Typhoon rainstorms often cause disasters in southern China. Quantitative precipitation estimation (QPE) with the use of polarimetric radar can improve the accuracy of precipitation estimation and enhance typhoon defense ability. On the basis of the observed drop size distribution (DSD) of raindrops, a comparison is conducted among the DSD parameters and the polarimetric radar observation retrieved from DSD in five typhoon and three squall line events that occurred in southern China from 2016 to 2017. A new piecewise fitting method (PFM) is used to develop the QPE estimators for landfall typhoons and squall lines. The performance of QPE is evaluated by two fitting methods for two precipitation types using DSD data collected. Findings indicate that the number concentration of raindrops in typhoon precipitation is large and the average diameter is small, while the raindrops in squall line rain have opposite characteristics. The differential reflectivity (Z_DR) and specific differential phase (K_DP) in these two precipitation types increase slowly with the reflectivity factor (Z_H), whereas the two precipitation types have different Z_DR and K_DP in the same Z_H. Thus, it is critical to fit the rainfall estimator for different precipitation types. Enhanced estimation can be obtained using the estimators for specific precipitation types, whether the estimators are derived from the conventional fitting method (CFM) or PFM, and the estimators fitted using the PFM can produce better results. The estimators for the developed polarimetric radar can be used in operational QPE and quantitative precipitation foresting, and they can improve disaster defense against typhoons and heavy rains.

The development and application of operational polarimetric radar (PR) in China is still in its infancy. In this study, an operational PR quantitative precipitation estimation (QPE) algorithm is suggested based on data for PR hydrometeor classification and local drop size distribution (DSD). Even though this algorithm performs well for conventional rainfall events, in which hourly rainfall accumulations are less than 50 mm, the capability of a PR to estimate extremely heavy rainfall remains unclear. The proposed algorithm is used for nine different types of rainfall events that occurred in Guangzhou, China, in 2016 and for an extremely heavy rainfall event that occurred in Guangzhou on 6 May 2017. It performs well for all data of these nine rainfall events and for light-to-moderate rain (hourly accumulation 50 mm) and the extremely heavy rain at stations where total rainfall exceeded 300 mm within 5 h in this extremely heavy rainfall event. To analyze the reasons for underestimation, a rain microphysics retrieval algorithm is presented to retrieve D_m and N_w from the PR measurements. The DSD characteristics and the factors affecting QPE are analyzed based on D_m and N_w. The results indicate that compared with statistical DSD data in Yangjiang (estimators are derived from these data), the average raindrop diameter during this rainfall event occurred on 6 May 2017 was much smaller and the number concentration was higher. The algorithm underestimated the precipitation with small and midsize particles, but overestimated the precipitation with midsize and large particles. Underestimations occurred when D_m and N_w are both very large, and the severe underestimations for heavy rain are mainly due to these particles. It is verified that some of these particles are associated with melting hail. Owing to the big differences in DSD characteristics, R(K_DP, Z_DR) underestimates most heavy rain. Therefore, R(A_H), which is least sensitive to DSD variations, replaces R(K_DP, Z_DR) to estimate precipitation. This improved algorithm performs well even for extremely heavy rain. These results are important for evaluating S-band Doppler radar polarization updates in China.

At present, the raindrop size distribution (DSD) fitting method and traditional neural network method are the main quantitative precipitation estimation (QPE) algorithms. The former is not enough to express the spatiotemporal variability of the DSDs using generalized $Z-R$ parameter relationship, and the latter is limited by the number of network layers, network structure and computing power. Their performance cannot both meet actual needs. In this research, we propose an alternative approach to dual-polarization radar (DPR) QPE. In particular, a radar-gauges dataset (RGD) is constructed for QPE based deep learning using radar raw data and gauges measurements for landfall typhoon in South China, and a deep learning model is designed and trained using this dataset. The model is applied to radar data to produce rainfall estimates. Preliminary results show the promising performance of this novel method compared to traditional QPE estimators.