초록 ▼

· 예측 선행 인자를 사용한 동아시아 몬순 발달의 1개월 및 계절 통계 예측모델 개발
1. 동아시아 몬순 및 장마 발달에 영향을 주는 선행 인자 분석 및 지표·대기·해양 인자 선정
- Forward stepwise regression method를 통해 주요 선행 인자 선정
- Regression, composite 분석 등을 수행하여 통계적 뿐만 아니라 역학적 인과관계 규명
2. 통계 예측 시스템의 구축 및 활용 가능성 규명
- 여러 상이한 선행인자로 구성된 선형회귀모형 개발
- F-test 및 V

· 예측 선행 인자를 사용한 동아시아 몬순 발달의 1개월 및 계절 통계 예측모델 개발
1. 동아시아 몬순 및 장마 발달에 영향을 주는 선행 인자 분석 및 지표·대기·해양 인자 선정
- Forward stepwise regression method를 통해 주요 선행 인자 선정
- Regression, composite 분석 등을 수행하여 통계적 뿐만 아니라 역학적 인과관계 규명
2. 통계 예측 시스템의 구축 및 활용 가능성 규명
- 여러 상이한 선행인자로 구성된 선형회귀모형 개발
- F-test 및 Variance Inflation Factor 검증 등을 통해 모델의 안정성 진단 및 확보
- 단순평균 방식과 가중치(weighting)를 주는 앙상블 방식을 적용하여 예측 스킬 검증
- 앙상블 예측을 통해 향상된 스킬 도출
3. 여름철 장마의 특징 및 메커니즘 상세 분석
- 장마 분석을 위해 회귀분석, 합성분석, 상관관계 분석 등을 수행
- 역학적 해석을 위해서 LBM, WRF 등의 모형을 사용하여 장마의 특징 분석
- 인도몬순과 남중국해의 대류 강제력에 의해 형성된 특이한 장마패턴 제시
· 북진하는 계절내 진동과 관련된 동아시아 여름철 강수밴드의 변동 이해
1. 서태평양에서 북진하는 계절내 진동의 장기변동성 분석
- 계절내 성분의 장기 평균된 CISO에 대하여 1990년대 중반 전후 비교
- 북동아시아 지역의 CISO 특징 변화와 그와 관련된 북진하는 계절내 성분의 위상속도 비교
2. 북진하는 계절내 진동을 이용한 동아시아 강수밴드의 통계적 예측모델 개발
- 북서태평양에서 북진하는 계절내 성분의 주성분에 대한 선행인자 분석
- 통계적 예측모형 구성 및 예측 성능 평가

Abstract ▼

Ⅱ. Objectives
∘∙ Develop a statistical prediction model for the East Asian summer monsoon.
∘∙ Examine dominant physical modes for the East Asian summer monsoon on the interannual/intraseasonal time scale.
∘∙ Develop an indices for the onset and withdrawal of the East Asian summer monsoon/ C

Ⅱ. Objectives
∘∙ Develop a statistical prediction model for the East Asian summer monsoon.
∘∙ Examine dominant physical modes for the East Asian summer monsoon on the interannual/intraseasonal time scale.
∘∙ Develop an indices for the onset and withdrawal of the East Asian summer monsoon/ Changma.
∘∙ Analyze and develop potential precursors of the Changma rainfall in the land surface condition, atmospheric features and oceanic variables.
∘∙ Construct an ensemble forecasting model using a diverse statistical models for the Korean Changma precipitation, and evaluate the predictability.
∘∙ Analyze physical characteristics and mechanisms of Changma occurred over the Korean peninsula since the year 2011.
∘∙ Evaluate the predictability of dynamical long-term prediction model used in KMA, and discuss potential solution for enhancing the predictability.
∘∙ Compare the performance of the constructed ensemble statistical model and the KMA dynamical model for the seasonal Changma forecast
Ⅲ. Backgrounds and necessities of the study
∘∙ Variability of the East Asian summer monsoon (EASM) and Korean Changma is important for the human life and society because this induces numerous natural disasters such as flood, drought, heat wave, and surge. The Korean peninsula is located in a very subtle area of midlatitudes where can be affected by the variations occurred in the tropics, subtropics, middle latitudes, and arctic regions. Therefore, a correct understanding of the interannual variability of EASM/Changma is necessary. Furthermore, empirical forecast model is needed to obtain an enhanced prediction of the EASM/Changma.
∘∙ As said, the EASM/Changma have a tremendous impact on the social and economic activities. Current understanding of the physical mechanisms is still lacking. To construct a skillful statistical forecasting model, proper predictors must be developed with sufficient physical reasoning.
∘∙ Previous studies suggest that the factors for influencing the EASM/Changma are tropical (Madden-Julian Oscillation, Western North Pacific convection, ENSO, and etc.), middle latitudinal (Tibetan heat source, Jet stream, Western North Pacific subtropical high, North Atlantic sea surface temperature and etc.), and Arctic (Arctic oscillation, North Atlantic oscillation, Eurasian snow cover, and etc.) variabilities. These variations should be evaluated in terms of potential predictor for the seasonal forecast. The importance of Arctic and tropical factors are emphasized from the extraordinary rainfall event over the Korean peninsula during early July in 2011 (Seo et al., 2012).
∘∙ Assessment of prediction skill of Changma from a new seasonal forecast model used in KMA has not been performed. This will be compared to the prediction skill of the statistical prediction model developed in this study.
Ⅳ. Contents and Scope
Ⅴ. Outcomes of the study
∘∙ Investigate physical mechanisms and dominant modes related to the interannual variability of Changma.
Dominant modes of the EASM are revealed from the variability of large-scale air masses discerned by equivalent potential temperature, and they are found to be dynamically connected with the anomalous sea surface temperatures (SSTs) over the three major oceans of the world and their counterparts of prevailing atmospheric oscillations or teleconnection patterns. Precipitation over East Asia (EA) during July is enhanced by the tropical central Indian Ocean warming and central Pacific El Niño-related SST warming, the northwestern Pacific cooling off the coast of NEA, and the North Atlantic Ocean warming. Model experiments(LBM, CFS) show reasonable evidence of the related physical mechanisms.
∘∙ Build a statistical model using most recent statistical methods.
To predict the interannual variations of EASM rainfall, four predictors are considered which are found to be dynamically connected with the anomalous sea surface temperatures (SSTs) and prevailing atmospheric oscillations. Using these factors and data from the preceding spring seasons, we build a multiple linear regression model for seasonal forecasting. The cross-validated correlation skill far exceeds the skill level of contemporary climate models.
∘∙ Develop an onset and withdrawal indices for Changma.
In spite of several decades of researches on the EASM, questions still remain on many aspects of the EASM that include its proper definition and relevant large-scale dynamical and thermodynamical features. Therefore, we clarifies the definition of EASM index using the meridional gradient of equivalent potential temperature and precipitation related to the position of the front. Then, the onset and withdrawal of Changma are calculated by the criteria with the equivalent potential temperature and the geopotential height and the upper-level wind. It turned out this index is more stable than in any other indices.
∘∙ Investigate EASM/Changma precursors from land surface, atmospheric variables and oceanic variables.
A statistical forecast model for Changma precipitation is proposed, which was constructed with three physically based predictors. A forward-stepwise regression was used to select the predictors that included sea surface temperature (SST) anomalies over the North Pacific, the North Atlantic, and the tropical Pacific Ocean. The dynamical processes associated with the predictors were examined prior to their use in the prediction scheme. All predictors tended to induce an anticyclonic anomaly to the east or southeast of Japan, which was responsible for transporting a large amount of moisture to the southern Korean Peninsula. The predictor in the North Pacific formed an SST front to the east of Japan during the summertime, which maintained a lower-tropospheric baroclinicity. The North Atlantic SST anomaly induced downstream wave propagation in the upper troposphere, developing anticyclonic activity east of Japan. Forcing from the tropical Pacific SST anomaly triggered a cyclonic anomaly over the South China Sea, which was maintained by atmosphere-ocean interactions and induced an anticyclonic anomaly via northward Rossby wave propagation (P-J teleconnection).
∘∙ Develop and verify an ensemble prediction model from various statistical models.
Statistical forecast models for the prediction of the summertime Changma precipitation have been developed in this research. As effective predictors for the Changma precipitation, the springtime sea surface temperature (SST) anomalies over the North Atlantic (NA1), the North Pacific (NPC) and the tropical Pacific Ocean (CNINO) has been suggested in the previous study. To further improve the performance of the statistical prediction scheme, we select other potential predictors and construct 3 additional statistical models. The selected predictors are the Northern Indian Ocean (NIO) and the Bering Sea (BS) SST anomalies and the spring Eurasian snow cover anomaly (EUSC) and the Western north pacific outgoing longwave radiation anomalies (WNP). Then, using the total four statistical prediction models, a simple ensemble-mean prediction is performed. The resulting correlation skill score (0.92) which is about 19% increase in the skill compared to the prediction model by Lee and Seo (2013). The EUSC and BS predictors are related to a strengthening of the Okhotsk high, leading to an enhancement of the Changma front. The NIO and WNP predictors induce the cyclonic anomalies to the southwest of the Korean peninsula and southeasterly flows toward the peninsula, giving rise to an increase in the Changma precipitation
∘∙ Analyze characteristics and mechanisms of Changma.
We are focussed on trying to improve performance of physical-statistical forecast model through the understanding of the interannual variation of the EASM/Changma. In 2011, the SST anomalies from the North Atlantic and Tropical Western Pacific Ocean influenced the EASM. In 2013, the Indian Ocean forcing has been an important factor in EASM rainfall. Using this observational analysis, we selected other potential predictors and developed an ensemble statistical forecast model through the EASM analysis during the 2011-2013. Changma rainfall in 2014 was successfully estimated using the ensemble statistical forecast model.
∘∙ Develop a method to improve forecasting skill of the KMA seasonal forecast model.
We evaluate the performance of Changma prediction using the seasonal prediction model of KMA (GloSea5). The predictions initiated on February and March showed that the cyclonic anomalies are induced to the south of the Korean Peninsula and leading to an weakening of the WNPSH, which gives rise to an decrease in the Changma precipitation. The predictions from April and May exhibited that an anticyclonic anomaly are induced to the east of Japan. This pressure field was responsible for transporting a large amount of moisture to the southern Korean Peninsula. To improve the operational seasonal forecast skill of the KMA, it is required to enhance the forecast skill of sea surface temperature filed.
∘∙ Compare forecasting skill of statistical model and KMA dynamical seasonal forecast model in KMA.
The North Atlantic and Western north pacific SSTAs induced the cyclonic anomalies to the south of the Korean peninsula and leading to an weakening of the WNPSH during the 2014 Changma season. This pressure distribution gave rise to an decrease in the Changma precipitation. The prediction of four statistical forecast models which are the component of an ensemble forecast model also had the same results (i.e., below-normal precipitation). In comparison with the skill level of the KMA seasonal forecast model, the prediction skill of the ensemble forecast model is extremely high. The predicted western north pacific SSTAs in the KMA seasonal forecast model were not as strong as the observed.

목차 Contents

• 표지 ... 1
• 제출문 ... 2
• 보고서 요약서 ... 3
• 요약문 ... 5
• SUMMARY ... 13
• CONTENTS ... 19
• 목차 ... 20
• 제1장 연구개발과제의 개요 ... 21
• 1. 연구개발의 목적 ... 21
• 2. 연구개발의 필요성 ... 21
• 3. 연구개발의 범위 ... 24
• 제2장 국내외 기술개발 현황 ... 26
• 1 . 국외 기술개발 수준 ... 26
• 2. 국내 기술개발 수준 ... 26
• 3. 국내외 기술개발 현황 ... 26
• 제3장 연구개발수행 내용 및 결과 ... 29
• 제1절 동아시아 몬순/장마의 시종 지수 개발 및 동아시아 몬순 통계예측모델 개발 ... 29
• 제2절 예측 선행 인자를 사용한 장마 강수 계절 통계예측모델 개발 ... 60
• 제3절 기상청 장기예보모델의 동아시아 몬순/장마 예측성 향상 방안 연구 ... 93
• 제4절 북서태평양 아열대고기압 지수를 이용한 북동아시아 여름철강수의 진단 ... 103
• 제5절 NCEP 계절예측시스템(CFS)과 정준상관분석(CCA)을 이용한 북동아시아 여름철 강수의 공간 패턴 예측 ... 110
• 제6절 KMA GloSea5와 원격상관을 이용한 북동아시아 여름철 강수량 예측과 지연 앙상블 기법 ... 119
• 제4장 목표달성도 및 관련분야에의 기여도 ... 129
• 1. 목표 달성도 ... 129
• 2. 연구개발목표의 달성도 ... 133
• 3. 관련분야의 기술발전에의 기여도 ... 135
• 제5장 연구개발결과의 활용계획 ... 136
• 1. 활용방안 ... 136
• 2. 기대성과 ... 136
• 제6장 연구개발과정에서 수집한 해외과학기술정보 ... 137
• 1. 국제회의 참가 실적 ... 137
• 2. 해외 관련 과학기술정보 ... 138
• 제7장 참고문헌 ... 148
• 끝페이지 ... 153