Analysis of Dual-Polarimetric Radar Observations of Precipitation Phase during Snowstorm Events in Jiangsu Province, China

Author:

Wang Lei12ORCID,Wang Yi1,Liu Mei1,Chen Wei12,Li Chiqin1

Affiliation:

1. Jiangsu Meteorological Observatory, Nanjing 210041, China

2. China Meteorological Administration Hydro-Meteorology Key Laboratory, Beijing 100081, China

Abstract

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.

Funder

Open Foundation of China Meteorological Administration Hydro-Meteorology Key Laboratory Projects

Youth Innovation Team of China Meteorological Administration

Key Research and Development Plan of Jiangsu Province

CMA “Open Bidding for Selecting the Best Candidates” Project

Basic Research Fund of CAMS

Publisher

MDPI AG

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