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PNU CGCM 앙상블 예보 시스템의 겨울철 남한 기온 예측 성능 평가

Evaluation of PNU CGCM Ensemble Forecast System for Boreal Winter Temperature over South Korea

조세라|안중배|이준리
한국기상학회 (Korean Meteorological Society)|2018.12|대기 (Atmosphere)|제28권 제4호|pp. 509-520|DOI 10.14191/Atmos.2018.28.4.509

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참고문헌

키워드

seasonal predictionPNU CGCMensemble forecastdeterministic forecastprobabilistic forecast
주제분류대기과학

초록

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with 3 × 3 contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

Abstract

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with 3 × 3 contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

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PNU CGCM과 WRF를 이용한 남한 지역 기온 예측성 검증김영현, 김응섭, 심교문, 안중배, 정명표, 정하규한국기상학회 · 대기2018 · 제28권 제4호PNU CGCM V1.1을 이용한 12개월 앙상블 예측 시스템의 개발류상범, 안중배, 이수봉한국기상학회 · 대기2012 · 제22권 제4호접합대순환모형의 초기조건 생산방법에 따른 북반구 겨울철 기온과 해수면 온도의 계절 예측성 비교 연구안중배, 이준리한국기상학회 · 대기2015 · 제25권 제2호기상청 기후예측시스템(GloSea6) 과거기후 예측장의앙상블 확대와 초기시간 변화에 따른 예측 특성 분석김지영, 박연희, 이조한, 지희숙, 현유경한국기상학회 · 대기2022 · 제32권 제4호PNU CGCM-WRF Chain을 이용한 남한 지역 폭염 장기 계절 예측성 평가김영현, 김응섭, 심교문, 안중배, 최명주한국기상학회 · 대기2019 · 제29권 제5호지역기후모델을 이용한 상세계절예측시스템 구축 및 겨울철 예측성 검증강현석, 권원태, 김문현, 박수희, 변영화한국기상학회 · 대기2011 · 제21권 제2호

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