Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Geoenvironmental Disasters Pub Date : 2024-03-04 DOI:10.1186/s40677-023-00262-5

Unashish Mondal, Anish Kumar, S. K. Panda, Devesh Sharma, Someshwar Das

{"title":"Comprehensive study of thunderstorm indices threshold favorable for thunderstorms during monsoon season using WRF–ARW model and ERA5 over India","authors":"Unashish Mondal, Anish Kumar, S. K. Panda, Devesh Sharma, Someshwar Das","doi":"10.1186/s40677-023-00262-5","DOIUrl":null,"url":null,"abstract":"The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms\ndue to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Integrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety.\nBased on this work, future research can improve severe weather forecasting models’ accuracy and reliability.","PeriodicalId":37025,"journal":{"name":"Geoenvironmental Disasters","volume":"90 1","pages":""},"PeriodicalIF":3.8000,"publicationDate":"2024-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoenvironmental Disasters","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s40677-023-00262-5","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

The current research investigates into the application of various thunderstorm indices to predict severe thunderstorm occurrences during the monsoon season across four distinct regions in India. Methods: The study assesses the prediction model’s efficacy using various skill scores and the Weather Research and Forecasting (WRF) model has been integrated for 30 h with double moment microphysics scheme NSSL-17 which accurately reproduces vertical and meteorological measures. Furthermore, it investigates fifteen thunderstorm indices derived from the ERA5 dataset to identify the most effective index for forecasting severe thunderstorms. The results indicate that combining thunderstorm indices with skill scores, such as the Heidke Skill Score and True Skill Statistic, enhances the accuracy of severe thunderstorm predictions in the Indian monsoon season. The accurate predictions rely on determining optimal thresholds for each index. The study emphasizes the importance of using multiple indices rather relying solely on single measure for predicting severe thunderstorms. Advanced indices like the Energy Helicity Index (EHI) and Supercell Composite Parameter (SCP) perform well in forecasting extreme severe thunderstorms due to their strong reliance on wind shears. The EHI (> 1), and SCP (≥ 3.5), STP (≥ 1.2) along with low SRH at 3 km (100 m2/s2), indicated no evidence of helicity or tornado activity during the event. On the other hand, the CAPE, K Index, and VT Index demonstrate robust predictive capabilities for non-severe category thunderstorms. Integrating numerous thunderstorm indices improves meteorologists’ forecasts, ensuring public safety. Based on this work, future research can improve severe weather forecasting models’ accuracy and reliability.

查看原文本刊更多论文

利用 WRF-ARW 模型和 ERA5 对印度季风季节有利于雷暴的雷暴指数阈值进行综合研究

目前的研究调查了各种雷暴指数在预测印度四个不同地区季风季节严重雷暴发生情况中的应用。方法：研究使用各种技能评分来评估预测模型的功效，并将天气研究和预报（WRF）模型与双矩微物理方案 NSSL-17 集成 30 小时，该方案准确地再现了垂直和气象测量结果。此外，它还研究了从ERA5数据集得出的15个雷暴指数，以确定预报严重雷暴的最有效指数。结果表明，将雷暴指数与 Heidke Skill Score 和 True Skill Statistic 等技能分数相结合，可提高印度季风季节强雷暴预测的准确性。准确预测有赖于确定每个指数的最佳阈值。该研究强调了使用多种指数而非仅仅依靠单一指标预测严重雷暴的重要性。能量自转指数（EHI）和超级暴风圈综合参数（SCP）等高级指数在预报极端严重雷暴时表现出色，因为它们对风切变的依赖性很强。EHI (> 1)、SCP (≥ 3.5)、STP (≥ 1.2) 以及 3 km 处的低 SRH (100 m2/s2)表明，在此次事件中没有出现螺旋或龙卷风活动的迹象。另一方面，CAPE、K 指数和 VT 指数显示了对非严重类别雷暴的强大预测能力。在这项工作的基础上，未来的研究可以提高恶劣天气预报模型的准确性和可靠性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Geoenvironmental Disasters Social Sciences-Geography, Planning and Development

CiteScore

8.90

自引率

6.20%

发文量

期刊介绍： Geoenvironmental Disasters is an international journal with a focus on multi-disciplinary applied and fundamental research and the effects and impacts on infrastructure, society and the environment of geoenvironmental disasters triggered by various types of geo-hazards (e.g. earthquakes, volcanic activity, landslides, tsunamis, intensive erosion and hydro-meteorological events). The integrated study of Geoenvironmental Disasters is an emerging and composite field of research interfacing with areas traditionally within civil engineering, earth sciences, atmospheric sciences and the life sciences. It centers on the interactions within and between the Earth''s ground, air and water environments, all of which are affected by climate, geological, morphological and anthropological processes; and biological and ecological cycles. Disasters are dynamic forces which can change the Earth pervasively, rapidly, or abruptly, and which can generate lasting effects on the natural and built environments. The journal publishes research papers, case studies and quick reports of recent geoenvironmental disasters, review papers and technical reports of various geoenvironmental disaster-related case studies. The focus on case studies and quick reports of recent geoenvironmental disasters helps to advance the practical understanding of geoenvironmental disasters and to inform future research priorities; they are a major component of the journal. The journal aims for the rapid publication of research papers at a high scientific level. The journal welcomes proposals for special issues reflecting the trends in geoenvironmental disaster reduction and monothematic issues. Researchers and practitioners are encouraged to submit original, unpublished contributions.