Abstract: With Surfer, Grads as a platform for direct space interpolation was widely used in meteorological rasterization of air temperature data, whatever the spatial interpolation technique (Spline, IDW, Lagrangian, Hennite interpolation, etc.), do not take into account the effects of topography on the air temperature distribution, In recent years with the expansion of GIS technology applications, the method of regression model by geographic factors (elevation, longitude, latitude, etc.) combined with spatial interpolation was used in gridbased regional climate factors and get good results. In this paper, used regression analysis methods combined with GIS spatial interpolation to rasterization of year mean air temperatures from 1971 to 2010 in Xinjiang area, the 99 meteorological stations(10 of them in order to verify) that has complete observations involved in the calculation. We use the following method for air temperature data rasterization in Xinjiang region, Firstly, establish the average temperature multiple regression model with the air temperature data that measured by weather station (excluding test station) for the output variables, and the longitude grid data, latitude grid data and altitude grid data of meteorological stations for the input variables, obtain the regression equation and the temperature residuals data for each weather station; Secondly, calculate the air temperature grid data (regular part) of the each observations station use the digital elevation model (DEM), the latitude grid data and longitude grid data by the regression equation, and then the residuals grid data (irregular part) to be rasterized with a spatial interpolation method(Three methods including IDW, Kriging and Spline); Finally, the two parts of the data grid computing has been to estimate the actual temperature. The authors used this method to rasterization the air temperature grid data of the Xinjiang region for many years (the average temperature data for 40 years, most recently 2010, the hottest years 2007 and the coldest years 1984). Comparative and analysis of residual data interpolation with inverse distance weighting method (IDW), ordinary kriging (Kriging) and Spline (Spline) method, overall, the result of mean absolute errors (MAE) and Root Mean Squared Interpolation Error (RMSIE) from crossvalidation tests is IDW gives lowest errors. The other question is, even if we use the best method (IDW) to create raster data of the air temperature in Xinjiang, the rasterized grid of error is significantly larger than the most other provinces in China, mainly due to Xinjiang’s unique geospatial and sparse distribution of meteorological observation site, so how to improve the accuracy of simulation grid is the key of the future rasterized grid in Xinjiang.

Key words: GIS, spatial interpolation, air temperature, grid