| 数值天气预报和气候预测的集合预报方法:思考与展望 |
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| 引用本文： | 段晚锁,汪叶,霍振华,周菲凡.数值天气预报和气候预测的集合预报方法:思考与展望[J].气候与环境研究,2019,24(3):396-406. |
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| 作者姓名： | 段晚锁 汪叶 霍振华 周菲凡 |
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| 作者单位： | 1.中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029;中国科学院大学,北京1000492.中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室(LASG),北京100029;中国科学院大学,北京100049;河南大学数学与统计学院,河南开封4750043.中国气象局国家气象中心,北京 1000814.中国科学院大气物理研究所云降水物理与强风暴重点实验室(LACS),北京100029 |
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| 基金项目： | 国家重点研发计划2018YFC1506402,国家自然科学基金项目41525017 |
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| 摘 要： | 从初始误差、模式误差以及两者综合影响的角度,综述了天气、气候集合预报方法的研究进展,指出了传统方法的优势,同时也评论了这些方法的局限性,提出了对未来先进集合预报方法的一些思考,以及需要解决的挑战性问题和可能的应用。
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| 关 键 词： | 初始误差 模式误差 集合预报 |
| 收稿时间： | 2018/10/18 0:00:00 |
Ensemble Forecast Methods for Numerical Weather Forecast and Climate Prediction: Thinking and Prospect |
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| DUAN Wansuo,WANG Ye,HUO Zhenhu,ZHOU Feifan.Ensemble Forecast Methods for Numerical Weather Forecast and Climate Prediction: Thinking and Prospect[J].Climatic and Environmental Research,2019,24(3):396-406. |
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| Authors: | DUAN Wansuo WANG Ye HUO Zhenhu ZHOU Feifan |
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| Affiliation: | 1.
State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;2.
University of Chinese Academy of Sciences, Beijing 100049;3.
School of Mathematics and Statistics, Henan University, Kaifeng, Henan Province 475004;4.
National Meteorological Center, China Meteorological Administration, Beijing 100081;5.
Key Laboratory of Cloud-Precipitation Physics and Severe Storm (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029 |
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| Abstract: | This article reviews the research progress of ensemble forecast methods for weather and climate from the perspective of initial errors, model errors, and the combined effects of the above two. The authors clarify the advantages of traditional methods; meanwhile, they comment on the limitations of these methods. Finally, the authors propose some ideas on developing more advanced ensemble forecast methods, raise some challenging problems that need to be resolved, and address possible applications of the ensemble methods. |
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| Keywords: | Initial error Model error Ensemble forecast |
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