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Prediction of Precipitation in the Western Mountainous Regions of China Using a Statistical Model
Joint Authors
Sun, Yuan
Hu, Yijia
Ha, Yao
Zhu, Yimin
Luo, Zhixian
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-22
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
During the summer in the western mountainous regions of China (WMR), the disasters such as mountain floods, landslides, and debris flows caused by heavy rain occur frequently, which often result in huge economic losses and many casualties.
Therefore, it is of great significance to predict the precipitation accurately in these regions.
In this paper, a statistical model is established to predict the precipitation in the WMR using the linear regression statistical method, in which the summer area-averaged precipitation anomaly in WMR is taken as the predictand and the prewinter Niño3 SST is taken as the predictor.
The results of the return cross test for the historical years from 1979 to 2008 and independent sample return test from 2009 to 2018 show that this statistical model has a good performance in predicting the summer precipitation in the WMR, especially in the flood years.
It has better skill in the prediction of WMR precipitation than the dynamical model SINTEX-F.
American Psychological Association (APA)
Hu, Yijia& Sun, Yuan& Ha, Yao& Zhu, Yimin& Luo, Zhixian. 2020. Prediction of Precipitation in the Western Mountainous Regions of China Using a Statistical Model. Advances in Meteorology،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1126863
Modern Language Association (MLA)
Hu, Yijia…[et al.]. Prediction of Precipitation in the Western Mountainous Regions of China Using a Statistical Model. Advances in Meteorology No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1126863
American Medical Association (AMA)
Hu, Yijia& Sun, Yuan& Ha, Yao& Zhu, Yimin& Luo, Zhixian. Prediction of Precipitation in the Western Mountainous Regions of China Using a Statistical Model. Advances in Meteorology. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1126863
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1126863