Journal of Earth System Science ( IF 1.3 ) Pub Date : 2021-03-12 , DOI: 10.1007/s12040-020-01549-z M Mohapatra , Naresh Kumar , Krishna Mishra , Sunitha Devi
Abstract
The major objective of any national weather forecasting services is to provide weather forecast and warnings and other meteorological related information to the public and government for the safety of life and property and economic activities. The heavy rainfall causes huge loss to the public in form of flood and landslide in varying severity mainly during monsoon season (June–September). Hence its accurate prediction is essential and the accuracy of prediction needs to be verified quantitatively to evaluate its strength and weakness. The National Weather Forecasting Centre (NWFC) of India Meteorological Department (IMD) issues heavy rainfall (HR) warnings for the safety of life and property of the public. In this study, verification of operational heavy rainfall (HR) warning issued by NWFC of IMD for 36 sub-divisions of India is carried out. The verification scores presented in the study are for 24 hrs (D1), 48 hrs (D2) and 72 hrs (D3) lead period average warning skills during 2014–2018 and year-wise trend of the HR warnings for the period 2002–2018. In general, it is observed that there are significant improvements in skill scores in recent years. The improvement in D3 is at higher rate as compared to D1 scores. The improvement in the recent years is mainly due to improvement in model resolution and data assimilation in the Numerical Prediction (NWP) Models runs by Ministry of Earth Sciences (MoES), Government of India and their interpretation and utilization by the forecasters for objective consensus forecast using an objective decision support system and synoptic value addition.
Research highlights
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There is significant improvement in heavy rainfall warning skill of India Meteorological Department during monsoon season in recent two years (2017 and 2018) as compared to 2002–2016.
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The skill scores namely, Probability of Detection (PoD), Critical Success Index (CSI) and Heidke Skill Score (HSI) has improved by 48%, 46% and 33%, respectively, as compared to mean of scores between 2002–2016 for Day 1 (D1) warning.
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In Day 3 (D3) warning, there is an improvement by 69%, 54% and 54% in PoD, CSI and HSS respectively during 2017–2018 as compared to mean of 2013–2015. The improvement in D3 warning is at higher rate as compared to D1 warning.
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In general, the skill scores are higher over the regions with higher frequency of heavy rainfall and lower over less prone regions of heavy rainfall.
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These improvements in the forecast warning skill may be attributed to availability and use of latest forecasting models with high resolution and better data assimilation. Apart from the above, the structured monitoring of the monsoon circulations parameters, interpretation of NWP models guidance through Forecast Demonstration Project (FDP), objective consensus through decision support system and subjective consensus amongst the forecasters through video conference contributed significantly improved HR warning in recent years.
中文翻译：
印度国家天气预报局季风季节（2002-2018年）的强降雨预警评估
摘要
任何国家天气预报服务的主要目的是为公众和政府提供生命，财产和经济活动的安全天气预报和警告以及其他与气象有关的信息。暴雨主要在季风季节（6月至9月）以严重程度不同的洪水和滑坡形式给公众造成巨大损失。因此，其准确的预测至关重要，并且需要定量验证预测的准确性以评估其优缺点。印度气象局（IMD）的国家天气预报中心（NWFC）发出强降雨（HR）警告，以保障公众生命和财产安全。在这项研究中，IMD的NWFC针对印度的36个分区进行了操作性强降雨（HR）警告的验证。研究中提供的验证分数是2014-2018年期间的24小时（D1），48小时（D2）和72小时（D3）提前期的平均警告技能，以及2002-2018年期间的人力资源警告的年度趋势。通常，可以发现，近年来，技能得分有了很大的提高。与D1分数相比，D3的改善率更高。近年来的改进主要是由于印度政府地球科学部（MoES）开展的数值预测（NWP）模型的模型分辨率和数据同化的改进以及预报员对客观共识预报的解释和利用使用客观决策支持系统和天气附加值。2014-2018年的提前期平均48小时（D2）和72小时（D3）的预警技能，以及2002-2018年期间的人力资源预警的逐年趋势。通常，可以发现，近年来，技能得分有了很大的提高。与D1分数相比，D3的改善率更高。近年来的改进主要是由于印度政府地球科学部（MoES）开展的数值预测（NWP）模型的模型分辨率和数据同化的改进以及预报员对客观共识预报的解释和利用使用客观决策支持系统和天气附加值。2014-2018年期间提前期48小时（D2）和72小时（D3）的平均警告技巧以及2002-2018年期间的人力资源警告的年度趋势。通常，可以发现，近年来，技能得分有了很大的提高。与D1分数相比，D3的改善率更高。近年来的改进主要是由于印度政府地球科学部（MoES）开展的数值预测（NWP）模型的模型分辨率和数据同化的改进以及预报员对客观共识预报的解释和利用使用客观决策支持系统和天气附加值。可以发现，近年来技能得分有了显着提高。与D1分数相比，D3的改善率更高。近年来的改进主要是由于印度政府地球科学部（MoES）开展的数值预测（NWP）模型的模型分辨率和数据同化的改进以及预报员对客观共识预报的解释和利用使用客观决策支持系统和天气附加值。可以发现，近年来技能得分有了显着提高。与D1分数相比，D3的改善率更高。近年来的改进主要是由于印度政府地球科学部（MoES）开展的数值预测（NWP）模型的模型分辨率和数据同化的改进以及预报员对客观共识预报的解释和利用使用客观决策支持系统和天气附加值。
研究重点
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与2002-2016年相比，最近两年（2017年和2018年），印度气象局在季风季节的强降雨预警能力有了显着提高。
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与2002-2016年间的平均得分相比，该技能的得分分别为检测概率（PoD），关键成功指数（CSI）和海德克技能得分（HSI），分别提高了48％，46％和33％。第1天（D1）警告。
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在第3天（D3）警告中，与2013-2015年平均值相比，2017-2018年PoD，CSI和HSS分别提高了69％，54％和54％。与D1警告相比，D3警告的改进率更高。
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通常，技能得分在暴雨频率较高的地区较高，而在暴雨频率较低的地区较低。
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预测警告技能的这些改进可能归因于具有高分辨率和更好的数据同化功能的最新预测模型的可用性和使用。除此之外，季风环流参数的结构化监测，通过预报示范项目（FDP）进行的NWP模型指导的解释，通过决策支持系统的客观共识以及通过电视会议在预报员之间的主观共识都极大地改善了人力资源预警。
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