中国地面气温和降水网格化数据精度比较

摘要

The primary goal is to evaluate and discuss the validation of gridded precipitation and temperature data by means of three products (ANUSPLIN,SHERPAD,and Optimal Interpolation Method)at 0.5°× 0.5° spatial resolution,using the daily data of precipitation and temperature from the network of 2419 national ground meteorological stations and 839 basic/reference meteorological stations from the National Meteorological Information Center (NMIC) of China in 2013.Results show that the accuracy of gridded precipitation and temperature data is higher,when the number of stations is greater.The correlation coefficient increases and the root mean square error (RMSE) decrease with increasing station density,which are different and change with seasons and months.The correlation coefficient and RMSE calculated from gridded precipitation in summer are better than those of the whole year,and the correlation coefficient calculated from gridded temperature in summer is worse than that of the whole year.The time series of evaluation indexes has greater uncertainty and larger fluctuation range for gridded precipitation than for gridded temperature.The comprehensive evaluation of the gridded precipitation data based on the climatic background filed by the Optimal Interpolation Method of NMIC and the elevation of the gridded temperature data by ANUSPLIN of NMIC are better than others.%采用2013年国家气象信息中心逐日的839个中国基准、基本气象站,2419个国家级地面气象站,3万个逐小时中国地面站气温和降水数据,利用ANUSPLIN、SHERPAD和OI插值方法进行网格化,比较了日尺度上不同方法插值不同站网密度站点数据的精度.结果表明:台站点数量多的插值结果,对降水或气温描述的准确性高,相关性随站点密度增加而增大,均方根误差随站点密度增加而减少.不同方法得出的相关性、均方根误差随季节变化明显,且差异较大,并具有月尺度变化特征.夏季站点数量相同的不同插值结果,降水场的相关系数、RMSE等评分指标均优于年平均,气温的相关系数则低于年平均.降水场的评估指标时间序列不确定性更大,波动范围大于气温.国家气象信息中心制作的基于气候背景场OI插值的2419站降水场和考虑高程ANUSPLIN插值的2419站气温的综合评价较好.