刘森峰,段安民. 2017. 基于青藏高原春季感热异常信号的中国东部夏季降水统计预测模型[J]. 气象学报, 75(6):903-916, doi:10.11676/qxxb2017.066
基于青藏高原春季感热异常信号的中国东部夏季降水统计预测模型
A statistical forecast model for summer precipitation in eastern China based on spring sensible heat anomaly in the Tibetan Plateau
投稿时间:2017-01-09  修订日期:2017-06-19
DOI:10.11676/qxxb2017.066
中文关键词:  青藏高原春季感热  中国东部夏季降水  统计预测  最大协方差分析
英文关键词:Spring Tibetan Plateau sensible heat  Precipitation in eastern China  Statistical forecast  Maximum covariance analysis
基金项目:国家自然科学基金(91337216、91637312)、CAS项目(XDA11010402)、中国财政部和科技部公益性行业(气象)专项(GYHY201406001)、NSFC-广东联合基金(第二期)超级计算科学应用研究专项及国家超级计算广州中心支持。
作者单位E-mail
刘森峰 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, 北京, 100029
中国科学院大学地球科学学院, 北京, 100049 
 
段安民 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, 北京, 100029
中国科学院大学地球科学学院, 北京, 100049
南京信息工程大学气象灾害预报预警与评估协同创新中心, 南京, 210044 
amduan@lasg.iap.ac.cn 
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中文摘要:
      使用1980-2014年由青藏高原中东部的地面气象观测台站观测资料计算得到的地表感热通量以及中国东部高分辨率的降水格点资料,在年代际变化和年际变率两个时间尺度上,使用最大协方差分析方法研究了青藏高原春季感热与中国东部夏季6、7和8月降水的关系,基于最大协方差关联因子的时间尺度分解回归分析方法建立了一个降水统计预测模型。青藏高原春季感热的各个关联预报因子与中国东部夏季各月降水的相关分析表明,在年代际成分中,6、7和8月在中国东部绝大部分地区均存在显著相关,方差贡献分别为75.6%、99.9%和79.7%;在年际成分中,相关区域在6月是华南地区、华北沿海地区和江淮流域,7月是华南地区西南部、长江流域、东北地区东南部和黄河中下游地区,8月是东北地区和华南地区西部,方差贡献分别为42.7%、43.4%和32.0%。预测模型的解释方差分析和后报试验检验表明,7月对整个中国东部地区预测效果最好,6月主要在长江以南地区,而8月主要在东北地区和华南地区西部预测效果较好。该预测模型能很好描述青藏高原春季感热与中国东部夏季各月降水的关联性,并对局地降水实现较好的定量预测,具有在短期气候预测业务应用的价值。
英文摘要:
      Based on station observations and high resolution gridded precipitation data, the relationship between spring sensible heat in the Tibetan Plateau (STPSH) and summer precipitation in eastern China (SPEC) is investigated in terms of decadal change and interannual variability by using the maximum covariance analysis. Further attempt has been made to establish a statistical model for forecasting the SPEC using the timescale decomposed regression approach. Results indicate that for the decadal component, a significant correlation exists between the STPSH and SPEC in most part of eastern China in June, July and August with the explained variance fractions of 75.6%, 99.9% and 79.7%, respectively. For the interannual component, however, the significantly correlated regions are distributed in southern China, the coastal area of northern China, and the Yangtze-Huai River valley in June; in July, the correlated areas are located over the southwestern part of South China, the Yangtze River valley, the southeastern part of Northeast China and the middle-lower reaches of Yellow River; high correlation is found over Northeast China and the western part of South China in August. The explained variance fractions are 42.7%, 43.4% and 32.0%, respectively. The explained variance analysis and the hindcast examination suggest that the best prediction skill of this model occurs in most part of eastern China in July. The areas with high predictability are the southern region of the Yangtze River in June, and northeastern China and western part of South China in August. The model can reasonably describe the relation between the STPSH and SPEC and quantitatively forecast local precipitation in June, July and August. Therefore this model might be used for short-term operational climate prediction.
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