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http://dx.doi.org/10.14191/Atmos.2020.30.2.115

Extratropical Prediction Skill of KMA GDAPS in January 2019  

Hwang, Jaeyoung (School of Earth and Environmental Sciences, Seoul National University)
Cho, Hyeong-Oh (School of Earth and Environmental Sciences, Seoul National University)
Lim, Yuna (Department of Earth System Science, University of California)
Son, Seok-Woo (School of Earth and Environmental Sciences, Seoul National University)
Kim, Eun-Jung (Numerical Model Development Division, Numerical Modeling Center, Korea Meteorological Administration)
Lim, Jeong-Ock (Numerical Model Development Division, Numerical Modeling Center, Korea Meteorological Administration)
Boo, Kyung-On (Numerical Model Development Division, Numerical Modeling Center, Korea Meteorological Administration)
Publication Information
Atmosphere / v.30, no.2, 2020 , pp. 115-124 More about this Journal
Abstract
The Northern Hemisphere extratropical prediction skill of the Korea Meteorological Administration (KMA) Global Data Assimilation and Prediction System (GDAPS) is examined for January 2019. The real-time prediction skill, evaluated with mean squared skill score (MSSS) of 30-90°N geopotential height field at 500 hPa (Z500), is ~8 days in the troposphere. The MSSS of Z500 considerably decreases after 3 days mainly due to the increasing eddy errors. The eddy errors are largely explained by the eddy-phased errors with minor contribution of amplitude errors. In particular, planetary-scale eddy errors are considered as a main reason of rapidly increasing errors. It turns out that such errors are associated with the blocking highs over North Pacific (NP) and Euro-Atlantic (EA) regions. The model overestimates the blocking highs over NP and EA regions in time, showing dependence of blocking predictability on blocking initializations. This result suggests that the extratropical prediction skill could be improved by better representing blocking in the model.
Keywords
Prediction skill; GDAPS; blocking;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
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