| 时空投影模型(STPM)的次季节至季节(S2S)预测应用进展 |
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| 引用本文： | 徐邦琪,臧钰歆,朱志伟,李天明.时空投影模型(STPM)的次季节至季节(S2S)预测应用进展[J].大气科学学报,2020,43(1):212-224. |
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| 作者姓名： | 徐邦琪 臧钰歆 朱志伟 李天明 |
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| 作者单位： | 南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际联合实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044;南京信息工程大学气象灾害教育部重点实验室/气候与环境变化国际联合实验室/气象灾害预报预警与评估协同创新中心,江苏南京210044;夏威夷大学马诺阿分校大气科学学院国际太平洋研究中心,檀香山夏威夷96822 |
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| 基金项目： | 国家重点研发计划资助项目（2018YFC1505804） |
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| 摘 要： | 随着数值天气预报技术和季节动力预报系统的发展,短期天气预报及长期气候预测的能力持续提高,然而介于两者之间的次季节至季节(S2S,两周至三个月)预测技巧偏低,成为当今气象学界和业务服务的难题。南京信息工程大学国家特聘专家李天明教授团队于2012年研发了基于时空投影技术的统计预报模型(STPM),成功地对中国大陆降水和气温距平,以及区域极端降水、夏季高温、冬季低温和西太平洋台风群发事件等高影响天气进行提前10~30 d的预报,并在国家气候中心及多个省份开展了业务应用。STPM也成功应用于台湾春雨预报、南海季风爆发和ENSO预测等季节至年际变化的预测。本文对S2S预测的理论基础、STPM的发展和应用进行了完整的介绍,并讨论了S2S预测业务中所面临的挑战和未来展望。
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| 关 键 词： | 时空投影模型(STPM) 次季节至季节(S2S)预测 延伸期天气预报 可预报性来源 极端天气 |
| 收稿时间： | 2019/10/28 0:00:00 |
| 修稿时间： | 2019/11/20 0:00:00 |
Subseasonal-to-seasonal(S2S) prediction using the spatial-temporal projection model(STPM) |
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| HSU Pang-chi,ZANG Yuxin,ZHU Zhiwei,LI Tim.Subseasonal-to-seasonal(S2S) prediction using the spatial-temporal projection model(STPM)[J].大气科学学报,2020,43(1):212-224. |
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| Authors: | HSU Pang-chi ZANG Yuxin ZHU Zhiwei LI Tim |
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| Institution: | Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China,Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China and Key Laboratory of Meteorological Disaster of Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD), Nanjing University of Information Science & Technology, Nanjing 210044, China;Department of Atmospheric Sciences, International Pacific Research Center, University of Hawaii at Manoa, Honolulu 96822, USA |
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| Abstract: | With the current developments of numerical weather forecasting technology and seasonal prediction systems,the ability of short-term weather forecast and long-term climate prediction continues to improve.However,the prediction skill of the subseasonal to seasonal(S2S,two weeks to three months) system is relatively weak,and this has become a challenging issue for the meteorological community and operational services.In 2012,the research team led by Prof.Tim Li at Nanjing University of Information Science & Technology developed the spatial-temporal projection model (STPM).The STPM exhibits high skill in predicting the rainfall and temperature anomalies and extreme events in China,such as extreme precipitation,heatwave,extreme cold days and typhoon clustering events,at the lead time of 10 to 30 d.Real-time extended-range weather forecast have been carried out using the STPM at the National Climate Center and in several provinces.In addition to the subseasonal forecast,the STPM has also been successfully applied to the forecasts of spring rain in Taiwan,the onset of the South China Sea monsoon and ENSO.In the present paper,we introduce the physical basis of S2S prediction and the development and application of STPM,and discuss the challenges and future prospects of S2S prediction. |
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| Keywords: | spatial-temporal projection model(STPM) subseasonal-to-seasonal(S2S) prediction extended-range weather forecast source of predictability extreme weather |
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