ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 48 Issue 11 > 2022,48(11):1460-1474. DOI:10.7519/j.issn.1000-0526.2022.042102 Prev Next
Correction and Verification for Grid Refined Forecast of Temperature and Frost in Spring in Northern Xinjiang
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Abstract:
Using the 0.05°×0.05° temperature grid forecast guidance products issued by China Meteorological Administration and the hourly temperature data of land surface data assimilation system (CLDAS), this paper designs three intelligent grid temperature forecast correction algorithms based on the average filtering and corrects the 3 h temperature forecasts with 240 h lead time starting from 20:00 BT every day in the northern Xinjiang Plain from April to May 2019. Then the forecast effect of three kinds of revised products and guidance forecast products are compared and tested. The results show that the accuracy and stability of air temperature and frost forecast were obviously improved after the three filtering corrections. In the results of time-division test, compared with the original guide forecast product, the root mean square error (RMSE) of the three corrected products decreased by 0.79, 0.85 and 0.88℃ on average, the accuracy of temperature forecast increased by 6.11%, 6.38% and 6.46% on average, the accuracy of frost forecast increased by 3.00%, 5.81% and 7.31% on average, respectively. Moreover, the RMSE of 24 h frost forecast decreased by 4.21, 4.41 and 4.35 h, respectively. In the regional test results, the RMSE of the temperature of the three revised products decreased by 0.66, 0.71 and 0.90℃, and the accuracy of the temperature forecast increased by 5.7%, 6.1% and 6.1%, respectively. The accuracy of frost forecast increased significantly in the area of 600-1200 m above sea level in the southeast of Junggar Basin, increasing by 2.5%, 4.8% and 5.4% respectively, but not obvious in other frost areas. The RMSE of 24 h frost duration forecast was reduced by 0.81, 0.63 and 0.56 h. In comparison, the effect of the revised forecasts by the optimal ensemble algorithm is the best.
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Clc Number:
P457.3，X43
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