Optimizing the use of remotely sensed atmospheric data through four-dimensional data assimilation
Results with the GLA interactive forecast-retrieval-assimilation system, which directly uses satellite radiance data in the assimilation cycle, are presented. Forecasts made from initial conditions derived with this system are shown to be more skillful than those whose initial conditions came from a comparable assimilation using statistically based temperature soundings, or ones using no sounding data at all. Examples are also given of additional parameters which are produced through the physical inversion of the satellite radiance data during the interactive assimilation cycle. These are potentially useful for analysis purposes.