In order to improve correction ability and forecasting level of intelligent grid. Based on the slice data of Gansu Province of objective guidance product from Central Meteorological Observatory of China and daily grid temperature data from Chinese Land Data Assimilation System Version 2.0 (CLDAS-V2.0) of CMA, the maximum and minimum temperature of 0.05°×0.05° grid points in the eastern Hexi Corridor (101.0°E-104.5°E, 36.0°N-40.0°N) were corrected, tested and evaluated by using Kalman filtering method and sliding training correction method. The results are as follows: (1) For seasonal comparison, the mean absolute errors of maximum and minimum temperature of Kalman filter and sliding training correction products were both smaller than objective guidance product at all seasons, and all values were less than 2.00 ℃. The forecast accuracy of maximum and minimum temperature of Kalman filter and sliding training correction products were greater than 70% at all seasons. which the maximum temperature was 6%-13% higher and the minimum temperature was 8%-24% higher. (2) For spatial comparison, the mean absolute errors of the maximum and minimum temperature of Kalman filter and sliding training correction products were 1.00-2.00 ℃, but greater than 2.00 ℃ in a few areas. The forecast accuracy of maximum (minimum) temperature of Kalman filter and sliding training correction products were greater than 70% (60%-70%) in most areas, and greater than 80%(70%) in a few areas. (3) As a whole, the correction skills of maximum and minimum temperature of Kalman filter and sliding training correction products were basically positive, and were greater than 0.300 in a few seasons and a few areas. It showed that the two correction methods have good prediction and correction ability, which can provide certain technical support for the future temperature forecasting operations.
