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首页> 外文期刊>Comptes rendus >Improving the performance of precipitation outputs from Global Climate Models to predict monthly and seasonal rainfall over the Indian subcontinent
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Improving the performance of precipitation outputs from Global Climate Models to predict monthly and seasonal rainfall over the Indian subcontinent

机译：改善全球气候模式的降水量输出性能，以预测印度次大陆的月度和季节性降雨

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Skilful prediction of the monthly and seasonal summer monsoon rainfall over India at a smaller spatial scale is a major challenge for the scientific community. The present study is aimed at achieving this objective by hybridising two mathematical techniques, namely synthetic superensemble (SSE) and supervised principal component regression (SPCR) on six state-of-the art Global Climate Models (GCMs). The performance of the mathematical model is evaluated using correlation analysis, the root mean square error, and the Nash-Sutcliffe efficiency index. Results feature reasonable improvement over central India, which is a zone of maximum rainfall activity in the summer monsoon season. The study also highlights improvement in the monthly prediction of rainfall over raw GCMs (15-20% improvement) with exceptional improvement in July. The developed model is also examined for anomalous years of monsoon and it is found that the model is able to capture the signs of anomalies over different gridpoints of the Indian domain.
机译：对科学界来说，在较小的空间范围内对印度的夏季和夏季季风和季风降雨量进行熟练的预测是一项重大挑战。本研究旨在通过在两种最新的全球气候模型（GCM）上混合两种数学技术（即合成超级集合（SSE）和有监督主成分回归（SPCR））来实现此目标。使用相关分析，均方根误差和Nash-Sutcliffe效率指数评估数学模型的性能。结果表明与印度中部相比有合理的改善，印度中部是夏季季风季节降雨最多的地区。该研究还强调指出，与原始GCM相比，每月降雨量的预报数有所改善（改进了15-20％），而7月的情况则有所改善。还检查了已开发的模型的季风异常年份，发现该模型能够捕获印度域不同网格点上异常的迹象。

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