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

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 21; p. 5287
• Main Authors: Lee, Eunhee, Todling, Ricardo, Karpowicz, Bryan M, Jin, Jianjun, Sewnath, Akira, Park, Seon Ki
• Format: Journal Article
• Language: English
• Published: Goddard Space Flight Center MDPI 01.11.2022; MDPI AG
• Subjects: Altitude; assimilation; atmospheric motion vector (AMV); Blackbody; Clouds; Data assimilation; Data collection; Electric communication systems; Errors; Geo-Kompsat-2A (GK2A); height error; Jet streams (meteorology); Lower atmosphere; Meteorological satellites; Meteorology And Climatology; Quality assessment; Quality control; Southern Hemisphere; Tropical environments; Weather forecasting; Wind; Wind speed; Assimilation; Atmospheric Motion Vectors; Amvs; Geo-Kompsat-2a (gk2a); Adas; Height Error; Quality Control
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14215287

Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2Agenerates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data hasbeen disseminated via Global Telecommunication System (GTS) since 25 October 2019. This articleevaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmosphericdata assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800hPa in the northern and southern hemispheres. These biases are caused by observation heightassignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2Atends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially inthe lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in theatmospheric 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 thesequality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introducedto exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightlyimproved after assimilating GK2A AMVs along with other conventional, radiance, and satellitewinds which include AMVs made by the Himawari-8 satellite in nearly the same observational areaof 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 ofHimawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source toenhance the robustness of GEOS ADAS.