REAL-TIME RADAR QUANTITATIVE PRECIPITATION ESTIMATION USING MULTIVARIATE PROJECTION MODEL

Abstract

 Radar-based Quantitative Precipitation Estimation (QPE) is primarily stated as techniques representing the best-fit rain rate measured at the ground. The current QPE method relies on either CAPPI maximum value or lowest radar tilt value. In fact, some discrepancies due to lag time and spatial variance remain found which cause the errors systematically propagated over a period. Furthermore, uncertainty factors on a void interspace due to the existence of a gap between available radar beams and ground have caused misleading in determining the actual rain rate. The real-time QPE model in this study is intended to improve the nowcasting rainfall predictor system. The multivariate projection model is used to predict the actual rain rate through the entanglement of physically precipitation factors such as wind shear, relative humidity, evaporation rate, and vertical moisture flux obtained from atmospheric sounding data. The vertical profiles of rainfall at various CAPPIs are collected and used as the response variable by the use of physical factors as a predictor variable to obtain the parameter coefficient value. This value interprets the signature of rainfall at the observed CAPPIs which then could be used for actual rainfall projection at the lower altitudes by real-time. Finally, the validation is taken through radar-gauge cross-correlation representing the actual rain rate. The model is performing well when it has a high correlation, least bias, and zero lag time.