QuikSCAT scatterometer wind data impact on tropical cyclone forecasts by a mesoscale model

Abstract

  This study describes the positive impact of QuikSCAT Scatterometer data on tropical cyclone analyses and forecasts using a Mesoscale Model (MM5). QuikSCAT data is especially valuable because they are available in the data sparse genesis regions of tropical cyclones, and because they are available in cloudy and rainy conditions. The model used in the study, MM5 is known as fifth generation NCAR/Penn State Mesoscale model (MM5). In order to understand and investigate the impact of QuikSCAT Scatterometer wind data, simulation with and without assimilation of scatterometer data has been performed for a few tropical cyclone cases during the period 1999 to 2003. For a cyclonic situation, data of few ships of opportunity and of some coastal or island stations are only available. For the assimilation of observed data into MM5, a few passes of QuikSCAT at different times are available. These additional data strengthen the initial data for assimilation. The results showed that the initial field with the inclusion of scatterometer data was nearer to the actual situation. In the prediction experiment, it was also shown that the inclusion of satellite data improved the prediction up to 48 hrs.