基于集合Kalman滤波数据同化与偏差修正的热带气旋强度集合预报研究

The ensemble forecasting of tropical cyclone intensity based on EnKFdata assimilation and bias correction

中文关键词:  热带气旋, 集合预报, 强度预报, 偏差修正

英文关键词:Tropical cyclone, Ensemble forecast, Intensity forecast, Bias correction

中文摘要:

      采用基于集合Kalman滤波数据同化和偏差修正方法的集合预报技术来研究热带气旋的强度预 报问题。集合预报系统考虑初值误差和模式误差，利用MM5中尺度模式，采用Anthes kuo 、Grell和Betts Miller等积云参数化方案和High resolution Blackadar、Burk Thom pson、MRF等边界层过程的9组不同的组合，分别进行45、60和75 min的短时预报。对9个预 报结果采用“镜像法”，得到18个集合成员。将蓝金涡旋作为同化的观测场，18个集合成员 作为集合Kalman滤波的初始背景集合，采用ENSRF算法和逐点局地分析算法进行同化。同化 后的结果作为集合预报的初值，预报过程对模式参数采用前述9种组合，进行72小时预报。 通过求取偏差系数对预报结果进行修正，减小模式系统误差。选2003—2004年16个台风过 程作为预报个例，讨论偏差修正前后对预报结果的影响。实验结果表明，基于集合Kalman滤 波数据同化的热带气旋集合预报相对于非同化的集合预报对路径预报的改进效果优于强度预 报。平均而言通过偏差修正，强度集合预报的潜力得到挖掘，绝对误差明显减小，通过偏差 修正减小了强度集合预报均值的误差，进而使得预报概率密度函数均值向理论值靠近，从而 提高了概率预报的精度和合理性，因此基于集合预报的偏差修正分析方法，是改善热带气旋 强度预报水平的有效途径。

英文摘要:

      The techniques of ensemble forecasting based on EnKF(Ensemble Kalman Filter) and bias correction are applied to predict the tropical cyclone intensity by using MM5 model. Adopting the Anthes Kuo, Grell, and Betts Miller cumulus parameterization schemes, and high resolution Blackadar, Burk Thompson, and MRF PBL process parameterization schemes, nine groups model configuration are designed, and45 , 60 and 75 min forecasts are conducted for each situation. With the “mirror imaging method”, 18 different initial conditions are obtained. Taking the “Rankine vortex” as observational data and the 18 different initial conditions as the background ensemble, the EnKF data assimilation with EnSRF arithmetic are then carried out. Utilizing the 18 data assimilation results as the ensemble forecasting initial fields and taking 9 different model configurations, 72 h forecast is carried out. Two experiments are designed: one is the ensemble forecasting based on EnKF data assimilation, which is used to compare with non assimilation ensemble forecasting, and 6 typhoon cases occurred in 2004 are selected. The other uses the bias correction method to modify the ensemble forecasting results, in which 16 typhoon cases occurred in 2003 and 2004 are selected. So we can discuss the impact of bias correction. The results of the first experiment show that because the MM5 model has deficiencies in intensity forecast, the ensemble forecasting system with data assimilation has made little improvement compared with non assimilation. The results of the second experiment show that, as can be seen from the RSS index, the bias corrected intensity forecast makes great improvement compared with that of uncorrected. On the average, the absolute error of intensity forecast is reduced obviously than that of control prediction in every forecast time. The error for 48 and 72 h are 18 hPa， and 11 hPa respectively, which minishes 5 hPa forecast error at 48 h. By bias correction, the error of intensity ensemble forecast sample mean is reduced, and the PDF mean is closer to the theoretical value. Therefore, the quality of the intensity ensemble forecast results, particularly forecast probability, is increased. So the sample variance is reduced and a more reasonable intensity probability forecasting is obtained.