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Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China |
ZHU Jiang-Shan1,2, KONG Fan-You2, LEI Heng-Chi1 |
1Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
2Center for Analysis and Prediction of Storms, University of Oklahoma, Norman 73072, USA |
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Abstract A running mean bias (RMB) correction approach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spread-error relationship. Moreover, two methods for computing calibrated probabilistic forecast (PF) were also evaluated through the 57 case dates: 1) using the relative frequency from the RMB-corrected ensemble; 2) computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.
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Received: 03 December 2013
Revised: 26 January 2014
Accepted: 24 February 2014
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Corresponding Author:
Zhu Jiangshan
E-mail: zhujiangshan@mail.iap.ac.cn
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