Passive microwave remote sensing of rainfall considering the effects of wind and nonprecipitating clouds

摘要

abstract_textpIt has long been shown both in theory and in observation that emission from rain drops in a raining cloud results in upwelling brightness temperature above that caused by the sea surface alone, High brightness temperatures at microwave frequencies (e.g., 37 and 19 GHz) have usually been quantitatively associated with rainfall using physical or statistical models. By comparing concurrent special sensor microwave/imager and radar data, however, we noticed many cases where there is no appreciable rainfall in a field of view (FOV) which exhibits high brightness temperature (T-B) at 37 and 19 GHz. On the basis of calculations and past literature it is shown that such high brightness temperatures can be caused by nonprecipitating clouds and by wind. The effect of the wind is to create wave and high-emissivity foam on the sea surface, A model is developed to relate T-B to the fractional coverage of rain, f, within a FOV. The parameters of the model are calibrated by fitting the model to the observed brightness temperature and fractional rain coverage data. The critical parameter of the model, T-B,T-min, which is the threshold brightness temperature for the presence of rain, depends on the strength of the storm. The strength of the storm is characterized by the fraction of the FOVs within a large area that have T-B higher than 240 K, which is readily obtainable from satellite data alone. The instantaneous FOV rain rate R can then be obtained through the f similar to R relationship which is empirically derived using radar data. An algorithm has been proposed based on the T-B similar to f and f similar to R relationship. Application of the algorithm to TOGA-COARE and Darwin storms results in reasonable instantaneous FOV rain estimate. When averaged over the entire radar scan, a more accurate and unbiased areal rain estimate can be achieved./p/abstract_text