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Data assimilation for Numerical Smoke Prediction
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  • Edward Hyer,
  • Christopher P Camacho,
  • David A Peterson,
  • Elizabeth A Satterfield,
  • Pablo E Saide
Edward Hyer
Naval Research Laboratory

Corresponding Author:[email protected]

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Christopher P Camacho
Naval Research Laboratory
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David A Peterson
Naval Research Laboratory
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Elizabeth A Satterfield
Naval Research Laboratory
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Pablo E Saide
University of California Los Angeles
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Abstract

Skillful forecasts of weather phenomena in numerical models begin with the most accurate set of initial conditions achievable from observational datasets. The process of combining observations with numerical model predictions is called data assimilation. This chapter describes the types of observations available for data assimilation in models that predict the transport, fate, and impacts of smoke pollution. Observation properties needed for effective data assimilation are identified based on experiences with a variety of observation types in data assimilation experiments, compiled from the published literature. The second half of the chapter surveys the data assimilation methodologies that have been applied to smoke aerosols, and describes specific problems associated with the smoke observations that require innovative techniques in data assimilation. The chapter concludes by providing an outlook for future research and development in data assimilation for smoke prediction models. Data assimilation for prediction of smoke is an emerging area of development that promises to greatly improve forecast skill as new datasets and techniques are applied.
13 Nov 2023Published in Landscape Fire, Smoke, and Health on pages 105-125. 10.1002/9781119757030.ch7