Research on Quantitative Precipitation Estimation by Polarized Radar Using CNN
The ZH,ZDR and KDP of Guangzhou S-band dual polarization radar and rainfall data of 219 automatic meteorological stations in Foshan are used to form 8 datasets.Based on the convolutional neural network CNN,a radar quantitative precipitation estimation model is established,which will be used for ground precipitation estimation.The evaluating results of 8 datasets applied to the same precipitation estimation model are compared to each other.The results show that:The increase in the number of channels(N)of the datasets is beneficial to reduce the RMSE and improve CORR of the quantitative rainfall estimation results;For the datasets Z,Z_1～3 km and Z_6 min formed by ZH,as the number of channels increases,the performance of the data sets Z,Z_1～3 km and Z_6 min are gradually improved,and the RMSE of Z_1～3 km and Z_6 min are 4.71 and 3.78,which are-1.3%and 18.7%lower than that of dataset Z;the CORR of Z_1～3 km and Z_6 min are 0.82 and 0.88,which are 2.5%and 10%higher than that of dataset Z;Among other datasets composed of KDP and ZDR,the dataset Z_ZDR_KDP has the best fitting performance.The RMSE is 3.97,which is 14.6%lower than that of dataset Z,and the CORR is 0.86,which is 7.5%higher than that of dataset Z;The statistical results of RMSE,MBR,AE and RE for seven precipitation levels of 0.6～5 mm,5～10 mm,10～20 mm,20～30 mm,30～40 mm,40～50 mm and above 50 mm respectively,show that dataset Z_6 min has the highest rainfall accuracy.
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