ISSN 1000-0526
CN 11-2282/P
CN 11-2282/P
Archive > Volume 41 Issue 6 > 2015,41(6):719-726. DOI:10.7519/j.issn.1000-0526.2015.06.006 Prev Next
Study on Bias Correction for the 2 m Temperature Forecast of GRAPES_RAFS
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Abstract:
The 8times daily 3 h forecast of the 2 m temperature forecast of GRAPES_RAFS (Global and Regional Assimilation and Prediction System_Rapid Analysis and Forecast System) during the period from 20 June to 20 July in 2013 is analyzed in this paper. It is found that the forecast can show the diurnal variation of the 2 m temperature fairly well, but, some deviations exist between the forecast and the observation. For instance, the forecasted values are 3℃ lower on average than the observation in Western Sichuan Plateau in eastern Tibetan, Yungui Plateau and Wuyi Mountains, but 3℃ higher than the observations in North China. In order to diminish the influence from those deviations, bias correction is conducted by mean method, biweight method, moving mean method and movingbiweight method respectively. The values before and after the bias correction are analyzed and compared. The results show that the average error is reduced to (-1-1℃) in most regions while the RMSE (root mean square error) is less than 2.5℃. The bias correction is more effective in areas with larger deviations like Western Sichuan Plateau in eastern Tibetan where the absolute of the average error is reduced from above 3℃ to within 1℃ and the RMSE is reduced from above 4℃ to within 3℃. Comparing the four methods of bias correction, we see that the biweight method is generally as effective as the mean method, but it works better than the mean method at some particular sites. The moving methods are more effective than the nonmoving methods. The movingbiweight method is the most effective method, which can reduce the average error down to be within (-0.5-0.5℃) in most area of China and no larger than (-1-1℃).
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