Studys On Hybrid Ensemble Kalman Filer And Data Assimilation Imbalances

As one of the mainstream algorithms in the field of data assimilation,the ensemble Kalman filter(En KF)has been widely used in atmosphere,ocean and land applications.The rapid development of observation networks and forecast models has brought opportunities and chalenges to ensemble data assimilation.How to better extract information from dense temporal observations and improve hybrid ensemble data assimilation system have become research hotspots.Thus,this study focuses on the following two research areas:(1)The assimilation frequency has essential influences on the performances of data assimilation,especially for modern observation networks that have inhomogeneous observations in space and time.In order to effectively assimilate thousands of observation data per hour,especially satelite data,one straightforward strategy is to increase the assimilation frequency.However,more frequent assimilation may exacerbate the model imbalance and result in degraded forecasts.To combat the imbalance caused by ensemble-based data assimilation due to sampling error and covariance localization,three-and four-dimensional incremental analysis update(IAU)were proposed,which gradually introduce the analysis increments into model rather than intermittently updating the state.The tradeoff between the assimilation frequency and imbalances is systematically explored here by using a two-layer model and the NOAA GFS.Results from the idealized two‐layer model show that increasing assimilation frequency can reduce errors for state variables that are not sensitive to imbalances.For state variable that carries the signal of the external gravity mode and is sensitive to imbalances,increasing assimilation frequency without(with)IAU reduces(increases)errors.Without IAU,more frequent updates result in smaller increments and less insertion noise,while the initialization of IAU cannot effectively mitigate the imbalances with increased assimilation frequency.Results with a low‐resolution version of the NOAA GFS demonstrate that increasing assimilation frequency from 6to 2 h improves the errors and biases of forecasts verified with conventional and radiance observations.The diagnostic of surface pressure tendency shows that,unlike in the two‐layer model experiments,increasing assimilation frequency from 6 to 2 h results in less balanced forecasts,which may be due to a fixed localization value applied,and the model error and moisture process presented in real case simulations.Therefore,increasing assimilation frequency can lead to improved forecasts for variables insensitive to imbalances.(2)Hybrid ensemble-variational assimilation methods have been widely applied for numerical weather predictions.The commonly used hybrid assimilation methods compute the hybrid analysis increment using a variational framework and update the ensemble perturbations by an En KF.To avoid the inconsistence resulted from separated hybrid assimilation and En KF systems,IHGEn KF(Integrated Hybrid Gain En KF)proposed by Lei et al.(2021,in review)utilizes an alternate framework of an En KF to represent the hybrid assimilation method.The IHGEn KF is investigated here for typhoon Lingling(2019),using the regional model WRF and a cycling En KF system.Results from the 6-h priors verified relative to the radiance observations show that the HCDA produces smaller errors than the En KF,while the IHGEn KF has smaller errors than the HCDA.The IHGEn KF leads to improved 6-h forecasts of temperature,wind speed and specific humidity verified with conventional observations,especially for the reduced wind speed errors.Temporally and vertically averaged prior errors show that the IHGEn KF produces smaller wind speed errors than the HCDA and the En KF along the TC track,and applying different covariance localization to the ensemble perturbations and the climatological ensemble perturbations leads to even smaller errors.The IHGEn KF has stronger inflow at low levels compared to the HCDA and the En KF,which indicates a more coherent storm wind structure.Therefore,the advantages of the IHGEn KF,especially for improved forecasts of wind speed,have been demonstrated.