Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1007/s00382-018-04605-z |
Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning | |
Xu, Lei1; Chen, Nengcheng1,2; Zhang, Xiang1,3; Chen, Zeqiang1; Hu, Chuli4; Wang, Chao1 | |
2019-07-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53页码:601-615 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
英文摘要 | Seasonal precipitation forecasts at regional or local areas can help guide agricultural practice and urban water resource management. The North American multi-model ensemble (NMME) is a seasonal forecasting system providing precipitation forecasts globally. Bias correction and downscaling of the NMME is a critical step before applied at local scales. Here, the machine learning methods coupling with wavelet are used to correct the precipitation forecasts in NMME for 518 meteorological stations in China for eight models at 0.5-8.5months leads. Compared with the traditional quantile mapping (QM) approach, the wavelet support vector machine (WSVM) and wavelet random forest (WRF) methods exhibit obvious advantage in downscaling, with an overall average improvement of Pearson's correlation coefficient increasing by 0.05-0.3 and root mean square error (RMSE) reducing by 18-40mm (21-33%) for individual models. Both the spatial and seasonal patterns of downscaled results demonstrate the superiority of wavelet machine learning methods over QM. A spatial analysis indicates that the corrected NMME precipitation forecasts show the best skill in South China, with an average RMSE of about 30mm, while the worst skill in Central and Southwest China with a RMSE of 80mm. In spite of the correction, the uncertainties of seasonal precipitation forecasts in summer and extreme wet cases are still large. However, the WSVM and WRF methods may serve as an effective tool in the bias correction of NMME precipitation forecasts. |
英文关键词 | NMME Precipitation forecast Bias correction Wavelet Machine learning |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000471722400036 |
WOS关键词 | SUPPORT VECTOR REGRESSION ; REGIONAL CLIMATE MODEL ; DROUGHT PREDICTION ; SOIL-MOISTURE ; BIAS CORRECTION ; RIVER-BASIN ; TEMPERATURE ; SYSTEM ; DECOMPOSITION ; SIMULATIONS |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/184392 |
专题 | 气候变化 |
作者单位 | 1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Hubei, Peoples R China; 2.Collaborat Innovat Ctr Geospatial Technol, Wuhan 430079, Hubei, Peoples R China; 3.CMA, Key Lab Arid Climat Change & Reducing Disaster CM, Key Lab Arid Climat Change & Reducing Disaster Ga, Inst Arid Meteorol, Lanzhou 730020, Gansu, Peoples R China; 4.China Univ Geosci Wuhan, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China |
推荐引用方式 GB/T 7714 | Xu, Lei,Chen, Nengcheng,Zhang, Xiang,et al. Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning[J]. CLIMATE DYNAMICS,2019,53:601-615. |
APA | Xu, Lei,Chen, Nengcheng,Zhang, Xiang,Chen, Zeqiang,Hu, Chuli,&Wang, Chao.(2019).Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning.CLIMATE DYNAMICS,53,601-615. |
MLA | Xu, Lei,et al."Improving the North American multi-model ensemble (NMME) precipitation forecasts at local areas using wavelet and machine learning".CLIMATE DYNAMICS 53(2019):601-615. |
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