Abstract
AbstractRainfall estimation using polarimetric radar involves the combination of a number of estimators with differing error characteristics to optimize rainfall estimates at all rain rates. In Part I of this paper, a new technique for such combinations was proposed that weights algorithms by the inverse of their theoretical errors. In this paper, the derived algorithms are validated using the “CP2” polarimetric radar in Queensland, Australia, and a collocated rain gauge network for two heavy-rain events during November 2008 and a larger statistical analysis that is based on data from between 2007 and 2009. Use of a weighted combination of polarimetric algorithms offers some improvement over composite methods that are based on decision-tree logic, particularly at moderate to high rain rates and during severe-thunderstorm events.
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