This paper describes the improvements in the simulation of a heavy rainfall event due to the assimilation of surface wind observations from the Oceansat-2 scatterometer using ensemble Kalman filter (EnKF) technique. A heavy rainfall event over the southern peninsular region of India during the northeast Indian monsoon season is investigated in this paper using the Advanced Research Weather Research and Forecasting model. A control (CTRL) run where no surface wind observations are assimilated, as well as a 3-D variational (3DVar) run and an EnKF run wherein surface wind observations are assimilated using the 3DVar and EnKF techniques, is performed. Results indicate that the EnKF assimilation run simulates various meteorological fields, including precipitation fields during the rainfall event, better than the CTRL and the 3DVar runs. Qualitative and quantitative comparisons with Tropical Rainfall Measurement Mission precipitation observations indicate that the rainfall simulation shows improvement due to EnKF assimilation as compared with the other two model runs. Vertical profiles of area-averaged and time-averaged relative vorticities and temperature anomalies around the low-pressure system are also better reproduced in the EnKF experiment. Considering the importance of accurate real time simulations of heavy rainfall events associated with the Indian monsoon season, this paper provides encouraging results on the utility of EnKF technique as applied over the Indian region.Index Terms-Data assimilation, ensemble Kalman filter (EnKF), northeast monsoon, Oceansat-2 scatterometer (OSCAT), Weather Research and Forecasting (WRF) model, 3-D variational (3DVar).
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 flowdependent correlation structures for efficient data assimilation, the need for more studies on the impact of background error covariances is obvious.
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