ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 43 Issue 12 > 2017,43(12):1584-1593. DOI:10.7519/j.issn.1000-0526.2017.12.015 Prev Next
A Simple Grid Temperature Forecast Correction Method
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Abstract:
The grid element forecasting is the main business of China Meteorological Administration, and also the future development trend of weather forecasting operation. This article proposes the method of “station corrected value transfer to grid point” which uses traditional station temperature forecast issued by SCMOC of Central Meteorological Observatory and station temperature established by the regression method to correct the grid forecast data of ECMWF high resolution model 2 m temperature. The results show that (1) the accuracy rate of maximum and minimum temperature deviation less than 2℃ in 24-168 h of SCMOC was higher than that of ECMWF by 10.0% and 23.1% respectively. There was large systematic deviation in ECMWF temperature forecast, whose minimum temperature forecast was higher and maximum temperature forecast was lower. (2) The “station corrected value transfer to grid point” method could correct the systematic deviation of ECMWF temperature forecast, and at the same time, keep the spatial pattern of forecasted field and topography characterization described by the original model unchanged. (3) Using SCMOC temperature forecasts from 98 county stations in the study area to correct ECMWF grid forecast data and returned the results to 1289 village stations for testing, we found that the accuracy rate of 24 h minimum and maximum temperature deviation <1℃ increased by 22.8% and 11.9% compared to ECMWF, and the accuracy of deviation <2℃ increased by 29.7% and 17.4%. The absolute error of the minimum (maximum) temperature decreased 0.99℃ (0.69℃) and the mean error decreased 0.7℃ (-0.9℃). (4) Using the temperature forecast of the 98 county stations by the regression method to correct the grid field could correct the systematic deviation of ECMWF as well. Comparing the two methods, SCMOC difference transfer has a great advantage in minimum temperature correction, and the regression method is better in maximum temperature correction. In addition, the regression method could improve the hourly temperature forecast effect. This method has been successfully applied to Shaanxi fining grid forecasting system.
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Clc Number:
P456
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