Search

Based on ground observed data, S-band dual-polarization radar data, and ERA-5 reanalysis data, the statistical characteristics of polarimetric parameters and the application of melting layer (ML) and hydrometeor classification (HCL) products during eight snowstorm events in Jiangsu Province from 2020 to 2022 were investigated. A heavy snowstorm that went through different phases of rain, sleet, and pure snow and that occurred on 29 December 2020 was also analyzed as a typical example. The results showed the following: During the phase transition between rain and snow in the Jiangsu region, the basic reflectivity factor ZH ≥ 27 dBZ, the zero-order lag correlation coefficient CC ≤ 0.93, and the differential reflectivity ZDR ≥ 1.0 dB were important indicators for judging the melting layer while the specific differential phase KDP changed slightly. The snowstorm event was well observed and recorded by the Yancheng dual-polarimetric radar, whose low value area of CC coincided mostly with the melting layer. The ML products and HCL products based on fuzzy-logic hydrometeor classification algorithms can help identify the melting layer and the properties of precipitation particles. ML products are more reliable when the melting layer is high and can better show the trends of melting layer decline. They can certainly serve as a reference for detecting and judging precipitation phase changes in winter in Jiangsu Province. [ABSTRACT FROM AUTHOR]

The target of radar echo extrapolation is to predict the motion and development of radar echo in the future based on historical radar observation data. For such spatiotemporal prediction problems, a deep learning method based on Long Short-Term Memory (LSTM) networks has been widely used in recent years, although such models generally suffer from weak and blurry prediction. This paper proposes two models called Residual Convolution LSTM (rcLSTM) and Generative Adversarial Networks-rcLSTM (GAN-rcLSTM): The former introduces the residual module, and the latter introduces the discriminator. We use the historical data of 2017 and 2018 in the Jiangsu region as training and test sets. Experiments show that in long sequence forecasts, our model can provide more stable and clear images, while achieving higher CSI scores. [ABSTRACT FROM AUTHOR]