a numerical daily air quality forecast system for …lar.wsu.edu/docs/2004_vaughan_airpact_bams.pdfa...

549APRIL 2004AMERICAN METEOROLOGICAL SOCIETY |

T he expanding need for information that pervadesour society extends to a need to know about theenvironment in which we live. At the same time,

the explosion of information and computer technol-

ogy coupled with new and improved weather forecastsystems provides a foundation for conveying detailedenvironmental information to the public in near–realtime. In the Pacific Northwest, recognition of the needfor timely environmental information led first to theestablishment of the Northwest Modeling Consor-tium, and second to the development of an advancedoperational numerical weather forecast system as de-scribed by Mass et al. (2003). This system is based onthe fifth-generation Pennsylvania State University(PSU)–National Center for Atmospheric Research(NCAR) Mesoscale Model (MM5) and provides twicedaily forecasts covering Washington, Oregon, andpart of Idaho down to a horizontal grid scale of 4 km.The success of the consortium and the MM5 forecastsystem has now led to the development of a numeri-cal photochemical air quality (AQ) forecast system forthe Northwest. Initially developed with support fromthe Environmental Protection Agency (EPA) Envi-ronmental Monitoring for Public Access and Com-munity Tracking (EMPACT) program, and now op-erating with support from various consortiummembers, the Air Indicator Report for Public Access

A NUMERICAL DAILY AIRQUALITY FORECAST SYSTEM

FOR THE PACIFICNORTHWEST

BY JOSEPH VAUGHAN, BRIAN LAMB, CHRIS FREI, ROB WILSON, CLINT BOWMAN, CRISTIANA FIGUEROA-KAMINSKY, SALLY OTTERSON, MIKE BOYER, CLIFF MASS, MARK ALBRIGHT, JANE KOENIG, ALICE

COLLINGWOOD, MIKE GILROY, AND NAYDENE MAYKUT

Forecasts using mesoscale meteorological modeling and a photochemistry grid model, running

nightly, provide air quality predictions for a high-resolution Puget Sound domain via the Web.

AFFILIATIONS: VAUGHAN, LAMB, AND FREI—Laboratory forAtmospheric Research, Department of Civil and EnvironmentalEngineering, Washington State University, Pullman, Washington;WILSON—U.S. Environmental Protection Agency, Region X, Seattle,Washington; BOWMAN, FIGUEROA-KAMINSKY, OTTERSON, AND

BOYER—Washington Department of Ecology, Olympia, Washington;MASS AND ALBRIGHT—Department of Atmospheric Science,University of Washington, Seattle, Washington; KOENIG—Depart-ment of Environmental Health, University of Washington, Seattle,Washington; COLLINGWOOD, GILROY, AND MAYKUT—Puget SoundClean Air Agency, Seattle, WashingtonCORRESPONDING AUTHOR: Dr. Joseph Vaughan, Laboratory forAtmospheric Research, Department of Civil and EnvironmentalEngineering, Washington State University, Pullman, WA 99164-2910E-mail: [email protected]: 10.1175/BAMS-85-4-549

In final form 2 September 2003© 2004 American Meteorological Society

550 APRIL 2004|

and Community Tracking (AIRPACT) system runson a daily basis year-round. It produces detailedhourly maps of ozone and other pollutant concentra-tions, including primary particulate matter (PM), fora 24-h forecast period. These predictions are availableby 1400 UTC on the morning of the forecast on a pub-lic Web site (see www.airpact.wsu.edu). AIRPACT isunique nationally as the sole numerical modeling sys-tem producing daily, year-round, high-resolutionregional AQ forecasts with daily verification. In thispaper, we discuss the motivation for building a nu-merical AQ forecast system, describe the basic com-ponents of the system, present examples of forecastresults, and show how the forecast compares to avail-able observations.

Until recently, AQ forecasts were based uponanalysis of forecast meteorological conditions withconsideration of pollutant ventilation conditions.Currently, a number of metropolitan areas take themeteorological analyses a step further and incorpo-rate use of statistical empirical methods to predictprobable concentrations of ozone or other pollutants(U.S. Environmental Protection Agency 1999a, seewww.epa.gov/airnow/). AIRNow forecasts are pro-duced manually by AQ meteorologists. Beyond theseforecasts, numerical forecasts employing photochemi-cal Eulerian grid models have been employed on asummer-only basis in the eastern United States topredict ozone concentrations with various and nesteddomains (McHenry et al. 1999) and in support of spe-cial field study campaigns (Cai et al. 2002; Grell et al.2000). In Canada, there is a substantial ongoing ef-fort to provide operational numerical forecasts fortropospheric ozone and particulate matter throughthe Canadian Hemispheric and Regional Ozone andNOx System (CHRONOS, see http://gfx.weatheroffice.ec.gc.ca/chronos/index_e.html). This system sharesmany of the attributes of the AIRPACT system, butoperates with a 21-km grid scale for a large portionof the United States and Canada. Together, theCHRONOS and AIRPACT systems represent thestate of the art in operational, year-round, daily AQforecast systems.

The motivation for forecasting AQ begins with theneed of AQ agencies to provide the public with healthalerts when air pollution levels are expected to exceedstipulated levels. This is most often done using an airquality index (AQI) that applies throughout a met-ropolitan area. Improving the confidence in theseforecasts and providing more spatial and temporalinformation about elevated pollutant levels will helpsensitive sectors of the public make better decisionsregarding their daily activities. In addition to cuing

health alerts, forecasting pollution episodes can trig-ger emission control efforts to reduce the impact ofan episode. Offering free public transportation onhigh ozone days is an example of this approach thatis used in some areas. More detailed forecasts thathave a demonstrated reliability will improve the ef-fectiveness of these control efforts. Also, AQ forecastsrelated to prescribed forest fires and agricultural fieldburning are valuable for minimizing smoke impact onlocal populations or regional haze. The United StatesDepartment of Agriculture (USDA) BlueSky programhas just begun forest fire smoke predictions in theNorthwest (see www.blueskyrains.org). In northernIdaho, we successfully ran the ClearSky smoke fore-cast system for management of smoke from agricul-tural field burning (see www.ClearSky.wsu.edu) be-ginning in the autumn 2002 burn season. TheClearSky system runs in parallel with the AIRPACTsystem described herein, and models smoke plumedispersion.

Generating numerical AQ forecasts for the PugetSound region poses two primary problems. The firstproblem is in generating a reasonably accurate me-teorological forecast for the complex Puget Soundregion; consider the scale and diversity of landscapesthat are involved: the peaks of the Olympic and Cas-cade Mountains reach elevations as high as 4400 m;maritime straits exist in a variety of sizes and orien-tations; surfaces range from seawater to farmland,mountain forest, and snowfields; and incomingweather arrives from seaward, all characterized by adearth of meteorological stations. This regional to-pography is starkly different from that of other areasin the United States that are the focus of Eulerian AQmodeling efforts: the southeastern United States,Texas Gulf coast, and Los Angeles areas, for example.Thus, many of the challenges associated with AQ fore-casting for the Puget Sound area are inherited fromthe difficulty in weather forecasting for this area.Fortunately, the mesoscale meteorology group at theUniversity of Washington has developed a system forhigh-resolution weather forecasts for the Puget Sound(Mass et al. 2003). The second major problem is inusing the scarce resources available, in terms of emis-sions data, manpower, computational power, and(critically) AQ simulation codes, in a balanced man-ner to get the best predictions possible. The AIRPACTapproach to this second problem is the general topicfor this paper.

In addition to addressing AQ management issues,detailed daily numerical forecast operations can alsoreveal new information concerning pollutant behav-ior in a region. As will be noted later, AIRPACT pre-


dicted the periodic occurrence of an ozone “hot spot”west of downtown Seattle that had not been previouslyidentified. The AQ monitoring network has now beenmodified as a result of AIRPACT. Archiving the dailyAIRPACT runs builds an extended simulation thatcan be analyzed to investigate a range of scientificquestions. For example, the AIRPACT-archived re-sults from a 6-month period are being used for aninitial analysis of the health risk associated withSeattle-area diesel particulate emissions. Finally, au-tomated evaluation of the model performance on anongoing daily basis builds a comprehensive picture cov-ering a wide range of conditions for determining howwell current models represent atmospheric processes.

Given the uses for reliable AQ forecasts, it is not sur-prising that there is growing interest in the developmentof numerical AQ forecast systems. In the UnitedStates, the Air Quality Research Subcommittee of theCommittee on the Environment and Natural Re-sources (CENR) recently published a review of fed-eral programs and research needs related to AQ fore-casts (CENR 2001). This was followed by a workshopconducted as part of the U.S. Weather Research Pro-gram; results from the workshop have been summa-rized in Dabberdt et al. (2004). In both cases, there wasa call for more substantial efforts to build and oper-ate numerical AQ forecast systems. In this regard,AIRPACT is a prototype of what we can expect in thefuture.

AIRPACT SYSTEM. Domain. The AIRPACT do-main (Fig. 1) encompassesthe Puget Sound, the majormetropolitan areas of Se-attle and Tacoma, and thenorth–south I-5 interstatehighway corridor. The do-main consists of 67 (N–S) by62 (E–W) grid cells (4 km× 4 km each), forming asubset of the cells in the4-km domain used in theMM5 forecast system. Thevertical coordinate systemis the sigma system used inMM5, but MM5 layers havebeen combined to providefor 13 layers reaching an el-evation of ~5 km. This do-main was selected based onresults from earlier photo-chemical AQ simulationsusing a much larger do-

main (Barna et al. 2000; Barna and Lamb 2000) thatshowed little transport of pollutants from theVancouver, British Columbia, Canada, metropolitanarea into the urban Puget Sound region. Experienceshows that the domain appropriate for a modeling ap-plication typically changes over time as policy require-ments and/or computational and/or informational re-sources change. For this reason, the AIRPACT systemautomates the sharing of domain-specific informationthat must be universally available among AIRPACTsystem components. Thus, the fullest use of metadatadefining the domain is a significant implementationdetail throughout the system implementation.

System design for AIRPACT. As shown in Fig. 2, the AQforecast system consists of the MM5 (Grell et al. 1995),the California Meteorological wind field model(CALMET; Scire et al. 1995), a dynamic emission pro-cessing scheme, and the Eulerian California photo-chemical grid model (CALGRID; Yamartino et al.1992). The MM5 forecasts are produced at the Uni-versity of Washington as described by Mass et al.(2003) for a set of nested domains with grid scales of36, 12, and 4 km, with the outermost 36-km modelrun being initialized at 0000 and 1200 UTC each day.AIRPACT currently uses forecast hours 12–36 froma 4-km simulation based on a 12-km MM5 run ini-tialized at 0000 UTC. The CALMM5 and CALMETprocessors pass MM5 wind fields to CALGRID. Thus,the CALGRID 24-h simulation begins at 1200 UTC.In processing MM5 hourly data, CALMM5 extracts

FIG. 1. Map of the AIRPACT domain.

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temperature and shortwave radiation fields that aremade available for emissions processing, as discussedin a subsequent section. Previous applications ofCALMET/CALGRID in this region have examinedhistorical ozone episodes of interest, such as the PugetSound event of 11–14 July 1996 (Barna et al. 2001;Jiang et al. 2003), where meteorological data (surfaceanalyses and vertical soundings) and MM5 simula-tions were both used to drive CALMET. In theAIRPACT forecast application, however, no observa-tional meteorological data are available—only theforecast results provided from MM5.

The CALGRID version being used in AIRPACTemploys a detailed atmospheric chemistry kineticmechanism called Statewide Air Pollution ResearchCenter 1997 (SAPRC 97; Carter 1996; Carter et al.1997) to account for the gas phase chemical reactionsthat lead to the production of ozone and related prod-ucts, such as formaldehyde. In this mechanism, ex-plicit reactions are included for inorganic and someorganic species (i.e., NO, NO2, isoprene) while mostvolatile organic compounds (VOCs) are represented

by a few surrogate or lumped species(e.g., OLE1 represents fast-reactingolefins). Chemical boundary condi-tions are based upon results for cleanmaritime and continental condi-tions taken from the literature, aswell as from previous episodicmodel simulations in the area (Jianget al. 2003). Optionally, results fromthe last hour of a forecast are fedback into the next forecast as chemi-cal initial conditions.

To implement the numericalforecast system, scripts were devel-oped in a Unix environment to au-tomatically execute the various dataprocessors and model codes. Modelresults, including gridded emissionpredictions, are converted to networkCommon Data Form (netCDF) for-mat and visualization postprocessingis done using the Package for Analy-sis and Visualization of Environmen-tal Data (PAVE), an AQ visualizationpackage (Environmental Modelingfor Policy Development project atthe Carolina Environmental Pro-gram, see www.cep.unc.edu/empd/EDSS/pave_doc/index.shtml). All ofthe scripts and codes are run on asingle processor Compaq/DEC Al-

pha system; a 24-h simulation requires approximately70 min of computer time. Each forecast is completeand results available through the Web site by approxi-mately 1400 UTC on the day of the forecast.

Emissions calculations. Preparation of emissions forCALGRID is a critical aspect of AIRPACT. Due to thefantastically multifaceted variety of relevant emissionsources, getting accurate emissions data is a virtualimpossibility; thus, required emissions inputs are es-timated through the artful use of available data andplausible calculations. AIRPACT explicitly treats ma-jor point-source emissions (e.g., from power plants)and three types of area emissions: 1) mobile vehicleemissions, 2) biogenic emissions, and 3) anthropo-genic emissions (small point sources treated collec-tively as area sources). The emissions subsystems forbiogenic and mobile emissions are shown in Fig. 3.Area emissions from all three contributing types aregenerated for each hour of CALGRID simulation.

Vehicles generate emissions through engine ex-haust and also through six types of evaporative loss:

FIG. 2. Major system components for AIRPACT.


hot soak, diurnal, crankcase, running, resting, andrefueling. The first three of these evaporative losstypes are combined into a single EVAPORATIVE cat-egory, while evaporative losses during refueling arerelegated to anthropogenic area emissions (such as gasstations). Thus, mobile vehicular emissions are treatedin four categories: EVAPORATIVE, EXHAUST,RESTING, and RUNNING. The Washington Depart-ment of Ecology prepared the AIRPACT base casemobile emissions through a multistep process utiliz-ing MOBILE5b software, with modifications for theanticipated TIER2 standards (Koupal 1999; Kremer1999). Figure 3 illustrates how AIRPACT generatesa gridded mobile emissions inventory with hourlytime resolution, utilizing gridded MM5 surface layertemperatures, and reflecting observed temporal vari-ability of traffic. The traffic activity adjustments includethe effects of the day of the week and hour of the day.

Figure 3 also shows how AIRPACT generates bio-genic emissions by using a third generation biogenicemission modeling system called the Global BiosphereEmissions and Interactions System (GLOBEIS;Guenther et al. 2000), and hourly MM5 data. To avoidthe need to run GLOBEIS in real time, uniform MM5fields of surface temperature andshortwave downward radiation wereused as inputs to a single run ofGLOBEIS. The base case tempera-ture was set to 30°C and the photonflux was set to 1000 mmol m-2 s-1. Theresulting biogenic emissions (iso-prene, terpenes, and other VOCs)were then converted to the SAPR 97chemical species and used as a basecase for emissions processing. Spe-cifically, these gridded biogenicemissions are modified on a run-time basis using hourly gridded tem-peratures and shortwave downwardradiative flux from MM5, accordingto equations for isoprene, terpenes,and other VOCs from Guenther etal. (1993). Thus, for each hour ofCALGRID simulation, a base map ofspeciated biogenic emissions ismodified for predicted temperatureand light levels to project hourly bio-genic emissions.

Because point source emissionsdata generally reflect steady indus-trial operating conditions and typi-cally are available only on an annualaverage basis, we adapted an existing

point-source emission inventory for the region fromour previous episodic modeling studies (Barna et al.2000). In this case, the Washington Department ofEcology constructed a point-source emissions inven-tory from annually reported data with some modifi-cation for current operating conditions specific to theJuly 1996 period originally simulated. In AIRPACT,we used this inventory to produce emission files forweekday, Friday, Saturday, and Sunday, each definedas per local time. Then hourly data are read as re-quired from these four single-day files to construct atime sequence of hourly point emissions to match thedays of the week and the hours required for theCALGRID run. Development of a more current pointsource inventory with adjustment for specific condi-tions remains a future task for improving AIRPACT.

Anthropogenic area emissions are also available forfour generic days: weekday, Friday, Saturday, andSunday. As with the point emissions, anthropogenicarea emissions are recast to construct the time se-quence required for the CALGRID run.

An example of mobile NOx (NO and NO2) emis-sions is shown in Fig. 4 that illustrates the domina-tion of the Puget Sound mobile emissions by the Se-

FIG. 3. Schematic for processing biogenic and mobile emissions.

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attle metropolitan area and highway network.Figure 5 shows the urban-core diurnal mobile NOxemissions pattern with a rush hour peak in the 0600–

0700 PST (1400–1500 UTC) hour and a broader peakfrom 1500–1800 PST (2300–0200 UTC).

An example of biogenic isoprene emissions isshown in Figs. 6 and 7; the latter displays a diurnalcycle that reflects the functional dependence of iso-prene emissions on light and temperature. Area NOxemissions are shown in Fig. 8 and illustrate how areaemissions reflect population density.

FIG. 4. Emissions contour map for mobile NOx emissionsduring the afternoon rush hour. The red rectangleshows the subdomain averaged to represent the Seattleand Tacoma urban core areas, as shown in subsequentplots of diurnal patterns.

FIG. 5. Diurnal pattern of urban-core mobile NOx emis-sions as average cell value (g s-----1 cell-----1). Time steps 1–24 correspond to hours beginning 1200 UTC 10 Augthrough 1100 UTC 11 Aug. The urban core is the redrectangle shown in Fig. 4.

FIG. 6. Emissions contour map for biogenic isopreneemissions.

FIG. 7. Diurnal pattern of biogenic isoprene emissionsas average cell value (g s-----1 cell-----1) for the urban core.Time steps 1–24 correspond to hours beginning 1200UTC 10 Aug through 1100 UTC 11 Aug. The urbancore is the red rectangle shown in Fig. 4.


Simulation results. CALGRID results are generatednightly with animations of selected emissions, pollut-ant mixing ratios and statistics being made availablevia the project Web site (see www.airpact.wsu.edu).Examples of CALGRID forecasts for O3 and NOx mix-ing ratios are shown in Figs. 9 and 10 for a day fromthe Pacific Northwest 2001 (PNW2001) field program(discussed later). During this day, weather conditionswere mild with warm temperatures and light to mod-erate northwest winds. These conditions are quitetypical of periods with elevated ozone within the re-gion (Barna et al. 2000). These examples illustrate theexpected pattern for ozone formation downwind ofPuget Sound with elevated NOx and reduced ozoneconcentrations in the urban core and a “fishhook”pattern of elevated ozone downwind and surround-ing the urban core. The relatively high ozone concen-trations that appear to the west and southwest of theurban core had not previously been identified; sub-sequent monitoring generally confirmed existence ofthis AIRPACT-predicted ozone hot spot.

By generating daily forecast results, the AIRPACTsystem makes possible the analysis of (predicted) cu-mulative pollutant impact. For example, results accu-mulated during August 2001 are shown in Fig. 11 forthe number of hours with predicted O3 mixing ratiosabove 80 ppb. Also, results accumulated during thewinter of 2001–02 for diesel PM emissions (Fig. 12)

were provided to the Washington Department ofEcology for an initial evaluation of population expo-sure associated with diesel emissions.

FIG. 8. Emissions contour map for area NOx emissions.FIG. 9. The O3 simulation results for Puget Sound area.Urban Seattle is marked as S. Enumclaw is marked as E.

FIG. 10. The NOx simulation results for Puget Soundarea. The location of a power plant in Centralia, WA,is marked as CPP.

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Verification using surface and airborne sensors.AIRPACT simulation results are automatically com-pared (1 day later) with AQ observations from anautomated instrument network operated by theWashington Department of Ecology. These compari-sons of observed and predicted mixing ratios are dis-played as time series on the Web site on the day fol-lowing the forecast, such as shown in Fig. 13. Thisfigure is a good illustration of the variability in the skillof the system for simulating ozone. A summary ofthese evaluation results from a number of ozonemonitoring sites during the PNW2001 field campaignin August 2001 is shown in Fig. 14. There is consid-erable scatter of the ratio of observed/predicted O3mixing ratio at low mixing ratios and a tendency tounderpredict the maximum observed mixing ratios.Efforts are underway to investigate why this under-estimation occurs and whether it is the result of er-rors in meteorological, boundary condition, emission,or chemical factors.

Model performance statistics for O3 for August2001 are given in Table 1. Note that maximum ozoneobserved during August was 98 ppb, while the maxi-mum predicted ozone (at monitor locations only) was101 ppb. For analysis of historical ozone episodes us-ing photochemical grid models, the EPA defines ac-ceptable model performance in terms of a bias within15% and a normalized gross error less than 35%. Forthe forecast system, the August results generally meet

the bias criterion, but exceed somewhat the gross er-ror criterion. This is not surprising since the forecastresults cannot take advantage of meteorological ob-servations to improve the meteorological simulation,as is typically done in historical episode analyses. Wehave previously employed the MM5 using observa-tional nudging to improve the predicted wind fieldsfor modeling a specific ozone episode (Barna andLamb 2000). In that case, incorporating observedwinds in the MM5 simulation improved the O3 modelperformance statistics; the mean bias improved from–22% to –9% and the gross error improved from 31%to 24%. Key questions for numerical AQ systems are,1) What are useful measures of forecast performance?,and 2) What is an acceptable level of forecast skill?These questions need to be addressed in terms of bothscientific needs as well as the needs of the public forAQ forecast products.

Additional data for model verification come froman August 2001 North America Research Strategy forTropospheric Ozone (NARSTO) aircraft study calledPNW2001, conducted collaboratively by the PacificNorthwest National Laboratory, EPA Region 10, theWashington State Department of Ecology, the Uni-versity of Washington, and Washington State Univer-sity. PNW2001 was conducted in coordination withthe Canadian Pacific 2001 study in the Vancouver,

FIG. 11. Accumulated grid cell hours with simulatedozone greater than 80 ppb during Aug 2001. FIG. 12. Grid cell maximum 24-h average particulate

matter concentration due to PM diesel emissions dur-ing winter 2001–02.


British Columbia, area. AIRPACT results are beingevaluated by comparison to aircraft and ozonesondedata from PNW2001.

An area of high ozone SW of Seattle–Tacoma inFig. 9 is seen in Fig. 11 to be a commonly predictedfeature. This region of frequently elevated O3 waspredicted by AIRPACT but had not been observedprior to August 2001. Snow et al. (2003) found fromthe 10 August PNW2001 sampling flight that thisheretofore unobserved O3 plume appears to have ex-isted much as predicted, lending credence to the ba-sic correctness of the AIRPACT system. These pre-dictions both stimulated interest inusing model results to guide moni-toring efforts to discover previouslyuncharted O3 plumes and motivatedthe extension of the monitoring net-work to collect data from the vicinityof the AIRPACT-predicted hot spot.

In addition to the PNW2001 fieldprogram, preliminary evaluation ofAIRPACT has been completed usingthe routine monitoring system. Thisevaluation is complicated since 1)CO monitors are typically placed attraffic hot spots, and 2) such hot spotmonitoring reports elevated CO lev-els that are not well represented inthe 4-km grid of the AIRPACT sys-tem. Further, there are no VOC ob-servations made on a routine basis andthere is only one NOx monitor in the

area. For particulate matter,AIRPACT was modified totreat primary particulateemissions from onroad die-sel sources, but availablemonitoring data capturesboth primary and second-ary particulate from allsources. These difficultiesin a rigorous evaluation ofthe system highlight theneed for better coupling ofurban monitoring networksand numerical modelingtools such as AIRPACT.

In spite of the shortcom-ings of the monitoring sys-tem for evaluation pur-poses, we have examinedthe ability of AIRPACT tosimulate pollutant concen-

trations during several different months through theyear. For example, results for CO predictions shownin Fig. 15 indicate that the model overestimates COat low concentrations, but has relatively good perfor-mance for CO at elevated levels during July andOctober. However, in December the model signifi-cantly underestimates CO during periods of elevatedCO. This may be due to a larger impact of localsources during periods with significant stagnation.For NO2, the results for these different months (notshown) indicate overestimation at low concentrationsand underestimation at elevated concentrations, simi-

FIG. 14. Ratio of predicted/observed O3 vs observed O3 mixing ratioat five rural sites downwind of Seattle during Aug 2001.

FIG. 13. Diurnal observed and predicted O3 mixing ratios at the Enumclaw siteduring 9–12 Aug 2001.

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evated pollutant levels.However, it is not yet clearwhether this is caused byunderestimation of emis-sions, overestimation ofmixed layer depth or windspeed, or by the inherentproblems of comparingpoint observations to grid-ded model output.

Because of interest in theapplication of AIRPACT inproviding public alerts, it isalso important to evaluatethe use of the system tosimulate the correct AQI.In this regard, we need torecognize that the AQI hasvery distinct cutoffs based onpollutant concentrations:good, moderate, unhealthyfor sensitive groups, and un-healthy. Thus, the model canmiss the correct AQI even ifit is predicting a pollutantlevel within a few parts perbillion. In terms of the 1-and 8-h ozone standards,AIRPACT correctly pre-dicted the AQI category in98.4% and 97.6% of the pos-sible observations, respec-tively (all sites, all hours).However, the tendency ofthe system to underesti-mate peak ozone values isevident in the AQI sum-mary in Table 2; the num-ber of cases with moderateto unhealthy conditions isslightly underestimated.

CONCLUSIONS. Severalaspects of the AIRPACT sys-tem promise to provide

modelers with ample feedback, helping them to fo-cus available resources on model skill, emissions ac-curacy or other science, and code or data issues. Forexample, during the 10 August 2001 simulation dis-cussed herein, gaps in roadways can be seen in themobile emissions results in Fig. 4; since this simula-tion the mobile emissions data have been updated tocorrect this problem. Also, strong area emissions for

Enumclaw 23 60

Obs (ppb) 26 15 88Pred (ppb) 26 17 76P/O 1.2 0.8

Seattle –5 56


Pack forest 22 53


Rainier –6 35


North Bend 46 63


Issaquah 2 47


Yelm 27 54


All sites 15 51


TABLE 1. Summary of model ozone performance statistics for Aug2001. Mean, standard deviation, and maximum are shown as parts perbillion for observed and predicted ozone for each site and sitescombined. Mean and standard deviation for ratio of predicted overobserved (P/O) ozone are given and as simple ratios for each site andsites combined. Mean bias and gross error are given as percentagesfor each site and sites combined.

Mean Std dev Max Bias (%) Gross error (%)

lar to that shown previously for O3. For particulates,AIRPACT underestimated observed levels, but thiswas expected since the system only simulates primarydiesel emissions. However, it appears the degree ofunderestimation is more than can be explained by thefraction of diesel PM in the emission inventory.Collectively, these preliminary evaluations of forecastperformance suggest a general underestimation of el-


NOx have been detected in suspicious areas (Fig. 8)and traced back to spatial allocation problems formarine (ship fuel combustion) emissions; the spatialallocation of marine emissions merits further review.Last, the blocky pattern visible in biogenic isopreneemissions (Fig. 6) suggests that our use of GLOBEISresults in relatively coarse-scale emissions, prompt-ing a review of our biogenics processing. In general,AIRPACT’s automated animations and automatedverification support the identification of desired cor-rections and enhancements.

Despite minor problems identified by exercisingAIRPACT, this AQ simulation system is proving tobe a unique tool to study the performance (accuracy)of the CALGRID AQ model, the correctness of theemissions subsystems, and the adequacy of the asso-ciated emissions inventories. Evaluation of AQ fore-cast verification statistics with reference to categoriesof meteorological conditions can 1) help identify weakareas in the AQ modeling components, and 2) guideimprovements to these models, or 3) may motivate thereplacement of CALGRID with another model, forexample the Community Multiscale Air Qualitymodel (CMAQ; U.S. Environmental ProtectionAgency 1999b).

While AIRPACT’s automated verification compo-nent has provided useful feedback for model evalua-tion and correction of errors, there is still a pressingneed for a robust and capacious data storage andanalysis system to fully support post hoc analysis ofmodel performance, in concert with observed meteo-rology data and a wide variety of AQ data. We areworking to identify a database and analysis system(hardware and software) well suited to supporting thisAIRPACT requirement.

The AIRPACT project is demonstrating a highlyautomated real-time AQ forecasting approach in ap-plication to the Puget Sound area. The AIRPACTdesign targets linkage of real-time monitoring to fore-casting for purposes of verification and seeks to ap-ply AQ modeling both to educate the public and toalert sensitive individuals to potentially dangerouspollution episodes. AIRPACT is operating with a fullflow of emissions and generating daily AQ forecastsfor review. It is very indicative of the interest in thesystem that there has been a recent push to extendAIRPACT coverage both north into Canada andsouth into Oregon. Accordingly, the 4-km domain isbeing expanded to include Vancouver, British Co-lumbia, in the north, and Salem, Oregon, in the south.Also, additional tracer species are being added toAIRPACT for tracing air-toxics emissions. Thus,AIRPACT will soon provide an automated, long-term

simulation of air toxics for two major metropolitanareas in the Northwest, and these results will be di-rectly comparable to ongoing air-toxics monitoringprograms in both cities.

ACKNOWLEDGMENTS. The authors wish to recog-nize the assistance received from David Ovens of Univer-sity of Washington; Guangfeng Jiang, now with the RJ LeeGroup, Inc.; and Susan O’Neill, now with the USDA For-est Service FERA Lab. This work has been supported by the

FIG. 15. Ratio of predicted/observed CO vs observed COmixing ratios at an urban Seattle monitoring site dur-ing (a) Jul, (b) Oct, and (c) Dec in 2002.

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U.S. EPA EMPACT program and Boeing Endowmentfunds at Washington State University.

REFERENCESBarna, M., and B. Lamb, 2000: Improving ozone mod-

eling in regions of complex terrain using observa-tional nudging in a prognostic meteorological model.Atmos. Environ., 34, 4889–4906.

——, ——, S. M. O’Neill, H. Westberg, C. Figueroa-Kaminsky, S. Otterson, C. Bowman, and J. DeMay,2000: Modeling ozone formation and transport in theCascadia region of the Pacific Northwest. J. Appl.Meteor., 39, 349–366.

——, ——, and H. Westberg, 2001: Modeling the effectsof VOC/NOx emissions on ozone synthesis in theCascadia airshed of the Pacific Northwest. J. AirWaste Manage. Assoc., 51, 1021–1034.

Cai, C., and Coauthors, 2002: An experimental AQ fore-cast modeling system for the Northeast United States:A demonstration study. Preprints, Fourth Conf. onAtmospheric Chemistry, Orlando, FL, Amer. Meteor.Soc., CD-ROM, 4.2, 1–6.

Carter, W. P. L., 1996: Condensed atmospheric photo-oxidation mechanisms for isoprene. Atmos. Environ.,24, 4275–4290.

——, D. Luo, and I. L. Malkina, 1997:Environmental chamber studies fordevelopment of an updated photo-chemical mechanism for VOC reac-tivity assessment. Final Rep. pre-pared for California Air ResourcesBoard under Contract 92-345 byCenter for Environmental Researchand Technology, University of Cali-fornia, Riverside, 213 pp.

CENR, 2001: Air quality forecasting: Areview of federal programs and re-search needs. Air Quality Subcom-mittee of the Committee on Envi-ronment and Natural Resources,21 pp. [Available from NOAA Aer-onomy Laboratory, Office of theDirector, R/AL, 325 Broadway,Boulder, CO 80305-3328.]

Dabberdt, W., and Coauthors, 2004:Report of the U.S. Weather Re-search Program Workshop on airquality forecasting. Bull. Amer. Me-teor. Soc., 85, 563–586.

Grell, G. A., J. Dudhia, and D. R.Stauffer, 1995: A description of thefifth-generation Penn State/NCAR

mesoscale model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 122 pp.

——, S. Erneis, W. R. Stockwell, T. Schoenemeyer,R. Forkel, J. Michalakes, R. Knoche, and W. Seidl,2000: Application of a multi-scale, coupled MM5/chemistry model to the complex terrain of theVOTALP valley campaign. Atmos. Environ., 34,1435–1453.

Guenther, A. B., P. R. Zimmerman, P. C. Harley, R. E.K. Monson, and R. Fall, 1993: Isoprene and monot-erpene emissions rate variability: Model evaluationsand sensitivity analyses. J. Geophys. Res., 98 (D7), 12609–12 617.

——, C. Geron, T. Pierce, B. Lamb, P. Harley, and R. Fall,2000: Natural emissions of non-methane volatile or-ganic compounds, carbon monoxide, and oxides ofnitrogen from North America. Atmos. Environ., 34,2205–2230.

Jiang, G., B. Lamb, and H. Westberg, 2003: Using backtrajectories and process analysis to investigate pho-tochemical ozone production in the Puget Soundregion. Atmos. Environ., 37, 1489–1502.

Koupal, J. W., 1999: Development of heavy-duty gaso-line emissions inventories for the tier 2/sulfurNPRM. Environmental Protection Agency, Assess-ment and Modeling Division, Office of Mobile

1-h average O3

Good (O3 < 50 ppb) 3648 3666Moderate (51 ppb to 56 39

100 ppb)

Unhealthy for 3 2sensitive individuals(101 to 120 ppb)

Unhealthy (> 120 ppb) 0 1

8-h average O3

Good (O3 < 50 ppb) 4248 4270

Moderate (51 ppb to 29 22100 ppb)

Unhealthy for 7 0sensitive individuals(101 to 120 ppb)

Unhealthy (> 120 ppb) 5 0

TABLE 2. Summary of observed and predicted AQI for PugetSound during Jul 2002 for ozone.

Observed AQI Predicted AQIAQI range occurrences occurrences


Sources, March 26, 1999 Memo. to Docket A-97-10,Washington, DC, 23 pp. [Available online atwww.epa.gov/otaq/models/tier2/hdgas.pdf.]

Kremer, J. C., 1999: Modifications to heavy-duty dieselemissions rates, heavy duty mileage accumulation,and age distributions in MOBILE5b for tier 2/sulfurNPRM. Environmental Protection Agency, Assess-ment and Modeling Division, Office of MobileSources, March 26, 1999 Memo. to Docket A-97-10,Washington, DC, 4 pp. [Available online atwww.epa.gov/otaq/models/tier2/hddmobl.pdf.]

Mass, C. F., and Coauthors, 2003: Regional environmen-tal prediction over the Pacific Northwest. Bull. Amer.Meteor. Soc., 84, 1353–1366.

McHenry, J. N., N. Seaman, C. Coats, A. Lario-Gibbs, J.Vukovich, N. Wheeler, and E. Hayes, 1999: Real-timenested mesoscale forecasts of lower troposphericozone using a highly optimized coupled model nu-merical prediction system. Preprints, Special Symp.on Interdisciplinary Issues in Atmospheric Chemistry,Dallas, TX, Amer. Meteor. Soc., 125–128.

Scire, J. S., E. M. Insley, R. J. Yamartino, and M. E. Fernau,1995: A user’s guide for the CALMET meteorologicalmodel. Earth Tech Inc., 193 pp.

Snow, J. A., J. B. Dennison, D. A. Jaffe, H. U. Price, J. K.Vaughan, and B. Lamb, 2003: Aircraft and surface obser-vations in Puget Sound and a comparison to a regionalmodel. Atmos. Environ., 37, 4019–4032.

U.S. Environmental Protection Agency, 1999a: Guidelines fordeveloping an ozone forecasting program. Office of AirQuality Planning and Standards Rep. EPA-454/R-99-009,88 pp. [Available online at www.epa.gov/ttn/oarpg/t1/memoranda/foreguid.pdf.]

——, 1999b: Science algorithms of the EPA models-3 Com-munity Multiscale Air Quality (CMAQ) modeling system.Tech. Rep. EPA/600/R-99/030, 758 pp. [Available onlineat www.epa.gov/AMD/models3/doc/science/science.html.]

Yamartino, R. J., J. S. Scire, G. R. Charmichael, and Y. S.Chang, 1992: The CALGRID mesoscale photochemicalgrid model—I. Model formulation. Atmos. Environ., 26A,1493–1512.

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