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A Study of Objective Prediction for Summer Precipitation Patterns Over Eastern China Based on a Multinomial Logistic Regression Model

1
State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
2
Key Laboratory of Regional Climate-Environment for East Asia, Institute of Atmospheric Physics, University of Chinese Academy of Sciences, Beijing 100029, China
3
Institute for Marine and Atmospheric Research, Utrecht University, 3584 CC Utrecht, The Netherlands
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(4), 213; https://doi.org/10.3390/atmos10040213
Received: 19 February 2019 / Revised: 14 April 2019 / Accepted: 18 April 2019 / Published: 22 April 2019
(This article belongs to the Section Climatology and Meteorology)
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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. View Full-Text
Keywords: summer precipitation pattern; objective prediction; machine learning; multinomial logistic regression; selection of predictors; generalized ability summer precipitation pattern; objective prediction; machine learning; multinomial logistic regression; selection of predictors; generalized ability
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Gao, L.; Wei, F.; Yan, Z.; Ma, J.; Xia, J. A Study of Objective Prediction for Summer Precipitation Patterns Over Eastern China Based on a Multinomial Logistic Regression Model. Atmosphere 2019, 10, 213.

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