Abstract:Based on the 10 m wind speed forecasts during the summer of 2012 from the ECMWF in the TIGGE datasets,a Kalman filter bias-correction combining with a sliding weight method has been done to calibrate the ensemble perturbed forecasts.The effect of this calibration method is examined.Then,the jumpiness index is used to analyse the results before and after calibration.Results show that:(1)In general,the calibration method can effectively reduce the RMSEs of the 10 m wind speed ensemble forecasts at different start times.When the start time is 0000 UTC,the correction results are better in the middle and low latitudes.When the start time is 1200 UTC,the correction results are better in the middle and high latitudes.(2)The calibration method has a good effect on improving the dispersion of ensemble members.The Talagrand pictures show that U-type and L-type distributions decrease after calibration.(3)Analysis of RMSE shows that the 10 m wind speed ensemble forecasts from ECMWF has great inconsistency of prediction.After calibration,the period-average forecast inconsistency indices of ensemble mean are lower than before,showing that the Kalman filter bias-correction method can reduce the forecast inconsistency of the 10 m wind speed ensemble forecasts.(4)In terms of the period-average inconsistency features of the 10 m wind speed ensemble forecasts from ECMWF,all average period-average inconsistency indices increase with the forecast range,in agreement with the practical experience that the forecasts are usually more consistent at short forecast ranges.(5)The calibration method has better effects on reducing the frequencies of flip,flip-flop and flip-flop-flip.The flip happens more frequently than the other two,and the frequency of flip-flop-flip is the lowest.