Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

Abstract

Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2A generates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data has been disseminated via Global Telecommunication System (GTS) since 25 October 2019. This article evaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmospheric data assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800 hPa in the northern and southern hemispheres. These biases are caused by observation height assignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2A tends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially in the lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in the atmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC) criteria to select the most suitable GK2A AMV data for assimilation are presented based on these quality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introduced to exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightly improved after assimilating GK2A AMVs along with other conventional, radiance, and satellite winds which include AMVs made by the Himawari-8 satellite in nearly the same observational area of GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved, especially in the tropics and southern hemisphere after assimilating GK2A data in the absence of Himawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source to enhance the robustness of GEOS ADAS.

Direct Assimilation of Ground-Based Microwave Radiometer Clear-Sky Radiance Data and Its Impact on the Forecast of Heavy Rainfall

“…For indirect assimilation, the method of indirect assimilation converts radiance data into atmospheric variables before assimilating them into the model, introducing complicated retrieval errors that cannot be easily represented by an error covariance matrix [14,15]. Moreover, quality control can directly affect forecast results during the indirect assimilation process [16]. Direct assimilation, on the other hand, uses a radiative transfer model as an observation operator to directly assimilate radiance data, which can effectively avoid retrieval errors and extract more atmospheric information because of the more optimal utilization of radiance data [17,18].…”