Abstract
The prediction of summer precipitation patterns (PPs) over eastern China is an important and topical issue in China. Predictors that are selected based on historical information may not be suitable for the future due to non-stationary relationships between summer precipitations and corresponding predictors, and might induce the instability of prediction models, especially in cases with few predictors. This study aims to investigate how to learn as much information as possible from various and numerous predictors reflecting different climate conditions. An objective prediction method based on the multinomial logistic regression (MLR) model is proposed to facilitate the study. The predictors are objectively selected from a machine learning perspective. The effectiveness of the objective prediction model is assessed by considering the influence of collinearity and number of predictors. The prediction accuracy is found to be comparable to traditionally estimated predictability, ranging between 0.6 and 0.7. The objective prediction model is capable of learning the intrinsic structure of the predictors, and is significantly superior to the prediction model with randomly-selected predictors and the single best predictor. A robust prediction can be generally obtained by learning information from plenty of predictors, although the most effective model may be constructed with fewer predictors through proper methods of predictor selection. In addition, the effectiveness of objective prediction is found to generally improve as observation increases, highlighting its potential for improvement during application as time passes.
Highlights
Summer precipitation over eastern China, a region with a densely distributed population and cultivated/industrial lands, is mainly controlled by the East Asian summer monsoon (EASM)
The present paper introduces a novel approach to the statistical prediction of summer precipitation patterns (PPs) over eastern China by using machine learning or data-driving methods
In this study, we propose a systematic approach to the prediction of the summer PPs over eastern China, in which the predictors are objectively selected from a machine learning perspective
Summary
Summer precipitation over eastern China, a region with a densely distributed population and cultivated/industrial lands, is mainly controlled by the East Asian summer monsoon (EASM). In this study, we propose a systematic approach to the prediction of the summer PPs over eastern China, in which the predictors are objectively selected from a machine learning perspective This is conducted to extract as much useful information as possible from various predictors representing the relevant atmosphere, ocean, and land surface states, and to establish robust statistical relations between the preceding winter climate conditions and the summer PPs via assessing the effectiveness of the objective prediction model. The three PPs generally correspond to different climate conditions and are dominated by quite distinct large-scale atmospheric circulation patterns They are efficient and convenient in depicting summer precipitation over eastern China, and have often been used as indicators in many studies [2,29], in the operational prediction of summer precipitation by NCC-CMA.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
