Ensemble Kalman filter data assimilation for improved chemical tracer forecasts in a 2-D sea breeze model (Foremerly J10.10)
Amy L. Stuart, Univ. of South Florida, Tampa, FL; and A. Aksoy, J. W. Nielsen-Gammon, and F. Zhang
Ensemble-based Kalman filtering (EnKF) is an data assimilation approach that is undergoing significant investigation for many environmental modeling applications. Here, we study the use of EnKF in the context of a sea breeze and chemical tracer model. Sea breeze circulations are an important weather pattern affecting coastal areas. Since many cities are located near coasts, these circulations have impacts on the formation and transport of urban air pollution. We describe here a nonlinear, two-dimensional sea breeze model augmented with a chemical tracer algorithm and the ensemble-based Kalman filter technique used in our analyses. We apply this model to investigate (1) uncertainty in of tracer concentration predictions, (2) the effects on meteorological and chemical predictions of the EnKF assimilation of tracer concentration data, and (3) the potential use of EnKF data assimilation for the design of targeted observational networks.
Joint Session 3, Air Quality Forecasting (Joint with the 8th Conference on Atmospheric Chemistry and 14th Joint Conference on the Application of Air Pollution with the A&WMA)
Thursday, 2 February 2006, 8:30 AM-9:45 AM, A408
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