The impact of satellite-derived wind data assimilation on track, intensity and structure of tropical cyclones over the North Indian Ocean

Abstract

In the present satellite era, remote-sensing data are more useful to improve the initial condition of the model and hence the forecast of tropical cyclones (TCs) when they are in the deep oceans, where conventional observations are unavailable. In this study, an attempt is made to assess the impact of remotely sensed satellite-derived winds on initialization and simulation of TCs over the North Indian Ocean (NIO). For this purpose, four TCs, namely, ‘Nargis’, ‘Gonu’, ‘Sidr’ and ‘KhaiMuk’, are considered, with 13 different initial conditions. Two sets of numerical experiments, with and without satellite-derived wind data assimilation, are conducted using a high-resolution weather research and forecasting (WRF) model. The inclusion of satellite-derived winds through a three-dimensional variational (3DVAR) data assimilation system improves the initial position in 11 cases out of 13 by 34%. The 24-, 48-, 72- and 96-hour mean track forecast improves by 28%, 15%, 41% and 47%, respectively, based on 13 cases. The landfall prediction is significantly improved in 11 cases by about 37%. The intensity prediction also improves by 10–20%. Kinematic and thermodynamic structures of TCs are also better explained, as it could simulate heat and momentum exchange between sea surface and upper air. Due to better simulation of structure, intensity and track, the 24-hour accumulated rainfall intensity and distribution are also well predicted with the assimilation of satellite-derived winds.