基于相似卡尔曼滤波的安徽省WRF模式风速预报订正

doi:10.3969/j.issn.1673-503X.2023.04.005

Abstract

Abstract:

The error correction of the 10 m wind speed forecasted by the WRF model at 19 meteorological stations in Anhui province from April to December 2020 was carried out using the improved similar Kalman filter method.The results show that the average deviation of wind speed forecast is reduced from 1.35 m·s^-1 to 0.08 m·s^-1, which is about an elimination of the systematic error of the model.The root-mean-square error (RMSE) decreases from 1.77 m·s^-1 to 0.81 m·s^-1.When the average wind speed is more than 3 m·s^-1, the root-mean-square error of the wind speed forecast is reduced from 2.01 m·s^-1 to 1.19 m·s^-1, indicating that this method can not only effectively reduce the systematic error of the model, but also greatly reduce the random error of the model.The similar Kalman filter can correct the error of the physical process model which cannot be simulated accurately, improve the forecast accuracy of the model when the weather system changes dramatically and is suitable for the continuous forecast of meteorological elements for 24~72 hours.

Key words: Numerical weather prediction, Bias correction, Systematic error

CLC Number:

P457.5

Di WU,Hongqiang TIAN,Hui LIU,Jingjing WANG,Chenliang ZUO,Jingjing XU. Bias correction of wind speed forecasts for the WRF model in Anhui province based on the analog Kalman filter method[J]. Journal of Meteorology and Environment, 2023, 39(4): 31-37.

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References 23

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统计量	平均偏差/(m·s^-1)	平均绝对误差/(m·s^-1)	均方根误差/(m·s^-1)	相关系数
模式结果	1.35	1.47	1.77	0.67
24 h ANKF订正	0.08	0.63	0.81	0.69
48 h ANKF订正	0.10	0.64	0.82	0.68
72 h ANKF订正	0.11	0.64	0.83	0.68
24 h MOS订正	0.23	0.83	1.06	0.65

季节	订正情况	平均偏差/(m·s^-1)	平均绝对误差/(m·s^-1)	均方根误差/(m·s^-1)
春季	订正前	1.59	1.71	2.00
春季	订正后	0.09	0.70	0.90
夏季	订正前	1.26	1.39	1.70
夏季	订正后	0.04	0.61	0.79
秋季	订正前	1.34	1.43	1.68
秋季	订正后	0.19	0.60	0.76
冬季	订正前	1.16	1.33	1.72
冬季	订正后	-0.11	0.64	0.85

统计量	平均偏差/(m·s^-1)	平均绝对误差/(m·s^-1)	均方根误差/(m·s^-1)
模式结果	1.35	1.64	2.01
误差订正	-0.82	0.95	1.19

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Bias correction of wind speed forecasts for the WRF model in Anhui province based on the analog Kalman filter method

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