**Modeling Protocol**

**(Version 1.1)**



## Annual Application of MM5 to the Continental United States

**Prepared for:**

Mr. Pat Dolwick

USEPA/OAQPS

Prepared by:

Dennis McNally

T.W. Tesche

Alpine Geophysics, LLC

7341 Poppy Way

Arvada, CO 80007

(303) 42 1

- 2211

28 May 2002i **Table of Contents**

**1 INTRODUCTION** 

**................................ ................................**  **................................ ............................**  **1**

**- 1** 

1.1 B ACKGROUND

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

- 1

1.2 S TUDY OBJECTIVES

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

- 1

1.3 P URPOSE OF P ROTOCOL

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

- 1

**2 MODEL SELECTION** 

**................................ ................................**  **................................ ....................**  **2**

**- 1** 

**3 EP**  **ISODE SELECTION**

**................................ ................................**  **................................ ..................**  **3**

**- 1** 

**4 MODELING DOMAIN AND**  **DATA AVAILABILITY**

**................................ ..............................**  **4**

**- 1** 

4.1 M ODELING D OMAIN

................................ ................................ ................................ .................... 4

- 1

4.2 D ATA A

VAILABILITY

................................ ................................ ................................ ................... 4

- 1

**5 INPUT DATA PREPARATI**  **ON PROCEDURES**

**................................ ................................**  **........ 5** 

**- 1** 

5.1 F IXED D ATA I NPUTS

................................ ................................ ................................ .................... 5

- 1

5.2 V ARIABLE D ATA I NPUT

................................ ................................ ................................ ............... 5

- 1

5.3 M ULTI

- SCALE FDDA

................................ ................................ ................................ .................. 5

- 2

5.4 P HYSICS O PTIONS

................................ ................................ ................................ ........................ 5

- 3

5.4.1 Cumulus Parameterization

................................ ................................ ................................ . 5

- 3

5.4.2 Planetary Boundary Layer Scheme

................................ ................................ .................... 5

- 3

5.4.3 Explicit Moisture Scheme

................................ ................................ ................................ .. 5

- 3

5.4.4 Radiation Scheme

................................ ................................ ................................ ............... 5

- 3

5.4.5 Ground Temperature Scheme

................................ ................................ ............................. 5

- 3

**6 MODEL**  **PERFORMANCE EVALUATI ON** 

**................................ ................................**  **................ 6** 

**- 5** 

6.1 O PERATIONAL E VALUATION

................................ ................................ ................................ ....... 6

- 5

6.1.1 Scientific Evaluation

................................ ................................ ................................ .......... 6

- 5

**7 DIAGNOSTIC SIMULATIO**  **NS**

**................................ ................................**  **................................ .....**  **7**

**- 1** 

**8 DATA BASE AND REPORT**  **ING SUBMITTAL PROCED URES** 

**................................ .............**  **8**

**- 1** 

**9 REFERENCES** 

**................................ ................................**  **................................ ................................**  **. 9** 

**- 1** ii **List of Figures**

Figure 4

- 1: National ETA Computational Grid.

................................ ................................ ........................ 4

- 4iii **List of Tables**

Table 3

- 1: Model Simul ation Initialization Schedule.

................................ ................................ ............... 3

- 2

Table 4

- 1: MM5 Vertical Domain Specification.

................................ ................................ ..................... 4

- 2

Table 4

- 2: Meteorological Databases and Availabili ty.

................................ ................................ ............. 4

- 3

Table 6

- 1 : Possible Statistical Measures and Graphical Displays to be Used in the MM5 Operational

Evaluation

. ................................ ................................ ................................ ................................ ........... 6

- 6

Table 7

- 1: Pr oposed Episodic Sensitivity and Diagnostic Simulations.

................................ ................... 7

- 11

- 1

**1 INTRODUCTION** 

This document presents the protocol governing the application of the PSU/NCAR

Mesoscale Model (MM5) to the continental U.S. This computationally in tensive activity

is aimed at developing the meteorological fields needed to operate a variety of regional

scale oxidant, fine particulate, and acid deposition models. A companion protocol being

developed under this work assignment addresses MM5 applicatio n procedures to two (2)

episodic simulations over the western and eastern U.S.

**1.1 Background** 

Over the past half decade, emergent requirements for direct numerical simulation of

urban and regional scale photochemical and secondary aerosol air quality

— spawned

largely by the new particulate matter (PM 2.5 ) and regional haze regulations

— have led to

intensified efforts to construct high

- resolution emissions, meteorological and air quality

data sets. The concomitant increase in computational throughput of low

- cost modern

scientific workstations has ushered in a new era of regional air quality modeling. It is

now possible, for example, to exercise sophisticated mesoscale prognostic meteorological

models and Eulerian photochemical/aerosol models for the full annual p eriod, simulating

ozone, sulfate and nitrate deposition, and secondary organic aerosols (SOA) across the

entire United States (U.S.) or over discrete sub

- regions.

**1.2 Study objectives** 

Consistent with ongoing U.S. Environmental Protection Agency (EPA) prog rams, this

work assignment is aimed at developing high

- resolution, gridded meteorological data sets

that can be used to support urban and regional scale air quality modeling over the

continental United States. In this protocol, we lay out a technical appr oach for exercising

and testing the model over the entire U.S. for a full year at 36 km horizontal grid scale.

**1.3 Purpose of Protocol** 

This protocol documents the activities in performing the modeling required to support the

development of the meteorological model outputs. These activities include: (a) selection

of appropriate databases and modeling episodes, (b) evaluating the performance of the

meteorological model, (c) sensitivity and performance testing of the meteorological

modeling system, (d) delivery of the meteorological model outputs for subsequent use in

air quality modeling, and (e) documentation of the meteorological modeling study

findings. A companion report being developed under this work assignment will give an

in

- depth description of the MM 5 model evaluation process for both episodic and annual

average conditions.2

- 1

**2 Model Selection** 

The meteorological model selected for use in the study is non

- hydrostatic Penn State

University/National Center for Atmospheric Research (PSU/NCAR) mesoscale

m eteorological model (MM5) version 3.5. Described by Dudhia et al., 2001, the MM5 is

perhaps the most technically advanced public

- domain prognostic model available for

operational use in preparing inputs to urban

- and regional

- scale photochemical air quali ty

models.

The MM5 model is being continually updated. During the course this study the MM5

website will be monitored and if issues are discovered with this version of MM5, the

implications for this study will be discussed with the client representative.3

- 1

**3 Episode Selection** 

The recommended annual episode for this study is the year 2001. This year is attractive

because of the availability of observations for which to evaluate the meteorological and

subsequent air quality modeling, the lack of any major cl imatic anomalies, and the

opportunity of having an MM5 simulation completed in time for the 2000 to 2004

regional haze base planning period.

The full period to be modeled will extend from 16 Dec 2000 at 12Z to 15 January 2002 at

12Z. The model will be ru n in independent 5 day periods, with a new period starting

every 5 days. This scheme will allow 12 hours of model initialization ( i.e. spinup)

before the model calculations are deemed suitable for use in air quality models. A

sample simulation period is presented in Table 3.1. The purpose of the 5 day

reinitialization is to eliminate the error growth in long term simulations.

The model output files (MMOUT) will be split into separate files every 48 hours to

assure that the model output files do not exc eed the 2 Gigabyte file size limit on certain

computer systems.3

- 2

**Table 3** 

**- 1**  **: Model Simulation Initialization Schedule.**

Date Period 1 Period2

16 Dec. 12Z

17 Dec. 0Z

17 Dec. 12Z

18 Dec. 0Z

18 Dec. 1 2Z

19 Dec. 0Z

19 Dec. 12Z

20 Dec. 0Z

20 Dec. 12Z

21 Dec. 0Z

21 Dec. 12Z

22 Dec. 0Z

22 Dec. 12Z

23 Dec. 0Z

23 Dec. 12Z

24 Dec. 0Z

24 Dec. 12Z

25 Dec. 0Z

25 Dec. 12Z

26 Dec. 0Z

26 Dec. 12Z

27 Dec. 0Z4

- 1

**4 Modeling Domain and Data Availability** 

4.1 **Modeling Domain**

Horizontal Domain Definition: The computational domain for this application is

presented in Figure 4

- 1. The domain consists of an array of 165 x 129 grid cells with

36km horizontal spacing. This gr id was selected to maximize the coverage of the ETA

analysis region. This domain uses the recently selected “national Regional Planning

Organization (RPO)” grid projection which has a pole of 40 0

, - 97 0 with true latitudes of

33 0 and 45 0 . This domain is identical to the national emissions modeling surrogate grid

being prepared for the Lake Michigan Air Directors Consortium (LADCO) and the RPO

sensitivity modeling study grid (Baker, et al. 2002).

Vertical Domain Definition: MM5 will be exercised with 3 4 vertical layers with an

approximately 38 meter deep surface layer. The MM5 vertical domain is presented in

both sigma and height coordinates in Table 4

- 1.

**4.2 Data Availability** 

The data to be used in the annual MM5 application consists of meteorological mo del

derived inputs, geographic inputs and observed data. These data are used both for

running the MM5 model and for evaluating the results. The major datasets to be used in

this study, along with the availability for 2001 are presented in Table 4

- 2.

A ll major datasets have been downloaded from NCAR. The only documented data issues

are for the ETA initialization fields from 12Z 14 October to 6Z 17 October. For this

period the data may be contaminated by erroneous precipitation assimilation and suspect

greenness fraction. Special attention will be paid to the MM5 initialization and data

assimilation files during this period.4

- 2

**Table 4** 

**- 1**  **: MM5 Vertical Domain Specification.**

**k(MM5) sigma**  **press.(mb) height(m**  **) depth(m)** 

**34** 0.000 10000 15674 2004

**33** 0.050 14500 13670 1585

**32** 0.100 19000 12085 1321

**31** 0.150 23500 10764 1139

**30** 0.200 28000 9625 1004

**29** 0.250 32500 8621 900

**28** 0.300 37000 7720 817

**27** 0.350 41500 6903 750

**26** 0.400 46000 6153 693

**25** 0.450 5 0500 5461 645

**24** 0.500 55000 4816 604

**23** 0.550 59500 4212 568

**22** 0.600 64000 3644 536

**21** 0.650 68500 3108 508

**20** 0.700 73000 2600 388

**19** 0.740 76600 2212 282

**18** 0.770 79300 1930 274

**17** 0.800 82000 1657 178

**16** 0.820 83800 1478 175

**15** 0.840 85600 1 303 172

**14** 0.860 87400 1130 169

**13** 0.880 89200 961 167

**12** 0.900 91000 794 82

**11** 0.910 91900 712 82

**10** 0.920 92800 631 81

**9** 0.930 93700 550 80

**8** 0.940 94600 469 80

**7** 0.950 95500 389 79

**6** 0.960 96400 310 78

**5** 0.970 97300 232 78

**4** 0.980 98200 154 3 9

**3** 0.985 98650 115 39

**2** 0.990 99100 77 38

**1** 0.995 99550 38 38

**0** 1.000 100000 0 04

- 3

**Table 4** 

**- 2**  **: Meteorological Databases and Availability.**

**Dataset Use**  **Identifier Availability** 

**NCEP ETA Archives** “First

- guess” field for MM5 initialization and FDDA ETA Thru Dec ‘02

**NCEP ADP Global Upper Air Observation**

**Subsets** Objective analysis into MM5 initial, boundary and FDDA

fields DS353.4 Thru Feb ‘02

**NCEP ADP Global Surface Observations**  Used for objective analysis into MM5 initial, boundary and

FDDA fields DS464.0 Thru Feb ‘02

**Reynolds, Stoke and Smith Global SST Analyses**

**(weekly IO2)** Sea surface temperatures DS277.0 Thru Mar ‘02

**TDL Surface Hourly Obser vations**  Model Evaluation DS472.0 Thru Feb‘02

**FSL/NCDC Radiosonde Archive** Model Evaluation RAOBS Thru Dec ‘02

**NCDC 3240 Hourly Precipitation** Model Evaluation RAIN Thru Dec ‘024

- 4

**Figure 4** 

**- 1**  **: Annual MM5 Co mputational Domain.** 5

- 1

**5 Input Data Preparation Procedures** 

**5.1 Fixed Data Inputs** 

**Topography** Topographic information will be developed using the National Center for

Atmospheric Research (NCAR) terrain databases. The grid will be based on the 5 min

(~9 km) Geo physical Data Center global data. Terrain data are interpolated to the model

grid using a Cressman

- type objective analysis scheme. To avoid interpolating elevated

terrain over water, after the terrain databases are interpolated onto the MM5 grid, the

NCA R graphic water body database will be used to correct elevations over water bodies.

The terrain elevations are presented in Figure 5

- 1.

**Vegetation Type and Land Use:** Vegetation type and land use information is developed

using the most recently released 2 min. (~ 4 km) PSU/NCAR databases provided with the

MM5 distribution. Standard MM5 surface characteristics corresponding to each land use

category will be employed.

**5.2 Variable Data Input** 

**Atmospheric Data:** Initial conditions to the MM5 will be develo ped from operationally

analyzed fields derived from the National Centers for Environmental Prediction (NCEP)

ETA model following the procedures outlined by Stauffer and Seaman (1990). The

synoptic

- scale data to be used for this initialization (and in the a nalysis nudging discussed

below) will be obtained from the conventional National Weather Service (NWS) twice

daily radiosondes and 3

- hr NWS surface observations. These data include the horizontal

wind components (u and v), temperature (T), and relative hu midity (RH) at the standard

pressure levels, plus sea

- level pressure (SLP) and ground temperature (T

g ). Here, T g represents surface temperature over land and sea

- surface temperature over water. The so

called "first guess" NMC

- analyzed fields will be inte rpolated to several supplemental

analysis levels (950, 925, 900, 800, 750, 650, 600, 550, 450 and 350 mb) and then

modified by blending in the NWS standard rawinsonde data using a successive

correlation type of objective analysis that accounts for enhanced along

- wind correlation

of variables in strongly curved flow (Benjamin and Seaman, 1985). Subsequently, the

three

- dimensional variable fields will be interpolated onto the MM5's sigma vertical

coordinate system.

**Water Temperature:** Water temperatures will be derived from the global ETA skin

surface temperature database. It is recognized that these skin temperatures can lead to

temperature errors along coastlines. However, for this sort of analysis focusing on bulk

continental scale transport, this issue is likely not important. One of the proposed

sensitivity and diagnostic simulations presented in section 7 is aimed at addressing this

issue.5

- 2

**Clouds and Precipitation:** While the non

- hydrostatic MM5 treats cloud formation and

precipitation directly throu gh explicit resolved

- scale and parameterized sub

- grid scale

processes, the model does not require precipitation or cloud inputs. The potential for

precipitation and cloud formation enters through the thermodynamic and cloud processes

formulations in the model. The only precipitation

- related input required is the initial

mixing ratio field that will be developed from the NCEP and NMC data sets previously

discussed.

**5.3 Multi** 

**- scale FDDA** 

The multi

- scale Four Dimensional Data Assimilation (FDDA) technique deve loped at

Penn State (Stauffer and Seaman, 1990, 1994) and used in MM5 is based on Newtonian

relaxation, or nudging, which is a continuous assimilation method that relaxes the model

state toward the observed state by adding to one or more of the prognostic equations

artificial tendency terms based on the difference between the two states. It is said to be a

form of continuous data assimilation because the nudging term is applied at every time

step, thereby minimizing "shock" to the model solutions that may occur in intermittent

assimilation schemes.

The multi

- scale FDDA technique was developed by Stauffer and Seaman (1994) and

includes simultaneous use of two approaches outlined in Stauffer and Seaman (1990) and

Stauffer et al. (1991): (a) nudging toward g ridded analyses which are interpolated to the

model's current time step, and (b) nudging directly toward individual observations within

a time

- and

- space "window" surrounding the data. These two approaches are referred to

as "analysis nudging" and "obs nud ging", respectively. Analysis nudging is ideal for

assimilating synoptic data that cover most or all of a model domain at discrete times.

Obs nudging does not require gridded analyses of observations and is better suited for

assimilating high

- frequency a synoptic data that may be distributed non

- uniformly in

space and time (e.g., the intensive studies data). The routine observational networks to be

used in this study are not sufficiently dense enough to support obs nudging.

It is critically important to u nderstand the influence FDDA is having on the model

simulation. Properly applied, FDDA is a guide for the model, gently moving the model

estimates towards the analyzed synoptic fields. Improperly applied, FDDA is a

sledgehammer that forces the model towa rds observations in data rich areas while

degrading the model field away from the observations.

The nudging coefficients to be applied in this study are 2.5x10

- 4 sec - 1 for winds and

temperature and 1x10

- 5 sec - 1 for mixing ratio. These nudging coefficient s are relatively

weak and should not have an undesirably large impact on the model simulation. Only 3D

analysis nudging will be performed and thermodynamic variables will not be nudged

within the boundary layer. Several of the sensitivity and diagnostic s imulations proposed

in Section 7 are designed to better understand FDDA.5

- 3

As a sensitivity test, the “ZFAC mods” will be applied to the MM5 model source code.

These modifications exclude all data assimilation below a user specified level. The

lowest la yer to be used in this study is 850 Mbar. The ZFAC mods were developed to

correct a deficiency in the MM5 model where nocturnal jets were suppressed at night

because the FDDA analysis field was not able to resolve the feature. If the ZFAC mods

do not deg

rade the bias and error statistics of the model, the mods will be used in the base

configuration.

**5.4 Physics Options** 

This section presents physics options to be used in the meteorological modeling.

**5.4.1 Cumulus Parameterization** 

The Kain

- Fritsch (1993) cumulus parameterization will be used for this application. This

scheme uses a sophisticated cloud mixing scheme to determine entrainment/detrainment

and removing all available buoyant energy in the relaxation time. This scheme also

predicts both updraft and dow ndraft properties.

**5.4.2 Planetary Boundary Layer Scheme** 

The high resolution Blackadar PBL will be used. Several studies in the past few years

have focused on the impact of the PBL scheme for air quality related MM5 modeling,

with no one scheme being clearly p referred. One of the proposed diagnostic and

sensitivity test simulations outlined in Section 7 is designed to help address this issue.

**5.4.3 Explicit Moisture Scheme** 

The Dudhia Simple Ice scheme will be used. The simple ice scheme predicts both cloud,

rain w ater, and ice phases. No supercooled water is allowed and immediate melting of

water above freezing is assumed.

**5.4.4 Radiation Scheme** 

The Rapid Radiative Transfer Model (RRTM) (Mlawer et al. 1997) radiation scheme will

be used. This longwave radiation scheme is a new, highly accurate, and efficient method

that uses a correlated

- k model to represent the effects of the detailed absorption spectrum.

The model accounts for water vapor, carbon dioxide and ozone.

**5.4.5 Ground Temperature Scheme** 

The multilayer soil temp erature model will be employed. This model predicts

temperature in 1, 2, 4, 8, and 16 cm. layers with fixed substrate below using the vertical

diffusion equation. Thermal inertia is based on a force/restore scheme and includes5

- 4

vertically resolved tempera ture variation. This scheme allows for more rapid response of

surface temperature than a simple slab model.6

- 5

**6 Model Performance Evaluation** 

The specific procedures to be used in the model performance evaluation are being

prepared under Task A of this wor k assignment. This section presents an overview of the

evaluation process.

**6.1 Operational Evaluation** 

The operational evaluation refers to an assessment of the model's ability to estimate

correctly the atmospheric observations whether or not the process desc riptions in the

model are accurate (Tesche, 1991). It is an examination of how well the model

reproduces the observed meteorological fields in time and space consistent with the needs

of air quality models. The operational evaluation gives little, if any , information about

whether the results are correct from a scientific perspective or whether they are simply

the fortuitous product of compensating errors. Therefore, a "successful" operational

evaluation is a necessary but insufficient condition for achi eving a sound, reliable

modeling exercise.

It is difficult to identify the specific performance metrics that will be applied. The goal

of Task A of this work assignment is to identify new performance tests for long

- term

simulation models. Metrics that h ave been previously applied to episodic meteorological

model applications for air quality planning are presented in Table 6

- 1.

A challenge in evaluating large

- scale models it to determine the appropriate scale for

evaluation. It is very possible, even li kely, to have regional performance issues masked

in statistical evaluations because the signal is masked by different model behavior in

other parts of the domain. In performing the model performance evaluation it is also

important to understand the politi cal environment in which the science is conducted. For

these reasons the performance metrics will be conducted on a statewide basis, an RPO

basis, and a national level.

**6.1.1 Scientific Evaluation** 

The scientific evaluation addresses the realism of the basic me teorological processes

simulated by the model. This involves testing the model as an entire system as well as its

component parts. The scientific evaluation seeks to determine whether the model's

behavior, in the aggregate and in its component modules, i s consistent with prevailing

theory, knowledge of physical processes, and observations. The main objective is to

reveal the presence of bias and internal (compensating) errors in the model that, unless

discovered and rectified, or at least quantified, may lead to erroneous or fundamentally

incorrect policy decisions based on model usage.

The scientific evaluation ideally consists of a series of diagnostic and mechanistic tests

aimed at: (a) examining the existence of compensatory errors, (b) determining t he causes6

- 6

of failure of a flawed model, (c) stressing a model to ensure failure if indeed the model is

flawed, (d) provide additional insight into model performance beyond that supplied

through routine, operational evaluation procedures.

**Table 6** 

**- 1**  : Possible Statistical Measures and Graphical Displays to be Used in the MM5

Operational Evaluation

.

**Statistical Measure Graphical Display** 

**Surface Winds (m/s)**

Vector mean observed wind speed Vector mean modeled and observed wind

speeds as a function of time

Vector mean predicted wind speed Scalar mean modeled and observed wind

speeds as a function of time

Scalar mean observed wind speed Modeled and o bserved mean wind

directions as a function of time

Scalar mean predicted wind speed Modeled and observed standard

deviations in wind speed as a function of

time

Mean observed wind direction RMSE, RMSE

s , and RMSE

u errors as a

function of time

Mean p redicted wind direction Index of Agreement as a function of time

Standard deviation of observed wind speeds Surface wind vector plots of modeled and

observed winds every 3

- hrs

Standard deviation of predicted wind speeds Upper level wind vector plots every 3

- hrs

Standard deviation of observed wind directions

Standard deviation of predicted wind directions

Total RMSE error in wind speeds

Systematic RMSE error in wind speeds

Unsystematic RMSE error in wind speeds

Index of Agreement (I) in wind speeds

SKILL E skill scores for surface wind speeds

SKILLvar skill scores for surface wind speeds6

- 7 **Surface Temperatures (Deg**

**- C)** 

Maximum region

- wide observed surface

temperature Normalized bias in surface temperature

estimates as a function of time

Maximum region

- wide predicted surface

temperature Normalized error in surface temperature

estimates as a function of time

Normalized bias in hourly surface temperature Scatterplot of hourly observed and

mo deled surface temperatures

Mean bias in hourly surface temperature Scatterplot of daily maximum observed

and modeled surface temperatures

Normalized gross error in hourly surface

temperature Standard deviation of modeled and

observed surface temperat ures as a

function of time

Mean gross error in hourly surface temperature Spatial mean of hourly modeled and

observed surface temperatures as a

function of time

Average accuracy of daily maximum temperature

estimates over all stations Isopleths of ho urly ground level

temperatures every 3

- hr

Variance in hourly temperature estimates Time series of modeled and observed

hourly temperatures as selected stations

**Surface Mixing Ratio (G/kg)**

Maximum region

- wide observed mixi ng ratio Normalized bias in surface mixing ratio

estimates as a function of time

Maximum region

- wide predicted mixing ratio Normalized error in surface mixing ratio

estimates as a function of time

Normalized bias in hourly mixing ratio Scatterplot o f hourly observed and

modeled surface mixing ratios

Mean bias in hourly mixing ratio Scatterplot of daily maximum observed

and modeled surface mixing ratios

Normalized gross error in hourly mixing ratio Standard deviation of modeled and

observed surf

ace mixing ratios as a

function of time

Mean gross error in hourly mixing ratio Spatial mean of hourly modeled and

observed surface mixing ratios as a6

- 8 observed surface mixing ratios as a

function of time

Average accuracy of daily maximum mixing ratio Isopleths of hourly ground level mi xing

ratios every 3

- hr

Variance in hourly mixing ratio estimates Time series of modeled and observed

hourly mixing ratios at selected stations7

- 1

**7 Diagnostic Simulations** 

Determining the optimal configuration of the MM5 model for a specific application

requires performing multiple experiments to identify the suite of physics and

configuration options giving the “best” output fields. Unguided by diagnostic/sensitivity

simulations, this effort quickly exhausts available time and resources. Our proposed

approach, based on experience with both RAMS and MM5, is designed to quickly and

efficiently identify a suitable model configuration.

Notwithstanding prodigious increases in processing speeds on today’s computers, MM5

is still CPU

- intensive. Based on our test simulations over the national grid, MM5 will

take several computer weeks to simulate the full year of 2001. Thus, a concerted effort is

needed to minimize the overall computational burden.

To optimize the resources available for this study, the mo del sensitivity testing will first

focus on episodic modeling of winter and summer cases. February will be used for

wintertime cases and July will be used for the summertime cases. Once the episodic

simulations are complete, two annual simulations will b e performed using the two

episodic configurations judged by the study team and client representative to have the

most promise.7

- 1

**Table 7** 

**- 1**  **: Proposed Episodic Sensitivity and Diagnostic Simulations.**

Simulatio n Period Description Purpose

Baseline 1

- 20 Feb. 2001

1

- 20 July 2001 Simulation with the baseline model options presented

in this protocol Provide a basis of comparison.

NNRP 1

- 20 Feb. 2001

1

- 20 July 2001 Use NCAR/NCEP Reanalysis Project (NNRP)

initializa tion field with larger computational domain Examine the differences in model

estimation skill with different

initialization datasets.

ZFAC 1

- 20 Feb. 2001

1

- 20 July 2001 Include the “ZFAC mods”. Examine the potential for nighttime jet

suppression from low level FDDA.

No FDDA 1

- 20 Feb. 2001

1

- 20 July 2001 Remove all FDDA from simulation To test that FDDA is no having an

inordinately large impact on model

results

High FDDA 1

- 20 Feb. 2001

1

- 20 July 2001 Double the FDDA nudging coefficient Examine models sens itivity to the

nudging coefficient

Soil Moisture 1

- 20 July 2001 Adjust soil moisture to reflect precipitation anomalies

in 2001. Examine impact of soil moisture

parameters

Reisner

Microphysics 1

- 20 Feb. 2001

1

- 20 July 2001 Use the more advanced Reisner microphysics option

instead of simple ice Examine impact on precipitation skill

from more advanced moisture

microphysics

Reynolds SST 1 1

- 20 Feb. 2001

1

- 20 July 2001 Include higher resolution sea

- surface temperature

data Examine the influence of coarse

res olution earth skin temperature.

PBL Scheme 1 1

- 20 Feb. 2001

1

- 20 July 2001 Employ alternative PBL scheme Examine sensitivity of model to PBL

scheme

Land Surface

Model 1 1

- 20 Feb. 2001

1

- 20 July 2001 Employ Land Surface Model Explore ability of LSM model t o improve

model skill

1 Project resources preclude this diagnostic and sensitivity simulation from being performed by the contractor team. The simulation

will be performed by USEPA staff on a time available basis. It is possible that specification of the annual simulations may have to be

decided before these simulations are completed.8

- 1

**8 Data Base and Reporting Submittal Procedures** 

Documents, technical memorandums, and data bases developed in this study will be

submitted to the project sponsors for revi ew and approval during the course of the

modeling work.

A final technical report summarizing the entire meteorological modeling effort, including

data base development, model application and model performance evaluation will be

submitted to the project sponsors for review. In addition, all modeling input and output

data bases and model codes used in this study will be transferred via media in the

standard big

- endian IEEE MM5 binary format to the project sponsors to allow for proper,

independent review o f the modeling assumptions, inputs, and outputs.9

- 1

**9 References** 

Baker, K., et. al. 2002: “Draft Meteorological and Photochemical Model Performance

Sensitivity Project.”, Lake Michigan Air Directors Consortium, Des Plaines IL.

Dudhia, J, D. Gill, Y

- R Gou, K . Manning and W. Wang, 2001: PSU/NCAR Mesoscale

Modeling System User’s Guide (MM5 Modeling System Verion 3).

Kain, J.S. and J.M. Fritsch, 1993: Convective parameterization for mesoscale models:

The Kain

- Fritsch scheme. The representation of cumulus para meterization in mumerical

models, K.A. Emanuel and D.J. Raymond, Eds., Amer. Meteor. Soc., 246 pp.

Mlawer, E.J., S.J. Taubman, P.D. Brown, M.J. Iacono and S.A. Clough, 1997: Radiative

transfer for inhomogeneous atmosphere: RRTM, a validated correlated

- k m odel for the

longware. J. Geophys. Res., 102, 16663

- 16682.

Stauffer, D.R. and N.L. Seaman, 1990: Use of four

- dimensional data assimilation in a

limited

- area mesoscale model. Part I: Experiments with synoptic data. Mon. Wea. Rev.

, 118 , 1250

- 1277.

Stauff er, D.R., N.L. Seaman and F.S. Binkowski, 1991: Use of four

- dimensional data

assimilation in a limited

- area mesoscale model. Part II: Effects of data assimilation

within the planetary boundary layer. Mon. Wea. Rev.

, 119 , 734

- 754.

Stauffer, D.R. and N.L . Seaman, 1994: On multi

- scale four

- dimensional data

assimilation. J. Appl. Meteor.

, 33 , 416

- 434.

Tesche, T. W., 1991. Evaluation Procedures for Using Numerical Meteorological Models

as Input to Photochemical Models”, 7 th Joint Conference on Applicatio ns of Air Pollution

Meteorology, American Meteorological Society, 14

- 18 January, New Orleans, LA.