​​雷達能測風雨

Quantitative Precipitation Estimation (QPE) using variational algorithm

 

The accuracy of rain rate estimation using polarimetric radar measurements has been improved as a result of better characterization of data quality and rain microphysics. In the literature, a variety of power-law relations between polarimetric radar measurements and rain rate are described due to dynamic or varying nature of rain microphysics. A variational technique that concurrently takes into account observational error and dynamically varying rain microphysics is proposed in this study.  Rain rate estimation using the variational algorithm that uses event based observational error and background rain climatology is evaluated using Observing System Simulation Experiment (OSSE), and its performance is demonstrated in the case of an epic Colorado flood event. The rain event was between 11 and 12 September 2013. The results from OSSE show the variational algorithm with event based observational error consistently estimates more accurate rain rate than the R(ZHH,ZDR) algorithm. On the contrary, the usage of ad hoc or improper observational error degrades the performance of the variational method. Furthermore, the variational algorithm is less sensitive to the observational error of ZDR compared to the R(ZHH,ZDR) algorithm. The variational QPE retrieved more accurate rainfall estimation than dual-polarization QPE in this particular event, despite the fact that both algorithms used the same dual-polarization radar measurements from the NEXRAD.