气温数据栅格化中的几个具体问题

Some Practical Problems Related to Rasterization of Air Temperature

用地理信息系统软件ARC/INFO及4种空间插值方法,对中国617个气象站30年平均气温数据和1961年平均气温数据的栅格化试验发现,克立格插值方法的精度最高,反距离权重法次之,样条插值法的精度第三,趋势面插值方法的精度最低。利用多年平均气温数据和年平均气温的距平值进行气温数据的栅格化,虽然可以减少分析和计算量,但栅格化结果的精度比利用年平均气温数据直接进行栅格化的精度要低。气象站的实际高程与气象站经纬度对应的数字高程(DEM)上的高程不完全一致,但气温数据的栅格化完全依赖于DEM,用气象站实际高程建模进行气温数据栅格化的精度比用与气象站经纬度对应的DEM上的高程建模进行气温数据栅格化的精度要高。

Through an experiment of rasterization for 30 year mean air temperature and 1961's mean air temperature data from 617 meteorological stations in China with ARC/INFO and four interpolation methods, it was found that the Kriging method resulted in the highest precision, IDW method the second, Spline method the third, Trend method the lowest. The perennial mean air temperature and annual temperature anomaly can be used to rasterize annual air temperature data. This method will result in less analysis and computation but lower precision compared with the rasterization using annual mean air temperature data only. There is difference between meteorological stations' actual elevation and the DEM (Digital Elevation Model) values corresponding to the meteorological stations' longitudes and latitudes. Rasterization of air temperature data relies DEM fully. However, the model based on meteorological stations' actual elevation will result in higher precision for rasterization of air temperature data than that based on the DEM values corresponding to meteorological stations' longitudes and latitudes.