AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (12.4 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Article | Publishing Language: Chinese

Optimization of hydromateor classification algorithm for winter rain-snow transition events using dual-polarization radars in China

Zhaoqiang XU1,2Chong WU2( )Hao WANG1Liping LIU2Xingwen JIANG3
Chengdu University of Information Technology,College of Electronic Engineering (College of Meteorological Observation),Chengdu 610225,China
State Key Laboratory of Severe Weather Meteorological Science and Technology,Chinese Academy of Meteorological Sciences,Beijing 100081,China
Institute of Plateau Meteorology,CMA,Chengdu 610072,China
Show Author Information

Abstract

Melting Layers (ML) during winter precipitation events, characterized by low-altitude, high temporal variability and spatially heterogeneous melting, pose significant challenges to conventional dual-polarization radar Hydrometeor Classification Algorithms (HCA), resulting in substantially degraded performance in rain-snow transition regions. To address these challenges, we develop an improved HCA scheme using data from 115 dual-polarization radars and vertical profiles from 116 radiosonde stations across China (July 2023—July 2024). The scheme integrates three key components: Optimized spatiotemporal matching with radiosonde observations, real-time ML monitoring through Quasi-Vertical Profile (QVP) analysis, and three-dimensional ML identification using the Melting Layer Detection Algorithm (MLDA). These improvements enhance both spatiotemporal precision of ML detection and regional applicability of HCA, ultimately improving the discrimination between various hydrometeor types. The improved scheme effectively resolves issues related to rapid ML variability and spatial heterogeneity in winter, reducing the ML detection update interval from 6—12 h to 6 min and achieving spatial resolution of 1 km in range, 0.1 km in altitude, and 1° in azimuth. Sensitivity experiments using wintertime datasets from seven radars around Nanjing demonstrate that the improved scheme can accurately identify the rain-snow boundary, increasing overall classification accuracy by 11.91% and mixed-phase precipitation accuracy by over 50%. Validation against Present Weather Sensor (PWS) observations confirms that the near-surface classification accuracy exceeds 77% within 100 km. Statistics on winter algorithm activation periods from 115 radars nationwide reveal that the winter-specific algorithm operates for 18%—51% of the year at plain sites in Northeast, North, and Central China, as well as at high-altitude mountain sites, confirming the effectiveness of the improved scheme for wintertime dual-polarization radar hydrometeor classification.

CLC number: P412.25

References

【1】
【1】
 
 
Acta Meteorologica Sinica
Pages 806-822

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
XU Z, WU C, WANG H, et al. Optimization of hydromateor classification algorithm for winter rain-snow transition events using dual-polarization radars in China. Acta Meteorologica Sinica, 2026, 84(4): 806-822. https://doi.org/10.11676/qxxb2026.20250213

1

Views

0

Downloads

0

Crossref

0

CSCD

Received: 21 October 2025
Revised: 28 February 2026
Published: 25 August 2026
Copyright © 2026 Acta Meteorologica Sinica. All rights reserved.