한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증
The statistical prediction model for wintertime surface air temperature, that is based on snow cover extent and Arctic sea ice concentration, is updated by considering El-Nino Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). These additional factors, representing leading modes of in...
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한국기상학회
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ftseoulnuniv:oai:s-space.snu.ac.kr:10371/206481 2024-09-15T18:34:46+00:00 한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 Development and evaluation of statistical prediction model of monthly-mean winter surface air temperature in Korea 한보름 임유나 김혜진 손석우 손석우 2019-07-09 https://hdl.handle.net/10371/206481 https://doi.org/10.14191/Atmos.2018.28.2.153 한국어 unknown 한국기상학회 대기, Vol.28 No.2, pp.153-162 1598-3560 https://hdl.handle.net/10371/206481 doi:10.14191/Atmos.2018.28.2.153 000439202900004 78156 ART002356647 SEA-ICE GEOPOTENTIAL HEIGHT ARCTIC-OSCILLATION SNOW COVER VARIABILITY CIRCULATION WEATHER Statistical prediction model seasonal forecasts surface air temperature Article ART 2019 ftseoulnuniv https://doi.org/10.14191/Atmos.2018.28.2.153 2024-08-13T23:46:33Z The statistical prediction model for wintertime surface air temperature, that is based on snow cover extent and Arctic sea ice concentration, is updated by considering El-Nino Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). These additional factors, representing leading modes of interannual variability in the troposphere and stratosphere, enhance the seasonal prediction over the Northern Hemispheric surface air temperature, even though their impacts are dependent on the predicted month and region. In particular, the prediction of Korean surface air temperature in midwinter is substantially improved. In December, ENSO improved about 10% of prediction skill compared without it. In January, ENSO and QBO jointly helped to enhance prediction skill up to 36%. These results suggest that wintertime surface air temperature in Korea can be better predicted by considering not only high-latitude surface conditions (i.e., Eurasian snow cover extent and Arctic sea ice concentration) but also equatorial sea surface temperature and stratospheric circulation. N 1 Article in Journal/Newspaper Sea ice Seoul National University: S-Space |
institution |
Open Polar |
collection |
Seoul National University: S-Space |
op_collection_id |
ftseoulnuniv |
language |
unknown |
topic |
SEA-ICE GEOPOTENTIAL HEIGHT ARCTIC-OSCILLATION SNOW COVER VARIABILITY CIRCULATION WEATHER Statistical prediction model seasonal forecasts surface air temperature |
spellingShingle |
SEA-ICE GEOPOTENTIAL HEIGHT ARCTIC-OSCILLATION SNOW COVER VARIABILITY CIRCULATION WEATHER Statistical prediction model seasonal forecasts surface air temperature 한보름 임유나 김혜진 손석우 한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
topic_facet |
SEA-ICE GEOPOTENTIAL HEIGHT ARCTIC-OSCILLATION SNOW COVER VARIABILITY CIRCULATION WEATHER Statistical prediction model seasonal forecasts surface air temperature |
description |
The statistical prediction model for wintertime surface air temperature, that is based on snow cover extent and Arctic sea ice concentration, is updated by considering El-Nino Southern Oscillation (ENSO) and Quasi-Biennial Oscillation (QBO). These additional factors, representing leading modes of interannual variability in the troposphere and stratosphere, enhance the seasonal prediction over the Northern Hemispheric surface air temperature, even though their impacts are dependent on the predicted month and region. In particular, the prediction of Korean surface air temperature in midwinter is substantially improved. In December, ENSO improved about 10% of prediction skill compared without it. In January, ENSO and QBO jointly helped to enhance prediction skill up to 36%. These results suggest that wintertime surface air temperature in Korea can be better predicted by considering not only high-latitude surface conditions (i.e., Eurasian snow cover extent and Arctic sea ice concentration) but also equatorial sea surface temperature and stratospheric circulation. N 1 |
author2 |
손석우 |
format |
Article in Journal/Newspaper |
author |
한보름 임유나 김혜진 손석우 |
author_facet |
한보름 임유나 김혜진 손석우 |
author_sort |
한보름 |
title |
한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
title_short |
한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
title_full |
한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
title_fullStr |
한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
title_full_unstemmed |
한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
title_sort |
한반도 겨울철 기온의 월별 통계 예측 모형 구축 및 검증 |
publisher |
한국기상학회 |
publishDate |
2019 |
url |
https://hdl.handle.net/10371/206481 https://doi.org/10.14191/Atmos.2018.28.2.153 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
대기, Vol.28 No.2, pp.153-162 1598-3560 https://hdl.handle.net/10371/206481 doi:10.14191/Atmos.2018.28.2.153 000439202900004 78156 ART002356647 |
op_doi |
https://doi.org/10.14191/Atmos.2018.28.2.153 |
_version_ |
1810476736441221120 |