Improving the skill of medium range ensemble rainfall forecasts over India using MoES grand ensemble (MGE)-part-I

IF 1.9 4区 地球科学 Q3 METEOROLOGY & ATMOSPHERIC SCIENCES
Anumeha Dube, V. Abhijith, Ashu Mamgain, Snehlata Tirkey, Raghavendra Ashrit, V. S. Prasad
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Abstract

One of the key attributes of an ensemble prediction system (EPS) is the spread among the members. It plays a crucial role in conveying the uncertainty associated with the forecasted parameters. It is a quantitative measure of forecast uncertainty, provides a range of possible outcomes, and helps in the assessment of risk and decision making. Additionally, the spread can also serve as a diagnostic tool for assessing the reliability and variability among the ensemble members. If the spread is consistently narrow, it may indicate that the ensemble members are not diverse enough and the uncertainties may not be adequately captured resulting in sub-optimal decision making. In this study, the rainfall forecasts from two EPSs over India have been assessed during four monsoon seasons (2019–2022) with an aim to boost the ensemble spread by constructing a ‘Grand Ensemble’. The two high-resolution operational global EPSs of Ministry of Earth Science (MoES) in India are (i) National Centre for Medium Range Weather Forecasting (NCMRWF) EPS (NEPS) which has a 12 km grid, and 23 members and (ii) Global Ensemble Forecast System (GEFS) with a 12 km grid and 21 members. Both EPSs have been used for operational medium range forecasts out to Day-10 since 2018. The MoES Grand Ensemble (MGE) constructed by combining the two EPSs (NEPS & GEFS), features a higher spread and an improved Spread Vs Bias relationship compared to the constituent models. Further, the results indicate lowest CRPS in the MGE compared to the constituent EPSs, over the Indian land region. The improved performance of MGE is also demonstrated for moderate and heavy rainfall events using Brier Skill Score (BSS), Reliability Diagram and ROC curves.

Abstract Image

利用气象和环境科学部大集合(MGE)提高印度中程集合降雨预报技能--第一部分
集合预测系统(EPS)的关键属性之一是成员之间的传播。它在传递与预测参数相关的不确定性方面起着至关重要的作用。它是对预测不确定性的量化测量,提供了一系列可能的结果,有助于风险评估和决策制定。此外,频差还可以作为一种诊断工具,用于评估集合成员之间的可靠性和可变性。如果频差一直很窄,则可能表明集合成员的多样性不够,不确定性可能未被充分捕捉,从而导致决策失准。在这项研究中,对印度上空两个 EPS 在四个季风季节(2019-2022 年)的降雨预报进行了评估,目的是通过构建 "大集合 "来提高集合传播。印度地球科学部(MoES)的两个高分辨率全球运行 EPS 是:(i) 国家中期天气预报中心(NCMRWF)EPS(NEPS),其网格为 12 千米,有 23 个成员;(ii) 全球集合预报系统(GEFS),网格为 12 千米,有 21 个成员。自2018年起,这两个EPS都被用于运行至第10天的中程预报。由两个 EPS(NEPS & GEFS)组合而成的气象和环境部大集合(MGE),与组成模式相比,具有更高的展宽和更好的展宽与偏差关系。此外,结果表明,在印度陆地地区,与各组成 EPS 相比,MGE 的 CRPS 最低。利用布赖尔技能得分(BSS)、可靠性图和 ROC 曲线,还证明了 MGE 在中雨和大雨事件中的改进性能。
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来源期刊
Meteorology and Atmospheric Physics
Meteorology and Atmospheric Physics 地学-气象与大气科学
CiteScore
4.00
自引率
5.00%
发文量
87
审稿时长
6-12 weeks
期刊介绍: Meteorology and Atmospheric Physics accepts original research papers for publication following the recommendations of a review panel. The emphasis lies with the following topic areas: - atmospheric dynamics and general circulation; - synoptic meteorology; - weather systems in specific regions, such as the tropics, the polar caps, the oceans; - atmospheric energetics; - numerical modeling and forecasting; - physical and chemical processes in the atmosphere, including radiation, optical effects, electricity, and atmospheric turbulence and transport processes; - mathematical and statistical techniques applied to meteorological data sets Meteorology and Atmospheric Physics discusses physical and chemical processes - in both clear and cloudy atmospheres - including radiation, optical and electrical effects, precipitation and cloud microphysics.
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