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http://dx.doi.org/10.14191/Atmos.2021.31.2.229

Global Ocean Data Assimilation and Prediction System in KMA: Description and Assessment  

Chang, Pil-Hun (Operational Systems Development Department, National Institute of Meteorological Sciences)
Hwang, Seung-On (Operational Systems Development Department, National Institute of Meteorological Sciences)
Choo, Sung-Ho (Operational Systems Development Department, National Institute of Meteorological Sciences)
Lee, Johan (Operational Systems Development Department, National Institute of Meteorological Sciences)
Lee, Sang-Min (Operational Systems Development Department, National Institute of Meteorological Sciences)
Boo, Kyung-On (Operational Systems Development Department, National Institute of Meteorological Sciences)
Publication Information
Atmosphere / v.31, no.2, 2021 , pp. 229-240 More about this Journal
Abstract
The Global Ocean Data Assimilation and Prediction System (GODAPS) in operation at the KMA (Korea Meteorological Administration) is introduced. GODAPS consists of ocean model, ice model, and 3-d variational ocean data assimilation system. GODAPS assimilates conventional and satellite observations for sea surface temperature and height, observations of sea-ice concentration, as well as temperature and salinity profiles for the ocean using a 24-hour data assimilation window. It finally produces ocean analysis fields with a resolution of 0.25 ORCA (tripolar) grid and 75-layer in depth. This analysis is used for providing a boundary condition for the atmospheric model of the KMA Global Seasonal Forecasting System version 5 (GloSea5) in addition to monitoring on the global ocean and ice. For the purpose of evaluating the quality of ocean analysis produced by GODAPS, a one-year data assimilation experiment was performed. Assimilation of global observing system in GODAPS results in producing improved analysis and forecast fields with reduced error in terms of RMSE of innovation and analysis increment. In addition, comparison with an unassimilated experiment shows a mostly positive impact, especially over the region with large oceanic variability.
Keywords
Ocean data assimilation; GODAPS; NEMO; NEMOVAR; GloSea5;
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