Collaborative Research: Adaptive Sampling and Ensemble-Based Data Assimilation

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This project represents a collaboration between Dr. Bishop and Dr. Emanuel of MIT who is funded under ATM-9815114. The project will expand work undertaken by the principal investigators on adaptive sampling of the atmosphere. Heretofore, routine in-situ observations of the atmosphere have been made either from fixed locations at fixed times (e.g., by instrumented weather balloons) or from platforms of opportunity such as ships and commercial aircraft. Adaptive sampling uses information about the current and (estimated) future state of the atmosphere, such as estimates of the distribution of analysis uncertainty and forecast sensitivity, to determine locations where observations would lead to the greatest improvements in forecasts. Programmable observation platforms, such as manned or unmanned aircraft, are then directed to these special locations to make the observations. The principal investigators' work to data has developed, refined, and tested several adaptive sampling strategies. They will continue with these studies and extending to the problem of hurricane track prediction, one of the most important forecasting problems facing us today. A key component of this study will be the exploration of data assimilation and its role in optimally employing these new observations in the weather analyses. In particular, Drs. Bishop and Emanuel will be looking at the use of ensembles for the assimilation process and comparing this approach with current techniques. In the course of this work, the principal investigators will employ a hierarchy of models.