中国地面气温统计降尺度预报方法研究  

Surface temperature statistical forecasting downscaling research in China

作　　者：陈国华[1] 郭品文[1] 

机构地区：[1]南京信息工程大学气象灾害教育部重点实验室,江苏南京210044

出　　处：《大气科学学报》2016年第4期569-575,共7页Transactions of Atmospheric Sciences

基　　金：公益性行业(气象)科研专项(GYHY201006017)

摘　　要：利用中国752个基本、基准地面气象观测站2000—2010年地面温度日值数据,采用具有自适应特征的Kalman滤波类型的递减平均统计降尺度技术,对中国地面温度进行精细化预报研究。分析该方案的降尺度效果,并与常用插值降尺度方法进行比较。结果表明：1）递减平均统计降尺度技术相比插值方法有较大的提高,显著减小东西部预报效果差异,1～3 d预报的均方根误差减小了1.4℃;2）该方案1～3 d预报的均方根误差为1.5℃,预报误差从东南地区（均方根误差为1.4℃）向西北地区（均方根误差为1.8℃）逐渐增大,并且预报效果夏季优于冬季。因此,递减平均统计降尺度技术对中国地面温度进行精细化预报是可行的。The statistical downscaling technique based on large-scale numerical forecasting productions is an effective method for fine-scale forecasting.In China,researchers use interpolation methods such as bilinear interpolation and inverse distance interpolation to produce a downscaled forecast.In recent years,the Kalman filter-type self-adap- ting decaying average downscaling technique has been designed overseas for forecast downscaling,which is better than the MOS method.Based on a daily surface temperature dataset of 752 weather stations for the period 2000 to 2010 in China,a fine-scale prediction test with a low-resolution ＂forecast field＂ and fine-scale ＂analysis field＂ for daily average temperature, using the self-adapting Kalman Filter-type decaying average statistical downscaling technique,was designed,without the effect of forecasting error in numerical forecasting production. The result of the downscaled prediction was compared with the interpolation method and analyzed for its possibility of application in China. The decaying average technique in this paper filtered the observational data in order and determined the change of the dynamic system constantly.Then, systematic bias（called the ＂downscaling vector＂, DV） was estimated.Finally,the prediction outcome was then corrected by the bias.This was a kind of self-adapting bias-estimated method, similar to the Kalman filter and a statistical post-processing method.The DV, defined as the difference between the ＂forecast field＂ and ＂analysis field＂ at the same time, presents the statistical relationship and systematic bias between the forecast and analysis.The DV that is weight-averaged between the last DV and the forecast error at the same time is updated by the decaying average algorithm.Thus, we can extract error information between the forecast and the observational data to estimate the forecast bias.The result show that： （1） The 1-3 d forecast accuracy rate of the self-adapting Kalman filter-type decaying average statistical do

关 键 词：精细化预报 统计降尺度 插值 递减平均法 

分 类 号：P457.3[天文地球—大气科学及气象学]