8A.2
A review of adaptive observations

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Thursday, 27 January 2011: 8:45 AM
A review of adaptive observations
2A (Washington State Convention Center)
Sharanya J. Majumdar, Univ. of Miami/RSMAS, Miami, FL; and T. THORPEX Data Assimilation and Observing Strategies Working Group

A review of the field of adaptive observations has recently been conducted by the THORPEX Data Assimilation and Observing Strategies (DAOS) Working Group, under the auspices of the World Meteorological Organisation (WMO). In the context of this presentation, "adaptive observations" refers to the selection of supplementary observations that augment those observations that are conventionally assimilated into operational numerical weather prediction models. These extra observations can be selected in specific regions at chosen times, in order to improve a short- or medium-range forecast of a high-impact weather event. Examples include dropwindsondes launched from aircraft, new types of satellite data, and the inclusion of selected regular satellite observations (such as radiances) that are currently not included in data assimilation due to routine thinning procedures.

Since the advent of adaptive observations during the Hurricane Synoptic Flow experiments in the 1980s-90s, and the FASTEX experiment in 1997, several large and usually international field campaigns have taken place in which observations have been targeted at selected regions to improve forecasts of winter storms or tropical cyclones. Concurrently, collaborative efforts to improve the use of satellite data in numerical models have been ongoing. Numerous strategies have been proposed for the selection of adaptive observations, and an array of quantitative evaluations have been performed on the impact of assimilating these extra observations on numerical forecasts. Given the rapid evolution of this field during the past decade, the time is appropriate to review pertinent developments, successes, and shortcomings of these efforts, and to offer recommendations for advancing forecasting via adaptive observations in the future.