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The background error covariance structure influences a variational dataassimilation system immensely. The simulation of a weather phenomenon likemonsoon depression can hence be influenced by the background correlationinformation used in the analysis formulation. The Weather Research andForecasting Model Data assimilation (WRFDA) system includes an option forformulating multivariate background correlations for its three-dimensionalvariational (3DVar)  system  (cv6 option). The impact of using such aformulation in the simulation of three monsoon depressions over India isinvestigated in this study. Analysis and forecast fields generated using thisoption are compared with those obtained using the default formulation forregional background error correlations  (cv5) in WRFDA and with a base runwithout any assimilation. The model rainfall forecasts are compared withrainfall observations from the Tropical Rainfall Measurement Mission  (TRMM) andthe other model forecast fields are compared with a high-resolution analysisas well as with European Centre for Medium-Range Weather Forecasts (ECMWF)ERA-Interim reanalysis. The results of the study indicate that inclusion ofadditional correlation information in background error statistics has amoderate impact on the vertical profiles of relative humidity, moistureconvergence, horizontal divergence and the temperature structure at thedepression centre at the analysis time of the cv5/cv6 sensitivityexperiments. Moderate improvements are seen in two of the three depressionsinvestigated in this study. An improved thermodynamic and moisture structureat the initial time is expected to provide for improved rainfall simulation.The results of the study indicate that the skill scores of accumulatedrainfall are somewhat better for the cv6 option as compared to the cv5 option for atleast two of the three depression cases studied, especially at the higherthreshold levels. Considering the importance of utilising improvedflow-dependent correlation structures for efficient data assimilation, theneed for more studies on the impact of background error covariances isobvious

Impact of variational assimilation using multivariate background error covariances on the simulation of monsoon depressions over India