Simulation of Tropical Cyclones Over Indian Seas: Data Impact Study Using WRF-Var Assimilation System

The track and intensity prediction of TCs require accurate representation of the vortex in the model initial conditions. The sparsity of observations, both near the vortex and in the surrounding environment, causes either undetectability in standard analyses or poor analysis with ill-defined centers and locations. So, much emphasis over the years has been laid on improving the initial conditions of NWP models, particularly high-resolution mesoscale models in a number of ways. The initial errors obviously have a major impact on the forecast of cyclone tracks using numerical models. One way of overcoming the above difficulty is by improving the initial analysis with the assimilation of conventional and nonconventional observations, which include the development and testing of a range of assimilation methods in the numerical weather prediction (NWP) model. Unfortunately, conventional measurements used to initialize forecast models are unavailable over vast areas of the tropical oceans. So, the high-resolution data required for numerical prediction of TC can be derived by tracking cloud features in the satellite imageries, which provide a large amount of data over data-void regions of the oceans. These derived winds can be used to improve the initialization of the model for the TC forecast. The ability to provide high-density wind coverage over large regions of the tropics makes satellite winds particularly useful for studying TCs (Velden et al. 1998).