The development of a hybrid EnSRF-En3DVar system for convective-scale data assimilation

• Published in: Atmospheric research 2019-11, Vol.229, p.208-223
• Main Authors: Gao, Shibo, Min, Jinzhong, Liu, Limin, Ren, Chuanyou
• Format: Article
• Language: English
• Subjects: Hybrid EnSRF-En3DVar system; Mesoscale convective system; Radar data assimilation
• ISSN: 0169-8095; 1873-2895
• DOI: 10.1016/j.atmosres.2019.06.024

A coupled hybrid ensemble square root filter and three-dimensional ensemble-variational (EnSRF-En3DVar) radar data assimilation system, developed for the Weather Research and Forecasting model, was applied to a mesoscale convective system (MCS) that occurred over southeastern China on 5 June 2009. This hybrid system uses the extended control variable method to combine the static and ensemble flow-dependent forecast error covariance. The potential of the hybrid EnSRF-En3DVar system was first explored by comparing the derived results with those obtained using 3DVar and EnSRF approaches alone. Assimilation results showed the hybrid EnSRF-En3DVar system reduces the root mean square innovations for reflectivity and radial velocity, and better represents the pattern of the MCS than either 3DVar or EnSRF. This successful analysis improved quantitative reflectivity and precipitation forecasting skills, and helped forecast the MCS more realistically with regard to location, structure and intensity. Moreover, the root mean square error of the forecast wind, temperature and water vapor mixing ratio were found reduced by the hybrid EnSRF-En3DVar system when compared with the other methods. Diagnoses of the forecast fields showed the hybrid EnSRF-En3DVar system increases low-level cooling and mid-level warming within the convective area. Furthermore, sensitivity experiments testing the ensemble covariance weighting factor effect in the hybrid EnSRF-En3DVar method suggested that a stronger ensemble covariance weighting value led to improved precipitation forecast results. •A new hybrid EnSRF-En3DVar radar data assimilation system is developed and applied to an MCS case.•The improvements of the hybrid approach are found in the forecasting of the MCS comparing with 3DVar and EnSRF.•A stronger ensemble covariance weighting value lead to an improved  forecast results.