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Steven R. Hanna
Researcher at Harvard University
Publications - 183
Citations - 9593
Steven R. Hanna is an academic researcher from Harvard University. The author has contributed to research in topics: Atmospheric dispersion modeling & Plume. The author has an hindex of 48, co-authored 183 publications receiving 8979 citations. Previous affiliations of Steven R. Hanna include Pennsylvania State University & National Oceanic and Atmospheric Administration.
Papers
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Journal ArticleDOI
Air quality model performance evaluation
Joseph C. Chang,Steven R. Hanna +1 more
TL;DR: A review is given of a set of model evaluation methodologies, including the BOOT and the ASTM evaluation software, Taylor’s nomogram, the figure of merit in space, and the CDF approach.
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Flow and dispersion in urban areas
Rex Britter,Steven R. Hanna +1 more
TL;DR: In this article, the authors address flow and dispersion in urban areas at four scales: regional, city, neighborhood, and street, and address the most appropriate framework to study and quantify the result.
Book
Handbook on atmospheric diffusion
TL;DR: In this article, basic meteorological concepts are covered as well as plume rise, source effects, and diffusion models, and suggestions for calculating diffusion in special situations, such as for instantaneous releases over complex terrain, over long distances, and during times when chemical reactions or dry or wet deposition are important.
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Confidence limits for air quality model evaluations, as estimated by bootstrap and jackknife resampling methods
TL;DR: In this paper, a few alternate forms of the socalled bootstrap and jackknife resampling procedures are tested using a concocted data set with a Gaussian parent distribution, with the result that the jackknife is the most efficient procedure to apply, although its confidence bounds are slightly overestimated.
Journal ArticleDOI
FLACS CFD air quality model performance evaluation with Kit Fox, MUST, Prairie Grass, and EMU observations
TL;DR: The FLACS model as discussed by the authors was used to estimate the flow and dispersion around buildings and other large roughness obstacles, and the results were consistently fairly good, with a median of 86% of the predictions within a factor of two of the observations, a median relative bias suggesting a 20% underprediction, median relative scatter of about 50%, and a median 20% overprediction of the overall experiment maximum.