for the px_acm run for episode 1 are quite discouraging. The r un 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

finding s 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_acm

2 configuration will henceforth be considered the base case episode 1 for cross

sensitivity 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 empl oy 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 sh ows the model bias (blue) and

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

(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 Valle y, while melting snow and clouds

(figure 7) might inhibit mixing over the northern Mid

- Atlantic States.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.Draft

8 Figure 6. Like figure 5, except for pr ecipitation.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

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

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

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

12 Figure 10. Like figure 5, except for relative humidity.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/particula te 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 wou ld 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.Draft

14 Figure 12. Nighttime (07

- 10 UTC) average PBL heights for the 12

- km VISTAS r egion 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 placehold er and has no

physical meaning.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.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 fig ure 12, except for relative humidity.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 t heir nighttime wind speeds are higher than their daytime wind speeds (figure 12).

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

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

only show sp atial 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, 20 01 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 me aning.Draft

19 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 theDraft

20 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 rat io 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 plo t (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 statis tical 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 epi sode 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 ful l 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 repres ents 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 correspo nding 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 se em 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.Draft

21 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) c ross

- 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_et a

- my in purple.Draft

2 2 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 in dex of agreement. The px_acm case is blue, noah_mrf

red, multi_blkdr black, and noah_eta

- my purple.Draft

23 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 seco nd 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

- sensi tivity 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.Draft

24 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 a greement. 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 a bsolute 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.Draft

25 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.Draft

26 Figure 28. Episode 1 (J an 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_a cm case is

blue, noah_mrf red, multi_blkdr black, and noah_eta

- my purple.Draft

27 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 ar e 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.Draft

28 Figure 31. Episode 2 (Jul 13

- 28, 2001) cross

- sensitivit y 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.Draft

29 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 blu e, 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.Draft

30 Figure 33. Episode 2 (Jul 13

- 28, 2001) cross

- sensitivity mixing ratio a bsolute 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 o nly as placeholders and should

be ignored.Draft

31 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 sensitivi ties. 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 p lot 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 sh ould

be ignored.Draft

32 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, an d

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.Draft

33

The remainder of this report will focus primarily on statistical tables. These tables are episode

- composites

and are div ided into three sections. The top section contains bias, absolute error, coefficient of determination

(r 2 ), 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 co verage. 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 perh aps 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 stat istics 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 p recipitation 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 anal yzing 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 th

resholds 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 st atistic 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 sho w 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

(32.27 o ) is certainly in the ballpark. Given the weak s ynoptic forcing in this episode, it is likely that sub

synoptic 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, wit h 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 convec tion. 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.Draft

34

Total stats bias abserr r2 ia rmse

Temperatu

re (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 bi as 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 T hreshold (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.Draft

35

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

Temper ature (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.Draft

36

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.Draft

37

To augment the statistical tables des cribed 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 w hether the sensitivity produces better

results than the base case. For some metrics (e.g. r 2 , 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 tab le 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 statistic al 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 color

coding

.

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 differe nce 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 strik ing 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 t hat 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. ADraft

38 plot of this type is shown in figure 36 for Greensboro, NC, episode 1, 00 UTC. Note that at the surf ace 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_eta

my are expected and corr oborate 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.Draft

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 v ector 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.Draft

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.Draft

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.Draft

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. L ike table 11, except for 12

- km VISTAS domain.Draft

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.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 st ats 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 u verr

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.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.Draft

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

- 0.5 501

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.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 P OD 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_blkd r 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 C SI 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.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 r 2 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.Draft

49

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 composi te 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, excep t for 12

- km VISTAS domain.Draft

50

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.Draft

51

Total stats bias abserr r2 ia rmse

Temperature (K)

- 1.08

- 0.65

- 0.052

- 0.022

- 0.7857

Mixi ng 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

Tabl e 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, e xcept for 12

- km VISTAS domain.Draft

52 

## 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 t he 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, excep t 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 E

DAS 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 configurati on 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.Draft

53

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 uer r 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

- 0.00 31

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

T able 32. Like table 31, except for 12

- km VISTAS domain.Draft

54

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 s ignificantly cold

biased 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 recomm end 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 alterna tive 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 LITT LE_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 s hould” 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 r ecommended manner (i.e. with indirect soil nudging) requires that the

SFCFDDA file be available, which necessitates running one of those objective analysis preprocessors. The px

acm2 configuration used LITTLE_R only for that purpose. The px_acm4 configurat ion 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 outpu

t. 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 preci pitation 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

microphysi

cs.

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 t his 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 reasonab ly 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

expec tedly 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 im provement over the base case.Draft

55

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.Draft

56

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 R atio (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.Draft

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.Draft

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 comp osite 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

.Draft

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.

Tot al 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.Draft

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

Re l. 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 Thre shold (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.Draft

61

Total stats bias abserr r 2 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

- 0.0 046

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.Draft

62

Recall from the discussion of sensitivity px_acm4 that a slight improvement was found by using RAWINS

output to drive b oth 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 sensit ivity 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 sum mertime 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 applicati ons. 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 o C 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

unaffe cted, 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 mois ture 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 nud ging 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 drama tically. 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).Draft

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 ue rr 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

Mix ing 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

Tabl e 48. Like table 47, except for 12

- km VISTAS domain.Draft

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.Draft

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 u err 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 (de g, 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

- 0.0 244

Table 52. Like table 51, except for 12

- km VISTAS domain.Draft

66

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

W ind 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.Draft

67

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

- 0.0 005

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

Pc p 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.Draft

68

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 st ats 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

- 0.000 7

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.Draft

69

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 stati stical 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 do main.Draft

70

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 obvious ly 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 documentat ion. 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 w e 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 mois ture 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 stre ngth.

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 wintert ime episode 1. Note the general improvement in the surface statistics, especiallyDraft

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 a ll 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, bu t 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 sensitiviti es 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 precipitati on 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 sensiti vity 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. nort

hwest 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 precipit ation, 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

area s 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 accu

mulation 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 precipitatio n 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!Draft

72

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

- 0.0 019

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 port ion

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.Draft

73

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 (d eg, 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

Tabl e 65. Like table 64, except for 12

- km VISTAS domain.Draft

74

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.Draft

75 Figure 38. Episode 1 (Jan 2

- 21, 2002) composite precipitation fo r 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.Draft

76 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 accumulate d 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.Draft

77 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 gridde d 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.Draft

78 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 Pred iction 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 dif ference between the

px_acm sensitivity and the px_acm2 base case.Draft

79 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.Draft

80 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 o bservations. The bottom right plot shows the difference between the

noah_mrf sensitivity and the px_acm2 base case.Draft

81 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.Draft

82 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 p lot

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.Draft

83 Figure 46. Episode 1 (Jan 2

- 21, 2002) composite precipitation for the US la nd

- 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 confi guration, 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.Draft

84 Figure 47. Episode 1 (Jan 2

- 21, 2002) composi

te 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 f or 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.Draft

85 Figure 48. Epi

sode 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 pane l shows the corresponding plot for the base case px_acm2 model configuration.

The lower left plot shows the difference plot of the top panels.Draft

8 6 Figure 49. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the US land

- portion of the 12

- km domain i s 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 p

lot shows the difference plot of the top panels.Draft

87 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 th e difference between the px_acm sensitivity and the px_acm2 base case.Draft

88 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 gene rated 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 bott om right plot shows the

difference between the px_acm sensitivity and the px_acm2 base case.Draft

89 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 th e observations. The bottom right plot shows the

difference between the noah_mrf sensitivity and the px_acm2 base case.Draft

90 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 pa nel 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.Draft

91 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 p

lot 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.Draft

92 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 con figuration, 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.Draft

93 Figure 56. Episode 2 (Jul 13

- 28, 2001) comp osite 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 plo t 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.Draft

94 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 p anel 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_a cm2 base case.Draft

95 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 precipi tation

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.Draft

96 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 differenc e between the

px_acm5 sensitivity and the px_acm2 base case.Draft

97 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 p lot shows the

difference between the px_acm5 sensitivity and the px_acm2 base case.Draft

98 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 precip itation 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 observati ons. The bottom right plot shows the

difference between the px_acm5 sensitivity and the px_acm2 base case.Draft

99 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 display

s 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.Draft

100 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 diff

erence 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.Draft

101 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.Draft

102 Figure 65. Episode 2 (Jul 13

- 28, 2001) composite precipitation for the U S 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 confi guration, 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.Draft

103 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.Draft

104 Figure 67. Episode 1 (Ja

n 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.Draft

105 No te 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 nud ging 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 plo t shows the

difference between the px_acm_n sensitivity and the px_acm2 base case.Draft

106 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 precip itation 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 observat ions. The bottom right plot shows the

difference between the px_acm_n sensitivity and the px_acm2 base case.Draft

107 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 displa ys 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.Draft

108 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 t

he 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.Draft

109 

## 6 Conclusions from Sensitivity Modeling

Considering only the original four (five, inc luding 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 win ds 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,

inc luding 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_e ta

- 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 set

up produces the best results of the credible configurations, the cold bias still remains. The problem seem s 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 a re unavoidable.Draft

110



## 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 w e 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 not ably 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 n

otes, 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 i ce 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 ef fort 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 M M5 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 an other 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:Draft

111 Date tag Dura tion 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 jul 05_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 J an 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 J an 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_0 2 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, 2003Draft

112 

### B. Model Execution

To prepare the MM5 input files we execute the MM5v3.6.2 preprocessor system. The gene ral 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) T ranslates 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) glo bal 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 ex act 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 monthl y 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 in

terpolated 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 inDraft

113 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 preproce ssing 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 up per

- 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 “impr

ove” 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 execu ted 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 t he 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 in cluded in Appendix 9G.

MM5Draft

114

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 de creased 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

- Frit sch 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 show

s 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 i

s 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, inclus ive)

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 Ma rch 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 transm itted 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 summar y of the evaluation

products that will be available on that web site:Draft

115



### 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 S tatistical 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 R PO, 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)Draft

116

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 interval s. 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 b usy).

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 p recipitation, 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)



### Surf ace 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 d erive from the

Climate Prediction Center.

2) ir_sat: Model cfrac (alt) is compared with GOES 8 infrared imagery at 00Z and 12Z.Draft

117

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

- 0.8 0, 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 Pr oducts (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 (Profil er 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 Agreem ent (IA, in blue), the coefficient of

determination (r

2 , 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 ski

ll 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 defin e 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”Draft

118

means the model failed to predict measurable pr ecipitation 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 t he 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 o f 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 t o 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) Mixin g 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 th e 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 (F igure 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 precipitati

on (0.50 in threshold)

15) 24

- hr precipitation (1.00 in threshold)Draft

119 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 wi

nd 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)Draft

120

Labels in first part of table:

1) obsmean: Average of observations

2) modmean: Average of model pr edictions

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 it s 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 o f 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 f irst 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 metri c, 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 “TOTA L” does not appear in the variable name, then an animation ofDraft

121

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 spa tial 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 ro ot 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 met eorological

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 tempe rature 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 ri ght) as the x

- axis, as illustrated in figure 73.

We produce bias, error, and index of agreement plots for the following variables:Draft

122

1) Temperature (deg K)

2) Mixing Ratio (g/kg)

3) Relative Humidity (%)

4) Cloud cover

–

alt (%)

5) Wind Speed (m/s)

6) Wind direction (degre es), 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 se t to the

default white background.Draft

123 

### 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.Draft

124



### Mont hly 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 Accumulat ed 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 a nticipate 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 cluste r, 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

Re vised 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 Annu

al 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., an

d 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.Draft

125 Emery, C., E. Tai, and G. Yarwood, 2001. “Enhanced Meteorological Modeli ng 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

- generat ion 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. F ritsch, 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 c orrelated

- 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. A vailable 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 Associat ion)] 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 li quid 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 Meteoro logy, 40, 192

- 209.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, 2 21,

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,

VSPL OT = .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

NF UGBOX = 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.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_GLOB AL ',

'Data/DEM_30S_GLOBAL ',

LNDNAME = 'Data/LANDUSE.60 ',

'Data/LANDUSE.30 ',

'Data/LANDUSE.10 ',

' ',

' ',

' ',

LWNA ME = '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.1 0 ',

'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 analysesDraft

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 )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

endifDraft

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

#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

###############################################################################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.

#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/nullDraft

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

#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 = 0 1

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. ,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

&record1Draft

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'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.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

use sfc = .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

i fdatim =

- 1 / ! # of IC time periods to outputDraft

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,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