| The hybrid ensemble-variational data assimilation method combines the advantage of variational method which simultaneously assimilates varieties of observations with model constraints and the advantage of ensemble kalman filter which provides flow-dependent background error covariance.To establish an efficient cycling hybrid data assimilation and forecast system,study on flow-dependent background error covariance was carried out as the followings.Firstly,in order to improve the performance of variational data assimilation without increasing computational cost,a time-lagged ensemble-variational assimilation(TLEn-Var)method which introduces the flow-dependent time-lagged ensemble forecast error covariance into variational cost function was proposed.In single observation test,the characteristic of flow-dependence was showed by background error covariance derived from time-lagged ensemble.In cycle assimilation test,the analysis was improved by the TLEn-Var method compared with 3DVar.Because the time-lagged ensemble is based upon history forecasts,the TLEn-Var method proposed is not only computationally economic,but also efficient in performance.Thus this method is convenient for operational use.Secondly,an efficient regional hybrid EnVar data assimilation method using the globally augmented ensemble error covariance was proposed and tested in this study.This method used the global error covariance as the low resolution covariance,and the high-resolution dynamic ensemble forecast mean as the first guess in EnVar data assimilation and then re-centered the analysis to the updated high-resolution dynamic ensemble perturbations.The combination of the global and regional ensemble error covariance not only reduced the computational cost required by the ensemble forecasts,but also increased the degree of freedom of the ensemble error covariance and introduces the correct large-scale information which can better constrain the regional data assimilation.Thirdly,besides the traditional hybrid covariance data assimilation(referred to as"HCDA")method,the hybrid gain data assimilation(referred to as "HGDA")has been proposed recently to combine the ensemble Kalman filter and variational methods,showing potential advantages in global model.To evaluate the impact of HGDA on regional and meso-scale numerical weather prediction using WRF model over east China,both single observation tests and full cycling experiments for 3-weeks in July 2013 were conducted using the 3DVar,EnKF,HCDA and HGDA methods.In general,both of the hybrid data assimilation methods showed better results than EnKF and 3DVar.Especially,the HGDA method showed advantage benefiting from the utilization of optimal EnKF analysis mean and 3DVar analysis which equals to the linearly combination of the gain matrix,considering the total error variance.Fouthly,a unified variational and ensemble analysis algorithm in a variational framework to analyze both the mean state and ensemble perturbations was proposed.For the ensemble perturbation analysis,the newly-proposed algorithm is based upon the deterministic EnKF formulation without perturbed observations proposed by Sakov and Oke(2008),who demonstrated that their EnKF implementation performed superior to the EnKF with perturbed observations and comparable to other deterministic EnKF.This unified algorithm allows the mean state and ensemble perturbations updated in a similar way with some advantages for computational efficiency,flexible configuration,and reduced burden for software maintenance.Finally,as the first attempt to assimilate radiances from satellite Himawari-8(AHI)using WRFDA at convective scales,the added value of hourly AHI clear-sky radiances on convection-permitting analyses and forecasts of the "7.19" severe rainstorm that occurred over north China during 18-21 July 2016 was investigated.Analyses were produced hourly and 24h forecasts were produced every 6 hours.The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances.It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6h lead time,especially for heavy rainfall exceeding 100mm.Furthermore,the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances,eventually contributing to a better forecast of the severe rainstorm. |
