The impact of GMS (Geostationary Meteorological Satellite) data in the GDAPS (Global Data Assimilation and Prediction System)

Due to the extensive calculations, running a global model requires a high performance computer. The global model and analysis system, called GDAPS, has been set up in a Cray C9O environment. The five-day global forecast was made available twice a day. In order to improve the forecast performance, the assimilation with satellite cloud information is investigated. Since moisture data over the ocean is very sparse, many meteorologists have attempted to retrieve the moisture profile from other sources, such as satellite data, to supplement the sparsity of the moisture field. Using the supplementary data, they improved the NWP model forecast, especially the forecast of rainfall intensity. The Korea Meteorological Administration (KMA) have tried to supplement the meagerness of the moisture field over the ocean following the method developed by Baba (1987), which uses the GMS retrieved data as supplementary moisture data. KMA have used the method since May 1, 1996 with the help of the Satellite Division. The authors investigate the impact of GMS retrieved data on GDAPS