The Effect of Assimilating AMSU-A Radiance Data from Satellites and Large-Scale Flows from GFS on Improving Tropical Cyclone Track Forecast

Abstract

This study aimed to investigate the effect of assimilating either AMSU-A radiance data from satellites, large-scale flows from the Global Forecast System (GFS), or both together, on improving the track forecast of tropical cyclone (TC). The scale-selective data assimilation (SSDA) approach was employed for the assimilation of large-scale GFS flows, while the conventional 3D variational data assimilation (3DVAR) method was used for that of AMSU-A radiance data. The results show that assimilating either AMSU-A radiance data or large-scale GFS flows has a significant improvement on TC track forecast, but the improvement occurs within the first 72 h and after 72 h, respectively. When assimilating both AMSU-A radiance data and large-scale GFS flows, the forecast can take advantage of both data and thus lead to the smallest 5-day mean errors of the track forecast. These results are instructive to future operational TC track forecasting.

Keywords

• tropical cyclone (TC)
• track forecast
• radiance data assimilation
• scale-selective data assimilation (SSDA)