%0 Journal Article %A Chunqing DONG %A Yuanyuan GUO %A Lei ZHANG %A Jiaying HU %T Deviation Correction Method of Grid Temperature Prediction Based on CLDAS Data %D 2021 %R 10.11755/j.issn.1006-7639(2021)-05-0847 %J Journal of Arid Meteorology %P 847-856 %V 39 %N 5 %X

Based on CLDAS grid temperature data from National Meteorological Information Center of China, SCMOC grid temperature forecast data from Central Meteorological Observatory of China and temperature observation data at weather stations of Shanxi Province, the applicability of CLDAS temperature in Shanxi Province was evaluated comprehensively by using non-independence test method. And on this basis, based on CLDAS grid temperature data, the objective correction of SCMOC temperature forecast field was studied by using the sliding training period scheme. The results are as follows: (1) The complex terrain in Shanxi Province had a certain influence on the accuracy of CLDAS temperature, and the maximum temperature of CLDAS exhibited a better accuracy than the minimum temperature of CLDAS, which indicated that the influence of terrain on deviation of the minimum temperature was more significant, and the deviation of the minimum temperature in high altitude areas was negative generally, while that in low altitude areas was positive. (2) The deviation of CLDAS grid temperature had a continuity of time in space. After the simple deviation correction, the accuracy of the maximum and minimum temperature of CLDAS promoted by 1.1% and 9.7%, respectively, the revised temperatures were more consistent with observation. (3) Based on improved CLDAS grid temperature, the accuracy rate of SCMOC temperature forecast improved significantly by using the sliding deviation correction scheme. Compared to SCMOC, the accuracy rate of the 24-hour maximum and minimum temperature forecast in Shanxi Province in 2019 respectively increased by 2.7% and 4.7% after the sliding deviation correction. The quality of short-term temperature forecast after the sliding deviation correction had greatly improved, and it was superior to the subjective forecast of forecasters.

%U http://www.ghqx.org.cn/EN/10.11755/j.issn.1006-7639(2021)-05-0847