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Evaluations of CALPUFF, HPAC, and VLSTRACK with Two Mesoscale Field Datasets

Joseph C. ChangGeorge Mason University, Fairfax, Virginia

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Pasquale FranzeseGeorge Mason University, Fairfax, Virginia

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Kittisak ChayantrakomOld Dominion University, Richmond, Virginia

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Steven R. HannaGeorge Mason University, Fairfax, Virginia

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Abstract

Results of evaluations of transport and dispersion models with field data are summarized. The California Puff (CALPUFF), Hazard Prediction and Assessment Capability (HPAC), and Chemical/Biological Agent Vapor, Liquid, and Solid Tracking (VLSTRACK) models were compared using two recent mesoscale field datasets—the Dipole Pride 26 (DP26) and the Overland Along-wind Dispersion (OLAD). Both field experiments involved instantaneous releases of sulfur hexafluoride tracer gas in a mesoscale region with desert basins and mountains. DP26 involved point sources, and OLAD involved line sources. Networks of surface wind observations and special radiosonde and pilot balloon soundings were available, and tracer concentrations were observed along lines of whole-air samplers and some fast-response instruments at distances up to 20 km. The models were evaluated using the maximum 3-h dosage (concentration integrated over time) along a sampling line. It was found that the solutions were highly dependent upon the diagnostic wind field model used to interpolate the spatially variable observed wind fields. At the DP26 site, CALPUFF and HPAC had better performance than VLSTRACK. Overall, the three models had mean biases within 35% and random scatters of about a factor of 3–4. About 50%–60% of CALPUFF and HPAC predictions and about 40% of VLSTRACK predictions were within a factor of 2 of observations. At the OLAD site, all three models underpredicted by a factor of 2–3, on average, with random scatters of a factor of 3–7. About 50% of HPAC predictions and only 25%–30% of CALPUFF and VLSTRACK predictions were within a factor of 2 of observations.

Corresponding author address: Joseph Chang, School of Computational Sciences, MS 5C3, George Mason University, Fairfax, VA 22030-4444. jchang4@scs.gmu.edu

Abstract

Results of evaluations of transport and dispersion models with field data are summarized. The California Puff (CALPUFF), Hazard Prediction and Assessment Capability (HPAC), and Chemical/Biological Agent Vapor, Liquid, and Solid Tracking (VLSTRACK) models were compared using two recent mesoscale field datasets—the Dipole Pride 26 (DP26) and the Overland Along-wind Dispersion (OLAD). Both field experiments involved instantaneous releases of sulfur hexafluoride tracer gas in a mesoscale region with desert basins and mountains. DP26 involved point sources, and OLAD involved line sources. Networks of surface wind observations and special radiosonde and pilot balloon soundings were available, and tracer concentrations were observed along lines of whole-air samplers and some fast-response instruments at distances up to 20 km. The models were evaluated using the maximum 3-h dosage (concentration integrated over time) along a sampling line. It was found that the solutions were highly dependent upon the diagnostic wind field model used to interpolate the spatially variable observed wind fields. At the DP26 site, CALPUFF and HPAC had better performance than VLSTRACK. Overall, the three models had mean biases within 35% and random scatters of about a factor of 3–4. About 50%–60% of CALPUFF and HPAC predictions and about 40% of VLSTRACK predictions were within a factor of 2 of observations. At the OLAD site, all three models underpredicted by a factor of 2–3, on average, with random scatters of a factor of 3–7. About 50% of HPAC predictions and only 25%–30% of CALPUFF and VLSTRACK predictions were within a factor of 2 of observations.

Corresponding author address: Joseph Chang, School of Computational Sciences, MS 5C3, George Mason University, Fairfax, VA 22030-4444. jchang4@scs.gmu.edu

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