印度东海岸奥迪沙季风降雨多物理场集合预测评估

IF 1.9 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS pure and applied geophysics Pub Date : 2024-08-05 DOI:10.1007/s00024-024-03547-4
Anshul Sisodiya, Sandeep Pattnaik, Adrish Baneerjee
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引用次数: 0

摘要

为特定应用选择合适的参数化方案组合是天气研究和预报(WRF)模型用户非常关心的问题。本研究的目标是创建一种客观的方法来确定一套方案组合,以形成适合印度东海岸奥迪沙邦短期降水预报的多物理场集合(MPE)。在这项研究中,创建了云微观物理(CMP)和陆面模型(LSM,传统集合)的五个成员集合,以及针对 13 个季风低气压(MD)和 8 个深低气压(DD)案例的性能最佳的五个成员集合(优化集合)。共有 30 个组合(5 个 PBL * 5 个 CMP、5 个带有最佳 PBL 和 CMP 的 LSM 以及一个带有 ISRO 土地利用土地覆盖数据的组合)。模拟使用 WRF 4.1,以ERA5 再分析数据进行初始化,前置时间为 72 小时。降雨验证技能得分表明,集合成员的表现明显优于任何确定性模型。通过较高的相关系数和较低的均方根误差(RMSE),可以很好地预测集合成员的降雨特征,如地点、强度和发生时间。邻近集合概率也表明,集合成员有更高的概率检测到大暴雨到特大暴雨事件,并具有更高的空间精度。研究还得出结论,参数化的选择也会影响大尺度动态参数(温度、湿度、风、水文介质),进而影响相关降雨量。在对大尺度参数进行综合分析时,集合成员的偏差较小。此外,对水汽预算成分的综合分析表明,辐合项是水汽累积的最重要成分,导致季风低压系统期间的降雨。这些研究结果表明,所提出的方法是减少降雨预报偏差的有效方法。
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Evaluation of Multi-Physics Ensemble Prediction of Monsoon Rainfall Over Odisha, the Eastern Coast of India

Selecting proper parameterization scheme combinations for a particular application is of great interest to Weather Research and Forecasting (WRF) model users. The goal of this research is to create an objective method for identifying a set of scheme combinations to form a Multi-Physics Ensemble (MPE) suitable for short-term precipitation forecasting over Odisha, India’s east coast state. In this study, five member ensembles for Cloud Microphysics (CMP) and Land Surface Model (LSM, conventional ensemble) are created, as well as an ensemble of the top five performing members (optimized ensemble) for 13 Monsoon Depressions (MD) and 8 Deep Depression (DD) cases. There are a total of 30 combinations (5 PBL * 5 CMP, 5 LSM with best PBL and CMP, and one with ISRO Land Use Land Cover data). WRF 4.1 is used to carry out simulations, which are initialized with ERA5 reanalysis data and have a 72-h lead time. Rainfall verification skill scores indicate that ensemble members perform significantly better than any deterministic model. Rainfall characteristics such as location, intensity, and time of occurrence are well predicted in ensemble members as measured by a higher correlation coefficient and a lower RMSE. Neighbourhood ensemble probability also demonstrates that ensemble members have a higher chance of detecting heavy to very heavy rainfall events with more spatial accuracy. The study also concludes that choice of parameterization also affects large-scale dynamical parameters (temperature, humidity, wind, hydrometeors) and thus associated rainfall. Ensemble members exhibited less bias in the composite analysis of large-scale parameters. Furthermore, a composite analysis of moisture budget components revealed that the convergence term is the most important component of moisture accumulation, resulting in rainfall during the monsoon low-pressure system. These findings indicate that the proposed method is an effective method for reducing bias in rainfall forecasts.

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来源期刊
pure and applied geophysics
pure and applied geophysics 地学-地球化学与地球物理
CiteScore
4.20
自引率
5.00%
发文量
240
审稿时长
9.8 months
期刊介绍: pure and applied geophysics (pageoph), a continuation of the journal "Geofisica pura e applicata", publishes original scientific contributions in the fields of solid Earth, atmospheric and oceanic sciences. Regular and special issues feature thought-provoking reports on active areas of current research and state-of-the-art surveys. Long running journal, founded in 1939 as Geofisica pura e applicata Publishes peer-reviewed original scientific contributions and state-of-the-art surveys in solid earth and atmospheric sciences Features thought-provoking reports on active areas of current research and is a major source for publications on tsunami research Coverage extends to research topics in oceanic sciences See Instructions for Authors on the right hand side.
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