A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning

Xiaolei MEN; Ruili JIAO; Ding WANG; Chenguang ZHAO; Yakun LIU; Jiangjiang XIA; Haochen LI; Zhongwei YAN; Jianhua SUN; Lizhi WANG

doi:10.3878/j.issn.1006-9585.2018.18049

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Article Navigation > Climatic and Environmental Research > 2019 > 24(1): 116-124

Xiaolei MEN, Ruili JIAO, Ding WANG, Chenguang ZHAO, Yakun LIU, Jiangjiang XIA, Haochen LI, Zhongwei YAN, Jianhua SUN, Lizhi WANG. A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning[J]. Climatic and Environmental Research, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049

Citation:

Xiaolei MEN, Ruili JIAO, Ding WANG, Chenguang ZHAO, Yakun LIU, Jiangjiang XIA, Haochen LI, Zhongwei YAN, Jianhua SUN, Lizhi WANG. A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning[J]. Climatic and Environmental Research, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049

Citation:

Xiaolei MEN, Ruili JIAO, Ding WANG, Chenguang ZHAO, Yakun LIU, Jiangjiang XIA, Haochen LI, Zhongwei YAN, Jianhua SUN, Lizhi WANG. A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning[J]. Climatic and Environmental Research, 2019, 24(1): 116-124. doi: 10.3878/j.issn.1006-9585.2018.18049

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A Temperature Correction Method for Multi-model Ensemble Forecast in North China Based on Machine Learning

doi: 10.3878/j.issn.1006-9585.2018.18049

1.
School of Information and Communication Engineering, Beijing Information Science and Technology University, Beijing 100101
2.
School of Information Management, Beijing Information Science and Technology University, Beijing 100192
3.
Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
4.
School of Mathematical Sciences, Peking University, Beijing 100871
5.
University of Chinese Academy of Sciences, Beijing 100049

Funds:

Strategic Priority Research Program of Chinese Academy of Sciences A-XDA19030403

Strategic Priority Research Program of Chinese Academy of Sciences XDA19040202

Received Date: 2018-04-02
Available Online: 2018-07-04
Publish Date: 2019-01-20

Abstract

Abstract

Post-forecast data processing is critical for obtaining reliable local weather forecast. In this study, the authors developed three post-processing models based on ridge regression (Ridge), random forest (RF), and deep learning (DL) methods. The post-processing models were trained by observational and forecast data of daily 2-m above surface air temperature in North China (38°N-43°N, 113°E-119°E) from four numerical weather forecast (NWF) models (BABJ model from China Meteorological Administration, ECMF model from ECMWF, RJTD model from Japan Meteorological Agency, and KWBC model from NCEP, respectively), for the training period from February 2014 to September 2016, and then applied to the forecast period from October 2016 to September 2017. The forecast results of the post-processing models together with those of commonly-used multi-model ensemble mean (EMN) and individual NWF models were evaluated according to the root-mean-square error (RMSE). The main results are as follows:1) For the region as a whole, with the increase in the forecast lead time, all the NWF models, EMN and the post-processing models exhibit increasing RMSEs, but the RMSEs of the three post-processing models are all significantly smaller than those of EMN and individual NWF models; especially, DL is slightly better for the short-term (the first few days) forecast and RF is slightly better for the longer-term prediction. 2) The RMSEs are relatively smaller in the southeastern part of North China, approximately in the range of (38°N-41°N, 115.5°E-119°E) than else where; on average, DL is slightly better, and the RMSEs of the three machine learning models are between 1.24℃ and 1.26℃, while the EMN error is 1.69℃. 3) There are seasonal differences:The results of all the models are relatively good for the summer, but poor in general for the winter. All the three post-processing models perform better than EMN and individual NWF models, with a smallest average RMSE of 2.15℃ for Ridge compared with 2.45℃ for EMN.
- 2-m above surface air temperature,
- Multi-model ensemble mean forecast,
- Ridge regression,
- Random forest,
- Deep learning

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References(21)

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