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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2021.10
- 수록면
- 15 - 15 (1page)
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초록· 키워드
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Recent advances in science and computing power have significantly improved the prediction performance using numerical models. Nevertheless, the prediction skills of the state-of-art numerical models, coupled general circulation models (CGCMs), are still limited for certain regions such as Siberia and the eastern United States in winter. This study aims to improve the seasonal prediction skill of the boreal winter temperature in a CGCM using a dynamics-statistics chain. The predictors, statistically related to winter temperature over Siberia and the eastern United States, used here are the sea surface temperature (sea ice concentration, 2m air temperature) around the Barents-Kara Sea and snow cover in the Eurasian region, which are also highly associated with the Arctic oscillation for boreal winter.
The temporal correlation coefficient (root mean square error) of winter temperature produced by the new hybrid (dynamics-statistics chain) ... 전체 초록 보기
The temporal correlation coefficient (root mean square error) of winter temperature produced by the new hybrid (dynamics-statistics chain) ... 전체 초록 보기
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