摘要
利用1979—2015年中国国家气候中心整编的160站月平均气温和NCEP/NCAR全球大气再分析资料,从1979/1980—2008/2009年冬季前期500 h Pa高度场、200 h Pa势函数和850 h Pa势函数场选择预测因子,考虑不同时效因子的组合及其独立性,综合应用多因子回归集合、交叉检验集合、逐月滚动集合,建立了针对中国冬季气温的逐月滚动预测模型,并利用该模型对2010/2011—2014/2015年冬季气温进行了独立预测试验和检验。结果表明,综合运用多种集合可提高短期气候客观定量预测的可行性和稳定性。多因子回归集合能增加可预测站点数,交叉检验集合可减少因统计关系不稳定而产生的对预报效果的影响,逐月滚动集合的应用不仅增加了可预测站点数,而且使预测效果更加稳定。本文建立的预测模型可对中国冬季气温进行长时效的预测,且有一定的预报技巧,对实际的季节预测业务有重要应用价值。
With the development of social economy and the improvement of peoples living standard,the demand of country and society for the short term climate prediction is increasing.Though current methods including statistic,dynamical statistic and numerical methods for the prediction of surface air temperature in wintertime are more,the prediction lead time is usually short and the forecast skill is not stable.For example,the seasonal prediction of climate model for winter temperature is still low outside the tropics and the most models cannot give reliable results in many areas of China.So it is very important to carry prediction experiment of winter air temperature and expand valid prediction lead time,in order to meet the needs of the society.Based on NCC(National Climate Center of China)monthly surface air temperature data of160stations in China and NCEP/NCAR monthly mean reanalysis data during1979-2015,the predictive factors are selected from early winter geopotential height at500hPa and velocity potential at850and200hPa during1979/1980-2008/2009.Considering the combination of different predictive factors and their independence,the monthly rolling forecasting models are separately established by the multi variable regression ensemble,the cross validation test ensemble and the monthly rolling prediction ensemble,in order to perform independent predictive tests for the winter temperature in China during2010/2011-2014/2015.The velocity potential can reflect the external forcing source of atmospheric system,and500hPa height can denote the basic state of atmospheric circulation.Although the memory of internal evolution within atmosphere circulation is about a week or so,the initial time potential function at850and200hPa can reflect variations of the upper and lower level boundary forcing anomalies and their influences on the future atmosphere.Besides,it is simple and practical to select factors from the predictands on the above three levels.Results show that the multi variable regression ensemble(ENC1)may increase predictable station number.Combined using of the multi variable regression ensemble and the cross validation test ensemble(ENC2)can improve stability and prediction skill,which is negatively affected by unstable statistic relationship between predictor and predictand.The comprehensive ensemble of multi variable regression,cross validation test and monthly rolling prediction(ENC3)can not only increase the predictable station number,but also make the prediction more stable,which improves the feasibility and stability of objective quantitative prediction of short term climate.Although the data used in establishment of prediction model are less and not complex,the final prediction model,through the comprehensive application of the three ensemble methods,has a certain predictive ability for the winter surface air temperature in China,and the prediction lead time is relatively long.Therefore,the statistic model established here will make the long lead prediction reliable and effective with valuable skill,which is very important in practical use in short range climate prediction.In addition,the comprehensive application of multi ensemble methods can also be employed to correct the numerical model products by the establishment of dynamic statistical forecasting model.
作者
谭桂容
尹丝雨
王永光
TAN Guirong;YIN Siyu;WANG Yongguang(Key Laboratory of Meteorological Disaster,Ministry of Education(KLME)/Joint International Research Laboratory of Climate and Environment Change(ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disaster(CIC FEMD),Nanjing University of Information Science & Techno;National Climate Center,China Meteorological Administration,Beijing 100081,China)
出处
《大气科学学报》
CSCD
北大核心
2017年第6期749-758,共10页
Transactions of Atmospheric Sciences
基金
公益性行业(气象)科研专项(GYHY201206016
GYHY201306028)
国家自然科学基金资助项目(41475088)
关键词
逐月滚动预测
统计预测模型
多集合预测
冬季气温
monthly rolling prediction
statistic prediction model
multi ensemble prediction
winter surface air temperature
