Impact of data assimilation and air−sea flux parameterization schemes on the prediction of cyclone Phailin over the Bay of Bengal using the WRF-ARW model

Abstract

The present study investigates the impact of data assimilation for prediction of the landfalling cyclone Phailin (October 8–13, 2013) over the Bay of Bengal by using the Weather Research and Forecasting Advanced Research model and its 3D variational data assimilation system. The impact of the air−sea flux (ACF) parameterization scheme is also tested with an improved model initial condition. Nine experiments were conducted, six with and without data assimilation at three different initial conditions. Three more experiments were conducted with the available ACF parameterization scheme. In all experiments, the lateral boundary conditions were obtained from the Global Forecast System (GFS) at 0.5° × 0.5° resolution with a temporal resolution of 3 hr, and the initial condition was taken from GFS analysis. The results suggest that assimilation of observations is beneficial for the forecast of tropical cyclone track and intensity. They show that forecasted storm intensity is highly sensitive during the maximum value of intensity to the parameterization of the ACFs, and higher surface intensity was produced by a large surface exchange co-efficient for moisture. The distribution and magnitude of rainfall and reflectivity of the storm were compared with observed datasets from the Automatic Weather Station and Doppler Weather Radar, respectively. The results show that accumulated rainfall, as well as the structure of the cyclone, are fairly well captured by the model. The predicted higher diabatic heating rate and horizontal eddy diffusivity of heat are also found to be responsible for increasing wind speed of the storm.