Qpe (quantitative Precipitation Estimation) Merging Techniques Based On Multiplatform (radar,satellite) Data And Raingauge Data

To improve the radar QPE (Quantitive Precipitation Estimation) techniques, the probability-fitting technique (PFT) is adopted to localize the relationship between Z and I and the OI (Optimum Interpolation ) method is used to correct the radar precipitation estimation based on CINRAD/SA dataset of volume scan reflectivity and rain gauge data in the Guangdong area. In order to estimate large scale precipitation, multi-radar QPE (Quantitive Precipitation Estimation) was combined by weighting each radar by square multiplicative inverse of its RMSE (Root Mean Square Error) and the QPE output with resolution of 0.010×0.010 was obtained. As for CMORPH data, Combining precipitation data from raingauge observations and bias -corrected satellite estimates through the Optimal Interpolation, the QPE output with resolution of 0.1250×0.1250 was obtained. Finally, QPE data from multi-radar with resolution of 0.010×0.010 and CMPRPH data with resolution of 0.1250×0.1250 is merged. Error statistical analyses indicated that station gauge rain is most closely related to the average of nine points reflectivity above and that accumulating 6 minutes rainfall estimation to 1 hourly accumulations by the probability-fitting technique ( PFT) can significantly reduce the errors of the precipitation estimation in comparison with that of Z-I relationship by matching hourly raingauge and radar averaged reflectivity .Additionally, it was verified that the QPE precision of each single-Doppler radar with the same model is different; As for the multi-radar QPE,the result of OI combination of corrected value of single-radar precipitation estimation is better than that of OI combination of single-radar precipitation estimation. Trough cross-validation, some conclusions were obtained that the OI correction method can minimize the precipitation estimation errors to a certain extent and correcting single radar precipitation estimation in advance is important. It was also shown that the method of multi-dadar QPE combination is better than gauge's OI(Optimum Interpolation). Against the radar composite image hybrid reflectivity QPE, the precision of the former is higher than the latter. As for CMORPH data, using raingauge data to correct CMORPH data can reduce bias ; The satellite QPE merging with raingauge data is better than gauge's OI(Optimum Interpolation); QPE output with different kinds resolution has obvious regional differences; Merging is an effective way to create products from multiple sources of information and applied to take advantage of information from multiple platforms.