Abstract
Abstract. The background error covariance structure influences a variational data assimilation system immensely. The simulation of a weather phenomenon like monsoon depression can hence be influenced by the background correlation information used in the analysis formulation. The Weather Research and Forecasting Model Data assimilation (WRFDA) system includes an option for formulating multivariate background correlations for its three-dimensional variational (3DVar) system (cv6 option). The impact of using such a formulation in the simulation of three monsoon depressions over India is investigated in this study. Analysis and forecast fields generated using this option are compared with those obtained using the default formulation for regional background error correlations (cv5) in WRFDA and with a base run without any assimilation. The model rainfall forecasts are compared with rainfall observations from the Tropical Rainfall Measurement Mission (TRMM) and the other model forecast fields are compared with a high-resolution analysis as well as with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim reanalysis. The results of the study indicate that inclusion of additional correlation information in background error statistics has a moderate impact on the vertical profiles of relative humidity, moisture convergence, horizontal divergence and the temperature structure at the depression centre at the analysis time of the cv5/cv6 sensitivity experiments. Moderate improvements are seen in two of the three depressions investigated in this study. An improved thermodynamic and moisture structure at the initial time is expected to provide for improved rainfall simulation. The results of the study indicate that the skill scores of accumulated rainfall are somewhat better for the cv6 option as compared to the cv5 option for at least two of the three depression cases studied, especially at the higher threshold levels. Considering the importance of utilising improved flow-dependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.
Highlights
Numerical simulation of a weather phenomenon requires an efficient numerical model as well as an accurate initial state of the system
The u-wind observation which differs from the background with a magnitude of 1 m s−1 is assimilated using both the cv5 and cv6 option of the background error covariance (BEC) matrix
The aim of the current study was to investigate whether the formulation of control variables in the BEC matrix affect the impact of observations in a data assimilation system considerably
Summary
Numerical simulation of a weather phenomenon requires an efficient numerical model as well as an accurate initial state of the system. Data assimilation is the technique used to estimate the optimal state of the atmospheric system, utilising available meteorological observations as well as background information. The background and the observation information are weighted utilising their respective error covariances. Accurate specification of both observation and background errors is important for the success of a data assimilation system since these contribute to the improved analysis. The analysis x = xa represents a minimum variance estimate of xt given the observations y ∈ Rm as well as the error covariances of background and observations denoted by B and R respectively. For a numerical weather model with typically 107 degrees of freedom, the direct calculation of this term is not possible
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
