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气象:2019,45(4):469-482
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基于T639集合预报的我国2016年极端温度预报检验
郑嘉雯,高丽,任宏利,陈权亮,蔡宏珂
(成都信息工程大学,成都 610225, 国家气象中心,北京 100081, 国家气候中心,北京 100081)
Verification of China Extreme Temperature Forecasts in 2016 Based on T639 Ensemble Forecast
ZHENG Jiawen,GAO Li,REN Hongli,CHEN Quanliang,CAI Hongke
(Chengdu University of Information Technology, Chengdu 610225, National Meteorological Centre, Beijing 100081, National Climate Centre, Beijing 100081)
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投稿时间:2018-01-17    修订日期:2019-01-02
中文摘要: 基于我国T639集合预报系统的2 m温度模式实时预报和分析场资料,对历史气候百分位分布进行了估算,并对2016年我国极端高温和极端低温集合预报进行了检验评估。分析表明,对于某一区域代表站点,不同超前时间对应的气候百分位分布变化趋势均与分析场的基本一致,但不同区域代表站点之间以及同一站点不同季节之间的气候百分位分布则存在差异。Talagrand分布检验结果显示,T639集合系统对哈尔滨和长沙站的预报存在暖偏差,而对北京和拉萨站的预报则存在冷偏差,其余典型站表现出预报离散度偏小的特点。基于历史气候百分位定义,对2016年极端温度进行识别、预报和检验。TS评分结果表明,T639集合系统对于我国极端温度总体上具有一定预报性能,预报技巧在各个区域有较大差别,极端高温在江南和东北区域技巧相对较高、极端低温在华北和南方技巧较高。对于不同预报方法技巧比较显示,集合平均法对极端信号有平滑效果,总体技巧偏低,集合众数法对低温技巧增加明显,而采用集合最大值和最小值法对极端信号则有放大效果,对高温技巧增加明显,但对低温技巧则不明显。由此可见,采用合适的方法对集合预报中极端信息的正确提取至关重要。
Abstract:Based on the real time forecast and analysis data of 2 m temperature from the T639 ensemble forecast system in China, the climatic percentile distribution is estimated for both real time forecast and analysis data, and the forecasts of extreme high temperature events and extreme low temperature at typical stations over China in 2016 are verified and evaluated. The analysis results show that the forecasted climate percentile distribution at different lead time is basically the same as the percentile distribution of the analysis for a given station site, but they are different to some degree between different regions and different seasons. The results of the Talagrand distribution show that there is a warm bias in the forecasts of Harbin and Changsha Stations by the T639 ensemble system and a cold bias in the predictions of Beijing and Lhasa Stations. The other typical stations show a low ensemble spread. Based on the definition of historical climate percentile, the 2016 extreme temperature identified, forecasted and verified. The results of TS score show that the T639 ensemble system has a certain forecasting performance for extreme temperatures in China, but the prediction skill has apparent difference in different regions. The extreme high temperature has higher prediction skills in the south to the Yangtze River and the Northeast China, and the extreme low temperature has higher prediction skills in the northern and southern parts of China. The comparisons of different forecasting methods show that the ensemble mean method has a smoothing effect on the extreme signals and the overall prediction skill is low. The ensemble model method improves ob viously the extreme low temperature skill, while the method of using maximum or minimum in all ensemble members can amplify the extreme signals and increase significantly the prediction skill of extreme high temperature, but not clear for low temperature. Thus, we can see that proper extraction of extreme information in ensemble forecasting is of crucial importance.
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基金项目:国家自然科学基金项目(41875138)、国家科技支撑计划项目(2015BAC03B01)、气象预报业务关键技术发展专项(YBGJXM2017 04)和中央引导地方科技发展专项(ZY18C12)共同资助
引用文本:
郑嘉雯,高丽,任宏利,陈权亮,蔡宏珂,2019.基于T639集合预报的我国2016年极端温度预报检验[J].气象,45(4):469-482.
ZHENG Jiawen,GAO Li,REN Hongli,CHEN Quanliang,CAI Hongke,2019.Verification of China Extreme Temperature Forecasts in 2016 Based on T639 Ensemble Forecast[J].Meteor Mon,45(4):469-482.