DEVELOPMENT AND ASSESSMENT OF QUANTITATIVE PRECIPITATION ESTIMATION ALGORITHMS FOR S-, C-, AND X-BAND DUAL-POLARIZATION RADARS BASED ON DISDROMETER DATA FROM THREE REGIONS OF CHINA

Abstract:

The accuracy of quantitative precipitation estimation (QPE) for dual-polarization radars can be improved by using a localized rainfall estimation algorithm derived from the raindrop size distribution (DSD). In the present study, DSDs observed at Suzhou City, Jiangsu province; Yangjiang City, Guangdong province; and Naqu City, Tibet are analyzed during the rainy season together with the corresponding polarimetric variables for the above three regions. Most importantly, these DSD data are used to develop optimal “synthetic” QPE algorithms for S-, C-, and X-band dual-polarization radars, which will be built or upgraded in the three regions. Meanwhile, a new piecewise fitting method (PFM) is proposed. It has been found that the number concentration N(D) of small raindrops (D<1mm) is the highest in Suzhou, while that of larger raindrops (D>1mm) is the highest in Yangjiang. The characteristics of the differential reflectivity (ZDR) and specific differential phase (KDP) are significantly different in the three locations, suggesting that different rainfall estimators are needed for different locations. Further performance assessment of the QPE based on DSD data indicates that the PFM QPE algorithm (LDSD) performs better than the conventional fitting method (CFM), and the localized QPE algorithm can improve the QPE accuracy. Observations from S-band dual-polarization radars and rain gauges in the Southern China Monsoon Rainfall Experiment are implemented to verify the performances of the QPE algorithms proposed in the present study. It is found that compared with non-localized algorithms, the localized LDSD algorithm yields the best results with at least 7.66% and 8.43% reductions in the RMSE and NE, respectively, which implies that while polarimetric variables can reflect DSD characteristics, the localized QPE algorithm remains necessary.