气候变化领域集成服务门户(CCPortal): Revamping extended range forecast of Indian summer monsoon

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DOI	10.1007/s00382-020-05454-5
	Revamping extended range forecast of Indian summer monsoon
	Raghav P.; Borkotoky S.S.; Joseph J.; Chattopadhyay R.; Sahai A.K.; Ghosh S.
发表日期	2020
ISSN	0930-7575
起始页码	3397
结束页码	3411
卷号	55
英文摘要	Forecast of the Indian summer monsoon on an extended range (beyond the conventional one-week lead time) is critical for an agronomic economy like India. Although dynamic models have been quite successful in capturing and representing monsoon circulation, they fail to sustain this skill beyond the standard weather time scale (7–10 days). As such, the present study is directed at developing a computationally feasible, yet reliable method of statistical downscaling that further improves the present skill of global dynamic models for extended range forecasting. We quantitatively demonstrate the feasibility of this post-processing statistical module for improving the predictability of the dynamic Extended Range Prediction System (ERPAS), which is developed by Indian Institute of Tropical Meteorology, Pune, India and now operational in the country. It first develops climate cluster(s) (rainfall states in the present case), then builds a statistical relationship between these clusters and a set of appropriate climate variables using a robust and advanced classification technique known as Extreme Gradient Boosting (XGboost), and eventually delivers the real-valued precipitation at individual grid cells via a non-parametric regression. The module is able to skilfully capture the active and break phases of the Indian summer monsoon, and also subsequently project them for the ensuing regression component of the module. This approach shows to significantly boost the prediction skill over the Core Monsoon Zone of Indian mainland up to a lead time of 4 weeks. Our statistical downscaled model is comparable in the week 1 lead time but outperforms global models for 2nd, 3rd, and 4th weeks lead time. Nonetheless, this performance is retained only in the northern and central region, and not ubiquitous over the rest of India. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Extended range prediction; Indian summer monsoon; Multi-model ensemble; Statistical learning
语种	英语
scopus关键词	ensemble forecasting; machine learning; prediction; seasonal variation; statistical analysis; weather forecasting; India; Maharashtra; Pune
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/145247
作者单位	Department of Civil Engineering, Indian Institute of Technology Bombay, Mumbai, India; Indian Institute of Tropical Meteorology, Pune, India; Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Mumbai, India
推荐引用方式 GB/T 7714	Raghav P.,Borkotoky S.S.,Joseph J.,et al. Revamping extended range forecast of Indian summer monsoon[J],2020,55.
APA	Raghav P.,Borkotoky S.S.,Joseph J.,Chattopadhyay R.,Sahai A.K.,&Ghosh S..(2020).Revamping extended range forecast of Indian summer monsoon.Climate Dynamics,55.
MLA	Raghav P.,et al."Revamping extended range forecast of Indian summer monsoon".Climate Dynamics 55(2020).