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Performance of general circulation models and their ensembles for the prediction of drought indices over India during summer monsoon

机译:夏季季风期间印度环流模型及其综合预报干旱指数的性能

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摘要

The drought during the months of June to September (JJAS) results in significant deficiency in the annual rainfall and affects the hydrological planning, disaster management, and the agriculture sector of India. Advance information on drought characteristics over the space may help in risk assessment over the country. This issue motivated the present study which deals with the prediction of drought during JJAS through standardized precipitation index (SPI) using nine general circulation models (GCM) product. Among these GCMs, three are the atmospheric and six are atmosphere-ocean coupled models. The performance of these GCM's predicted SPI is examined against the observed SPI for the time period of 1982-2010. After a rigorous analysis, it can be concluded that the skill of prediction by GCM is not satisfactory, whereas the ability of the coupled models is better than the atmospheric models. An attempt has been made to improve the accuracy of predicted SPI using two different multi-model ensemble (MME) schemes, viz., arithmetic mean and weighted mean using singular value decomposition-based multiple linear regressions (SVD-MLR) of GCMs. It is found that among these MME techniques, SVD-MLR-based MME has more skill as compared to simple MME as well as individual GCMs.
机译:6月至9月的干旱(JJAS)导致年降水量严重不足,并影响了印度的水文规划,灾害管理和农业部门。有关空间干旱特征的预先信息可能有助于全国风险评估。这个问题推动了本研究的进行,该研究使用九种通用循环模型(GCM)产品通过标准化降水指数(SPI)处理了JJAS期间的干旱预测。在这些GCM中,三个是大气耦合模型,六个是大气-海洋耦合模型。将这些GCM的预测SPI的性能与1982-2010年期间的SPI进行比较。经过严格的分析,可以得出结论,GCM的预测能力并不令人满意,而耦合模型的能力要优于大气模型。已经尝试使用两种不同的多模型集成(MME)方案来提高预测SPI的准确性,即使用基于奇异值分解的GCM多元线性回归(SVD-MLR)算术平均值和加权平均值。发现在这些MME技术中,与简单MME和单个GCM相比,基于SVD-MLR的MME具有更多的技能。

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