Using Machine Learning Methods to Develop an Algorithm for Recognizing a Risk of Waterspout Occurrence off the Black Sea Coast of Russia

IF 1.4 4区 地球科学 Q4 METEOROLOGY & ATMOSPHERIC SCIENCES
O. V. Kalmykova
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引用次数: 0

Abstract

Every year about 50 waterspouts occur over the sea off the Black Sea coast of Russia. Over the past few years, the cases of waterspouts have occurred in the immediate vicinity of the coast with their subsequent destruction. The vortex destruction is often accompanied by short-term wind strengthening up to storm levels. The present study solves the problem of nowcasting the Black Sea waterspouts (building a detailed forecast of their formation for the next 2–6 hours) using machine learning methods. Learning by precedents is considered based on the labeled dataset of the radar characteristics of convective systems with and without waterspouts, models for classifying systems in terms of the risk of waterspout occurrence are constructed. The testing of the models showed that it is fundamentally possible to use them to diagnose systems with already formed waterspouts, as well as to identify the risk of waterspouts in advance (within two hours).

Abstract Image

使用机器学习方法开发识别俄罗斯黑海沿岸水龙卷发生风险的算法
摘要每年在俄罗斯黑海沿岸海域约发生 50 次水龙卷。在过去几年中,水龙卷都发生在海岸附近并随之被摧毁。漩涡破坏往往伴随着短期风力增强至风暴级别。本研究利用机器学习方法解决了黑海水龙卷的预报问题(对其未来 2-6 小时的形成进行详细预报)。在有水龙卷和无水龙卷的对流系统雷达特征标签数据集的基础上,考虑了先例学习,构建了根据水龙卷发生风险对系统进行分类的模型。对模型的测试表明,利用这些模型诊断已形成水龙卷的系统以及提前(两小时内)识别水龙卷风险是基本可行的。
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来源期刊
Russian Meteorology and Hydrology
Russian Meteorology and Hydrology METEOROLOGY & ATMOSPHERIC SCIENCES-
CiteScore
1.70
自引率
28.60%
发文量
44
审稿时长
4-8 weeks
期刊介绍: Russian Meteorology and Hydrology is a peer reviewed journal that covers topical issues of hydrometeorological science and practice: methods of forecasting weather and hydrological phenomena, climate monitoring issues, environmental pollution, space hydrometeorology, agrometeorology.
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