Abstract
In order to improve the accuracy of quantitative precipitation estimates, we correct radar reflectivity measurements by a “sorting and moving average” (SMA) method and with a POSS (precipitation occurrence sensing system) disdrometer. The correction procedure, optimized by a polynomial least-square fit of the data, greatly reduces errors in rainfall estimates. The Zc–R relationships for different cloud types are calculated using a classification algorithm. A VPR threshold algorithm is used to investigate the accuracy of rainfall estimates depending on the cloud type. For stratiform and convective cloud types, rainfall estimates were more accurate when the correction was taken into account, with statistical errors substantially reduced.The developed algorithm successfully reduced errors in the rainfall estimates and improved their accuracy. This new quantitative precipitation estimate (QPE) algorithm will improve the reliability of radar-based quantitative rainfall measurements and the accuracy of weather forecasts.
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