概率密度匹配方法在我国近海海面10 m风速预报中的应用

海面风速对航运及海上生产作业影响重大，但数值模式对于海面的风速预报仍存在较大误差。为降低数值模式海面10 m风速预报的系统性误差，提高海上大风预报准确率，基于2017—2019年中国气象局地面气象观测资料对ECMWF确定性模式的10 m风场预报结果进行检验评估，并采用概率密度匹配方法对模式误差进行订正。分析结果表明，概率密度匹配方法可有效地改善数值模式10 m风速预报的系统性误差，订正后风速在各个预报时效和风速量级的平均误差均较订正前有所降低。对于大量级风速的预报，经概率密度匹配方法订正后的风速预报的漏报率可减少10%以上。订正后12 h预报时效的8、9级风速预报的平均绝对误差分别由4.15 m/s、5.61 m/s降低至3.12 m/s、4.08 m/s，120 h预报时效的8、9级风速预报的平均绝对误差由7.38 m/s、9.35 m/s减小至6.46 m/s、8.07 m/s。在冷空气、台风大风天气过程中，基于概率密度匹配方法订正后的风速与实况观测更接近，能够为我国近海洋面10 m风速的预报提供更准确的参考。 

APPLICATION OF PROBABILITY DENSITY FUNCTION MATCHING IN THE OFFSHORE 10 M WIND SPEED FORECASTING IN CHINA

Sea surface wind speed has a significant impact on shipping and offshore operations, but there are still large errors in the sea surface wind speed forecasting by numerical models. In order to reduce the systematic error of the 10m sea surface wind speed forecasting by numerical models, and to improve the accuracy of sea wind forecasting, the present study uses the ground meteorological observational data from the China Meteorological Administration from 2017 to 2019 to assess the 10 m wind forecasting by the ECMWF deterministic model, and employs the probability density function matching method for bias correction. An analysis of the test data show that the probability density function matching method can effectively reduce the systematic errors in the 10 m wind speed forecasting by the numerical model. After the correction, the mean errors of wind speed in each forecast and each wind speed magnitude are reduced compared with those before the correction. For the prediction of high magnitude wind speeds, the missing forecast rate of wind speed forecasts corrected by probability density function matching method is reduced by more than 10%. The mean absolute error of the 8 and 9 magnitude wind speed 12 h forecast reduce from 4.15 m s and 5.61 m/s to 3.12 m/s and 4.08 m/s, respectively. The mean absolute error of the 8 and 9 magnitude wind speed 120h forecast reduce from 7.38 m/s and 9.35 m/s to 6.46 m/s and 8.07 m/s, respectively. In the prediction of cold air, typhoon and other strong winds, the corrected wind speed based on the probability density matching technique is closer to the observation, which can provide a more accurate reference for the 10m offshore wind speed forecasting in China.