Adaptive Observations: A Modeling Study and Fastex Experiment

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9634239 Emanuel Numerical weather prediction has advanced to the stage where further improvements are increasingly limited by the observational base. Numerical weather prediction models depend on the assimilation of observational data collected mostly form fixed locations. However, in certain extreme weather situations such as hurricanes or severe winter storms, additional observations are obtained by specially deployed platforms under the supposition that additional observations will improve the forecasts. This strategy certainly has been successful for hurricane track forecasting. It is then logical to explore whether the strategy of targeted observations could be applied more generally to weather forecasting and how such a strategy could be optimized. The principal investigators will investigate the possibility of improving forecasts by selectively observing the atmosphere at the locations and times where the forecast would most benefit from additional information. These locations are determined by real-time estimates of the likelihood of error in the analysis at the time of data assimilation and estimates of the sensitivity of forecast error to the presence or absence of observations. As an a-priori estimate of the likelihood of analysis error, a measure of the spread among members of an ensemble of 6- or 12-hour forecasts would be used. Such measure is the amplitude of the so-called "bred modes." Preliminary modeling studies show that targeting observations based on the amplitude of these modes leads to a significant reduction in the forecast error compared with randomly locating the same number of observations. Further improvements may be gained by targeting observations based on a model adjoint-derived measure of the sensitivity of forecast error to data in the next assimilation cycle. Thus, the primary goal of this research is to find the optimal observation targeting strategy based on both a-priori analysis error estimate and adjoint-derived forecast error sensitiv ities. These strategies will be tested using a hierarchy of numerical models and as part of a field experiment in conjunction with the international Fronts and Atlantic Storm Track Experiment (FASTEX). ***