## EVALUATIONANDSENSITIVITYANALYSESRESULTS OFTHEMESOPUFFIIMODELWITHCAPTEXMEASUREMENTS

by



### James M. Godowitch Atmospheric Sciences Modeling Division Atmospheric Research and Exposure Assessment Laboratory Research Triangle Park, North carolina 27711

ATMOSPHERIC RESEARCH AND EXPOSURE ASSESSMENT LABORATORY OFFICE OF RESEARCH AND DEVELOPMENT U.S. ENVIRONMENTAL PROTECTION AGENCY RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711

## EVALUATIONANDSENSITIVITYANALYSESRESULTS OFTHEMESOPUFFIIMODELWITHCAPTEXMEASUREMENTS

by

James M. Godowitch Atmospheric Sciences Modeling Division Atmospheric Research and Exposure Assessment Laboratory Research Triangle Park, North Carolina 27711

ATMOSPHERIC RESEARCH AND EXPOSURE ASSESSMENT LABORATORY OFFICE OF RESEARCH AND DEVELOPMENT U.S. ENVIRONMENTAL PROTECTION AGENCY RESEARCH TRIANGLE PARK, NORTH CAROLINA 27711

### DISCLAIMER


### The information inthis documenthas been fundedwholly or inpart by the United States Environmental ProtectionAgency. Ithas been subject to the Agency's peer and administrative reviews, andhas been approved for publicationas anEPA document.

AFFILIATION 

### The author is on assignment to the Atmospheric Sciences Modeling Division, Atmospheric Research and Exposure Assessment Laboratory. from the National Oceanic andAtmospheric Administration, U.S. Department of Commerce.

ii

### ABSTRACT The MESOPUFF II regional Lagrangianpuffmodel has been evaluated and testedwith the Cross-AppalachianTracer Experiment (CAPTEX) data base. The model was appliedto the six full-scale CAPTEX episodes in order to simulate the tracerplume formed from the 3-hourrelease of an inert, non-depositingperfluorocarbon tracer gas from either one of two selected sites. Surface tracermeasurements were made at 3 hour or 6 hour intervals over an extensive samplingnetworkof arcs downwind to 1100

### krn in the northeasternU. S. and southeasternCanada. An operational evaluationwas conductedby exercising the model transport and dispersion components with all default features set according to the user's guide. Transport in the model is simulatedby a mixedlayer averagedwind field and anupper levelwind field averaged from the mixingheight to 700 mb level. Puff growthwithin 100

### krn downwind is governedbyhorizontal andvertical dispersionparameters derived from expressions fitted to the standardPGT curves, while time dependent formulas are applied at greater distances. Model performancewas quantitatively determined from standard statistical measures of difference and correlationbetweenmodeled and observed tracer concentrations paired in time and location. The model overpredictedpeak andmean concentrations, whether paired in time and/or location. Modeledmean concentrations were within a factor of two of observedmeanvalues in four of the experiments. The overpredictions are primarily attributed to anunderestimation ofvertical dispersion during neutral stability conditions whichwere specifiedduring the afternoonhours after releases. Graphical maps and comparative tests between observed andmodeled concentrationpairs revealed that spatial displacements between the observed andmodeledplumes contributed to the large scatter and low correlations found in the evaluation statistics. Analysis ofplume centroidpositions revealed that the greatest changes in the separation of the observed andmodeledplumes occurred during the nocturnal periods. Revisions are suggested tobetter treat this source type in the model. Diagnostic model test results with optional wind fields in the model (e.g. surface, 850 mb single levelwind fields) andvarious dispersion options are compared to those from the operational model and the observed data. Asurface wind field transported the modeledplume considerably slower and generally to the left of the observedplume while the single level 850 mb wind field consistently shifted the modeledplume to the right (clockwise) of the actual plume location. Model results with the mixedlayer averagedwind field displayed thebest overall performance incomparisons of the position and time of impact of the peak concentrationwith the observedplume values at the 300

### krn arc. The 24-hpeak andplume average concentrationresults from model test runs, which focused on different options andvariations inkeyparameters of the the dry deposition and chemical transformationmodules, are compared tobase case values. All model runs were performedwith SOx emissions from a realistic elevatedpoint source. Peak S02 concentrations showed negligible variations when deposition or chemical transformationwere omitted however, mean

# so2 concentrations intheplume were more sensitive. Mean andpeak sulfate concentrations were more sensitive than S02 byvariations in the surface resistance andby changes inparameters controlling the chemical transformationrate.

iii

## CONTENTS
Abstract

......................................................... Figures

.......................................................... Tables

........................................................... Acknowledgements

................................................. 1. INTRODUCTION

.................................................. 2. DESCRIPTION OF MODEL COMPONENTS AND TECHNICAL FEATURES

........ Wind Fields andRelatedMeteorological Parameters

.......... DispersionParameters

...................................... Dry Depositionand Chemical TransformationMethods

......... Model Grid System

.......................................... 3. MODEL EVALUATION DATA BASE

.................................... Description of the FieldMeasurement Program

............... Data Preparation for theMeteorological Processor

.......... 4. MODEL EVALUATION PROCEDURES AND RESULTS

....................... OperationalModel EvaluationAnalyses andResults

.......... StatisticalResults for Mean and Peak Concentrations

... ComparativeAnalyses of Plume Patterns and Positions

... DiagnosticModel Test Results

.............................. 5. MODEL SENSITIVITY RESULTS

..................................... 6. SUMMARY AND CONCLUSIONS

....................................... REFERENCES
APPENDIX A Model test results for different puffrelease/samplingrates
..................

v 

### iii vi viii X 

### 1 4 7 

### 10 10 12 14 14 

### 18 30 

### 33 33 54 64 

### 82 

### 93 96

99

### Number 1



### 2 3


### 4

5

6

7



### 8

9


### 10

11 LISTOFFIGURES



### Flow diagram of the MESOPUFF II modeling system
.....···········
Surface sampling network for the CAPTEX fieldprogram ..········ Meteorological grid inMESOPAC II for the Dayton cases of the CAPTEX field program ..........................·········· Closest rawinsonde site (1 6) to each gridpoint in the MESOPAC II meteorological grid for Dayton cases ........···· Surface meteorological stations considered in the MESOPAC II runs for the CAPTEX evaluation. Boundaries for meteorological grids are denoted (heavy solid and dashed lines) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ········· Closest surfacemeteorological station 

### (1 25) to each gridpoint in the MESOPAC II meteorological grid for Dayton cases during CAPTLX
......................... Landuse categories in the MESOPAC II meteorological grid for the Dayton cases during CAPTEX
........................ Average concentrations obtained from sites with observed andmodelednonzero concentrations over the sampling periods-during a) CAPTEX #landb) CAPTEX #2
................... Same as lla-b, except for c) CAPTEX #3 and d) CAPTEX #4 Same as lla-b, except for e) CAPTEX #5 and f) CAPTEX #7 Cumulative frequency distributions of the model and observed concentrations from the entire data set
.... Cumulative frequency distributions ofmodel and observed concentrations for a) CAPTEX #l andb) CAPTEX #2

0 ••••••• 

### .

## .. . . . Same as 13a-b, except for c) CAPTLX #3 and d) CAPTEX #4


## ...... Same as 13a-b, except for e) CAPTEX #5 and f) CAPTEX #7

•••• 0 ••• 

### Observed (0) andmodeled 

### (*) plume patterns depicted from sites with nonzero concentrations during; a) CAPTEX #l andb) CAPTEX #2 .................................. Same as 8a-b, except for c) CAPTEX #3 and d) CAPTEX #4 Same as 8a-b, except for e) CAPTEX #5 and f) CAPTEX #7

vi 5


### 16
19
22

23

25


### 29

41 

### 42 43


### 44
46 47 48

56 57 58

### Number Figures(continued) 

### Page 12 Ratios of downwind distances ofthe model and observed plume centroidlocations (D /D0) 

### and separation distancebetweencentroidpgsitions to the observed downwind distance (Ds/D0) 

### versus time of day starting from the day ofrelease. Negative andpositivevalues for Ds/D0 

### denote themodelplume position is to the right and left of the observedplume, respectively. a) CAPTEX#landb) CAPTEX#2 results

.... . . . . . . . ....... . . . . . . . . 61

13

14
15
16

17 Same as 9a-b, except for c) CAPTEX#3 and d) CAPTEX#4 Same as 9a-b, except for e) CAPTEX #5 and f) CAPTEX#7 a) Differencebetween the downwind distances of the centroid locations of the model (D ) and observed (D0) 

### plume patternsversus time after

### r~lease. b) Separationdistance (Ds) betweenmodel and observed plume centroidlocations versus time after release. Symbols inboth figures denote events for each experiment; CAPTEX#l (0), #2 (X), #3 (triangles), #4 (squares), #5 (stars), and#7 (diamonds)

.................................. Upper airwind speed (0) 

### andwind direction (X) profiles at a) 18 GMT (i.e. 13 LST) andb) 00 

### GMT for CAPTEX#2

......... a) Wind speed (0) 

### andwind direction (X) profiles and b) temperature profile at Dayton, Ohio from CAPTEX#3 Tracer concentrationpattern from aircraft flight legs at different levels during the afternoonofOctober 3, 1983 during CAPTEX#3

............................................... Hourlyvariationofmixingheight (Z·) 

### from the MESOPAC II

### meteorologicalprocessor for Dayton, Ohio during September 18-19, 1983 of CAPTEX#l

...................... 62 63

65

72


### 74

75

81 18 Average 24-h

## so2 concentrationfields from the model base case run for3a) Day 1 andb) Day 2. Allvalues inunits ofJJg m- ............................................. 85-86 19 Average 24-h sulfate concentrationfields from the model base case for a)**~ay** 

### 1 andb) Day 2. Allvalues inunits ofJJg m- ............................................. 87-88

vii

### Number 

### 1

2 

### 3 4 5 6 

### 7 8



### 9


### 10 11 

### LISTOFTABLES



### Calculation sequence ofmeteorological parameters inMESOPAC II

................................················· Computation sequence of the MESOPUFF II model code

..············· Tracer release informationfor CAPTEX

............················ Tracer samplingperiods during each CAPTEX field experiment

.····· Upper airmeasurements and specifications

...........············· Surface meteorological station information

...........············ Hourly surface observations and specifications

...........····· Landuse categories and surface roughness lengths for MESOPAC II

. . . . . . . . . . . . . . . . ...............················ Input control parameters and default features of MESOPAC II runs for CAPTEX episodes

........................... Inputparameters and specifications for MESOPUFF II runs ......... MESOPUFF II model evaluationresults: observed and modeled concentrations paired in time and location 6 8 

### 15 15 21 24 

### 26


### 28
31 

### 32

35 

### 12 Test results ofmodeled and observed concentrations

13


### 14
15


### 16
17
18



### 19

20 

### paired in time and location

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 

### 37 Selected statistical evaluationresults for each event of the CAPTEX experiments

..................................... Distribution of observed andmodeled concentrations over the entire CAPTEX data set
............................... Peak observed andmodel concentrations paired in location only

....................................... Peak observed andmodeled concentrations paired in time only for the events of each CAPTEX experiments

..................... High-25 observed andmodeled concentrations unpaired in time and location for each CAPTEX experiment

............... Statistical results of observed andmodeled concentrations paired in time and location from sampling arcs for CAPTEX #l-4

............................................... Peak observed andmodeled concentrations unpaired in time and location for each arc from CAPTEX #l-4

..................... Comparative results for differentwind fields on peak concentration/plume transport at the 300

### km arc for CAPTEX #l

................................ viii 

### 39

49



# so
52

53

55

55

67

### Number 

### 21

22
23
24


### 25


### 26

27 

### 28


### 29
30
Al Tables(continued) 

### Comparative results for differentwind fields on peak concentration/plume transport at the 300 

### km arc for CAPTEX#2

............................... Comparative results for differentwind fields on at the 300

### km arc for CAPTEX#3

............................... Comparative results for differentwind fields on at the 300 

### km arc for CAPTEX #4

............................... Selectedmeteorological observations andmodel results at the release site for each CAPTEX experiment

................ Results of analysis of observedupper air windprofiles for the CAPTEX release days

................................... Peak concentrations for CAPTEX #l using various dispersion options

............. Comparison of observed andmodeledmixingheights Source characteristics and emission rates for all model sensitivity test runs ......................................... Results ofmodel sensitivityruns: Comparisons with 24-hbase case peak andmean concentrations

................... Results of sensitivityruns from variations in selectkey parameters in the model

............................ Differences inpeakvalues at the initial arc for different puff release/sampling rates
.....................

ix 

### 68
69

70

71



### 77

79 79



### 83


### 89
91
99

### ACKNOWLEDGEMENTS The author wishes to extend his appreciation to Terry Clark, Norman Possiel, and Jerome Heffter for their thorough reviews and helpful suggestions which were incorporated into this final report. Thanks are also expressed to Terry Clark for providing a copy of the CAPTEX data base and for the useful discussions concerning regional model evaluation methods. Appreciation is also expressed to James Reagan for his assistance with the land use inventory.

X

### SECTION1 INTRODUCTION 

### The ability of a regional-scale air qualitymodel to reproduce spatial pollutant concentration fields or depositionpatterns ona shorttermbasis is strongly dependent upon its formulations for simulating the transport and dispersionprocesses. Models need to treat the important diurnalvariations occurring inthe mean and turbulent structure of the three-dimensional wind flowwhich directs the path of and impacts the spread ofpollutantplumes. Thus, an evaluationof these crucial model components with fieldmeasurements is a critical element in establishing the credibity of anymodel. Although a comprehensive evaluationof all components in a model, including the chemical mechanism and removal methods, is highly desireable, the datanecessary to undertake such an efforthave been difficult to compile. Experimental field studies with certain tracer gases have provided valuable meteorological and concentrationmeasurements onregional scales, which allow evaluations to focus on the transport and dispersion components ofmodels. One of the most recent tracer data sets available for this purpose is the Cross-AppalachianTracer Experiment (CAPTEX). This field programwas was specifically designed and conductedwith the intent of acquiring an accurate tracer data set from an extensive surface sampling networkwith concurrentupper airmeteorological measurements for use in transport and dispersionmodel evaluation efforts. During the CAPTEX episodes, a chemically inert, non-depositingperfluorocarbon tracer gas was released at ground-level over a 3-hperiod from either one of two selected sites. Uncertainties oftenexisting inemissions were minimized as the tracer emissionrate was highly controlled and concentrations wer.e accuratelymeasured above a relatively low**ba~kground** 

### level. There was also no known interference from other sources to complicate the interpretation of the tracermeasurements. The CAPTEX fieldprogramwas conducted in the northeasternU.S. and southeasternCanada and the database consists of a challenging set of sevenexperiments withmeasurements spanningup to two diurnal cycles ineach episode. The results from a fewmodels whichhave been applied to particular cases or to the entire CAPTEX data sethave alreadybeenreported. Lee (1987) applied a particle-in-cellnumerical model to all CAPTEX cases and relied heavily onvarious graphical analyses to investigate model performance. Even thoughplots ofmodeled and observedplume patterns showedrelatively good qualitative agreement inmost cases, a scatter diagram ofmodeled and observed concentrationpairs showed considerable variability andno correlation. In fact, the large number of zero concentrations for either the observed orpredictedvalues indicated that notable spatialplume displacements musthave existed during the course of each experiement. The ARL (Air Resources Laboratory) Lagrangianpuffmodel, which distributes mass vertically into different layers and transports the mass by different trajectories at each level, was evaluated in severalways with the**CAPTL~** 

### database by Draxler (1987). Several model runs were performedwithvarious 1combinations of the meteorological surface and upper air data; some simulations applied finer spatial resolution and/or a more frequent temporal interval than the standard twice-daily profiles of the routine measurement network. Results were not conclusive as to whether improved spatial or temporal resolution in the input data produced more accurate model plume patterns. However, the best results for the plume trajectory were indeed found whenboth spatial and temporal resolutions were enhanced. Nevertheless, the predicted concentrations displayed order-of-magnitude differences from observed values when pairs were compared evenwhenusing the most highly-resolved input data. Thus, spatial plume displacements still were sufficient to produce large scatter and low correlations in concentrationpairs in time and location. Davis et al. (1986) demonstrated the ability of a Lagrangian puff model (MLAM)

. which allowed puffs to be vertically distributed and transported within sevenvertical layers and also simulated decoupling from the surface, to explain the plume patterns in two CAPTEX episodes. While no statistical results were provided, the graphical displays showed the ability of this model to treat the considerable vertical direction shear present in one of the CAPTEX cases simulated. Most recently, a four-dimensional data assimilation technique incorporated into a three dimensional hydrodynamic model was also applied to two CAPTLX cases by Kao andYamada (1988). who utilized itwith a random-particle diffusion model. Their graphical results of concentration pairs revealed notable scatter. Differences between observed and simulated wind components, which developed with time over the modeled plume history were mainly attributed for the discrepancies inplume centerline positions. Spatial displacements were found to be larger in the latter stages of each episode. Clearly, the statistical and graphical analyses from these efforts revealed the difficulty that current models have in successfully replicating the threedimensional wind flows and dispersion processes that characterize the observed data, as exemplifiedby concentration differences and plume pattern displacements between the models and observations.
In this report, the results of an evaluation and testing effort of the MESOPUFF II model (Scire et al., l984a,b) with the CAPTEX data base are presented. MESOPUFF II is a second generation Lagrangian puff model which was designed to treat transport, dispersion, chemical transformation, and the removal (dry and wet) processes impacting pollutant emissions from elevated point and/or area sources on a regional scale for short-term applications. Although the chemical and deposition modules were specifically developed for sulfur oxides and nitrogen oxides, any non-reacting and/or non-depositing gaseous species may be modeled by electing not to simulate these processes during model execution. Thus, no significant changes in the mode code were needed for this evaluation effort.
The current operational version of MESOPUFF II (version 4.3) was applied and evaluatedwith the measurements obtained from six CAPTEX episodes. The model can take advantage of two different layer-averaged wind fields derived from surface andupper air data to transport a pollutant plume, while puff dispersion is treated with traditional dispersion parameters. Section 2 describes the model system and provides the pertinent details about the methods employed in the model to simulate these processes, while Section 3 2

### discusses the CAPTEX database andpreparation of the various input data for the model runs. Model performancewas examinedwithvarious analyses techniques in order to investigate the model's ability to predict concentrations and plume patterns out to 1000

### km from the tracer release sites. Thus, the analysis tools included, butwere not limited to traditional statistical measures ofdifference and correlationbetweenmodeled and observed concentrations paired in time and/or location. While these statistical results provide useful quantitative information, the results ofprevious regional model evaluations with the CAPTEX databasehave also shown the utility of various graphical analysis andmore innovative quantitative measures, which helped clarify·observed andmodeledplume positions. Consequently, graphical displays are used to demonstrate qualitative spatial agreementbetween observed and simulatedplumes, while analysis ofplume centroid locations is the method applied to obtain quantitative measures of differences in the downwindposition and separation of the respective plumes. In Section4. all of these analysis techniques are employed in the evaluationprocess in order to obtain thebest overall picture of the capabilities and limitations of the model for this particular application. Uncertainty shouldbe considered in the analysis ofmodel predictions. The uncertainty is causedby errors in the measurements, model formulation andmodel input errors, as well as the inherentuncertainty associatedwith the stochastic nature of turbulence and diffusion of a plume. The uncertainty is not addressed directlywithin the evaluation analyses. However, any inadequacy in the input data is pointedoutwhere itmay contribute to model error. Particular attention in the analyses will focus onhighlighting shortcomings in the model's simulationof the transport and dispersionprocesses. Since the model evaluationwith the CAPTEX data is limited to an assessment of the transport and dispersion components of the model system, a select group ofmodel test runs was also undertaken to investigate the impact on pollutant concentrations from optional methods in the model or to changes to key technical parameters of the chemical transformation and dry deposition mechanisms. The model test runs simulated the SOx emissions from a single, elevatedpoint source and apply the same meteorological fields from one of the CAPTEX episodes. Section 5 discusses the results ofmodel test runs from comparisons of 24-hpeak and average plume concentrations from each test simulation to those of a base case run.

3

### SECTION2
DESCRIPTIONOFMODELCOMPONENTSANDTECHNICALFEATURES The principal program components of the model system and their important input/output features are depicted in Figure 1. In particular, separate computer codes perform the specific functions for processing various meteorological data (READ56, MESOPAC II), for computing pollutant concentrations (MESOPUFF II), and for postprocessing operations with the calculated concentrations from receptor sites and/or gridpoints (MESOFILE II). All of these model components were exercised during this evaluation and testing effort. An overview of the attributes of the model components is givenherein and the essential details of the methods used to compute the parameters relevant to the transport and dispersion processes, and treatments of dry removal and chemical transformation are also presented. This informationprovides some background to assist in the interpretation of the evaluation results and sensitivity test runs. Complete details about the various methods in the model programs canbe found in Scire et al. (1984a) and complete instructions for applying the model system and optional features are contained in Scire et al. (1984b). The READ56 processor program examines and reformats the twice-daily (i.e. 00 and 12 GMT) rawinsonde sounding measurements, and creates a formatted file of temperature and wind speed/direction profile data for each site. This program screens the profile measurements to determine if values at mandatory levels are missing. However, data quality assurance tests and inserting missingvalues must be performedby the user. MESOPAC II is the primary meteorological processor program. It generates hourly gridded fields of meteorological parameters from hourly surface meteorological observations, the upper air data files produced by READ56, and land use category information. Precipitation measurements are necessary when modeling wet episodes, otherwise these data are optional. Precipitation measurements were not used in this model evaluation effort as dry conditions existed during the CAPTEX episodes. Table 1 shows the execution sequence of the MESOPAC II program code and the order of calculation of meteorological parameters. The processor program is highly modular in that each parameter is computedwithin a separate**su~routine.** A single output file of gridded meteorological parameters is producedby MESOPAC II, which serves as the primary input file to the model program. Both processor programs were specifically designed to accept as input the various formatted meteorological data archived by the National Climatic Data Center inAshville, North Carolina. The National Weather Service (NWS) upper air data (TDF5600 format) and hourly surface observations (CDl444 format) are the expected data sets for these programs, although comparable observed data canbe substituted if all the necessary variables have been measured. Hourly measurements of ceilingheight, cloud cover, and nearsurface observations of temperature, pressure, wind speed and direction and relative humidity are required. ' 

### 4MESOPACII ControlParameter lngut3

MESOPUFFII ControlParameter Inputs

MESOFILEII ControlParameter lncuu

MESOFILEII

**Post:~rocassor**Program REA056Control Parameterlnouts

MESOPACIIMeteoroiQ<Oical

PreprocessorPrcqram

MESOPUFFIIDISPERSIONMODEL

**~(Optional)**

Concenrrauon Tables READ56UgperAir PreorocessorProgram

(Opuonal)

Coneantraucn Tables Users Edtts



### Figure 1. Flowdiagramof theMESOPUFF II modeling system. (Scire et al., 1984a)
5

### TABLE 1 

### CALCULATION SEQUENCE OFMETEOROLOGICAL PARAMETERS IN MESOPAC II

>READ USER INPUT FOR CONTROLAND TECHNICAL FEATURES
>READ UPPERAIR PROFILE DATA (00AND 12 GMT FORFIRST SIMULATION DAY)


### <ITERATE OVERDAY(S) *COMPUTE SUNRISE/SUNSET TIMES, HOURLY SOLAR ELEVATION ANGLES
>READ PRECIPITATION DATA (OPTIONAL)


### <ITERATE OVERHOURS
>READ HOURLY SURFACEMETEOROLOGICAL STATION OBSERVATIONS >IFHOUR IS 00 GMT, READ NEXT 12 GMT UPPERAIR PROFILE >IFHOUR IS 12 GMT, READ NEXT 00 GMTUPPERAIR PROFILE * CALCULATE SURFACEWIND FIELD AT GRID POINTS * CALCULATE PGT STABILITY CLASS AT GRID POINTS * CALCULATE SENSIBLE HEAT FLUX (H) AT GRID POINTS * CALCULATE FRICTIONVELOCITY (U*) AT GRID POINTS * CALCULATEMIXING HEIGHT (Zi) AT GRID POINTS * CALCULATE OBUKHOV LENGTH (L) AT GRID POINTS * CALCULATE CONVECTIVE VELOCITY (W*) AT GRID POINTS * CALCULATE LOWERWIND FIELD AT EACH GRID POINT * CALCULATEUPPERWIND FIELD AT EACH GRID POINT * OUTPUT/STORE ALL GRIDDED METEOROLOGICAL PARAMETERS
<

### END OFHOUR ITERATION


### <END OF DAY ITERATION * CLOSE FILES * END PROGRAM EXECUTION

6

### MESOPUFF II computes hourly surface pollutant concentrations on a 

### Cartesiangrid and/or at

### user-specified, nongridded receptor sites. The model applies a 

### Gaussianpuff superposition technique to simulate a continuous plumeby a series of individual puffs. The puff release and samplingrates, and source andemission informationmustbe suppliedby the user. Eachpuff is individually transported and itsmass is subjected to dispersion, deposition (dry and/orwet) and chemical transformation. Table 2 reveals the overall execution sequence of theMESOPUFF II code and the order of simulationof the physicalprocesses. Options exist thatpermit one ormore of the removal mechanisms tobe omitted or for inputvalues to override defaultvalues ofkey technical parameters. TheMESOFILE IIpostprocessorpackage is capable ofperforming various analyses of individual ormultiple concentration files produced by the model. Time-averaging ofconcentrations may beperformed in an evaluationeffort or statistical results canbe computed to investigate variations inconcentration fields producedby differentmodel runs. The latter function is particularlyuseful when conductingmodel sensitivity studies in a 

### comparison of different results to a base case. WIND FIELDS AND RELATED METEOROLOGICAL PARAMETERS Two different layer-averagedwind fields are generatedbyMESOPAC II in the default mode. Amixed-layer averagedwind field is determined from hourly surface observations and twice-dailyupper airprofile data inorder to represent the meanboundary layer flowpatternbetween the surface and mixingheight (Zi). An upperwind field is determined over the layer from Zi to the 700 mb height. The usermay override these default fields by selecting alternatewind fields according to particular input specifications (see Scire et al., 1984b). Optionalwind fields include; a single level wind field for surface, 850 mb, 700 mb, or 500 mb levels, orvertically averagedwinds from Zi to 850 mb or to 500 mb. Asinglewind fieldmodel is also possibleby specifying the same wind field selectionas the lower andupper levelwind fields. The methodology to determine the layer-averagedwind fields consists of several steps. Surfacewind components (us, vs) are determined at each gridpoint from the hourly surface stationobservations with the inverse distance-squared (l/d2) technique. An alignmentweighting factor is also appliedwhich gives greater consideration to measurements at sites more directlyupwind or downwind ofa gridpoint. The 00 GMT and 12 GMT rawinsonde profiles are assumed tobe representative ofneutral/unstable conditions and stable conditions, respectively. With the appropriate rawinsonde profile, layer-averagedwind speed and direction are computed from all measurements between the surface and the current Zi. Then the ratio of the layer-averaged and surfacewind speeds <Rws), and the angular differencebetween the layeraveraged and surfacewind directions (d8) at the rawinsonde site are determined Finally, the mixed-layer averagedhorizontalwind components (Uml•Vml) at a gridpoint for a particularhour are computedby applyingRws and d8 from the closestrawinsonde site to the spatially-interpolated surfacewind speed and direction, respectively, at that gridpoint. The layer-averagedupperwind field is determinedby linear interpolation in timebetween soundings and averaging all data over the layer from Zi 

### 7

### TABLE2 

### COMPUTATION SEQUENCE OF THE MESOPUFF IIMODEL CODE
>READ INPUTS FORTECHNICAL OPTIONS, GRID SIZE,**SOURCES/&~ISSIONS** 

### >READ THE GRIDDED WIND FIELDS FOR INITIALHOUR OF SIMULATION


### < ITERATE OVERTHE HOURS FORTHE SIMULATION
>READ OTHER GRIDDED METEOROLOGICAL PARAMETERS FOR CURRENT HOUR
>READ GRIDDED WIND FIELDS FOR NLXT HOUR * INITIALIZE CONCENTRATIONARRAYS
<

### PERFORM PUFF ITERATIONS * FOR NEW EMITTED PUFF

- COMPUTE PLUME RISE


### <PERFORM PUFF SAMPLING ITERATIONS * TRANSPORT THE PUFF * COMPUTE AND APPLY DISPERSION PARAMETERS TO PUFF,**ru~D** 

### TRANSFERMASS IF IN 3-LAYERMODE * CALCULATE AND APPLY CHEMICALTRANSFORMATION RATE(S) * CALCULATE AND APPLYWET REMOVAL * CALCULATE AND APPLY DRY DEPOSITION * SAMPLE PUFF TO COMPUTE CONCENTRATION CONTRIBUTION AT GRID POINTS/NONGRIDDED RECEPTOR LOCATIONS


### <END PUFF SAMPLING ITERATIONS
<

### END PUFF ITERATIONS * CALCULATE PUFF CONCENTRATIONS FOR NEXT HOUR'S CHEMISTRY *CALCULATEMULTI-HOURAVERAGE CONCENTRATIONS, IF SPECIFIED * PRINT/STOREMODELED CONCENTRATIONS


### <END HOUR ITERATIONS * END PROGRAM EXECUTION

8

### to the 700 mb height. The upper levelwind components (Uul• Vul) are derived at each gridpointby the l/d2 methodwith the summation of contri butions over the rawinsonde sites. The appropriate wind field for puff transport is automatically selectedby a comparison of the puff centerheight (Zp) and the current Zi on anhourlybasis The sourceheight and anyplume rise are summed**t~ obt~in**  

### Zp. For Zp<Zi, a puff is advectedwith the lower level**w~nd f~eld.**  

### IfZ>Zi, the upperwind field is used to transport the puff and its

## grow~h is governedby stability class 5 (i.e. PGT class Eby default). Puff splitting is notperformedby this model. However, when the multi-layermodel feature is exercised, puffmass canbe transferred between the mixed-layer andupper layer. For example, the amount ofmass removed from the mixed-layer depends on the fractional change of Zi since its peakvalue. This model feature is usually selectedwith the surface depletionmethod for dry deposition in an attempt to account for the large difference ofboundary layer resistance to mixing at night compared to daylighthours. The mixingheighthas particularly important roles in this model because it is involved inboth transport, asjust discussed, and in dispersionprocesses. Since Zi is the demarcation level defining the vertical extent of the mixed-layer averagedwind field and the starting height of averaging for the upper levelwind field, variations in Zi can lead to possiblynotable differences inpuff transport, especially when strongvertical shears in speed andjor direction exist. As inmany models, Zi is also the criticalheight scale invertical dispersion calculations. Thus, it is essential to model performance that the temporal behavior and spatial variations of Zi be simulatedrealistically. During the daylighthours, convective andmechanical mixingheights are computedwith the greater of the two values being selected as Zi at each gridpoint. The convectivemixingheight (Zic) is strongly controlledby the surface sensibleheat flux (H = 

### w'T') and the temperature structure aloft. Thebehavior ofHis highly dependent on the incoming solar radiation and the observed cloud cover. Eqs. (1) and (2) are used to compute Zic and the temperature discontinuity**(~T)** 

### at the mixed-layer top, respectively, at the nexthour (t+l) over anhourly time step (dt).


### 2 (Zic +**2H(l+E)dt/~** t

### ~T = 

### (2 E

### ~ H

### dt)~ t+l 

### +**~T /~**  

### t+l 

### (l)
(2)



### The potential temperature lapse rate**(~)** 

### is determined over a 200 mlayer (default) above Zic· An entrainment coefficient (E) is included in an effort to parameterize an additional contribution to zic growth from downward transport ofwarmer airwith a defaultvalue forE of 0.15. Adaytime mechanical mixingheight (Zim) is computed from the surface frictionvelocity (U*) and the Brunt-Vaisala frequency (Nb) according to; 

### 9

### where f is the Coriolis parameter. During nocturnalhours, the mixingheight is defined solelyby Zim• whichhas an even stronger dependency onU* according to the empirical expression inEq. 4.

The current defaultvalue for Cis 2400. The frictionvelocity is a function of the surface wind speed, surface roughness length (Z0), 

### and a stability adjustment factor (Scire et al.,1984a). The landuse type for each gridpoint should (3)



### (4)

be accurately specifiedbecause Z0 

### is assigned internallybased on representative values for the broad landuse types of Sheih et al. (1979). 

### DISPERSION PARAMETERS Eachpuff in the model is horizontally symmetric with a Gaussian distribution. Puff growth is governedby the horizontal andvertical dispersionparameters, ay and 

### az. Values of these parameters are computed bypower law functions which were derived from fits of the Turner (1970) curves. The form of these functions is givenby;

a = 

### AXb (5)



### where Aandb varywith stability class and are different for ay and 

### az (Scire et al., 1984b). Eq. (5) is applied to puff growth out to downwind distances (X) of 100

### km (default). Thereafter, puff growth is determined with the time-dependent formulas ofHeffter (1965). The crossover distance where the change inmethods occurs may be overidden. An optional feature in the model uniformly disperses puffs throughout the mixed-layer depth immediately afterbeing released. The stability class for each gridpoint is determined eachhour according to the Turner (1964) method. Since these PGT stability classes are defined according to cloud cover, ceilingheight, solar' elevation angle, and the surface wind speed, values canvary across the model grid eachhour. Stability classes range from 1 (A very unstable) to 6 (F- moderately stable). 

### DRY DEPOSITION AND CHEMICALTRANSFORMATIONMETHODS Although the tracer gas which is the subject of this evaluation effort is nondepositing and chemically inert, the methodologies that simulate dry removal processes and determine the transformation rates in MESOPUFF II are describedbecause the results of the sensitivity analyses in Section 5 are obtained fromvariations to the default methods. 10

### Both of thesephysical processes are highly complex in the atmosphere and revisions to model formulations are oftenperformed as new experimental informationbecomes available. Methods to simulate theseprocesses mustbe species dependent. While MESOPUFF II is capable of simulating SOx and NOx simultaneously, the informationcontained in this overviewis limited to this model's treatment of

## so2 and a secondaryproduct, fine sulfate (S04). Dry depositionhas received greater recognition over the past decade as an important removal process ofpollutants over regional and larger scales Consequently, more resources havebeen devoted to obtaining experimental dry depositionmeasurements ofcertainpollutants (Hales et al., 1987), whichhas providedmore information to improve ourunderstanding of depositionprocesses andhas advanced the development of depositionparameterizationmethods. Therehas alsobeen a notable evolution in the treatment of dry deposition inmodels from the simple technique of specifying a constant depositionvelocity of a specificpollutant, as in the first generation ofMESOPUFF (Benkley and Bass, 1979) to a physically realistic methodwhich attempts to account for the spatial and temporal variations inmeteorological parameters and surface features that influence dry removal processes. Currently. atmospheric and surface resistances are commonly employed to compute spatially-averaged depositionvelocities for grid cellsby considerationof the proportion ofvarious landuse types within each cell (Walcek, et al., 1986). The dry depositionmethod inMESOPUFF II is also parameterizedby a depositionvelocity (Vd). In this scheme the removal processes consist of the vertical turbulent diffusion ofpollutants through a turbulent atmospheric surface layer and a laminar sublayer, which is followedby uptake into the vegetative or surfacematerial. Individual resistances are used to represent the ability of a species to transfer through the atmosphere and surface cover. The depositionvelocity is computed as the inverse of the sum of individual resistances according as follows;
·(6)



### where ra is the aerodynamic resistance, rb is the quasi laminar boundary layer resistance in the proximity of the canopy or surface interface and rs is the surface resistance. Values of ra and rb are temporally and spatiallyvariable sinceboth are dependent upon the meteorological conditions and landuse. These aerodymamic resistances are computed from;
(k

### U*)-1

### (ln(z/Z0)

- 

### Sh (kU*)-1kB-l (6a) (6b)

where Sh is a stability correction factor and Bis a surface transfer function. The value ofkB-l for S02 is 2.6. However, rb is assumed to have a constantvalue of 1000 s/m for S04. Specifications for other modeledpollutants are given in the user guide (Scire et al., 1984b).
11Surface resistances for

## so2 are a function of land use and stability class. The default values are specified internally in the module for summer conditions only and are the same as those in Sheih et al. (1979). For

## so4, the total resistance is determinedby the sum of ra and rb because rs is currently assigned to be zero in the module.
In the multi-layer mode of the model, only puff mass within the mixed layer is subjected to dry deposition. In contrast, the source depletion method is exercisedwhen the single layer mode of the model is selected. The treatment of chemical transformation in the atmosphere has also shown great advances. There is a hierarchy of methods to simulate chemical reactions and their rates that vary from the linear treatments with fixed transformation rates to sophisticated chemical mechanisms containing numerous reactions which require considerable computer resources. Chemical conversion of

## so2 to

## so4 inMESOPUFF II is controlled by a transformation rate which is computed from a parameterized expression. Scire et al. (1984a) described the photochemical model mechanism and analyses of the model simulation results which lead to defining the primary variables that dominated the transformation rates. Expressions for transformation rates were developed from statistical regression analyses of many photochemical model run results for a range of conditions. In particular, the hourly transformation rate (k) for S02 to sulfate for the daytime period is computed from;



### + 

### (7)

where R is total solar radiation (kwatt/m2), 03 is the background ozone concentration (ppm), Sis the stability class, and RH is relative humidity(%). These parameters were found to be the dominant variables from regression analysis of the photochemical model results. Thus, chemical conversion of

## so2 is enhanced in the model under conditions of high solar radiation, strong dispersion, high background

### o3, and high humidity. The default background 03 concentration in the model is currently 80 ppb, although the model is capable of accepting measured 03 concentrations at sites within the model domain. Model run results in Scire et al. (1984a) indicated values for k were 2-3% per hour during the daytime, which were comparable to existing experimental results from field studies (Wilson, 1981). During the nocturnal period, the

## so2 oxidation rate is set to 0.2% h-1

. Equations comparable to (7) have also been developed for NOx transformation rates. GRID SYSTEM The grids for MESOPAC II andMESOPUFF II are based on a Cartesian coordinate reference frame. Since it is not constructed on a latitudelongitude framework, alignment differences between N-S grid lines and the true N-S direction lines defined by longitude canbecome serious for grid domains larger than a few hundred kilometers, especially at middle andhigh latitude regions. An approach used to minimize the impact of 

### 12

### this effect on the grid systemfor the CAPTEX regionwillbe noted later. Ameteorological grid defines the set of gridpoints where the meteorological parameters are computedbyMESOPAC II. Currently, the computer code limits the size to 40 x 40 grid indices. The computational grid inMESOPUFF II canbe defined tobe the same size as themeteorological grid or a subset of it. When a puff travels beyond the computational grid, it is eliminated from further consideration inconcentrationcalculations. Asampling grid defines the set of griddedreceptorpoints where concentrations are computedand this gridmay be identical to or a subset of the computational grid. The model is also capable ofcomputing concentrations at so-callednongriddedreceptor locations. This model feature canbe particularlyuseful in a model evaluation, such as in this application to the CAPTEX tracer sampling sites, since measurement networks are not designed according to a uniformly spaced grid. Model executiontime is also reduced significantlyby restricting concentration calculations to the nongridded receptor locations with an option to omit calculations for the sampling grid. The grid size and gridpoint spacingmustbe definedby the user in each application. Additional details and an example configurationof the model grid sjstems arepresented in Scire et al. (1984b).

13

### SECTION3

MODELEVALUATIONDATABASE


### DESCRIPTION OF THE FIELD MEASUREMENT PROGRAM The field study phase of CAPTEX consisted of seven individual tracer releases from either Dayton, Ohio or Sudbury, Ontario over the period from mid-September to late October 1983. Information about each tracer experiment is given inTable 3. With the exception of experiment #6, which was very short to permit the start up of the next release from Sudbury, the amount of tracer gas emitted for these experiments was comparable. All releases were from ground level and a constant emission rate is assumed over a 3-hour period during each full-scale experiment. The first four tracer releases were undertaken from Dayton and the release periods were during afternoon hours (Table 3). In contrast, both Sudbury releases were conducted at night. Nevertheless, the meteorological conditions at both sites permitted strong vertical mixing and the prevailing wind pattern in each case provided the desired transport of the tracer plume across the study region. Dayton releases were performed when the region was under the influence of large high pressure systems. At the release site, wind flows were from the southwest or west and mostly fair or partly cloudy sky conditions existed. The Sudbury releases occurred after cold front passages with northwesterly winds and mostly cloudy skies. Brown et al. (1985) provides detailed synoptic maps and discusses the weather conditions for each CAPTEX episode. The field study region during CAPTEX encompassed the northeastern U. S. and southeastern Canada. The surface sampling network shown in Figure 2 was designed to encompass this area. It consisted of an array of individual arcs deployed at approximately 100 km intervals extending from 300 

### km to about 1100 km from of Dayton. Each sampling site was designated by a 3-digit number (e.g. 302); the leading digit (3) signifying the arc number inhundreds of kilometers and the next two digits (02) representing a site's relative position along each arc with numbers increasing from south to north. The experimental measurement plan was to commence sampling concurrently at all sites along a given arc prior to the expected arrival of the tracer plume and to obtain measurements over the entire duration of plume passage. Each site was equipped with an automatic sequential sampler that was preset to collect tracer concentrations over a prescribed period. The tracer sampling system is described by Ferber et al. (1986). Tracer sampling began at the nearest arc soon after the start of a release, and at 3-h or 6-h intervals later at subsequent arcs. The sampling interval was set to 3-h at sites on the 300-km arc for Dayton releases and at some Canadian sites after the Sudbury releases to provide better temporal resolution near the release point. All other measurements were generally made over 6-h intervals. Amaximum of six consecutive samples were obtained at each site,


### 14

### TABLE3 

### TRACER# RELEASE INFORMATION FOR CAPTEX
EXPT SITE DATE TIME AMOUNT RELEASED RATE (#) 

### (1983) (LST) (kg) (g/s)

1 DAYTON* 9/18 1200-1500 208 19.26 2 DAYTON 9/25 1205-1505 201 18.61 3 DAYTON 10/2 1400-1700 201 18.61 4 DAYTON 10/14 1100-1400 199 18.44 5 SUDBURY 10/25-6 2245-0145 180 16.67 6 DAYTON 10/28 1030-1100 32 2.96 7 SUDBURY 10/29 0100-0400 183 16.94

* Released fromWright-PattersonAir Force Base, otherwise at DaytonInternationalAirport (DAY). # 

### Tracerusedwas perfluoromonomethylcyclohexane (C7H14)

TABLE4 

### TRACER SAMPLING PERIOD FOREACH CAPTEXFIELD EXPERIMENT
EXPT START SAMPLING STOP SAMPLING$ DURATION (#) 

### (DATE-TIME (LST) (DATE/TIME (LST) (hours)

1 9/18-1400 9/20-0800 42 2 9/25-1300 9/28-0400 63 3 10/02-1600 10/04-1900 51 4 10/14-1300 10/16-1000 45 5 10/26-0300 10/28-0300 48 6* 10/28-1100 10/29-0800 21 7** 10/28-0100 10/30-0800 33 

### $ Sampling end time for most sitesup through the 1000km 

### arc. *Canadian siteswere notput inoperation inanticipation of#7. **One site continuedsamplinguntil 11-31/1500

15

# • • 152 052 0 854 

# • • 853 

# • 756 0 108 

# • 912 107 654 

# • 

# • 106 

# • 0 556 910 0 0II

0 614 0 810

00 710 0 I-' 0 908 0\ 612 009 0 0 0 70B BOB 610 0 412 0 007 0 608 0 50B 706 806 G 0 410 0 0 0 0 0314 0 320 310316 312 0 506 408 0 0 310 406 

# • 504 0 

# • 604 502 

# 0

Figure 2. Surface sampling network for the CAPTEX field program.

### so measurements spanned either 18 or 36 hours. Inmost cases this sampling strategywas 

### successful since the arrival and departure times of the tracer plume, andpeak concentrations couldbe determined at each arc to within the averaging interval of the measurements. Table 4 lists the start time andmost common ending time of sampling to reveal the overall duration of each experiment. Measurements at a few sites on the most distant arcs continued even later than those posted inTable 4. The samplingperiod for all experiments, with the exception of#6, extendedover at least one diurnal cycle. Measurementperiods for the four Dayton experiments covered episodes of approximately two full days before the actual tracerplume departed the samplingnetwork. The acquisition ofupper airmeteorological data onmore frequent temporal and finer spatial scales was also an important ingredient of the CAPTEX fieldprogram. During the experimentalperiods the NationalWeather Service rawinsonde sites in the CAPTEX region launched four rawinsondes daily at 6-hour intervals, insteadof the routine twice-daily soundings. These supplemental soundings were made at 06 and 18 GMT. Even though the meteorological processorwas not designed to accept these profiles as input, Zi observations from these soundings may be compared to modeledvalues and the intermediatewind profiles are also useful in diagnosing and interpreting the causes ofmodelvariations. There were 10 additional upper air sites deployed in the regionduring this experimental program to provide greater spatial density of the rather sparse NWS rawinsonde network. Rawinsonde soundings were also performed during the tracer release periods atboth release sites. Several research aircraftwere specially-equippedwithmodified tracer samplers for the CAPTEX studies. Sampling flights were conducted to obtain the horizontal dimensions of the tracerplume atvarious levels at different downwind distances and aircraft soundings were conducted to investigate the vertical distribution of tracer. The aircraft flight paths for each experiment and listings of the tracer concentrations measured along each flight path are providedby Ferber et al. (1986). Since the aircraft tracer measurements represented spatially-averaged concentrations sampled over several minutes along a flight path atvarious heights above ground, these measurements will notbe directlyused in the quantitative statistical analysis of the model results. However, the aircraft measurements providedvaluable information about the tracerplume position, and its spatial dimensions atvarious times and distances from the release point. The aircraft data canbehelpful in an examination ofmodeled and observedplume locations and inqualitative assessments ofmodeled and observedconcentrations whenpossible. Results of analyses of the horizontal andvertical distribution of the aircraft tracer data havebeenpresentedbyRaynor et al. (1984) and Stunder et al. (1986), respectively. The tracermeasurements collected at each sampling sitewere subjected to quality assurance audits and tests (Lagomarsino et al., 1987). Tracer concentrations were provided inunits of femtoliters per liter (fl 1-1) which is equivalent to parts per.l015

.

### Accardi~~ to Ferber et al. (1986), an ambientbackground**concentrat~on** 

### of 3.4 fl 1 was subtracted from all tracermeasurements. The final database containedconcentrations to the nearest 1 fl 1-l at each site with accompanying date/time (GMT) information, 17and sampling interval (3 or 6 hours). The data base contained no entries for sampling periods when tracer concentrations were not collected at a site or when measurements were reiectedby quality assurance procedures. A conversion factor of 1 fl 1-

- 1.56xlo-ll g m 3 was applied to modeled concentrations for their evaluation against observations. 

### DATA PREPARATION FORTHE METEOROLOGICAL PROCESSOR PROGRAM Prior to the selection of surface stations and data preparation for the model evaluation runs, a meteorological grid for MESOPAC II was designed with sufficient size and adequate resolution to cover the CAPTEX region. Itwas also decided to use different grid sizes for the Dayton and Sudbury cases. The meteorological grid was set to 30 x 19 for the Dayton cases and 30 x 24 for both Sudbury releases. However, the grid point spacing of 37

### km was the same for both grids. Therefore, the grid domain covered an 1100

### km x 700

### km area for the Dayton cases and an 1100

### km X 888 

### km region for the Sudbury cases. The selection of the grid point spacing was partlybased on the resolution of the land use inventory which will be discussed later. This combination of grid size and the grid point spacing provided sufficient coverage and spatial resolution of the study region. Figure 3 shows the meteorological grid for the Dayton cases. The release site is situated a sufficient distance from gridboundaries and the domain extends eastward to include the 1000 km arc. The northern boundary of the grid extended to 47°N for the Sudbury cases. These grid sizes appeared to stretch the applicability of this model grid system to a maximum limit due to the convergence of longitude lines with latitude. Differences in alignment of grid and longitude lines, taking the overall domain into consideration, were minimized by defining the N-S grid lines in the middle of the model domain to be parallel with longitude lines. Thus, alignment differences increased slowly toward the E-W boundaries. Amagnetic tape of the CAPTEX data base contained the rawinsonde data, surface tracer concentrations, aircraft tracer data, ancillary meterological measurements from several towers, and terrainheights over the CAPTEX region according to the format specifications documented in Ferber et al. (1986). The rawinsonde sounding measurements at the release sites and the regular NWS upper air network sites were processed for input to MESOPAC II. Since a survey of the rawinsonde measurements from the 10 additional sounding sites revealed that winds were consistently missing for many levels within the boundary layer, these sites were not included in the meteorological processor runs. The wind and temperature records for each sounding were combined from separate blocks on the tape. An additional complicationwas that the significant wind and temperature reporting levels did not necessarily correspond, as temperatures were often detected at fewer levels. Consequently, an upper air profile with temperatures and winds at common levels up to 500 mb was constructedby deriving values by linear interpolation at heights where the other parameter was measured. The formatted upper air data files were reviewed to insure that soundings existed at 00 and 12 GMT for all days in the simulationperiod required to model each experiment. These files were also examined to make sure values existed at all mandatory levels (i.e. 1000, 850, 500 mb) because the MESOPAC II code ceases executionwhen an upper air profile or data are missing. Table 5 

### 18

# \ 0



# ... ._PI

### 41.ocf

# r-~~--------------------1

Figure 3. 0 

### YSB

Meteorological grid inMESOPAC II for the Dayton cases of the CAPTEX field program.contains information about the upper air measurements required as input to MESOPAC II. These upper air data were subjected to various quality control/ assurance tests during processing and archival procedures (Ferber et al., 1986) Figure 4 displays the closest rawinsonde station to each gridpoint by a number from 1 to 6. There were six rawinsonde sites available for the Dayton cases. It is evident from Figure 4 that each site is assumed to represent a rather large section of the model grid, ranging from 300 km to over 400 

### km in most directions. The hourly surface meteorological observations at all regularly reporting stations in the northeasternUnited States and for several stations in Ontario were also obtained, as the processor cannot compute fields of meteorological parameters without hourly surface measurements. Since the number of stations available was much greater thanMESOPAC II's limit of 25 sites, a single group of stations was selected and their observations were prepared for model input. Figure 5 shows the full set of stations selected for the model evaluation. These sites were chosen in order to cover the entire CAPTEX region and to also provide a relatively uniform intersite spacing across the region. The hourly observations at each release site were included as input for the experiments with a release from that site. Table 6 lists the entire group of stations whose observations were use as input for the model runs. A set of 25 stations was used in all cases, however, a few sites were only used in the Dayton experiments and other stations were substituted for the Sudbury cases. In particular, stations closer to a release location were selected in an attempt to provide better definition of the flow pattern around the time of release. For example, two stations in Indiana and one inMichiganwere included in the Dayton cases, but these stations were excluded from the model runs for the Sudbury releases in lieu of sites closer to Sudbury. Although the dimensions of arrays in the program code couldbe easily increased to accommodate data at additional stations, the purpose of the operational evaluation was to exercise the code as specified in the user guide. Figure 6 reveals that the greater number of surface sites is translated into finer spatial resolution than that for the upper air stations. There are notable elevation differences among the stations in Table 6. The relatively level terrain across Ohio and Ontario gives way to higher elevations and complex terrainvariations among the stations across Pennsylvania and New York. Nevertheless, elevation data are not required by MESOPAC II since flat terrain is assumed by this model. The MESOPAC II code contains no provisions for missing data and execution stops if it detects missing data or an error occurs in the date/time information. Therefore, it is also imperative that the hourly surface observation files be carefully scrutinized for missing data and that editting corrections be performedbefore model execution. Fortunately, only a small fraction of observations were missing from the 25 selected sites. In most cases a single hour of observations was missing, although 2 or 3 consecutive hours of missing data occurred occasionally. In all instances, values were estimatedby linear interpolation from observations taken immediatelybefore and after the missing hour(s). Table 7 presents the surface meteorological variables required as input to MESOPAC II. The information about each variable is taken from CD144 (Card Deck 144) format specifications. The files ofhourly observations were also reviewed and values were derived when a single variable was missing.


### 20

### TABLE5 

### UPPERAIRMEASUREMENTS AND SPECIFICATIONS#

VARIABLE UNITS RESOLUTION

Pressure mb 0.1mb Height m, MSL* 1 m Temperature degrees (OK) 

### 0.1° Wind speed m/s 0.1 m/s Wind direction degrees (0) 

### 1 0 

### #Units and resolutionof the format requirements inMESOPAC II. * altitude above mean sea level



### 21N N STATT011

**IIU~C**U OF CLOS!:ST RAIIIIISOIIOE STATfON TO EACit GRIO POTNT HULTIPLY ALL VALUES aY 10 •• 0 1? 6 6 6 6 6 6 6 6 6 6 6 6

**~** 5 5 5

**~** 

### s 5 5 5 5 3 3 3 3 1 1 3 ' 1- 1::1 \ 6 6 ' + 1- 17 \ 6 "' \

# . 16 \ 6 \ • +

**1~** \ 6 6 \ • + 

### n ' o

**~>** \ • + 1l \ 6 6 \

# . 12 \ 6 6 ' • + 11 \ 6 6 \ + + 1-J \ 6 6 ' + +

**~** \ 6 6 \ • +

**(~** \ 1 1 ' 1- + 7 ' 1 ' + + 0 \ ' • + 5 \ \ +

**~** \ 1 \ + + J \ \ + + l \ + 6 6 + f, 6 + 6 6 + 6 6 <i 6 + 6 6 + + 6 6 

# • 6 l + + 6 6 + 6 6 + 1 + +
+ .+ 1 

# •
+ + 1 1 + + 1- + • 1 1 + 6 + 6 

# • 6 + 6 + 6 + 6 + 6 + 6 + 6

+ I + + 1 +

+ + 6 + 6 + 6 + 6 + 6 t 6 + 6 6 + 6 + 6

+ 1 + 1 

# • 1 + + 1 + 6 + 6 + 6 + 6 + 6 + 6 

# • 6 6 + 6 + 6 +
+

t
+ 

# • 1 + + 6 + 6 + 6 + 6 

# • 6 + 6 + 6 t 6 6 

# • 6 + 1

+ 1 +

1-
+ 1 t + 6 6 + 6 + 6 + 6 + 6 + 6 t 6 + 6 t 6 + 6 +
t 1 t + 1 t 1 + + 6 + 6 t 6 t 6 t 6 

# • 6 + 6 + 6 +

**~** + 1 

# • 4 + 4 + 1 + 1

+
+ 1 \ + t + t + + + + t \ 1 1 1 1 1 ' + + + t t + t + + + 6 6 + 6 + 6 t 6 

# • 6 

# • 6 + 6 + 

## c 4 

# • 4 4 + 4 + 4 + 4 + 4 + 4 6 + 6 + 6 6 + 6 + 6 + 6 + 6 4 

# • 4 + 4 + 4 + 4 t 4 4 + 4 t 4 + + 5 t 5 + 5 + 5 t 5 + 5 + 5 t

**~** t 4 + 4 

# • 4 + 4 4 t 4 t 4 + 4 5 + 5 + 5 t 5 + 5 + 5 

# • 5 + 

### s + 4 

# • 4 + 4 + 4 + 4 + 4 + 4 + 4 + 4 5 + 5 + 5 + 5 + 5 + 5 + 5 t 5 t 5 4 t 4 t 4 + 4 + 4 t 4 t 4 t 4 5 t 5 + 5 + 5 t 5 

# • 5 + 5 

# • 5 + 5 t 4 

# • 4 4 + 4 t 4 + 4 t 4 t 4 + 5 + 5 + 5 + 5 t 5 + 5 t 5 t 5 + 5 5 + 4 + 4 + 4 + 4 + 4 + 4 + 4 + 5 t 5 5 + 5 + 5 t 5 + 5 t 5 + 5 t 5 

# • 4 + 4 + 4 + 4 + 4 + 2 + 2 + 5 t 5 + 5 + 5 + 5 5 + 5 + 5 -t 5 + 5 + 4 t 4 + 4 + 4 + ?. 2 + 2 + 5 + 5 + 5 + 5 t 5 + 5 + 5 t 5 t
t 

## s + 5 4 + :! + 2 

# • 2 

# • 2 + 2 + 5 t

5 t 

# • 5 + 

### s t 5

t 5 + 5 t 5 t 2 + 2 t 2 + 2 

# • 2 + 2 + 5 t

t 5 + 5 + 5 5 + 5 t 5 

# • 5 + 5 + 5 + 2 + 2 2 + 2 t 2 t 2 + 5 +
+ 5 + 5 + 5 + 5 + 5 + 5 t 5 + 5 + 5 + 2 + 2 t l + 2 + 2 + l + 3 t 3 + J 

# .. 3 + 3 + 3 + 

# • 3 + l + 3 + 3 + 2 + 2 + 2 t 2 t 2 t 2 J + J + 3 + 3 

# • 3 + 3 + 3 + 3 + J 

# .. 3 + J + 3 + 2 t l t 2 + 2 t 2 J + 3 + 3 3 t 3 + 3 + 3 + J + 3 + J t J + J + 5 

# • 2 + 2 + 2 + 2 J + J + 3 3 t J + J t J 

# • 3 

# • J + J
J + J + 3 + 2 + 2 + 2 + 2 t + t + t t + t • + + + + t + 4 4 4 4 4

**~** 2 2 2 2 2 2 2 2 2 2 + + + + + + + + + + t + + t 3 + J + 3 + J + 1 + J + J + J + J 

# • J 

# • J + J 

# • 1 

# • J + 2 + 2 + 2 

# • J t J + J + J + J + J t J 

# • J + J + J + .5 + J + J + J + J + 2 

# • 2 + J +
+ 3 + 3 + J + 3 

# • J + J + J + 1 + J t 3 + J + J + J + 2 + 2 • + + 2 2 2 • + + 

# ----------------------------------------------------------------------------------------- 2 3 Figure 

### h. 5 6 1 

### r 9 10 11 12 11 14 15 16 17 1a 19 20 21 22 21 24 25 26 21 23 Z9 30
Closest rawinsonde site (l

- 6) to each grid point in the MESOPAC II meteorological grid for Dayton cases.

# • •

Figure 5. 

# • CMH 

# • DUJ

# • 

# • ELM



# • !PT

Surface meteorological stations considered in the MESOPAC II runs for the CAPTEX evaluation. Boundaries for meteorological grids are denoted (heavy solid and dashed lines).

### TABLE6 

### SURFACE METEOROLOGICAL STATION INFORMATION
LOCATION SITE ID Albany, NY ALB Altoona, PA AOO Avoca, PA AVP Buffalo, NY BUF Cleveland, OH CLE Dayton, OH DAY Dubois, PA DUJ Elmira, NY ELM Erie, PA ERI Flint, MI FNT Ft. Wayne, IN FWA GrandRapids,MI GRR Harrisburg, PA CXY Indianapolis,IN IND Messina, NY MSS Newark, NJ EWR Philadelphia, PA PHL Pittsburgh, PA PIT Parkerburg, WV PKB *Sault Ste Marie,MI SSM

Syracuse, NY SYR Toledo, OH TOL Williamsport, PA IPT Youngtown, OH YNG Canadian Sites: London, ONT. YXU *Ottawa, ONT. YOW *Sudbury, ONT. YSB

Trenton, ONT. YTR Alternate Site: #Columbus, OH 

### CMH LATITUDE (DEG. N)

42.75 40.30 41.33 42.93 41.42 39.90 41.18 42.17 42.08 42.97 41.00 42.90 40.22 39.75 44.93 40.70 39.88 40.50 39.27 46.47 43.12 41.60 41.25 41.27
43.05 45.32 46.62 44.12

40.00 LONGITUDE (DEG. W) 73.80 78.32 75.53 78.73 81.87 84.20 78.90 76.90 80.18 83.75 85.20 85.50 76.85 86.25 74.85 74.17 75.25 80.22 81.57 84.37 76.12 83.80 76.92 80.67
81.09 75.67 80.80 77.53

82.88 ELEVATION (m ASL) 89. 448. 289. 215. 245. 306. 556. 291. 225. 234. 252. 245. 107. 246.

65.

9. 8. 

### 374. 194. 221. 124. 

### 211. 160. 362.
278. 126 348.

81.

254. * applied in the Sudbury cases only inplace of FWA, IND, YNG # 

### Substitutedfor DAY in a test case only

24N lJl HESOPUFF VERSIOH 4.0 LEVEL 870421

STATIOII truttBER OF CLOSEST StmFACE HET. STATiotlTO EACII GRID POUlT HULTIPLY AllVALUES OY 10 MM 0 19 ' 14 14 14 14 12 12 12 12 12 25 25 25 25 25 24 24 24 24 24 24 24 24 17 17 17 17 17 17 17 17 ' + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 18 ' 14 14 14 14 12 12 12 12 12 25 25 25 25 25 25 24 24 24 24 24 24 24 24 17 17 17 I7 I7 I7 I7 \ + + + + + + + + + + + • + + + • + + + + + + + + + + + + + + 17\ 14 I4 14 14 12 12 12 12 12 25 25 25 25 25 25 24 24 24 24 24 24 24 24 17 17 17 17 17 17 17 \ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 16\ 14 14 14 14 12 12 12 12 12 25 25 25 25 25 25 4 4 24 24 24 24 24 24 21 21 17 17 I7 1 1 ' + + + + + • .. + + + + + + + + + • .. + + + + + + + .. + + + 15 ' 14 14 14 14 12 12 12 12 12 25 25 25 25 25 25 4 4 4 24 24 24 24 21 21 21 21 1 1 1 1 \ + .. + • + + + + + + + + + + + + + + + .. + + + + + + + + + 14\ 14 14 14 14 12 12 12 12 12 25 25 25 25 25 25 4 4 4 4 4 24 21 21 21 21 21 1 1 1 1 \ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 13\ 14 14 14 14 14 I2 12 12 12 25 25 25 25 25 25 4 4 4 4 4 21 21 21 21 21 21 1 1 1 1 \ + + + • • t + • • t • + • + + + .. + + + + + + .. + + + + + + 12 \ 14 14 14 14 14 12 12 12 I2 25 25 25 25 25 10 10 4 4 4 4 9 9 21 21 21 21 I 1 1 1 ' + + + + + + + + + 11 ' 14 14 14 14 14 12 12 12 12 ' + + + +
.. + + .. + 10 ' 14 14 14 14 22 22 22 22 22 • + + + .. + + + 5 25 25 10 10 10 10 4 + + + + + + + + 5 5 5 10 10 10 10 10 \ + + + + + t t

.. 9 \ 13 13 13 13 22 22 22 22

\+ + + + + + 8 \ 13 13 13 13 13 22 22 22

' + + + + + + + • 7 ' 13 13 13 13 13 22 22 22

\ + • + + + • t • 6 \ 13 13 13 13 13 7 7 22

\ + t + • • • + • 5 \ 15 15 13 13 7 7 7 7

' • • + 4 \ 15 15 15 + 7 + 7 + 5 + 5 + 5 

# • 5 + 7 + 5 + 5 

# • 5 + 5 

# • 5 + 5 + 5 + + + + + 5 23 23 10 10 + + + + + 5 23 23 23 8 + .. t t + 5 5 23 23 2J 8 + + + + + + 5 5 19 19 19 19 + + + + + + 5 19

.19 19 19 19 + + + + + + + + 7 20 20 20 19 19. 19 19 \ + • + 3 ' 15 15 15

' + • + 2\ 15 15 15

' + + + 1 ' 15 15 15 + 7 + 7 + 7 + 7 + + 7 + 7 + 7 + 7 + + 7 + 7 

# • 7 

## • 7 + '+ + + + + • + + + 7 7 20 20 20 20 20 19 19 19 • • + + + + + • + + 7 7 20 20 20 20 20 20 20 19 + + + • + + + + + • 7 7 20 20 20 20 20 20 20 20 ' + + + + + + + + + + + + 8 + 8 + 8 + 8 

# • 2 + 2 + 2 + 2 

# • 2 

# • + 4 + 8 + 8 + 8 + 8 + 8 + 2 + 2



## .. 2 

# • 2 + 2 + + 4 + 8 + 9 + 9 + 9 + 9 + 9 + 9 + 9 + 9 + + 9 21 + .• 9 J + 1 + 3 + 1 + I + 1 + 1 + 1 + 1 + + + + + 8 16 16 16 16 + + + • • 8 16 16 16 16 + 3 + 3 + 3 + 3 + 3 + 3 + 3 + 3 + + + + 3 3 11 11 + + + + 3111111 + 8 + 2 + 2 + 2 + 2 + 2 + 2 

# • + + • + 2 16 16 16 + + • • 2 6 6 6 + • + + 2 6 6 6 

# • 6 + 6 + + + + + + + 3 J 3 11 11 11 1I + + + + + + + 6 3 J 11 11 11 11 + + + + + + + 6 18 18 18 11 11 11 + + + + + + + 6 18 18 18 18 11 11 + 2 + 2 + 2 

## • 2 + 6 + 6 + 6 + 6 + t 6 + 6 + 6 + + + + + + + + + 6 6 18 18 18 18 18 11 + + + + + + + + 6 16 16 18 16 18 16 18 • + + + + + + + 6 16 18 16 18 16 18 16 + + + + + + + + +



# -----------------------------------------------------------------------------------------------------------------------

1 2 J 4 5 6 7 6 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 JO

Figure 6. Closest surface meteorological station (l-25) to each grid point of the MESOPAC II meteorological grid for Dayton cases during CAPTEX.

### TABLE7 

### HOURLY SURFACE OBSERVATIONS AND SPECIFICATIONS



### VARIABLE UNITS

Wind speed knots Wind direction degrees (0) 

### Temperature Fo Pressure (station) Inches Hg Relative humidity % 

### Cloud cover Tenths (xlO) Ceilingheight Ft, AGL# Weather code

(Precip. type)* * CardDeck 1444 specifications # 

### height above ground level

26 

### RESOLUTION

1 10° 1 0.01

1 1 

### 100

### However, no quality assurance tests wereperformedon the surface observations sincevarious qualitycontrolprocedures are routinely conductedduring the data archivalprocessby the National Climatic Data Center. It shouldalsobe statedthat these surface observations do not represent actual time-averagedmeasurements over 1-hourperiods. The hourly observations are actually taken over a relatively short intervalprior to eachhour. It isparticularlynotable thatwinddirection is recorded only to the nearest 10°. There is no randomnumber technique inMESOPAC II to generate a value for the units place and observeddirections are multipliedby 10 in the model. While wind speedis reported to the nearest 1 knot, observedwind speeds less than 3 knots are rare due to instrument response limitations. These light wind conditions areusuallyreported as calm (i.e. zero wind speed). The landuse type must alsobeprovidedat each gridpoint in the meteorological domain. Alist of the landuse categories and their associated surface roughness lengths built into the preprocessor code are given inTable 8. Since landuse informationwas not part of the CAPTEX database, landuse types were obtained from an existing in-house landuse inventory (Page,l980). Briefly, this inventory contained the percentage contributions of 12 different landuse types for cells covering 1/6° latitudeby 1/4° longitude areas (i.e. approximately 18.5 km 

### square grids). The landuse cellwhose coordinates were closest to each gridpointwas determined and the landuse type exhibiting the largestpercentage was selectedto represent the landuse at each grid point. Atmost gridpoints, a single landuse type was dominantwith percentages greater than70%. There were a few cases when the agriculture (Category 1) and forest (Category4 or 5) 

### categories were the major land use types andexhibitednearly equivalentpercentages. Category 2 

### (see Table 8) 

### was selectedas a representative compromise in these situations because only one landuse category couldbe specifiedfor each grid point. It is acknowledged that this is a simplification to the realworldwhere the landscape usually exhibitsvarious landuses on subgrid scale of this model. Figure 7 displays the gridfor Daytonexperiments with the landuse categories. The principalwaterbodies (12) include the Great Lakes and Atlantic Ocean. Agricultural cropland (1) dominates Ohio while forested (5) 

### regions cover central Pennsylvania, andparts ofNew York state andOntario. There are a 

### few gridpoints defined as urban landuse (11). The spatial pattern inFigure 7was also found to closelycorrespondwith the coarser grid scale landuse map presentedby Sheih et al. (1979).

27

### TABLE8 

### LAND USE CATEGORIES AND SURFACE ROUGHNESS LENGTHS FORMESOPAC II


### CATEGORY
1* 2* 3 

### 4 5* 6 

### 7 8 

### 9 10 

### 11* 

### 12* NOTE: LAND USE DESCRIPTION ROUGHNESS LENGTH Z 0 (m)

crops and pasture crops, woodland, and grazing land irrigated crops grazed forest and woodland ungrazed forest and woodland subhumid grassland and semiarid grazing land openwoodland grazed desert shrubland swamp marshland urban water 0.20 0.30 0.05 0.90 

### 1.00 0.10 0.20 0.30 0.20 0.50 

### 1.00 0.0001

Contents of table taken from Sheih et al. (1979) 

# * land use types input at grid points in the model domain



### 28N I.D NESOPAC VERSIOII 1.1 LEVEL 660326

UtiO USE CATEGORIES FOR EACII GRID POitiT MULTIPLY ALL VALUES BY 10 MM 0 19 ' 12 lZ 12 ' • • + 18 ' 12 2 2 \ + + + 17\ 12 2 2 \ • • + 16\ 2 2 2 \ + + + 15\ 2 2 2 ' + • + 14 \ 2 2 2 ' + • + 13 ' 12 2 2 \ + + + 12 \ 12 1 1 ' + 11 ' 1 \ + 10 \ 1 \ + 9 ' 1

' + 8 \ 1

' + 7 \ 1

\

# . 6 ' 1

\ + 5 \ 1

\ + 4 ' 1 \ t3 \ 1

' t2 ' 1

' t- 1 \ 1



# ' + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1t1 + 1 + 1 + + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 2 + 2 + 2 + 2 + 2 

## • 2 

# • 2 + 1 

# • 1 t 1 + 1 

# • 1 + 1 + 1 + 1 + 1 1



# .. 1



# .. 1 t 2 + 2 + 2 + 2 + 2 + 2 + 2 + 1 + 1 + 1 t 1 t 1 t 1 

# • 1 + 1 + 1 + 1 + 1 t 1 + 2 + 5 + 5 + 2 + 2 + 2 + 2 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1 + 1



# .. 1



# .. 5 5 12 12 12 12 12 12 12 + + + .. +
.. • + + 5 12 12 12 12 12 2 12 12 + + + + + + + + + 5 12 12 12 12 1 1 1 1 + + + + + + + • + 2 12 12 12 12 1 1 1 1 + + + • + + + + + 12 1 1 12 12 1 1 1 1

+ + + • + + + + + 1 2 1 12 12 1 1 1 1 + + + + + + + + + 2 2 1 12 1 1 1 1 1 + + + + + + + + • 1 1 1 1 1 1 1 1 12 5 5 5 5 5 5 5 + • + + + + + 5 5 5 5 5 5 5 + + + + + + + 1 12 1 1 2 2 2 + + + + + + + 1 1 1 1 1 1 12 + + + + + + + 1 1 12 12 12 12 12 + + + .. .. .. + 12 12 12 12 12 12 12

.. • .. .. + .. • 2 2 1 1 1 2 2

.. + + + + .. .. 12 12 2 2 1 2 1 + + 1 11

.. + 1 11 + t

.. .. + + .. t + 2 + 2 + 5



### ... 5 

## • 2 t2 + 2



### ... t 2 t 

## z + 5 t 5 + 5 + 5 + 5 t 2 + 5



### ... 5 + 2 t 

# • 2 

# • 2 

### ... 1 + 2 12 1 1 12 12 12 12 12

+
.. .. +
.. .. + .. 1 12 12 12 12 12 2 2

.. + t- +
.. .. 1 12 12 12 12 12 + + + .. + + 1 1 12 12 11 2

.. t + .. .. + 1 1 1 1 1 2

.. .. + .. + .. 1 1 1 1 1 2 + .. .. + .. .. 1 1 1 2 1 2

.. t

.. .. + 1 1 1 2 1 2 t t
.. .. + .. 1 1 1 2 1 1

.. .. + .. .. + 1 1 5 5 2 2 + t + t t
.. 2 5 5 5 2 2



# .. .. 

# .. • t 

# .. • 2



# .. 2



# .. 2 

# • 2 

## • 2 

# • 2 

## • 2 t 5 t 5 t + • 2 1 

# • 

# • 2 2 + + 2 2 + + 2 2 + + 2 11

.. + 2 1 + + 2 1 t 5 1



# .. .. 5 1



# .. + 5 + 5 + 2 + 2 + 2 + 2 + 2 + 5 + 5 t- + 2



### ... 2 + 2 

## • 5 + 5



### ... 5t5 + 5 + 2 + 5 

# • 5 

# • 2 + 5 

## • 5 

## • 2 

# • + 2 + 5



# .. 5



### ... 5 + 5



### ... 5



### ... 5 

## • 2 + 5



### ... 1 

# • + 2 

# • 5



### ... 5 

# • 5



### ... 2



### ... 5 + 2 + 1



# .. 1



# .. 1



### ... 

# • 2 

# • 5



### ... 5 

# • 2 + 2 

# • 1 

# • 1 + 1 + 1 

## • 5 + 5 

# • 2



### ... 1 + 12

+ 2 + 2 + 2 + 1 + 2 + 1 + 5 + 2 + 2 + 2 + 1 + 1 + 2



# .. 5



# .. 1 + 1



# .. 2



# .. 1 ·+ 2 + 5



# .. 2



# .. 2



# .. 2 + 2



# .. 1



# .. 5 + 2



# .. 2 + 2 + 1 

# • 2 + 2



### ... 1 

# • 1



# .. 1



# .. 2



# .. 5



# .. 5 + 5 t 2



# .. 2



# .. 2 + 2 1 t 2 t 5 t 5 t 5



# .. 5 t 1 + 2 t 2 + 

### z



# .. .. 2 5

.. + 2 2

.. 

# .. 5 5

.. 

# .. 5 1

.. t 1 1

.. t 2 11 t t 2 11 + t 2 2

.. t 1 12 t t 1 t 2 t 5 t 5



# .. 5



# .. 5 t 5 t 

### z t 2 t 5 5 5 t t 5 12

.. .. 5 

### z t t 5 5 + t 5 5 t t 5 5

.. t 5 5 t t 2 2 + 2 t 5 t 2 + 2 1 t 2 t 5 t 5 t 5 t 5 t 2



# .. 5 t 5



# .. 5 t t t
.. 5 2 2 5 t
.. t t 5 2 2 2 t
.. .. .. 2 2 5 2 t t t
.. 2111111 t + t t 1 11 12 lZ t •
.. • 2 2 12 12 t
.. t t 5 5 12 12

.. t t + 5 12 12 12 t t

... + 5 12 12 12 + t 

# .. ...



# ----------------------------------------------------------------------------------------------------------------------- 1 2 4 5 6 7 8 9 10 11 12 13 14 15 16 17 16 19 20 21 22 23 24 25 26 27 28 29 30 Figure 7. Land use categories in the MESOPAC II meteorological grid for the Dayton cases during CAPTEX.

### SECTION4
MODELEVALUATIONPROCEDURESANDRESULTS The MESOPUFF 

### II modeling systemwas applied to the six full-scale CAPTEX experimental cases. Experiment #6 was omitted in this model evaluation effort because it was limited to a half-hour release with few nonzero measured concentrations over the sampling network. All model runs and data analyses were performed on a Sperry Univac 1182 computer system. The important inputvariables and standard features specified in the MESOPAC 

### II processor runs are given in Table 9. All MESOPAC 

### II runs began at hour 00 EST (midnight, Eastern Standard Time) of a release day and the number of simulationhours was sufficient to cover the entire sampling period of each experiment. All technical default features in the operational model runs were applied according to the user guide (Scire et al., 1984b). The data files of hourly surface observations from 25 selected stations, the maximum number permitted, were input in each case and data files of upper air measurements for 6 and 7 rawinsonde sites were used for Dayton and Sudbury cases, respectively. The gridded meteorological fields generatedby the processor were stored on a single disk file in each case and an output file was also produced with results printed at two hour intervals for use in analysis and interpretation of model results. The MESOPAC 

### II program required about 400,000 bytes of computer core during execution and computational times varied from 400 to 600 seconds for these episodes spanning two to three simulation days. The input values and specifications for the MESOPUFF 

### II model runs are presented in Table 10. Due to the conservative nature of the tracer, the wet/dry deposition and transformation processes were not simulated. The computational grid in the model was defined to be identical to the meteorological grid. The emission rate (g s-1) was slightly different in each case as individual rates were computed from the actual amount of tracer emitted over the release period (see Table 3). The tracer gas existed under ambient temperature and exhibited neutral bouyancy. Thus, no plume rise occurred. The height of the release nozzle was specified to be l mabove the surface in all model runs. The selection of rates for puff release and puff sampling is an important element for any puff model because sufficient puff overlap must occur under all wind speeds encountered in an application for the model to properly simulate plumes. The results of several model test runs conducted with various combinations of rates are listed in Appendix A. Results showed that individual peak concentrations at 300

### krn arc sites differed by 3% or less from the average peak value computed over all test runs. Since these results indicated that modeled concentrations were not sensitive to combinations of rates between 2 and 12 puffs h 1 at the nearest arc, the puff release rate and puff sampling rate were both set to 4 h 1 in all model runs. However, an optional feature was selected for these model runs which allowed the puff sampling rate to increase, if necessary, in order to insure sufficient puff overlap. The start time for
30

### TABLE9 

### INPUT CONTROL PARAMETERS AND DEFAULT FEATURES OF MESOPAC IIRUNS FOR CAPTEX EPISODES 

### FEATURE
METEOROLOGICAL GRID GRID POINT SPACING TIME STEP SIMULATION PERIOD

REFERENCE TIME ZONE METEOROLOGICAL STATIONS UPPERAIR STATIONS WIND FIELDS ,(default)
OUTPUT FIELDS
COMPUTER REQUIREMENTS

31 

### SPECIFICATION
30 x 19 (Dayton), 30 x 24 (Sudbury) 37 km 1

### hour VARIABLE startat 00 EST onthe day ofrelease and go to end of sampling period (e.g. range from 56 to 87 h) EasternStandardTime (EST) 25 hourly surface sites 6 - Dayton , 7 - Sudburycases Mixed-layer average (Sfc. to Zi), upper layer average (Zi to 700 mb) Storage on diskfile, printer output at 2-hour intervals 400,000bytes; 10 minutes CPU time ona SperryUnivac 1182 computer

### TABLE 10 

### INPUT PARAMETERS AND SPECIFICATIONS FORMESOPUFF IIRUNS
TRACER EMISSIONS
COMPUTATIONAL GRID TIME STEP PUFF RELEASE RATE PUFF SAMPLING RATE WET/DRY DEPOSITION/CHEMICAL TRANSFORMATION PUFF DISPERSION**Sfu~PLING** 

### GRID CALCULATIONS
COMPUTATION REQUIREMENTS

32 

### 3-hour release at constant rate Source Height 1 m; 

### neutralbuoyancy IDENTICAL TO METEOROLOGICAL GRID 1

### hour 4 

### perhour 4 perhour PROCESSES NOT SIMULATED
aty and a2 

### according to PGT curves

b 

### 100kffi;tirne dependent expressions ofHeffter (1965) at greater distances

deactivated; calculations at nongridded site coordinates only 400,000 bytes of core

### model execution corresponded with the beginning hour for each release. The model code was modified slightly to stop newpuffs from being emitted after 3 hours. Thereafter, only existingpuffs were tracked and sampled during the remainder of the simulationperiod. Hourly tracer concentrations (g/m3) were computed at the coordinates of all operational tracer measurement sites in the model domain, instead of at sampling gridpoints. This feature reduced the execution time of individual model runs to less than two minutes for this application.
In order to compare the modeled results with the surface tracer measurements, the MESOFILE II postprocessor programwas applied in order to compute 3-h or 6-h average concentrations from the model output file over the same time periods as the measurements and modeled concentrations were also converted into the same units as the measurements (fl 1 1). 

### OPERATIONALMODELEVALUATIONANALYSESANDRESULTS Sets of statistical measures of differences (residuals) and correlation between observed and modeled concentration pairs are presented from time and/or space pairings in order to provide quantitative evidence of model performance. Concentrationpairs withboth observed and modeled values equal to zero were excluded in the calculation of the statistical measures because neither the observed nor the modeled plumes had an impact in these cases; 1088 out of 1895 pairs were in this category. In addition, analyses of observedplume concentration patterns at individual time periods revealed a large number of concentrations of 1-2 fl 1-1; some at sites separated from the main plume distributionby adjacent sites which exhibited zero concentrations. Specifically, measurements at these isolated locations were believed to be anomolous since there was considerable uncertainty as to whether a measured concentration so close to the natural background really indicated an actual impact by the tracer plume. Potential error in the analytical procedures used to determine concentrations might be attributed for some of these values above background (Ferber et al., 1986). However, instead of eliminating all such extremely lowvalues, a screening procedure redefined 

### 73 values to be zero where an observed concentration

### of~ 2 fl 1-l occurred at any outlying site separated from the tracer plume pattern. Other observed

### conce~trations**~** 2 fl 1-l at sites next to locations with nonzero tracer concentrations were not changed in the data set since itwas deemed reasonable to assume that the tracer plume had indeed impacted such locations. Therefore, the final evaluation data set contained all pairedvalues that formed the union ofboth the perceived actual and predictedplumes. As a consequence of applying these screening criteria, the full data set was composed of 

### 734 concentrationpairs from the six modeled experiments. Statistical Results For Mean and Peak Concentrations Statistical results were computed from observed and modeled concentration pairs matched in time and location for each experiment and over the entire data set. These criteriaprovide a stringent test of model performance and a critical test ofmodel skill as each predicted concentrationwas compared to the measuredvalue at the same site and for the same time period. 33The results in Table 11 reveal that MESOPUFF II overpredicted mean concentrations in each case. Nevertheless, the predictedmean concentration (P) was within a factor of two of the observed mean value (0) in experiments #2,#4, and#7 and for the entire data set. The overall mean residual (d) was -33.8 fl 1-1, while individual values ranged from -58.3 fl 1-l for CAPTEX #l to a low of 12.8 fl 1-l in CAPTEX #2. The most significant model overprediction occurred in CAPTEX #3 as P was five times greater than 0; however, the observed mean concentration in CAPTEX #3 was considerably lower than the mean values of the other experiments. Results of additional analyses will be presented later in an effort to explain this large discrepancy. Since no absolute standards have been established for defining acceptable model performance, statistical results like those in Table 11 are primarily useful in an intercomparison of different models. However, comparable statistics for other models have yet to appear. Nevertheless, results of several statistical measures inTable 11 did indicate that considerable scatter existed among the paired concentrations. The standard deviations of the residuals (SDd) are much larger than the observed mean concentrations. Furthermore, there is greater variability in the modeled results as exemplifiedby the standard deviations of the modeled concentrations (SDP), which are greater than corresponding standard deviations for observed values (SD0). The results for the average absolute residual (AAR) and root mean square error (RSME), when compared to observed concentrations, also indicate that notable scatter exists between modeled and observed concentrations. In addition, the Pearson correlation coefficient (R) is also generally near zero which reveals no linear relationship among the concentration pairs for these individual data sets and over the entire data set. Similar results for the statistical measures were also obtained by Policastro et al. (1986) when the predicted results of eight regional models, includingMESOPUFF II, were evaluated in time and space against measurements from two different tracer field studies. Large scatter and low correlations were generally found between model and observed results, and most models also displayed a tendency to overpredict mean concentrations. Consequently, scatter plots of observed versus modeled concentrations showedmany pairs where either the observed or the predicted value was zero (Policastro et al., 1986; Draxler, 1987; Lee, 1987). Thus, it was evident that the modeled plume was either displaced away from particular sites where the actual tracer was measured or modeled concentrations impacted the particular sites at different times that the observed plume. Some statistical measures were strongly sensitive to these circumstances and the ensuing results reflected situations when plume displacements occurred. In particular, significant scatter (e.g. large values for d, AAR, and RMSE) and low correlations were obtained when observed and modeled plumes exhibit little or no overlap, even though modeled mean concentrations may have compared favorably with observed mean values (Policastro, et al., 1986). However, the statistical measures could only provide partial information concerning regional model performance and more innovative analyses, to be shown, are certainly required to provide better quantitative information to assist in assessing and understanding the causes of model/measured differences. 

### 34

### TABLEll 

### MESOPUFF IIMODELEVALUATIONRESULTS: OBSERVED AND MODELED CONCENTRATIONS PAIRED INTIMEAND LOCATION STATISTIC$ CAPTEXEXPERIMENT #l #2 

### #3


### N 143 247 124 0 

### 22.6 45.7 10.0 SDO 

### ±138.1 ±172.0 ±31.4 

## p 80.9 58.5 49.9 SDP 

### ±252.8 ±231.1 ±163.2 d 

### -58.3 12.8 39.9 SDd 

### ±165.5 ±295.5 ±168.2 AAR 

### 71.8 95.7 57.1 

### RMSE 174.9 295.2 172.2 R 

### 0.80 -0.05 -0.07 #4 

### #5 

### #7


### 96 68 56 54.9 41.7 79.7 ±172.2 ±112.7 ±135.4 90.5 86.0 113.5 ±226.5 ±166.8 ±214.8

-35.7 -44.4**~33.8** 

### ±294.6 ±178.4 ±283.0

123.6 103.2 185.5 295.2 182.6 282.5 -0.08 0.23 -0.27 

### $ - units are fl/1, except for N and R N - number of concentrationpairs 

### ALL
734 38.6 ±143.3 72.4 ±218.6**~33.8** 

### ±244.6 95.7 246.7

0.14

0 = 

### mean observed concentration , P = 

### mean modeled concentration SD = 

### standard deviation about the mean d = 

### average residual = 

### 0 P AAR = 

### average absolute residual 

### RMSE = 

### root mean square error R = 

### Pearson correlation coefficient



### 35Objective tests between the concentration pairs were performed in order to provide additional quantitative comparative results which could provide a better overall picture of the model's capabilities to simulate the spatial plume pattern over the time frame of these experiments. In particular, comparative test results that demonstrate the relative amount of correspondence between modeled and observedplumes couldprovide relevant information about the abilities of the transport and dispersion components of the model to simulate the movement and growth experiencedby the tracer plume. Table 12 contains results in the form of percentages for certain comparative tests on the concentration pairs. One of the interesting tests is the percentage of pairs where both observed and modeled values are nonzero (Tl). High percentages for Tl demonstrates that a substantial overlap must have existedbetween the modeled and observed plumes for the sampling periods of an experiment, which also suggests the overall speed and path, and growth of the plume was accurately simulated. Conversely, low percentages would reveal that the area of intersectionbetween the respective plumes was rather small which implies inadequate treatment of plume transport/dispersionby the model. The percentages for Tl in Table 12 vary among these cases with the highest being 46.9% for CAPTEX #4. Tl was less than 20% for experiments #l, #3, #5, and #7 which indicated rather small areas of intersection for the observed and modeled plumes during these cases. Additional evidence of spatial displacements between the respective plumes is providedby test results showing the percentages when either the observed (T2) or the predicted (T3) concentrationwas zero while its counterpart was nonzero. Low percentages for T2 and T3 would suggest a strong spa_tial correspondence of the respe-ctive tracer plumes. However, these results show a notable number of pairs where either the observed or predicted concentration was zero. In fact, results indicate that either T2 or T3 was greater than 50% in some experiments with most values for T2 and T3 above 30%. The best combination of T2 and T3 is found for CAPTEX #4. which also displayed the highest value for Tl. Finally, the results for T4, the percentage of cases where observed concentrations were greater than modeled values, vary with values ranging from about 35% to 54%. Results from the T4 test are sometimes used as indicators of the performance of dispersion in a model. For example, when T4 is significantly greater than 50%, it might be suggested that the modeled plume exhibited less horizontal spread than the observedplume. Nevertheless, T4 results are difficult to interpret in this manner and are also complicatedby the potential effects from both transport and dispersion. Even though the model certainly overpredicted the mean concentrations, as shown by the earlier statistical results, spatial displacements due to differences in the speed and/or the directional components of transport between the model and observed plumes are believed to be the principal factors responsible for the variations inT4 results among these cases. Mean concentrations and selected statistical measures for each experiment are presented in Table 13 to illustrate model behavior on an event by event basis. An examination of Table 13 indicates that observed mean concentrations generally exhibited an increase with time and then gradually decreased toward the last sampling period. This temporal trend reflects the arrival of the tracer plume accompanied by generally high concentrations 36

### TABLE12 

### TEST RESULTS OFMODELED AND OBSERVED CONCENTRATIONS PAIRED IN TIME AND LOCATION CAPTEX EXPERIMENT TEST #1 #2 #3 #4 #S #7 ALL
N 143 247 124 96 68 56 734 T1 

### (%) 16.8 33.6 16.1 46.9 19.1 16.1 26.4 (P&O >0) T2 (%) 52.4 32.0 33.9 27.1 48.5 37.5 37.6 (0=0, P>O) T3 (%) 30.8 34.4 50.0 26.0 32.4 46.4 36.0 (P=O, 0>0) T4 (%) 35.7 53.0 54.0 44.8 35.3 48.2 46.7 (O>P)

37

### TABLE 13 

### SELECTED STATISTICAL EVALUATION RESULTS FOR EACH EVENT OF THE CAPTEX EXPERIMENTS
EX.PT MM/DD-HR N 0 p d AAR 

### NNO NNP T1 T4 (LST) (fl/1) (fl/1) (fl/1) (fl/1) 

### (%) (%) 1 9/18-14 4 5.8 59.0 -53.2 53.2 2 0 25. 0. 1 9/18-17 5 552.2 1012.8 -460.5 615.5 0 1 75. 

# so. 1 9/18-20 15 23.7 155.9 -131.3 140.7 3 4 53. 40. 1 9/18-23 4 0.8 29.0 -28.2 29.8 2 2 0. 

## so. 1 9/19-02 21 16.6 61.8 -45.1 75.5 8 10 14. 52. 1 9/19-05 3 0. 118.7 -118.7 118.7 3 0 0. 0 1 9/19-08 26 4.5 49.2 -44.7 52.5 16 6 8. 31. 1 9/19-14 26 3.2 38.1 -34.8 41.3 17 9 0. 35. 1 9/19-20 26 2.0 36.6 -34.6 38.1 17 7 8. 31. 1 9/20-02 14 2.4 13.0 10.6 15.0 7 5 7. 36. 2 9/25-13 4 0.2 51.0 -50.8 51.2 3 1 0. 25. 2 9/25-16 5 0.2 827.6 -827.4 827.8 4 1 0. 20. 2 9/25-19 6 45.7 419.0 -373.3 464.0 3 2 17. 33. 2 9/25-22 14 130.4 155.8 -25.4 285.9 5 8 7. 57. 2 9/26-01 8 311.6 118.8 192.9 421.4 4 2 25 38. 2 9/26-04 24 125.6 33.3 92.3 150.1 6 13 21. 

### 71. 2 9/26-10 33 32.6 32.7 -0.1 50.0 9 13 30. 70. 2 9/26-16 36 21.0 33.6 12.6 42.3 10 10 42. 47. 2 9/26-22 37 17.8 17.2 0.6 23.1 9 11 43. 54. 2 9/27-04 32 17.7 8.6 9.1 21.9 8 13 31. 59. 2 9/27-10 25 11.7 8.7 3.0 16.7 6 8 36. 52. 2 9/27-16 10 5.1 9.7 -4.6 13.8 6 1 30. 40. 3 10/02-16 1 1.0 0. 1.0 1.0 0 1 0. 100. 3 10/02-19 4 18.0 1.5 16.5 17.5 2 2 0. 50. 3 10/02-22 6 50.8 257.5 -206.7 308.0 4 1 0. 17. 3 10/03-01 16 3.6 118.8 -115.1 122.4 7 9 0. 56. 3 10/03-04 4 5.0 5.5 -0.5 10.5 1 3 

### 0. 75. 3 10/03-07 29 6.9 39.5 32.6 45.2 11 14 10. 48. 3 10/03-13 27 6.7 28.9 -22.3 32.8 8 10 30. 48. 3 10/03-19 15 12.3 49.1 -36.9 49.1 5 5 27. 40. 3 10/04-01 12 12.6 4.7 7.9 12.6 4 7 8. 67. 3 10/04-07 5 12.0 0. 12.0 12.0 0 5 0. 100. 3 

### 10/04-13 5 1.8 0. 1.8 1.8 0 5 0. 100.

38

### TABLE 13 

### (CONTINUED) EXPT DATE/TIME N 0 

### p 

### d AAR 

### NNO NNP 

### T1 T4

4 10/14-13 1 0. 67.0 -67.0 67.0 1 0 0. 0. 4 10/14-16 7 133.9 167.6 -33.7 301.4 5 2 0. 29. 4 10/14-19 5 276.6 521.0 -244.4 784.4 3 1 20. 20. 4 10/14-22 16 47.8 230.0 -72.2 

### 138.7 3 7 38. 62. 4 10/15-01 9 30.1 77.0 -46.9 102.0 3 3 33. 33. 4 10/15-04 24 58.8 51.8 7.1 70.1 4 5 58. 

## so. 4 10/15-07 6 27.0 62.3 -35.3 69.0 3 0 33. 17. 4 10/15-10 16 9.6 33.2 -23.6 26.3 5 0 69. 19. 4 10/15-16 8 13.2 11.2 2.0 14.0 2 3 38. 62. 4 10/15-22 9 8.8 7.3 1.5 7.2 1 4 44. 67. 4 10/16-04 10 0. 0. 10 10 5 10/26-03 4 86.2 0. 86.2 86.2 0 4 0. 100. 5 10/26-09 9 181.1 176.8 4.3 181.1 2 4 33. 56. 5 10/26-15 14 57.5 118.5 -61.0 175.1 6 5 14. 43. 5 10/26-21 12 2.8 130.5 127.7 128.7 6 2 33. 17. 5 10/27-03 13 0.6 74.5 -73.8 74.2 10 1 8. 8. 5 10/27-09 10 0.6 2.0 1.4 2.4 7 2 0. 20. 5 

### 10/27-15 3 1.3 0. 1.3 1.3 0 3 0. 100. 5 10/27-21 1 1. 0. 1. 1. 0 1 0. 100. 7 10/28-23 0 0. 0. 7 10/29-02 3 1. 0. 1. 1. 

### 0 3 0. 100. 7 10/29-05 3 35.7 190.7 -155.0 155.0 2 0 33. 0. 7 10/29-08 14 25.6 220.8 -195.2 234.6 8 2 21. 14. 7 10/29-11 4 179.5 8.0 171.5 187.5 1 3 0. 75. 7 10/29-14 16 75.9 107.3 -31.4 179.7 6 6 25. 44. 7 10/29-20 11 196.0 63.2 132.8 259.2 2 9 0. 82. 7 10/30-02 3 32.7 83.3 -50.7 116.0 2 1 0. 33.
NN0 , 

### NNR

- number ofobserved ' 

### predictedzero concentrations. T1 P nd0both

### >0 T4

- 0 

### > p



### 39at the closest arc or sites a few hours after release, and this peak was followed by an general decrease in the mean concentration due to the plume being dispersed as it traveled across the sampling region. Modeled concentrations also exhibited a similar temporal pattern, although the time variation of mean model values was often out ofphase with the time series of observed mean values. The residuals were usually negative as the model appears to overpredict inmost events. The largest biases were usually found during an early time periodwhen concentrations also happen to be highest. Differences in the time and location of impact of the peak concentration are attributed for these significant departures between the observed and modeled mean concentrations. The results for the number of observed (N0) and predicted (N ) concentrations being zero coupled with the low Tl results

## indica~e important spatial plume displacements. In fact, during some events there was no intersection of the respective plumes (i.e. Tl = 0). Low values of Tl were more common during the early events and also near the end of an experiment. Differences in the time of initial impact of the respective plumes at the first arc (or closest sites for the Subdury cases) can produce low Tl values soon after release. At the later stages of an experiment a more rapid departure of the observed plume from the sampling region or large spatial displacements between the plume patterns existing on the sampling domain was also believed to lead to small fractions in Tl. Averaged concentrations for each event were also computed separately from observed and modeled concentrations (excluding any zero values) to assess differences in magnitude between the plumes with time. The results are presented as a function of time after release in Figure Sa-f. For CAPTEX #l-4 (Figures Sa-d), the highest observed and modeled average plume concentration occurredwithin 12 h after release as a result of plume impact at the 300 km arc sites. The modeled values (X) replicated the time history and magnitudes of observed (O) concentrations best in#2 and #4. For a few events there were very few or no observed nonzero concentrations, which was responsible for the values near or on the abscissa. These results also show the overall decrease in average plume concentrations with time. Concentration standard deviations were comparable to mean values for most events. The results for CAPTEX #5 and #7 (Fig. Se-f) did not exhibit concentrations at early periods as high as the Dayton cases; probably because the plumes did not intersect sites or an arc as close to release point as in the Dayton cases. Despite the log scale for concentration, it appeared that average modeled concentrations from these calculations were more oftenhigher than average observed values, which suggests that peak concentrations were overpredicted, although more definitive information about model performance at peak concentration levels will be presented later. Cumulative frequency distributions were determined to establish the relative agreement between the observations and predicted values across the range of concentrations. Although frequency distributions do not demonstrate information about model skill, since values are not paired in time or location, the results can provide evidence about how well the modeled concentrations replicate the magnitude and overall distribution of observed values. The results from the entire data set in Figure 9 concur with the earlier tests that large numbers of either observed or modeled concentrations in the evaluation data set were zero. The cumulative frequency curve for observed concentrations lies above that for the modeled values. The 

### 401QA

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### TIME AFTER RELEASE (h) Figure 8. Same as 8a-b, except for c) CAPTEX #3 and d) CAPTE\#4

42

## e CAPTEX 5 10 4 

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43.c:- .c:-**~**

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# MESOPUFF II (clashed) OBSERVED 

# (solid)**OL-~--L_LJ~Ji~~----L_~I_JI_LILILILIIui**____

## L-~I_JI_LI~I~I~II~~-----L-~~~~~ 10° 10 10 2 

## 10 3 

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# CONCENTRATION (fl/1)
Figure 9. Cumulative frequency distributions of the model and observed concentrations from the entire data set.

### separationbetweenthe curves increasedfrom 1 to 10 fl l-1
, which indicated that a greaternumber of observedvalues exist at these low concentrations compared to modeledresults. The separationremains about the same between 10 and 100 fl 1-1

. However, the gapbetween the curves closedrapidly above 100 fl 1-l as a relatively greaternumber ofmodeled values occur athigher concentrations. Individual cumulative concentration distributions are shown in Figures lOa-f to illustrate the variability in the distributions among these experiments. The best overall agreementbetween the cumulative distribution curves of observed andmodeled concentrations appeared inexperiments#2,4, and#7. CAPTEX #2 and#4 also happened to exhibit thebest results for plume overlap (i.e. highest Tlvalues inTable 13) among these experiments. The frequency distributionresults of observed andmodeledvalues inTable 14 quantitatively reinforce the previous cumulative distribution findings. These results that reveal a relatively largenumber ofobserved concentrations still existed at 1 fl l-1
, however, there was no basis to eliminate ormodify these values since all were found at sites on the edge of the observedplume. These results alsoverify that largernumbers of observedvalues occurred at low concentrations. Acrossoverpoint appeared at about 25 fl 1-1
, as the frequencies ofmodeledvalues were greater at higher concentrations. In fact, these results provided strong evidence that the model overpredicted thehighest concentrations because twice as many modeledvalues are found in the intervals above 100 fl 1-l observed values. The ability of a model to predict the highest concentration should certaiplybe an important aspect of an evaluationeffort, since models are often applied in order to assess peak concentrations in regulatory situations. Inthe evaluation for peak concentrations, results were determined from observed andmodeledvalues paired in time only or location only. In·fact, there were very few cases where the modeledpeak concentration occurred at the same site and time as the observedpeak concentration, which is notunexpected inview of the spatialplume displacements reported earlier. Table 15 contains the results for peak observed andmodeled concentrations paired in location only. This evaluation framework allows peak observed andmodeled concentrations to occur at different time periods at a given site. These results show that the model overpredictedpeak concentrations for all experiments. Except for CAPTEX #3, the peak modeled concentrations were within a factor of two ofpeak observedvalues. The smallestnegative residuals are found inCAPTEX #2 and#4. In these experiments, the results for Tl inTable 12 indicated thatplume overlap was higher and the other test results were more favorable than in the other cases. The largest overpredictions ofpeak concentrations occurred in CAPTEX #l and#3. As reported earlier, the mean observed concentration for CAPTEX#3 was unusually low inrelation to the other cases. Results of analysis ofpeak concentrations at the 300 km 

### sites, where the highest observed concentrations occurred for the Dayton cases, revealed that the mainportion of the observedplume did indeedpass to the west of the 300 km 

### arc in this case. This explains the low observedpeak andmean concentrations for this experiment and the reason for the significant 45

### a CAPTEX

# #1**100r-~~ilniTIT--,-,-rrnTn=~==~~-~-~-~~~~-~ F--~-~-TT-.IIinl** **~~~~**

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## MESOPUFFII (dashed) OBSERVED (solid)

t I I II I I 

### ,I I I I I I I I I 102 103 10• 

### CONCENTRATION (fl/1) Figure 10. Cumulative frequency distributions ofmodel and observed concentrations for a) CAPTEX#l andb) CAPTLX #2. 46,..-....**~**

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Figure 10. Same as lOa-b, except for c) CAPTEX#3 andd) CAPTLX#4
47_......._

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f 

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## #7 **100~~,~,~~~~~~---,~TTTn"~l~-f-rTTr;a~~~~~~,,,~"nT"**

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**~~ ~~~~~~~~~**  10° 10 1 10 2 10 3 10" 

### CONCENTRATION 

## (fl/1) Figure 10. Same as lOa-b, except for e) CAPTEX #5 and f) CAPTLX #7 48

### TABLE 14 

### DISTRIBUTION OF OBSERVED AND MODELED CONCENTRATIONS FOR ENTIRE CAPTEX DATA SET


### CONCENTRATION OR NUMBEROFVALUES INTERVAL (fl/1) 

### OBSERVED MODELED 0 1437 1425



# ------------------------------------------------------ 1 

### 68 36 2 53 50.4% 25 34.8% 3-5 57 49 5-10 53 

# so

------ ------ 11-15 29 30 16-25 51 36.9% 49 40.9% 26-50 47 67 50-100 42 46



### ------ 

# ------ 100-150 14 24 151-200 11 25 201-250 8 10.7% 16 20.2% 251-300 3 

### 12 301-400 7 12 401-500 6 6



### ------ 

# ------ 501-600 2 4 601-701 0 4 701-800 1 0.9i 2 2.8% 801-900 0 

### 3 900-1000 1 0



### ------ ------ 1001-2001 5 1.1% 8 2.1% >2001 0 

### 2



# ------ ------

49

### TABLE 15 

### PEAK OBSERVED AND MODELED CONCENTRATIONS PAIRED IN LOCATION ONLY

E..XPT N 0 p d 

### AAR 

### RMSE R T4 

### (O>P)

# 

### (fl/1) (fl/1) (fl/1) (fl/1) (fl/1) 

### (%)
1 42 66.1 176.4 -110.3 141.3 277.4 0.83 31.0 ±251.2 ±427.0 ±257.6 2 58 86.3 133.2 -46.9 180.6 482.6 -0.09 60.3 ±229.3 ±406.2 ±84.5 3 43 17.7 109.0 -91.4 115.4 256.2 -0.ll 

### 34.9 ±48.6 ±231.7 ±242.2 4 26 111.0 151.7 -40.7 208.7 451.8 -0.12 57.7 ±262.6 ±345.8 ±458.8 5 25 95.2 178.8 83.6 126.0 196.4 0.61 20.0 ±169.0 ±225.4 ±181.4 7 28 lll.

### 3 173.9 -62.6 236.5 344.2 -0.22 42.9 ±171.5 ±263.2 ±344.7
ALL 69 197.4 391.1 193.6 326.0 579.9 0.22 29.0 ±315.7 ±526.3 ±550.6

50

### overpredictions was because the modeledplume directly intercepted this arc. Other results inTable 15 also reveal large scatter and low correlations betweenpeakpairs matched in space. The absolute value of residuals and RMSE are large relative to observedpeakvalues and correlationcoefficients are generally small andnegative. Acomparative analysis ofpeakconcentrations was also performed for observed andmodeledvalues paired in time only. With this framework, thehighest observed concentrationis comparedwith the peakmodeledvalue from any site for the same time period. Table 16 contains the results for the events of each experiment. Travel time is also givenas the number ofhours from the start time ofrelease to thebeginninghour of each sampling interval. Clearly, inmost of the events the modeledpeak concentrations were greater thanobservedvalues, as exemplifiedby the large number ofnegative residuals. An encouraging result, however, is that the highestmodeled concentrationwithin an experiment occurred at the same time as the corresponding observedpeakvalue in#1,#4, and#5. This result suggests that there was apparently good agreement in the transport speed and directionbetween the modeled and actualplumes during the initial hours after release. On the otherhand, highestmodeledpeakconcentrations for experiments #2 and#7 occurredmuch earlier than the observedhighest values in these cases. Since these results are unpaired in space, it is also interesting to note that a survey of all 60 events revealed that in only 3 cases the observed andmodeledpeakconcentrations during a particular eventwere found at the same site. However, therewere 21 events where thepeakvalues occurredatdifferent sites along the same arc. Larger displacements obviously existed in another 25 events where the peak observed andmodeledvalues were located at sites on different arcs. Finally, the relative positions ofpeakconcentrationpairs in the remaining 11 cases were indeterminant since either the observedormodeledvaluewas zero. These results also reveal that the model generallyoverpredicted peakvalues andnotable time and/or spatial displacements also existed betweenobserved andmodeledpeakconcentrations. Statistical results inTable 17 were computedover thehigh-25 observed andmodeled concentrations fully unpaired in time and location for each experiment and for the overall data set. These results are also notunexpectedbased on the previous analyses of the peakconcentrations and frequency distributions. The model consistently overpredicted for this group of concentrations evenwhenvalues are completelyunpaired. The absolute values of thebias were less than the average peak observed concentrations for experiments #2, #4, and#7- Since time and location constaints are irrelevant in this comparison, the peakconcentration differences given inTable 17 were not likely attributed to transport speed anddirectionvariations of the observed andmodeledplumes. These results and those inprevious tables for peakconcentrations suggest that dispersion of the modeledplumes may notbe as great as that experienced by the actual tracerplumes. Results of analysis of additional model runs tobe discussed laterwill attempt to confirm this hypothesis and identify the underlying cause(s). The configurationof the samplingnetworkpresentedan opportunity to investigate the four Daytonexperimental results separately from the Sudbury cases. Consequently, a statistical analysis was performedon this 

### 51

### TABLE 16 

### PEAK Of1SEI<VElJ Atm

**~HlllELEO** 

### f:ONCENTI!ATIONS PAIRED IN TIME FOR EACH EVENT OF THE CAPTEX EXPERIMENTS EVENT £' 3 ll 

### 5 6 7 8 9 10 11 

### 12 CAPTEX #1 

# --------------------------------------------------------------------- AVE + 

### SO TlATE/TIME (00/IIR) 18/11\ ln/17 lfl/

### 2ll lH/23 19/02 19/05 19/08 19/14 19/20 20/02 TIME FIWM RELEASE (h) 

### 2 5 

### n 11 14 17 20 26 32 38 Peak Obs. (fl/l) 

### 22 1586 121 2 89 0 47 16 11\ 12 191 +

### 492 Peak

**l~odeled** (tl/l) 

### 153 2367 862 67 283 224 2£'4 242 197 60 468

# +668 RESIDUAL (Op

- Pp) -131 -7B1 -741 -65 -194 -224 -177 -226 -183 -1\8 -277

# +262 CAPTEX #2 TlAI17111TtlOO/IIR) 25/13 25/16 25/19 25/22 26/01 26/04 26/10 26/16 n;22 27

### ;o4 27/10 27/16 TIME FROM RELEASE (h) 1 

### 4 7 lll 

### 13 lfi 22 28 34 40 46 5£' Peak Obs. ( f 1/l) 

### 1 1 228 1348 1575 1399 200 96 73 96 43 44 425 +

### 618 Peak 1100ELED (fl/l) 

### 156 2767 1430 1()£'0 381 413 245 214 144 50 27 26 573

# +779 RESIDUAL (Op

- Pp) -155 -2766 -1202 328 1194 986 -45 -118 -71 46 16 18 -148

# +1017

- Vl 

### CAPTEX #3 N 

### DATE/TIME (00/IIR) 02/16 02/19 02/22 03/01 03/0403/07 03/13 03/19 04/01 04/07 04/13 TIME FROM RELEASE (hrs) 2 5 13 ll 

### 14 17 23 29 35 41 47 Peak Obs. (fl/l) l 7l 

### 304 21 1S 63 68 Hi2 79 28 3 74 + 

### 89 Peak 110DELED (fl/1) 

### 0 4 874 1416 22 261\ 

### 201 294 28 0 0 282

# +426 RESIDUAL (Op

- Pp) 67 -570-1395 -7 -201 -133 -132 51 2B 3 -208

# +433 CAPTEX #4 

### D7CIT7TTI1E (DD/1/R) 14/13

### l'f/Tii 14719 14;22 15/01 15/04 

### 15/07 15/lU 15/16 15/22 16/04 TIME FIWt1 HELEASE (h) 2 5 8 11 14 17 20 23 29 35 41 Peak Obs. (fl/l) 0 

### 935 1350 248 216 210 156 40 37 28 0 293 +

### 439 Peak /100ELED (fl/1) 

### 67 B30 1448 687 491 39£' 221 147 51 35 0 397

# +426 RESIDUAL (Op

- Pp) -67 105 -98 -439 -275 -182 -65 -107 -14 -7 0 -104

# +142 CAPTEX #5 DATE/TIHE (lJD/IIR) 26/03 26/09 26/15 26/21 27/03 27/09 27

### /l /21 TIME FROM I<ELEASE (h) ll 

### 10 11i 22 28 34 40 46 Peak Ohs. ( fl/1) 

### 167 41J0 384 33 125 10 2 1 

### 148 + 

### 181 Peak 110DELED (fl/l) 

### 0 794 4713 382 272 5 0 

### 0 241

# +277

**I~ESIDUAL** 

### (Om

- Pm) 11i7 -331J -91J -31J9 -11J7 5 2 1



### - 93

# +178 CAPTEX 

### #7 DATE/TIME (DO/IIR} 28/23 29/02 29/05 29/08 29/11 29/lll 29/20 30/02 TIME FROM RELEASE (h) -2 l I] 

### 7 10 13 19 25 Peak Ohs. (fl

### I1 ) l l 

### 107 309 277

**~46** 

### 553 91J 

### 236 +

### 224 Peak HODELED (fl/l) 0 () 

### £'99 1041 32 578 li52 £'30 354

# +351 RESIDUAL (Op

- Pp) 1 1 

### -192 -732 245 -32

- 99 -132 -118

# +280

### TABLE17 

### HIGH-25 OBSERVED AND MODELED CONCENTRATIONS UNPAIRED IN TIME AND LOCATION FOREACH CAPTEXEXPERIMENT

EXPT 

### 0 p 

### P25/025 d 

### (£fJ1) (flJf) (fl/1)
1 121 ±311 379 ± 502 3.1 -259 ± 237 2 341 ±437 470 ± 578 1.4 129 ± 240 3 43 ± 59 230 ± 300 5.3 187 ± 246 4 196 ± 293 319 ± 352 1.6 -123 ± 146 5 113 ± 161 230 ± 204 2.0 -117 ± 86 7 178 ± 152 253 ± 258 1.4 75 ± 118
ALL 637 ±436 1010 ± 559 1.6 -373 ± 222

± one standard deviation



### 53subset of concentration pairs to compare observed and modeled concentrations for each arc from Dayton cases. The results for mean concentrations in Table 18 were derived from pairedvalues at all sites along each arc from CAPTEX #l-4. These results confirm that the largest overpredictions occurred at the closest arc with the largest negative residuals at the 300 and 400

### km arcs. The mean observed concentration at the 300

### km arc was significantly greater thanvalues at other arcs by as much as a factor of 10. Another notable result is that observed mean values appear to display no strong trend with downwind distance between the 400

### km and 1000

### km arc over these cases. The modeled mean concentrations, on the otherhand, definitely exhibit a gradual decrease with distance. The most significant differences between the respective mean values existed from the 400 

### km to 600

### km arcs. The results for the peak concentrations in Table 19 are similar to those for mean values over these arcs. The peak observed and modeled values were computed from the highest concentration at each of the sites along the arcs for these cases. These results show that model overpredictions of peak concentrations were also certainly largest at the 300

### km arc. While the peak observed concentration at the 300 

### kill arc is considerably larger than peakvalues at any of the other arcs, there is also an indication of a secondary peak in the results for the 700

### km arc. This feature was believed to be attributable to fumigation of elevated tracer material to the ground during the radid growth of the mixing height to higher levels over the morning period of the day after release. Comparative Analyses of Plume Patterns and Positions The graphical results in Figure lla-f are shown to give qualitative displays of the overall observed and simulated plume patterns for each of the modeled CAPTEX experiments. The overall spatial patterns of the observed and modeled plumes are depicted by symbol(s) at the sites that experienced a nonzero observed (0) and/or modeled 

### (*) concentration(s) during any sampling event of an experiment. The sites where both nonzero observed and predicted concentrations occurred represent an overlap region of the actual and modeled tracer plumes over the entire course of a particular experiment. During CAPTEX #l-4 (Figures lla-d) with releases from Dayton, both modeled and observedplumes crossed the 300 km arc in northern Ohio, although some spatial displacement in the positions of plume impact are evident even at this arc. The modeled plume appeared to overlap the observed tracer plume very well at the 300

### km arc during CAPTEX #l (Figure lla), however, the observedplume traveled north of the modeled plume as itpassed across Lake Ontario and upper New York state before exiting the sampling region. The modeled plume during CAPTEX #2 (Figure llb) intercepted the 300 

### km arc to the left (i.e. counterclockwise) of the observed plume, however, there appears to be better overall spatial agreement in the plume patterns farther downwind in this case. In contrast, the modeled plumes were shifted to the right (clockwise) of the observed plumes at the 300

### km arc during CAPTEX #3 and #4 in Figures llc and lld, respectively. The spatial discrepancies between the respective plumes are particularly noticeable from the 300

### km to 500 

### km arcs during CAPTEX #3. In fact, the observed plume nearly missed the 300 

### km arc 

### 54

### TABLE18 

### STATISTICALRESULTS OF OBSERVED AND MODELED CONCENTRATIONS PAIRED IN TIME AND LOCATION FROM SAMPLING ARCS FOR CAPTEX#l-4


### ARC N NN 

## 0 p d AAR 

### T4 (O>P) (SITES) (PAIRS) (fl/1) (fl/1) (fl/1) (fl/1) 

### (%)
300 16 91 

### 140.8 230.3 -89.5 321.8 41.8 400 9 71 12.0 110.7 -98.7 109.5 23.9 500 12 75 18.7 55.4 -36.7 53.7 30.7 600 10 78 15.2 34.3 -19.2 41.5 41.0 700 10 93 18.8 27.0 -8.2 39.3 55.9 800 13 112 16.8 14.1 2.7 27.0 67.5 900 8 63 12.2 12.5 -0.3 22.1 60.3 1000 3 27 13.7 13.4 0.3 17.6 63.0

TABLE19 

### PEAK OBSERVED AND MODELED CONCENTRATIONS UNPAIRED IN TIME AND LOCATION FROM CAPTEX#l-4 

### ARC N NN 0 p d AAR 

### T4 cd;1) (d/1) (fl/1) (fl/1) 

### (%)
300 12 12 439.0 1041.7 -602.7 983.5 16.7 ±658.2 ±924.8 ±1136.8 400 8 8 61.1 386.1 -307.0 307.0 0.0 ±69.8 ±328.6 ±354.8 500 9 9 71.7 170.4 -98.8 107.7 22.2 ±59.1 ±95.7 ±94.2 600 9 9 75.2 113.3 38.1 64.6 22.2 ±63.6 ±78.9 ±65.6 700 9 9 84.8 135.2 -50.4 123.6 33.3 ±74.2 ±82.6 ±132.6 800 12 12 71.0 59.3 11.7 53.5 58.3 ±41.4 ±53.5 ±75.2 900 6 6 62.5 70.7 -8.2 80.8 50.0 ±34.3 ±111.5 ±125.5 1000 2 2 55.5 123.0 -67.5 67.5 0.0 ±37.5 ±73.5 ±36.1 55"<> 0 0


### ...



## ~---~a~_:C.:_A.:_PT.:_E:_X~#-1_R_Es_u_L_T_s _____ •/(.1.oa'

R



### b 

### CAPTEX #2 RESULTS

R

JfJ4.Cq'

**2~---..L**

## ___ .....i.:_

___ **~~L3"-.....LL--------;**

**~**

**~** 

# .

**~** 

### Figure 11. Observed (0) andmodeled 

### (*) plume patterns depicted from sites withnonzero concentrations during a) CAPTEX #l andb) CAPTEX#2 

### 56

## c CAPTEX #3 RESULTS



## • 0 0 R



# ..

0 Q 0 

### d CAPTEX #4 RESULTS "

**~----** ____ ____::._ __________ •/147.oc'

R



### ------~------L_------~~B_d_

### __ **----------~~H~W** <> 

# .. "' Figure 11. Same as 11a-b, except for c) CAPTEX#3 andd) CAPTEX#4 57

### e CAPTEX #5 RESULTS
R

**~--~----L---~~_.J1__j_**

## _______ 1&/{

### ,8.w



# ..

'b 0 .,; 

## -y-----f _c_A_PT_E_X__:_#_7_R_E_s_u_L_Ts
_____

## ~/{

### .r.w R

0 0 0

0 .,; 

# .. 

# ..

**------~------L_------~~~d_------------~&/{~-(1(1'** 0 

# .. 

# .. Figure 11. Same as lla-b, except for e) CAPTEX#5 and f) CAPTEX #7 58

### during#3, as thebulk of it apparently traveled over extreme northwestern Ohio. Clearly, thebest overall spatial agreementbetween the plumes existedduring CAPTEX#4. Results of analysis ofwinds tobe shown later will indicate thatverticalwind direction shearwas comparatively smaller on this case than in the other Dayton episodes. Figures lle-f show the plume patterns during CAPTEX #5 and#7 for the Sudbury releases. There was an interestingdifference between these cases as the modeledplume traveled to the right (clockwise looking downwind) of the observedplume during CAPTEX#5 and to the left (counterclockwise) of the observedplume during#7. There is also evidence that the spatial separationbetween the respective plumes increasedwith distance downwind for these two cases. However, no firm conclusions canbe reached about the relative spatial agreementbetween the plumes at anyparticular time periodwithin an experiment from Figure lla-f since each showed only the overall spatial 'footprint' of the modeled and observedplumes over a particular experimental episode. Individual graphicalmaps of the concentrationdistributions, too numerous to displayherein, are needed to demonstrate the patterns of the observed andmodeledplumes at each time period. Another analysis technique thathas emerged to assess spatial displacements betweenmodeled and observedplume patterns relies onplume centroid positions. With this technique, the difference inrelative plume positions andplume separationcanbe explored. Acentroid locationof a plume is definedherein as the density-weightedmaximum concentrationcoordinate position (Xc,Yc). The plume centroidpositions for the modeled and observedplumes were determined at each time period from concentrations at sites with Eq. (8).**~** 

### xj•Cj**I~** 

### cj**~** 

### Yj.cj 

## 1**~** 

### cj (8a) (8b)



### InEq. 8, xj-Yj are the latitude-longitude coordinates of each site (j) with a nonzero concentration (C), where Crepresents either an observed or modeled concentration over a particular time period (t). The summation is performed over all sites separatelywith the observed andmodeledplumes. Using the results of Eq. 8, the downwind distances of the observed (D0) 

### andpredicted (Dp) plume centroid locations with respect to the particular release sitewere determined. In addition, the results of Eq. (8) were also applied in order to compute the separationdistance (Ds) between the observed andmodeledplume centroidpositions. These distance parameters have been advocatedby Clark et al. (1988) in an assessment of long range model performance. For example, the statistical and test results presentedearliermay notbe able to distinguish between the performance of two different models in situations whereboth calculate zero concentrations at the same sites. That is, one model's plume patternmay slightlymiss sampling sites, while the plume from another model may be displacedby a much greater distance from the same measurement sites. With these plume distance parameters, the downwind distance ratio 

### 59(Dp/D0) and a normalized separation distance ratio (Ds/D0) can be applied to more rigorously assess and distinguish between the performance of different models. For example, values of Dp/D0 that are greater than unity indicate the modeled plume traveled faster than the observed plume, while ratios less than unity signify the modeled plume was transported slower than the observedplume. On the otherhand, the values of Ds could implicate the speed and/or direction components of transport. However, if both plumes were the same distance downwind (i.e. Dp/D0=1), a separation between the respective plumes would be entirely attributable to directional errors by the model. The results of these ratios for each CAPTEX episode are presented in Figure 12a-f. Values have been plotted at the mid-point in time of each sampling interval. Focusing on the CAPTEX #l-4 (Fig. 12a-d), most of the values of Dp/D0 are less than 1, which reveals the modeled plumes traveled slower than the observed plumes. In particular, the evidence in some of these cases showed that the modeled plume tended to travel more slowly than the actual plume during the first overnight period. The sign convention adopted for the ratio of Ds/D0 in these figures specifies whether the modeled plume centroid was to the left (minus) or to the right (positive) of the observed tracer plume. The most dramatic shifts in Ds/D0 also occurred during the nocturnal hours of the first night. During the stable night hours, large vertical shears inboth speed and direction are expected to develop in associationwith the nocturnal low leveljet. In addition, graphical maps of plume concentration patterns during the morning periods of the second day in these cases also verified that the leading edge of the observed plume was impacting sites along arcs considerably farther downwind than the modeled plume. An examination of the MESOPUFF II code revealed that the modeled plume was always transported by the lower wind field (mixed layer averaged) even during the nocturnal hours. Transport of the modeled plume was not switched to the upper layer wind field because with a surface-based nonbouyant release as in this application, Zp always remained less than Zi. Thus, even though the mixed layer depth dropped to relatively low values at night, Zi continued to be greater than Zp. Plume transport by the mixed layer averaged field, which at night is representative of the wind flow over a relatively shallow layer, also explains why the observed tracer probably outpaced the modeled plume during the nocturnal period. At night when significant vertical shears in speed and direction are prevalent, the mixed layer averagedwinds were certainly slower than upper level wind speeds which more likely advected the observed tracer plume. In addition, plume separation was also magnified because larger differences inwind directionbetween these layers often exist during stable conditions at night. Examinations of nocturnal wind profiles during the CAPTEX episodes revealed notable vertical shears inboth speed and direction, although there was variability from among the cases. The results for the two Sudbury cases where the releases were at night under moderate winds behind cold fronts differed somewhat from the previous cases and each other. The results for CAPTEX #5 in Figure 12e were closer to ideal as higher winds and smaller vertical shears were responsible for transporting the rather narrow plumes rapidly off the U. S. east coast (see Figure 8e for plume patterns). Results for CAPTEX #7 in Figure 12f are 

### 601.5

1.0 

### 0**~**

### .5 

### c::

0.0

-.5

-1.012 

### CAPTEX

0



### X X



# ---------------------------------------------- X X X X X X

0 0 0 0 0 0 0 

# °

----------------------------------------------

I 

### I,I I I, I 

### I,I I I 

### I,I 20 00 OA. 08 12 16 20 00 OA. 03 12 14 18 22 02 06 10 14 18 22 02 06 10

1.5-



### 0**~.5** £::!:::

0.0

-.5 

### TIME (LST)
b CAPTEX 2 I I I'

### I'I I 

### I' I 

### I'I'I I

### I 

## Dp/Do o - 

## D,/00

## ~ X 

# 3 ------------------------------x-----x-----)(---j

X X X X X X 

# 1

_______________________________________________] 0 0

# ~ 0 

### J

# ~ 0 0 0 0 0 0 0 

### J
-1.0 I lr I 

### I,I I

### I I 

### I I 

### I I 

### I I

### I I 

### I,I I 

### I I

### I I 

### I I 

### I,I I

### I I 04 08 12 16 20 00 04 03 12**0~** 06 10 14 18 22 02 06 10 12 16 14 18 

### TIME (LST) Figure 12. Ratios of downwind distances of the model and observed plume centroid locations (D /D0) 

### and separation distancebetweencentroidpgsitions to the observed downwind distance (Ds/D0) 

### versus time of day starting from the day ofrelease. Negative andpositivevalues for Ds/D0 

### denote the model plume position is to the right and left of the observedplume, respectively. a) CAPTEX#l andb) CAPTEX #2 results. 

### 611.5

1.0 

### 0 ::;:.5 

### a::

-.5 

### CAPTEX 3

X 0 X 

### X X 

# -----------------;c--------x------------------ 0 X X 0 0 0 

## 4**----------------------------------------------~** 0 0 

# j
0 0

# -1.0U-~~~~~~LLLULW~~~~~~LLLL~~~~~~~~~~~~~ 12 16 20 00 04 08 16 20 00 08 12 14 18 22 

## o: 06 10 14 18 22 02 06 10 

### TIME (LST)
d CAPTEX 4

X 1.5 X X
1.0
---------------------------------------------- 0 X X X X X 0 X 0 0 

# -----------------------------------------------3 0 0 0 0 0 0 

# j

04 08 12 16 20 00 04 08 12 14 18 22 02 06 10 14 18 22 

## oz 06 10 

### TIME (LST) Figure 12. Same as 12a-b, except for c) CAPTEX#3 and d) CAPTEX#4 621.0 

### 0 ':;:.5 0::::

0.0

-.5

2.0

1.5 X



### -----------------------X-----~---------------- X X 0 0 0

_______________________ Q
______________________ 0

04 08 12 20 00 04 08 12 16 20 02 06 10 14 18 22 02 06 10 14 18 22 

### TIME (LST)

f 

### CAPTEX 7

I

X X 0 X X 

# ---------x------------------------------------

0.0 ---------------------------------------------- 0 0 -.5 0 0 0 00

**-1.0~'**Ll

**LLLLLLLLLLLLLLLLLLLLLLLLLLLLLL~LLLLLLLL~~~** I I I,I,I, I I I I I I I I I I,I I I I I, 00 04 08 12 16 20 00 04 08 12 16 02 06 10 14 18 22 02 06 10 14- 18 22 

### TIME (LST) Figure 12. Same as 12a-b, except for e) CAPTEX#5 and f) 

### CAPTEX#7 

### 63in agreement with the overall plume patterns shown earlier in Figure llf as the modeled plume traveled to the left (looking downwind) of the observed plume and at a faster pace, which was a departure from the other cases. The non-normalizedvalues of these parameters are presented in Figure 13 to show the actual difference between Dp and D 0 and the magnitude of Ds. The results have been plotted against time after release for all episodes. Anotable feature is that the largest differences in downwind distance between the respective plume centroid locations appeared from 12-24 hours after releases with values around 200 

### krn. This time frame also coincidedwith the nocturnal period for the Dayton cases. The largest values of Ds were also found during this time period as values near 300

### km occurred among these cases. Some caution must be exercisedwhen interpreting the results for the later time periods because the bulk of the observed plume probably already moved beyond the sampling domain. Consequently, greater uncertainty exists with these points as some values may not accurately reflect the true differences between the respective plumes as the observed plume centroids were derived from plume remnants still being sampled at the farthest arcs from the release locations. Nevertheless, these results have providedvaluable quantitive information about the magnitude of spatial plume displacements and helped focus on the probable causes for deviations inplume transport by the model. 

### DIAGNOSTICTESTRUNRESULTS Additional processor and model runs were conducted in order to exercise some of the optional selections for the wind field and dispersion methods and to compare these results with those already obtained from the operational default model runs. It was decided to conduct the diagnostic runs for the Dayton cases only with the emphasis in the analysis on the peak concentration results at the 300 

### krn arc. This test strategy was selected in order to uncover the extent of the differences in the plume impact location and timing for the various wind fields and to identify the cause of the model overpredictions of peak concentrations which were pronounced at the 300

### krn arc sites. A single optional feature was exercised in each of the diagnostic runs while all other default parameters and methods remained unchanged. The input surface and upper air data files were identical to those in the default runs. Although MESOPAC II contains options for several different wind fields, it was decided to limit the diagnostic runs to two alternative wind fields; single-level wind fields for the surface and the 850mb levels. Either of these wind flows might be selected in certainmodel applications. Winds at the 850 mb level are generally representative of the flow at the top of the daytime mixing layer, and it is expected to provide a strong contrast from the transport providedby a surface wind flow. MESOPAC II processor runs were performed to produce meteorological files for each case with these wind field selections. Of particular interest are differences in the location and travel time for the peak concentration and its magnitudeat the 300 km arc for the different wind fields andhow the results compare with those of the observed plume. Tables 20-23 present the comparative results for CAPTEX #l-4, 

### 64

## DOWNWIND DIFFERENCE 400 

### I I
0 t:. 200 0

# * 0 0 0 0 ,.--.... 0



## E x? 0 t:. .:::t. X X XO )!(0 X 0 t:. X

........... X

0 0 0 t:. 

# * X 0 0 0 

### 2 X *t:.



### Ia. IE! 0 t:. 0 

# * 0

0 0 0 0 -400 I I 101 I I I I 0 10 20 30 40 50 

## HOURS AFTER RELEASE
SEPARATION DISTANCE 400 I I I 

### I I I I I I 

### I I I I I 

### I I I I I 

## I 0 0 

# J

0

# ~ 0 

# l 300 

# l .c. 0 0 XI

,.--.... 0 

# l

E 0 

### * 0 

# J 2;,0o t:. X 

### _j 

## J "' 0 

# J 0 X 0 A X t:. X 

### I A X -tr.c. 

# l 100 )60 X 

## xo X 0 Q X 

# l t:. 

## I 

# * 0 

# l 0 

## o*o 

# J
0 

# * J 0 I 

### I 0 I I I I I I I I I I I I I I I I

0 10 20 30 40 50 

## HOURS AFTER RELEASE Figure 13. a) Difference in the downwind distances of the centroid positions of themodeled (D ) and observed (D0) 

### plume patternsversus time after ?elease. b) Separationdistance (D ) betweenmodeled and observedplume centroid locationsversus s time after release. Symbols inboth figures denote events for each experiment; CAPTEX#l (0), #2 (X), #3 (triangles), #4 (squares), #5 (stars), and#7 (diamonds). 

### 65respectively. The individual results will be discussed separately in this section. The azimuth angle of each site with respect to true north are also given to help quantify the angular difference in the relative positions of plume impacts with the different wind fields. The position of each site in these tables is consistent with the configuration of this arc, as shown earlier in Figure 2, with site numbers increasing from right (east) to the left (west). Table 24 presents the hourly NWS surface wind observations and results of certainparameters from the operational model runs for the release period. This table is provided with the intention of giving supplemental information for the interpretation of the model results. Surface winds in Table 24 do indeed show that the Dayton releases all occurredunder primarily southwesterly wind flows. Consequently, for this wind flow and with the configuration of the sampling arc, a modeled plume impacting a site to the right (left) of the observedplume location on the 300 krn arc would indicate a greater influence by a more westerly (southerly) component in the wind field. The results in Table 20 for CAPTEX #l indicate that the location and timing of impact of the peak concentrationwith the mixed layer (default) wind field compares favorably with the observed results. Site 316 was the location of impact for both the observed and model default peak concentration and the transport times were also the same at 5 h after the start of release. In contrast, the surface wind field caused the modeled peak concentration to occur much later and at a site slightly to the left of the observed results. The 850mb winds transported the modeled plume at about the same speed as the observed plume but directed it to the right of the observed peak concentration location. These results are to be expected when wind directionbecomes more westerly with height which was evident from the rawinsonde profiles; the wind flow at 850mb had a greater westerly component than the surface winds. It appears that winds averaged over the mixed layer simulated the observed transport very well during the afternoon period in this case. The results in Table 21 for CAPTEX #2 are certainly somewhat different from the CAPTEX #l in the relative positions of plume impact. During this experiment the model default peak concentration intercepted the arc much faster and to the left of the location of the observedpeak value. In fact, several 'model-specific' sampler sites were included in the model run in an effort to better define the modeled plume position in this case. The default peak occurred at a site about 25° to the west (left) of the observed peak. Mixed layer averaged wind directions at Dayton in Table 24 were indeed more southerly than the surface winds, which indicated why the modeled plume moved to the left of the observed plume position, but this did not explain why the mixed layer averagedwind possessed a greater southerly component. However, the afternoon and the regular evening (00 GMT) wind profiles at Dayton in Figure 14 appeared to show why the modeled plume behaved in this manner. The 00 GMT profile ,which was used to determine the modeled winds, revealed that wind directionbacked with height from 230° at the surface to 250° near 1000, while the sounding at 18 GMT indicated that wind direction displayed the more typical veering with height during the release period. Consequently, d8 derived from the surface and layer-averaged directions was positive from the 00 GMT profile, which produced the stronger southerly component found in the mixed 

### 66

### TABLE20 

### COMPARATIVE RESULTS FORDIFFERENT YIND FIELDS ON PEAKCONCENTRATION/PLUME TRANSPORT AT THE 300 KMARC FOR CAPTEX#1
RELEASE

- 9/18 (1200 320
AZIMUTH (0) 

### (24.5)

0 1



### D~~JTIME$ 18/1700 TRAVELTIME

MODELWIND
DEFAULT

SURFACE

850 mb *(h) 

### 5 FIELD

0



### 176 18/2300 11 0 

### - 1500LST) 

### SITENUMBER 318 316 314 (36.2) (42.5) (45.3)
425 1586 197 18/1700 18/1700 18/1700 5 5 5

116 2367 1568 18/1700 18/1700 18/1700 5 5 5 4456 752 163 18/2000 18/2300 19/0200 8 11 

### 14

0 0 0

$TIME IS THE START HOUR OF THE SAMPLING

**I~TERVAL** 

### 312 (55.4)
0

0

0

1961 18/1700 5 

# *TIME INTERVAL FROM THE START OF RELEASE

**&~D** 

### TO BEGINNING OF OF SAMPLING INTERVAL

67 310 (60.5)
0

0

0

642 18/1700 5

### TABLE21 

### COMPARATIVE RESULTS FOR DIFFERENT YIND FIELDS ON PEAK CONCENTRATIONS/PLUME TRANSPORT AT THE 300 KMARC FOR CAPTEX#2
RELEASE 9/25 (1205-1505 

### LST) SITENUHBER 324* 323* 322* 321* 320 318 316 314 312 310 

### AZI.(0) ( 

### 1.5) (4.7) (10.8)(16.7)(24.5)(36.2)(42.5)(45.3)(55.4)(60.5)

# g~~JTIME TRAVELTIME (h) MODELWIND FIELD
DEFAULT 9 320 2767 26/01 25/16 25/16 13 4 4 

### SURFACE 12 79 286 26/07 26/07 26/07

19 19 19 850 mb 0 0 0

* 

### 'Model-specific' sites 64 1575 768 407 12 10 26/04 26/02 26/04 26/01 26/04 26/04

16 14 16 13 16 16

1430 390 20 0 0 0 0 25/19 25/22 26/04 7 10 16 459 1106 1324 873 656 92 74 26/10 26/10 26/13 26/13 26/16 26/19 26/19 22 22 25 25 28 31 31 0 0 0 533 1002 303 0 25/19 25/22 2·5/22 7 10 10 

### only

68

### TABLE22 

### COMPARATIVERESULTS FORDIFFERENTYIND FIELDS ON PEAKCONCENTRATIONS/PLUME TRANSPORTAT THE 300 KMARC FOR CAPTEX#3
RELEASE

- 10/02 (1400-1700 LST) 

### SITENUHBER 320 318 316 314 3-12 310 308 306
AZI. (0) 

### (24.5) (36.2) (42.5) (45.3) (55.4) (60.5) (68.8) (74.4)

# g~~~HR 304 0 2 0 0 0 0 0 02/2200 03/0100 TIME (h) 8 11 

### MODELWIND FIELD



### -·-----------------------------------------------------------------------

DEFAULT 0 0 102 38 1416 524 8 0 02/2200 02/2200 03/01 02/2200 03/0100 8 8 11 

### 8 11 

### SURFACE 258 10 3 11 

### 0 0 0 0 03/0400 03/0400 03/0700 03/0700 14 14 17 17 850 mb 0 0 0 0 0 14 754 1357 03/0100 03/0100 03/0100 11 11 

### 11



### 69

### TABLE23 

### COMPARATIVE RESULTS FOR DIFFERENT WIND FIELDS ON PEAK CONCENTRATIONS/PLUME TRANSPORT FOR THE 300 KM ARC FOR CAPTEX #4

RELEASE

- 10/14 (1100-1400 LST) 

### SITENUHBER 320 318 316 314 312 310 308 306 304 302 

### AZI.(0) 

### 24.5 36.2 42.5 45.3 55.4 60.4 68.8 77.4 83.9 93.1

Omax 0 0 0 

### DAY/HR TRAVEL TIME (h) MODELWIND FIELD
DEFAULT 0 0 0



### SURFACE 65 799 1859 15/07 15/01 15/01 20 14 14 

### 850 mb 0 0 0



### M missing measurements 0

0



### 1492 15/04 17

0



### 70 9 

### 15/19 8

0



### 231 15/04 17 0 

### 1350 14/19

8

0



### 119 15/04 17 0 

### 33 14/19

8



### 1448 14/19 8 

### 0

0 0



### 145 14/16 5

### 0

0 M

0



### 0



### 626 14/16 5 M



### 0

0



### 81 14/19 8

### SITEDATE TABLE24 

### SELECTED METEOROLOGICAL OBSERVATIONS AND MODELRESULTS AT THE RELEASE SITE OF EACH CAPTEXEXPERIMENT SFC OBS. MODELMLWINDS$ HR 

# ws WD 

### SPEED DIR PGT TEMP (LST) 

### (m/s) (Degx10) 

### (m/s) (Deg) CLASS (oF) (DAY-9/18) #1 12 7.7 22 13.4 224.4 4 86 13 9.3 24 16.0 245.0 4 87 14 8.8 21 15.3 216.7 4 89 15 8.2 22 14.3 224.8 4 89 CLOUD COVER (tenths)
0 4 4 4



### -------------------------------------------------------------------------

(DAY-9/25) #2 12 4.1 24 9.4 230.1 3 66 10 (250)* 13 

### 5.2 20 12.8 189.5 4 68 10 (250)* 14 6.2 22 15.6 206.6 4 70 10 (250)* 15 3.6 22 10.1 205.2 4 69 10 (100)*



## --------------------------------------------------------------------------

(DAY-10/2) #3 14 7.2 24 17.3 266.0 4 81 0 15 5.7 22 13.7 248.7 4 82 0 

### 16 4.1 23 10.1 258.5 3 81 0 17 5.2 23 12.4 257.6 4 81 0



# --------------------------------------------------------------------------

(DAY-10/14) #4 11 

### 7.7 25 14.8 269.4 4 49 0 12 7.2 26 14.5 280.9 4 51 0 13 7.2 24 14.9 264.2 4 53 0 14 8.2 25 16.3 271.9 4 55 0



### _______________________________________ .,.

### __________________________________



### (SUD-10/25-26)

#5 23 4.6 32 4.6 319.9 4 37 8 (11)* 00 3.6 32 3.6 319.9 4 37 8 (16)* 01 5.2 33 5.2 329.8 4 39 8 (18)* 02 5.7 31 5.6 310.0 4 39 8 (13)*



# ---------------------------------------------------------------------------

(SUD-10/29) #7 01 8.2 33 17.9 326.8 4 32 4 02 6.2 33 13.0 326.9 4 32 8 (30)* 03 3.1 33 8.8 339.7 4 30 8 (24)* 04 6.2 33 13.0 326.9 4 30 4 $Mixedlayer (ML) averagedwinds at the gridpointnearest release site 

# * Ceilingheight inhundreds of feet above ground

71

### STATION 72429 DATE: 9/25/83 TIME18 GMT .3000

\\ 

## 19 -J I 

### l:l 2000 fo 

### o:l: '' 

## E '0



# - I / 

### 1- ,0' 

## ::r: >' l:l**~** \ L.U 1000 \Q 

## ::r: I I 

### 0

# ~

\\\**~~~ ~D**  

## ol**-'?--~~** I '::;
- r

## ~ 2
.... 

# ! 110 

# ? I 

### I 0 50 100 150 200 250 JOO 

### SPEED 

### {m/s),DIRECTION {0) 

### bottom

### axis

STATION 72429



# - ...J l:l 2000 

### o:l: 

## E



# -

l.!.J 

## :c 1000

250 270 280 290 

### SPEED(m/s) ,DIRECTION (0) 

### bottom

### axis Figure 14. Upper airwind speed (O) andwind direction (X) profiles at a) 18 GMT (i.e. 13 LST) andb) 00 GMT for CAPTEX**~2.** 

### 72

### layer averagedwind directions inTable 24 for this case. Another wind profile from a rawinsonde launch at 20 GMT was also very similar to the 18 GMT profile, so theverticalvariation inwind direction exhibited in the 00 GMT sounding didnot occur during the time periodof the tracer release. This case demonstrated the impact onmodel results when the upper air datawere not representative of the conditions at an earlier time. It is apparent thatbettermodel results mighthavebeen obtained if one of the upper airprofiles taken during the release periodwas substitutedfor the 00 GMT profile, or if themodel was capable ofusing rawinsonde data atmore frequent time intervals. The largest spatial difference in the location ofplume impact between the observedplume andmodeledplume with the mixed layer averaged winds occurred inCAPTEX#3 (Table 22). The exact location andmagnitude of the actual tracerpeak concentration couldnotbe accuratelyjudged in this case since thebulk of the tracer plume definitely traveled to the west of this arc. Some of the tracer plume impacted site 320 located on the western end of the arc. It is estimated from Table 22 results that the peak concentration locationfrom the model default runwas shifted at least 30° to the right of the positionwhere the observedplume may have passed at this downwind distance. The 00 GMT windprofile at Dayton inFigure 15 reveals that significantvertical shear in direction existedwithin the mixed-layer in this case, as wind directions varied from 200° at the surface to almost 270° at Zi. This large verticalvariation in direction also explains the large differences inplume impact time and locationbetween the model runs with surface and 850 mb wind fields. The release period for #3, being slightly later in the afternoon compared to the other Dayton releases, may also have contributed to the variations in the plume's trajectory and dispersion. Inparticular, tracer gas emittednear the end of the release period at 17 LST hadmuch less time to be mixed through theboundary layer as convective turbulence is already less vigorous and is rapidly decreasing during the late afternoonhours. Tracermeasurements from aircraft flights provided additional valuable information about the impact ofvertical direction shear coupled with the timing of the release for CAPTEX #3 on the transport and dispersion of the tracerplume. An aircraft flight legwas conducted across northern Ohio at 1500 maround midnight. Atracerplume was discovered at this levelwith the highest concentrationbeing 1466 fl 1-l in the same relative position as modeledpeak concentration from the mixed layerwind fields. However, the real suprise occurred from the tracer measurements from aircraft flight legs over southeasternPennsylvania during the afternoon of the next day, October 3. The vertical cross-section inFigure 16 showed evidence of a coherent tracer plume aloftwith a concentration as high as 2961 fl 1-1

. Clearly, this portionof the tracerplume was transportedbywesterlywinds which existed in the upper part of the boundary layer. Additionally, for concentrations to be so high, this plume sectionhad experienced littleboundary layer-based turbulent activity, whichwouldhave dispersed it and fumigated it to the surface. Surface sampling sites measured zero concentrations in this area. The multi-layer Lagrangianmodel byDavis et al. (1987), which contained sevenvertical layers up to nearly 3000 m, displayedmuch greater success in simulating thebroad spread and decoupling of the tracerplume pattern due to the large vertical shears during this episode. 73STATION 72429 DATE: 10/3/83 TIMEOO GMT .3000

II Q 0-

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### TEMPERATURE

## (OC) Figure 15. a) Wind speed (0) andwind direction (X) profiles and b) temperature profile at Dayton, Ohio from CAPTLX #3 

### 74..J tiJ I:

**~** ...... t..l Q :::1 !- !- ..J <:: 

## COHCEHTRAT!OH CFL/L)

RELEASE 3 3 0CT Z1Z 

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### 48 41 

### 42 43 45 LATITUDE 

### Figure 16. Tracer concentrationpatternfrom aircraft flight legs at different levels during the afternoon of October 3, 1983 during CAPTEX

**~3.** 

### (Ferber et al., 1986)



### 75The results for CAPTEX #4 in Table 23 reveal that the default model plume exhibited relatively good correspondence in time and position with the observedpeak concentration. The modeled peak occurredwithin 10° to the right of site where the observedplume impacted and the respective travel times were in agreement. Both results also show that the tracer plume was narrow in this case. The results for the single layer wind fields were similar to those in#l and #2; the modeled plume using surface wind fields traveled to the right and slower than the observedplume and the 850 mb winds caused the modeled plume to impact to the right of the observedpeak concentrationposition. Although the experiments from Sudburywere not examined in these model test runs, it is of interest to assess the plume impact locations for these two cases, especially on the north side of Lake Ontario. For CAPTEX #5, the observed and modeled plumes (Figure 8e) impacted sites 752 and 652, respectively. which were both about 400 

### km from Sudbury andjust north of Lake Ontario (Figure 2). More importantly, the difference in azimuth angle with respect to Sudburybetween these sites was about 8°. In CAPTEX #7, the observed plume traveled to the right of the modeled plume (Figure llf) and the angle difference in azimuth angle for the sites in this case was about 13°. Interestingly, in these cases and in some of the Dayton cases the angular separation of the respective plumes was comparable to the resolution of the surface wind direction observations. The upper air wind profile data collected during the release periods and at the regular 00 GMT launch time were analyzed to further explore reasons why modeled speeds and directions differed from those inferred from the actual tracer plume measurements out to the 300

### km arc. The results in Table 25 were determined from profiles at Dayton and for Buffalo (BUF) to represent the Sudbury cases. The notable values are the ratio of the mixed-layer averaged to surface wind speeds (WSml/WS0) and difference between the wind directions. In particular, most of the afternoon wind speed ratios were lower than values at 

### 00 GMT. Wind direction differences inTable 25 also tended to be larger at 

### 00 GMT than in the earlier profiles. Stronger turbulent mixing in the afternoon period likely reduced vertical shears in speed and directionwithin the mixing layer, however, vertical variations appeared to become stronger by evening. The changes over the time period of these profiles were also consistent with the results reported by Shreffler (1982). From analysis of a large wind profile data base, he found that the diurnal variations of wind speed ratio and wind direction difference over the mixed layer exhibitedminimum values during the afternoon period with gradual increases during the evening to maxima at night. Thus, the mixed layer averaged fields computedby the model are believed to be slightly overestimating transport speeds during the daytime period, however, this feature was difficult to assess with the 3-h averaging period for these cases. On the otherhand, there was some evidence of an effect onwind direction in the model as the previous results indicated that the default model plumes were shifted slightly to the right of the observedplumes in CAPTEX #4 (Table 23) and CAPTEX #l (Table 20). The other important feature to be examined is the significant model overprediction of peak concentrations reported earlier. The same dispersion method, as outlined in Section 2, was applied in these model runs with the 76

### TABLE25 

### RESULTS OFANALYSIS OF OBSERVED UPPERAIRWIND PROFILES FROM THE CAPTEXRELEASE DAYS
EXPT SITE DATE-TIME ws 1 WDml wsml WDB-WDml WD -WDz· z. (MM/DD-GMT) (mis) (Deg) WSo ( eg) (8eg)

**~** 

### (mY 1 DAY 9/18-1800 12.5 229.0 2.4 1.0 -12.0 1254 9/18-2000 13.1 239.0 2.0 -19.0 -27.0 1855 9/19-0000 12.7 221.5 1.8 11.5 -22.0 1675 2 DAY 9/25-1800 5.3 220.0 1.7 -20.0 -21.6 1294 9/26-0000 7.5 237.9 3.0 12.1 11.8 1439 3 DAY 10/2 -1800 4.9 258.3 1.1* -26.0* -39.0* 1808 10/3 -0000 7.3 226.4 2.4 -26.4 -55.0 1732 4 DAY 10/14-2000 9.2 249.2 1.5 -9.2 -30.6 1217 10/15-0000 8.0 239.8 2.0 -19.8 -38.6 972 5 BUF 10/26-1800 9.8 290.1 1.4 9.9 12.4 2293 10/27-0000 11.7 315.0 1.7 -25.0 -30.0 2070 7 BUF 10/29-1800 8.5 335.5 1.2 14.5 6.5 1254 10/30-0000 6.2 330.5 3.1 -0.5 -8.1 1175
NOTE:wsml - mixed-layermeanwindspeedfrom observedprofile data WDml mixed-layermeanwinddirectionfrom observedvalues wso 

# - surface observedwind speedat the rawinsonde site WDo 

# - surface observedwind directionat the rawinsonde site WDzi

- observedwinddirection at themixingheight zi

# - observedmixingheight; winds averagedup to this level * Missing observed surfacewind; values atnexthigher level applied; this resultmay slightlyunderestimate the actual condition.

77different fields. In the three Dayton cases where the peak concentration was readily resolved (Tables 20, 21, and 23), model predictions with the default wind scheme were greater than observedpeakvalues by varying amounts. Since dispersion is dependent on travel time after 100

### km in the model, modeled peakvalues differed for the various wind fields. Focusing on the default model results in Tables 20-23, the overpredictions were smaller for #l (Table 20) and #4 (Table 23) where model transport times agreed with observedvalues. The most sizeable overprediction of the peak concentrationby the default model run occurred in CAPTEX #2 (Table 21) as the modeled plume arrived much earlier than the observedplume. Differences in the width of modeled and observed plumes were difficult to assess even for the site spacing of this network, however, the results at 300

### km suggested that modeled and observedplumes exhibited comparable horizontal spread. Nevertheless, the consistent model overprediction strongly implied that dispersionwas greater for the observed plumes. Since puff growth is controlledby dispersion parameters, whose variations are dependent on stability class, the results for PGT class in Table 24 are illustrative of a possible cause for the model overpredictions. Neutral stability (PGT class 4) was often found during these afternoon hours when unstable conditions might be expected, although based on the observed wind speeds the specification of a neutral stability class appears appropriate. A further examination of processor output revealed stability class 4 was indeed prevalent at the gridpoints in Ohio during the afternoon hours. Consequently, the initial growth of the model plume, particularly in the vertical dimension, was much more limited due to the significantly slower increase in the dispersion parameters under neutral stability when compared to that under the unstable PGT classes (Turner, 1970). In particular, the coefficients of the power law curve for 

### az for neutral stability in the model were derived from a fit to the so-called D2 curve in Pasquill (1974), which is the neutral curve for the vertical dispersion parameter in Turner (1970). However, greater vertical dispersion in the model would have been produced under the Dl curve for neutral stability in Pasquill (1974). In any event, the principal cause for overpredictionby the model in this application was attributed to underestimation ofvertical dispersion. More evidence to support this explanation will be given next from additional model test runs. Diagnostic model test runs were also performed to assess the impact on peak concentrations by applying optional variations to the default dispersion method or selecting the uniform vertical mixing feature in the model. Results in Table 26 for CAPTEX #l were representative of those found in model runs for the other Dayton cases. The peak concentrations at the plume centerline with the uniform vertical mixing optionwere lower than values from the default model run. Since the observed peak concentration was 1586 fl 1 1 in CAPTEX #l (see Table 20), the peakvalue using the vertical mixing option was certainly more comparable and suggested that distributing the plume vertically over the depth of the mixed layer was more representative in this case. In fact, a further examination of operational model**out~ut** indicated that 

### az was much less than zi at the 300

### km arc,**wh~ch** revealed the modeled plume had not yet become well-mixed even out at this distance. Thus, it is evident that more rapid growth of

### az in the model wouldhave provided an improvement in the prediction of peak concentrations.
78

### TABLE26 

### PEAK CONCENTRATIONS FOR CAPTEX #lUSING VARIOUS DISPERSION OPTIONS
SITENUMBER MODELRUN 320 318 316 314 312
DEFAULT METHODS* 0 116 2367 1568 0 UNIFORMVERTICAL 0 69 1446 980 0 MIXING OPTION@ CROSSOVER DISTANCE# 0 133 2586 1722 0 AT 50 km 

### CROSSOVER DISTANCE# 0 126 3075 2020 0 AT 10 km


### * PGT dispersion curves applied out to 100 km, 

### time dependent growth according to Heffter (1965) at greater distances. @Puffs verticallydistributed over the mixed-layer after release insteadof a2 

### growth. # Downwind distance where puffdispersionchanges from PGT curves to time dependentmethod.

TABLE27 

### COMPARISON OF OBSERVED AND MODELED MIXING HEIGHTS
ALL PAIRS 0

### <

## cc ::5 

### 8/10 0 

### <

## cc :::; 

### 4/10 TIME (GMT) 18 00 18 00 18 00

N 59 73 38 49 18 28 ziO 

### (m) 1456. 1479. 1368. 1369. 1300. 1307. ±533. ±496. ±437. ±431. ±453. ±409.
ziP 

### (m) 1145. 1166. 1244. 1191. 1355. 1313. 

### ±317. ±326. ±270. ±339. ±324. ±331. d (m) 311. 312. 124. 177. -56. -6.
AAR (m) 

### 468. 471. 336. 402. 283. 340. R 0.22 0.15 0.35 0.22 0.56 0.34 O>P 

### (%) 66.1 67.1 57.9 59.2 44.4 39.3 F2 

### (%) 84.7 89.0 92.1 95.9 88.9 100. 

## cc = 

### cloudcover F2 = 

### percent ofobservedandmodeledvalues within a factor of 2
79Additional results in Table 26 revealed that reducing the crossover distance caused even higher peak concentrations compared to the default distance (i.e. 100 km). Clearly, these results indicated that the time dependent dispersion formulas provided even slower growth than that offered by PGT class 4 in this application. Since itwas noted earlier that accurate specification of Zi was also important to both transport and dispersion in this model, a brief examination of the modeled mixingheight is warranted. The diurnal variation of Zi at the grid point closest to Dayton over the two day period of CAPTEX #l is depicted in Figure 17 to illustrate the typical temporal behavior and magnitude of mixing height derivedby the MESOPAC II processor. The most dramtic change in Zi occurred around sunset when a switch in methods takes place. Mechanical Zi's at night were generally much lower than the daytime convective Zi's which were primarily controlledby the variation in the sensible heat flux. However, the term containing the thermal gradient aloft in Eq. 1 also appears to be counteracting the surface sensible heat flux term in this case. This effect is attributed to the gradual decreases in zi in the late afternoon hours prior to the substantial drop observed at sunset. Of particular interest is how well the modeled mixing heights compare to observedvalues. Therefore, a data set of observed and modeled Zi's was developed. An observed Zi, defined as the height of the elevated temperature inversionbase, was paired against a modeledvalue from the closest gridpoint to a rawinsonde site at the same hour. Observed values were easily obtained from the temperature profiles because a strong inversionwas present during the CAPTEX episodes (e.g. Figure 15). Statisitical results were computed for Zi pairs at 18 and 00 GMT. For the latter time, however, it was decided that the modeled convective Zi at 23 GMT was appropriate to compare with an observed value since sunset had already occurredby 00 GMT at some sites. The results in Table 27 contain the results for both time periods for separate categories of cloud cover. The model performedbest under clear to partly cloudy conditions which generally prevailed, as the bias (d) was smaller and the correlationwas higher than for the mostly cloudy cases. It is apparent that the underpredicted Zi for the few cases when overcast conditions prevailed. Finally, no strong conclusions about the general applicability of this method to model zi shouldbe drawnbecause the data set was relatively small and represented a rather limited set of temporal cases and meterological conditions.



### 80

# - E 1- 

# :r: (.!) 

## w 

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# '6**0~--~----~----~----~--~----~----~----~----~--~** 

### 00 

## 10 20 06 16 

# TIME OF THE DAY (HOURS) Figure 17. Hourlyvariation ofmixingheight (Zi) from the MESOPAC II for Dayton, Ohio for September 18-19, 1983 of CAPTEX #l. 

## 02

### SECTION5
MODELSENSITIVITYRESULTS Flexibilitybuilt into the design of model code canbe advantageous to the testing ofvarious model components. MESOPUFF II contains considerable flexibility which allows certain changes inmethods or variations in technical parameters to occur through input values (Scire et al., 1984b). Nevertheless, the intent of this testing effort was not to conduct a comprehensive sensitivity study of the various model components. Practical considerations of time and computer costs necessitated a limit to the number of model test runs for selected model components. It was decided to place major emphasis of this sensitivity analysis on the dry deposition and chemical transformation algorithms, which were not exercised in the CAPTEX evaluation effort. Furthermore, no evaluation of the methods for these processes was possible during the development effort that led to this version of the model. Finally, there was a desire to report for the first time the impact from some of the options in this model. The main purpose of this sensitivity effort was to investigate the impact on surface concentrations for a typical short-term averaging period (i.e. 24 h) from selected changes in methods available in the model or from differences in particular key technical parameters involved in the two model components. Differences in concentrations between the test cases and a model base case are emphasizedhereinbecause Morris et al. (1988) already examined the responses of deposition velocity and transformation rates of the modeled pollutant species to ranges in atmospheric parameter variations, land use types, and for differences in technical parameters involved in these model components. Although various types of sensitivitymethods are available (Gelinas andVayk, 1979), the test procedure adopted for this effort consisted of varying a single option or parameter in a model test run while holding all other inputs and model features the same as those in a base case. This testing strategy permitted a comparative assessment of the relative impact of the variation in each test run to the base case results. Consequently, all model test runs were performedwith the same input meteorological fields; namely, the gridded results producedby MESOPAC II in the default mode for CAPTEX #l. The identical source characteristics and emissions from a single realistic elevatedpoint source were input to each model run. Table 28 contains the source information and emission rates used in all model runs. The location of this single source was set to the same grid coordinates as the Dayton tracer release site. The elevated source height and strong buoyancy of the plume also exercises plume rise and fumigation methods in the model. A single source was chosenbecause the analysis and interpretation of the effects on ground-level concentrations from one point source are more straightforward and model runs are less expensive than for multiple sources. These test runs focused on computing

## so2 and sulfate 

## (so4) concentrations with the emission rates specified in Table 28. 

### 82

### TABLE28 

### SOURCE CHARACTERISTICS AND EMISSION RATES FORALLMODEL SENSITIVITY TEST RUNS
Point source location


# - Grid coordinates ofDayton tracer release site Stackheight


# - 304mAGL Stack diameter


# - 8 

### m Exitvelocity 22 

### m/s Exit temperature 407 K 

### so2 

### emission rate

- 7064 g/s so4 

### emissionrate 

### 53 gjs

83Several other aspects of the model base case and test runs should also be identified. All model runs commenced at midnight and model execution continued for a 48 h period. Source emissions in Table 28 were continuous over the 2-day simulationperiod, which was of sufficient duration for the source plume to travel across the entire model domain while being subjected to the various physical processes. Additionally, pollutant concentrations were computed at gridpoints on the model sampling gridwhich was set to the same size as the meteorological grid in the CAPTEX application. It was desireable to analyze concentrations on the denser, uniform model grid in order to take advantage of the capabilities of the MESOFILE II postprocessor program. Computational procedures were specially designed into this program to performvarious analyses on different gridded concentration fields from independent model runs. The methods described in Section 2 for dry deposition and chemical transformation were applied in the model base case run. The 24-h average concentration fields of so2 from the model base case run for Day 1 and 2 are displayed in Figures l8a and 18b, respectively. The results in Figure 18a show that the plume had reached the eastern edge of the model grid by the end of Day 1. The 24-h average plume patterns onboth days are similar because of prevailing southwesterly winds within the mixed-layer with stronger westerly winds in the upper layer field. The highest so2 concentrations are found close to the source and values generally decrease with distance downwind due to the dispersion, andboth deposition and transformation processes reducing so2 levels in the plume. The 24-h sulfate concentration patterns for the two simulation days from the base case run in Figure l9a and 19b are identical to those for so2, except so4 concentration levels are considerably lower. However, in contrast to the results for so2, highest sulfate concentrations are found several hundred kilometers downwind of the source on both days. This is a consistent feature for some pollutants, such as sulfate, which is primarily formed from chemical reactions during transport across a region. Since the plume patterns from all model test runs were also identical to the base case distributions because the same transport and dispersion conditions were applied in all cases, concentration differences were examined from the set of gridpoints defining the plume pattern. Thus, analyses by the postprocessor was restricted to the grid points with nonzero concentrations. The results consist of percent deviations of a test case concentration (Ct) from a corresponding model base case value (Cb). The results of peak and plume average concentrations for so2 and so4 over the two 24-h periods from initial model test runs are given in Table 29. The peak concentration was defined as the highest value found from the entire gridded plume pattern over the time period, while average plume concentrations were computed from all nonzero values. These initial test runs were conducted to examine the impact on concentrations when one of these physical processes was not modeled, which represents an extreme situation

.. The**resul~s** in**Table_2~** show minor differences inpeak so2**concentrat~ons** when**e~ther depos~t~on**  or transformation processes were omitted. This is to be expected since peak so2 concentrations were generally found close to the source where these processes have not yet had much 

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### Figure 18. 5 6 7 6 9 10 11 ll 15 16 17 111 19 zo Zl zz 

### Average 24-h

## so2 concentration fields from the model

**bas~** 

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### TABLE

### 29 

### RESULTS OF MODEL SENSITIVITY RUNS: COMPARISONS WITH 24-HOUR BASE CASE PEAKAND MEAN CONCENTRATIONS

MODELRUN

BASE CASE No Dep. only No Trans£. only No Vert. Gauss. Distribution* 1-layermode*

BASE CASE No Dep. only No Trans£. only No Vert. Gauss. Distribution* 1-layermode* VARIATION IN PEAK SO ccb ct);cb 

### (%) DAY 1 DAY 2

-1.6 -0.6

1.2 -0.5 168.5 -97.7

-0.1 0.0
PEAK

## so4 (Cb

- Ct)/Cb 

### (%)

-8.4 -28.5 91.3 89.3 22.6 21.5

0.0 0.0 VARIATION IN PLUME AVERAGE S02 (Cb

- Ct)/Cb 

### (%) DAY 1 DAY 2

-15.5 -24.6

-7.2 -8.8

3.2 25.9
-2.4 -2.9

PLUME AVERAGE S0

### 4 (Cb

- Ct)/Cb 

### (%)

-8.6 -17.4 93.1 94.0 14.7 26.5
-1.7 1.9 NOTE: Cb

- base case

**concentrat~on** 

### Ct

- test case concentratlon * Both deposition and transformationappliedas indefaultmode

89impact on so2. However, there was a greater effect on the average S02**concentrat~ons** since these values were derived over the entire plume pattern For example, in the model run where no depositionwas permitted, the average plume concentration on Day 2 was nearly 25% higher than the base case concentrations. The results in the case with no S02 loss by chemical transformation indicated that the mean plume concentrations were less than 10% greater than the base case. On the otherhand, it is evident from Table 29 that transformationhas a much greater impact on so4, as peak concentrations without transformationwere around 90% lower than those in the base case. In this simulation, the peak so4 concentrations were located from 500- 700 

### krn downwind of the source position. Other results in Table 29 are from the model test run with no vertical Gaussian dispersion (i.e. uniform vertical mixing). In this case, peak S02 concentrations were particularly sensitive to the type of vertical dispersion technique. Peak values were substantiallyhigher than those in the base case using the Gaussianvertical dispersion parameter because the plume became distributed immediately through the entire mixed-layer under this optional method. Since this option feature producedhigher peak concentrations and slightly greater deposition near the source, lower mean plume concentrations were obtained. For Day 2, the impact on the mean concentration is comparable, except in an opposite sense to the results without deposition. Peak and mean so4 concentrations were reduced under this dispersion option. In particular, the peak S04 concentration for both days was more than 20% lower than the base case values. In contrast, there appeared to be little impact on peak and mean concentrations in the 1-layer model mode where the source depletion method for dry deposition was applied as pollutant mass over the entire vertical extent of the plume was subjected to dry removal. Additional model test runs were performed to determine the impact of prescribed changes in rs for so2 and sulfate. The processor results indicated that PGT class 4 (neutral) was often determined to represent the stability during the afternoon periods. The surface resistances for so2 for the various land use categories is 100 s/m for the unstable classes and 300 

### sjm for neutral stability. During the daytime period, rs is generally the dominant resistance to pollutant uptake since the sum of the two atmospheric resistances are often much less than rs. Amodel run was performedwith rs redefined to be 100 s/m for neutral class, which essentiallywas an attempt designate these cases as being unstable in the model. However, the results in Table 30 show that the effects of this change are rather small on the peak and plume average concentrations of so2 and so4. On the otherhand, the results for a test run where the default value of rs for so4 was modified from its default value of 1000 sjm to 100 sjm reveal comparable impacts on peak and mean so4 concentrations. No differences occurred in so2 values, but the peak and plume average so4 concentrations for this model run were between 15-20% lower thanbase case values. The current default rs value limits the maximum Vd for sulfate to 0.1 em s-1, which might be too lowbased on some experimental results and the analysis reported by Morris et al. (1988). Thus, a refinement in the formulation for rs for so4 appears to be warranted. The background ozone concentration (03bg) is an important parameter 90

### TABLE30 

### RESULTS OF SENSITIVITY RUNS FROM VARIATIONS IN SELECT KEY TECHNICAL PARAMETERS IN THE MODEL

MODELRUN

BASE CASE modifiedrs (S02) 

### modifiedrs (S04) 03bg = 

### 40 ppb 03bg = 

### 120 ppb stackzs = 

### 150m VARIATION IN PEAK SO (Cb

- Ct)/tb 

### (%)
DAY 1

1.8 0 -0.3

0.3 -147.7 DAY 2

0.01 0 

### -0.2

0.1 -210.3 VARIATION IN PLUME AVERAGE SOz

(Cb

- Ct)/Cb (%)


### DAY 1 DAY 

### 2

4.5 4.4 0 0 

### -1.5 -1.7

1.2 1.4 -37.2 -20.3



# ----------------------------------------------------------------------- PEAK SO (Cb

-



# Ct)/~b (%) 

### BASE CASE modifiedrc(so2) 1.2 2.7 modifiedrs(S04) 17.0 18.2 03bg = 

### 40 ppb 19.0 18.0 03bg = 

### 120ppb -16.0 -15.0 stackzs = 

### 150m 21.0 30.1

NOTE: 

## o3bg

- backgroundozone concentration r - surface resistance z: - height above ground of stacktop

91 PLUME AVERAGE

## so4 

### (Cb Ct)/Cb 

### (%)

1.4 15.8 19.1 -16.1 1.1 2.3 18.5 18.2 -15.2 4.4in the determination of the transformation rate (k). Clearly, ozone generally exbihits a strong diurnal variation and large differences in ozone levels occur between air masses. Consequently, model test runs were conducted to assess concentration differences to a ±50% variation in

## o3b from its current default value of 80 ppb. In fact, k is larger by a f§ctor of two when

### o3b - 120 ppb compared to 40 ppb with all other parameters being equal

## (Mor~is et al., 1987). The results for these model runs inTable 30 reveal little effect on

## so2, however, the changes in 

## so4 peak andmean concentrations are more important. Rather clean and polluted air masses are representedby

## o3bgbeing 40 and 120 ppb, respectively. Peak and plume average

## so4 concentrations in the test cases appeared to be equally affected, as results with 03bg = 40 ppb were 18-19% lower and those with

## o3b**~** 120 ppb were 15-16% higher thanbase case values. Thus, the suffate concentration levels were fairly sensitive to variations in

## o3b values with this transformation method, Therefore, representative

### ozon~ levels from measurements made within the model domain shouldbe input for particular applications. The height of a pollutant release has a strong influence on the ground-level concentrations; the rationale for higher stacks during the past two decades was that peak concentration reductions would be produced with higher stackheights (zs). Amodel run was performed with zs = 150 m, which is approxiately one-half the height of the stack used in the base case. The final results shown inTable 30 reveal that peak

## so2 concentrations indeed were considerably higher onboth days compared to base case values, while increases in the plume mean

## so2 concentrations were not as dramatic. 

### Anotable result with the lower stack, however, is that peak sulfate values were 21% and 30% lower than base case peakvalues on Day 1 and 2, respectively. Thus, the change in stackheights may also have had an impact on secondary pollutant levels. It is recognized that these results represent only a small subset of a much larger group of possible model test runs which could explore the effects more features and variations in the numerous parameters incorporated into the current model. However, these limited set of model runs present a clearer picture of the impact on modeled concentrations when an important process is excluded or when certain key technical parameters in the dry deposition and chemical transformation methods are modified. More evaluation and testing efforts of this modeling system are certainly advocated.

92

### SECTION6

SUMMARYANDCONCLUSIONS The MESOPUFF II regional Lagrangianmodelhas beenevaluated and tested withmeasurements from six full-scale CAPTEX episodes to assess its ability to simulate long-range transport and dispersion of an inert, non-depositing tracerplume. Aneutrally-buoyant tracer gas was released over a 3-hour period from ground-level during four cases from Dayton, Ohio and during two cases from Sudbury, Ontario under fair weather conditions. The results revealed that the model overpredictedbothmean andpeak concentrations. The overpredictions were most pronounced for the Dayton releases at the first two sampling arcs. Since differences inhorizontal plume spreadwere not evident during the first day of transport, itwas concluded that the primary cause for the model overpredictions was an underestimationofvertical plume growth for neutral stability, whichwas the stability class most often specified during the afternoon release periods. It is suggested that a different dispersion formula for the Gaussianvertical dispersionparameter for neutral conditions (the Dl curve established by Pasquill (1974)) be incorporated and testedwithin the current framework of the model. This revisionwouldprovide for more rapidvertical plume spreadwith the default Gaussian dispersion distance-dependent scheme under neutral conditions, a stability class frequently selected from the widelyusedmethodologyby this model and others. The statistical results also showed that large scatter and low correlations occurredbetweenmodeled and observed concentrations paired in time and space for bothmean andpeakvalues. This reflected the inability of this model, like other similar models, to accurately replicate the speed and/or direction of the tracer plume. Thus, the large concentration differences where observed andmodeledplumes didnot overlap were primarily attributed to trajectory errors. However, the statistical analyses didnot provide sufficient information for an assessment of the causes for model errors in transport. Graphical displays and a technique to**det~rmine** 

### plume centroidpositions which indicated the size of the spatialplume differences, greatly assisted in characterizingmodel trajectory errors. Spatial displacements ofvarying amounts were evident from graphical maps ofmodeled and observedplume patterns with time. Plume centroidpositions, derived as the concentrationweighted locations from non-zero values at 6-hour intervals, were used in quantifying differences in the downwind distance and the separationdistance between observed andmodeledplumes. Specifically, results fromMESOPUFF II indicated that the observedplume moved faster and/or along a different path during the nocturnal period andwas generally found further downwind of the modeledplumeby the second daytime period in these episodes. The plume separationdistance, the difference incentroid locations between the observed andmodeledplumes, ranged from 100-300

### km during these cases at downwind distances of 500-1000

### km. The most rapid increase inplume separation also occurred during the nocturnalperiods.

93The CAPTEX tracer emissions consisted of non-buoyant, ground-level releases. For this source type, puffheights inMESOPUFF II were always less than the mixing height which causedmodeled plumes to be continually transportedby the lower layer wind field. This feature is believed to be a shortcoming of the model design, especially during the nocturnal period when greater vertical shears in speed and direction often prevail. It was concluded from analyses of the plume centroidpositions that the modeled plumes must have been advectedby slower winds derived over the shallow mixed layer at night, while observed plumes had been transported along a different trajectory by faster winds at higher levels. In the case of an elevated source emitting a buoyant plume which rises to even higher levels, the model wouldhave switched the plume transport at night to the upper layer wind field when the mixing height droppedbelow the height of the puff center. Based on these results, model applications for multi-day simulations of ground-level, non-buoyant point emissions are not recommended. The model's treatment for this source type could be improved if the height of the puff center after release was redefined to be one-half of its vertical dimension and this revised puff center height reached a limit when vertical puff growth extended up to the mixing height. Four other regional-scale models have also been applied on two or all of the CAPTEX episodes with results being reported in the literature. These models contained greater sophistication in transport (e.g. more vertical layers) and different dispersion techniques. Unfortunately, comparable statistical measures and plume displacement parameters were not reported for three of the four models and an extensive comparison could not be made. Nevertheless, the available results for the other models also displayed large variabilities when observed and modeled concentrations were compared and concentration differences were primarily attributed to spatial discrepancies in plume patterns due to trajectory errors. The most extensive results with CAPTEX data were reported by Draxler (1987) who tested a Lagrangian puff model which utilized several wind layers and permitted puff splitting. Observed-modeled plume separation distances with this model appeared to be smaller than those for MESOPUFF II, especially for the cases exhibiting the greatest speed and directional shears. However, plumes simulatedby MESOPUFF II appeared to be displaced to the same side with respect to the observedplumes (i.e. either to the right or left) as those with the multi-layer model. _Draxler (1987) reported that average spatial displacements for the 24-42 hour period after each release varied from 90-189 

### km among the CAPTEX episodes, while individual separation distances with MESOPUFF II were found to be 100-300

### km as noted above. It must be noted, however, that Draxler's model runs included rawinsonde profile data obtained at 6-hour intervals during CAPTEX. Better temporal resolution of flow fields was translated into reduced trajectory errors when the more frequent upper-air profile data and higher spatial resolution of observed data were included inhis model simulations. These supplemental profiles were not used in the MESOPUFF II model simulations since they are not available on a routine basis for model applications. Qualitatively, the results from MESOPUFF II appeared to be competitive with those of the other models. Therefore, radical changes ofMESOPUFF II and its processors are probably not warranted that could eliminate its advantages ofbeing computationally practical and easy to use for single 94

### sources. Indeed, the model shouldbe expected to performbetter for a ground-level point source ifrevisions identifiedearlier are instituted. The feasibility ofpuff splittingwithin the currentmodel's framework shouldalsobe exploredas a step towardbetter addressing directional shear-induceddispersion. Any firmjudgment of the comparative capabilites of the models for general applicationwas notpossiblebasedon the reported CAPTEX results. Ifconclusions couldhavebeenmade, theywouldnecessarilybe temperedby the rather small number ofcases, the specific source characteristics, and the narrow set ofmeteorological scenarios during CAPTEX. Consequently, a simultaneous evaluation ofMESOPUFF II anda hierarchy of other models using anagreedupon set of evaluationmeasures with a broader database(s), which should include elevatedpoint source data, is certainlyneeded to provide definitive evidence ofrelativemodelperformance. In the interim, the MESOPUFF II model shouldcontinue tobe considered a usefulmodeling tool for short-term, regional applications. Diagnostic tests of optional features in the model provided distinct differences inplume transport and dispersionfrom the defaultmethods. Themixed-layer averagedwind field (i.e. default lower levelwind field) was found tobe superior to single levelwind fields at the surface or 850 mb height in simulating the impact time and locationof the tracer plume at the 300 km arc. Results suggested that it shouldremain as the preferredwind field for representingboundary layer transport when applying this model. Model test runresults with the uniformvertical mixing option indicated thatpeak concentrations were much closer to observedvalues than those from the default Gaussian dispersionparametermethods. With the uniformvertical mixingmethod, puffs are completely dispersed through the entire extent of theboundary layer after release. These test results gave more evidence thatmodeledplumes required greatervertical mixing during the afternoonrelease periods. In lieuof the suggested revision to the Gaussian dispersion scheme noted earlier, the selectionof this optional dispersionmethod appears tobe an attractive alternative since itproduces the desiredvertical dispersionwhenneutral conditions are prevalent. Alimited group ofmodel sensitivityruns was also performed in an effort to examine the impact on 24-hpeak andplume average S02 and sulfate concentrations fromvariations incertainkeyparameters and changes inmethods in the dry deposition and chemical transformationcomponents of the model. The modelbase case and test case runs were exercisedwith the same meteorological fields from CAPTEX #l and the same emissionrates from a large, elevatedpoint source. Results showed that sulfate concentrations were more sensitive to the selectedvariation ina parameter ormethod. Inparticular, sensitivityrunresults with differences in the surface so4 resistance or ±50% differences inbackground ozone concentrationproducednegligible vari ations in so2 peak andmean concentrations; however, the impact on sulfate concentrations was from 15-20%. This finding is particularlyrelevant to regionalmodel applications since thehigher concentration levels for sulfate were found atmuch greater downwind distances thanwas so2. It follows that so4 is influenced to a greater extentby transformation and deposition -processes. Amore extensive evaluationof these modules is advocatedwith suitable experimental data.


### 95

### REFERENCES Benkley, C. W. andA. Bass, 1979: Development ofmesoscale air quality simulationmodels; Vol. 3. User's guide to MESOPUFF. EPA/600/7-80-058, U.S. Environmental ProtectionAgency, Available as NTIS PB80-227796. Brown, R., M. Leach, G. Raynor, and p_ Michael, 1985: Asummary of the weather documentation for the 1983 Cross-AppalachianTracer Experiments. Atmospheric Sciences Division, BrookhavenNational Laboratory, Upton, New York 11973. Clark, T. L., R. Cohn, S. Seilkop, R. Draxler, andJ. Heffter, 1988: Comparison ofmodeled andmeasured tracer gas concentrations during the Across NorthAmericaTracer Experiment (ANATEX). Paper at the 17th International Techn. Meeting ofNATO-CCMS onAir Poll. Modeling and Its Application, Cambridge, Engl., Sept. 19-22, 1988, 13 pp. Davis., W. E., A. R. Olson, B.T. Didier, andD. W. Damschen, 1986: Acomparison of meso-alphaMLAM model results with observations from CAPTEX. Preprints, FifthJoint Conf. onApplications ofAir Poll. Meteorol., Arner. Meteorol. Soc., Boston, MA., 

### p.69-72. Draxler, R.R., 1987: Sensitivity of a trajectorymodel to spatial and temporal resolution of the meteorological data during CAPTEX. J. Clim. andAppl. Meteorol.,26, 1577-1588. Ferber, G. J., 

### J. L. 

### Heffter, R. R. Draxler, R. J. 

### Lagomarsino, F. 

### L. Thomas, R. N. Dietz, and C. M. Benkovitz, 1986: CrossAppalachianTracer Experiment (CAPTEX 83). Final Report NOAA Tech. Memo. ERL ARL-142, Air Resources Laboratory, Silver Spring, MD. Gelinas, R. J., 

### andJ. P. Vayk, 1979: Systematic sensitivity analysis of air quality simulationmodels. EPA/600/4-79-035. U. S. Environmental ProtectionAgency. Hales, J. M., B. B. Hicks, and J. 

### M. Miller, 1987: The role ofresearch measurement networks as contributors to federal assessments of acid deposition. Bull. Arner. Meteorol. Soc.,68, 216-225. Heffter, J. 

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### 1965: The variation ofhorizontal diffusionparameters with time for travel periods of one hour or longer. J. 

### Appl.Meteorol., 4, 153-156. Kao C. andT. Yamada, 1988: Use of the CAPTEX data for evaluations of a long-range trnasport numerical model with a four-dimensional data assimilation technique. Mon. Wea. Rev., 116, 293-306. Lagomarsino, R. J., 

### F. L. Thomas, T. J. Weber, and L. 

### R. Greenfield, 1987: Cross-AppalachianTracer Experiment (CAPTEX 83) QualityAssurance of Surface Samples. Environmental Measurements Lab., EML-462, New York, New York 10014. 96

### Lee, I. Y., 1987: Numerical simulations ofCross-Appalachiantransport and diffusion. Boundary-LayerMeteorol., 39, 53-66. Morris, R. 

### E., R. 

## c. Kessler, S. G. Douglas, andK. R. Styles, 1988: Rockymountainaciddepositionmodel assessment: Evaluationofmesoscale depositionmodels for use incomplex terrain. EPA/600/3-88-088. 

### U. S. Environmental ProtectionAgency. Available as NTIS PB88-167 481. Page, S. H., 1980: National landand landcover inventory. 

### U. S. Environmental ProtectionAgency, Washington, D.C. Pasquill, F., 1974: Atmospheric Diffusion (2ndEdition). Ellis Horwood Ltd., Chichester, Engl. 228pp. Policastro, A. J., M. Wastag, L. Coke, R. A. Carhart, W. E. Dunn, 1986: Evaluationof short-termlongrange transportmodels (Vol I, II). EPA-450/4-86-016a, b. U. S. Environmental ProtectionAgency. Available as NTIS documents PB87-142337, PB87-142345. Raynor, G. S., R. N. Dietz, andT. W. D'Ottavio, 1984: Aircraft measurements oftracer gas during the 1983 Cross-Appalachian Tracer Experiment. Proc. ofFourthJoint Conf. onApplications of Air PollutionMeteorol., Amer. Meterol. Soc., Boston, MA, 

### 162-165. Scire, J. S., F. W. Lurmann, A. Bass, S. R. Hanna, 1984a: Development of the MESOPUFF II dispersionmodel. EPA/600/3-84-057, Environmental ProtectionAgency, ResearchTriangle Park, NC. Available as NTIS PB No. 84-183 753. Scire, J. S., F. W. Lurmann, A. Bass, and S. R. Hanna, 1984b: User's Guide to theMESOPUFF II model andrelatedprocessorprograms. EPA/600/8-84-013, Environmental ProtectionAgency, ResearchTriangle Park, NC. Available as NTIS PB No. 84-182 775. Sheih, C. M., M. L. Wesely, andB. B. Hicks, 1979: Estimated dry depositionvelocities of sulfur over the easternUnited States and surroundingregions. Atmos. Environ.,13, 1361 1368.**Shreffl~r,** 

### J. H., 1982: Intercomparisons ofupper air and surface winds inanurban region. Boundary-LayerMeteorol.,24, 345-356. Stunder, B. J. B., J. L. Heffter, andR. R. Draxler, 1986: Long range transportmodel evaluationusing CAPTEX aircraft and surface data. Vertical tracer distribution. Preprints, FifthJoint Conf. onApplications ofAir PollutionMeteorol., Amer. Meteorol. Soc., Boston, 

### MA., 65-68. Turner, D. B., 1964: Adiffusionmodel for anurban area. J. Appl. Meteorol.,3, 83-91. Turner, D. B., 1970: Workbook of atmospheric dispersionestimates. 97

### Publ. No AP-26, Office ofAir Programs, U. S Environmental ProtectionAgency., 84 pp. Available as NTIS PB 191 482. Walcek, C. J., 

### R. A. Brost, J. 

### S. Chang, andM. L. Wesely, 1986: S02, sulfate andHN03 depositionvelocities computedusing regioanl land use andmeteorological data. Atrnos. Environ.,20, 949-964. Wilson, W. E., 1981: Sulfate formation inpoint source plumes: Areview ofrecent field studies. Atrnos. Environ., 15, 2573-2590.

98

### APPENDIXA

MODELTESTRESULTSFORDIFFERENTPUFFRELEASE/SAMPLINGRATES 

### Aseries ofMESOPUFF II model runs for the CAPTEX#l casewas performed withvarious puff**r~lease** 

### and samplingrates. The results presentedbelow were generated frompeaktracer concentrationvalues at the initial tracer measurement arc at 300

### kmwhere thehighest concentrations were found. An averagepeakconcentrationvalue (CAVE) was computedfrom the results of eachmodel testruns andthe individualpeakconcentrationswere normalized by this average value to determine the amount of difference among the test runs. Differences over this set ofpuffrelease/samplingrates indicated relatively little sensitivity to the peakconcentrations at this distance under the conditions of this experiment. Similar test runs are advocated in other applications to determine sensitivityofmodeled concentrations to these rates.

TABLElA 

### DIFFERENCES IN PEAKVALUES AT THE INITIALARC FOR DIFFERENT PUFF RELEASE/SAMPLING RATES

RUN RELEASE RATE SAMPLING RATE Cmax/CAVEmax CPU TIME (puffs/h) ( #/h) (s)

1 2 2 0.997 12.7 2 2 4 0.966 22.1 3 4 2 1.020 22.4 4 4 4 0.984 41.0 5 

### 8 2 1.023 41.8 6 12 2 1.024 61.6 7 12 4 0.986 117
0

### 6

99