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Label distribution learning with climate probability for ensemble forecasting

文献类型：期刊论文

作者Yang, Xuebing4; Wu, Yajing3,4; Zhang, Wensheng2,4; Tang, Wei1
刊名INTELLIGENT DATA ANALYSIS
出版日期2020
卷号24期号:1页码:69-82
关键词Ensemble forecasting label distribution learning post-processing domain knowledge
ISSN号1088-467X
DOI10.3233/IDA-184446
通讯作者Yang, Xuebing(yangxuebing2013@ia.ac.cn)
英文摘要In meteorology, ensemble forecasting aims to post-process an ensemble of multiple members' forecasts and make better weather predictions. While multiple individual forecasts are generated to represent the uncertain weather system, the performance of ensemble forecasting is unsatisfactory. In this paper we conduct data analysis based on the expertise of human forecasters and introduce a machine learning method for ensemble forecasting. The proposed method, Label Distribution Learning with Climate Probability (LDLCP), can improve the accuracy of both deterministic forecasting and probabilistic forecasting. The LDLCP method utilizes the relevant variables of previous forecasts to construct the feature matrix and applies label distribution learning (LDL) to adjust the probability distribution of ensemble forecast. Our proposal is novel in its specialized target function and appropriate conditional probability function for the ensemble forecasting task, which can optimize the forecasts to be consistent with local climate. Experimental testing is performed on both artificial data and the data set for ensemble forecasting of precipitation in East China from August to November, 2017. Experimental results show that, compared with a baseline method and two state-of-the-art machine learning methods, LDLCP shows significantly better performance on measures of RMSE and average continuous ranked probability score.
WOS关键词SEASONAL CLIMATE ; WEATHER
资助项目National Natural Science Foundation of China[U1636220] ; National Natural Science Foundation of China[61602482]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000516758100005
出版者IOS PRESS
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/38394]  
专题精密感知与控制研究中心_人工智能与机器学习
通讯作者Yang, Xuebing
作者单位1.Publ Meteorol Serv Ctr China Meteorol Adm, Beijing, Peoples R China
2.Foshan Univ, Sch Math & Big Data, Foshan, Guangdong, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Res Ctr Precis Sensing & Control, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Xuebing,Wu, Yajing,Zhang, Wensheng,et al. Label distribution learning with climate probability for ensemble forecasting[J]. INTELLIGENT DATA ANALYSIS,2020,24(1):69-82.
APA Yang, Xuebing,Wu, Yajing,Zhang, Wensheng,&Tang, Wei.(2020).Label distribution learning with climate probability for ensemble forecasting.INTELLIGENT DATA ANALYSIS,24(1),69-82.
MLA Yang, Xuebing,et al."Label distribution learning with climate probability for ensemble forecasting".INTELLIGENT DATA ANALYSIS 24.1(2020):69-82.

入库方式： OAI收割

来源：自动化研究所

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