[보고서]아·태 기후정보서비스 및 연구개발

정홍상

아·태 기후정보서비스 및 연구개발 원문보기

보고서 정보
주관연구기관	APEC기후센터 Apec Climate Center
연구책임자	정홍상
참여연구자	Steven Crimp , 박종선 , 박남식 , 현민호
보고서유형	최종보고서
발행국가	대한민국
언어	한국어
발행년월	2017-02
과제시작연도	2016
주관부처	기상청 Korea Meteorological Administration(KMA)
등록번호	TRKO201700002873
과제고유번호	1365002452
사업명	아태기후정보서비스및연구개발
DB 구축일자	2017-09-02
키워드	기후예측/변화.다중모델 앙상블.여름철 계절내 진동.융합연구.기후정보서비스.Climate Prediction/Change.Multi-Model Ensemble.Boreal Summer Intraseasonal Oscillation.Interdisciplinary Research.Climate Information Services.

초록 ▼

가. 아태지역 실시간 고품질 기후예측 시스템 운영 및 기술개발
◦ 기후예측시스템 운영 및 품질개선
- 다중모델 앙상블 6개월 예측 시스템 운영 및 개선(I)
- 기후예측 활용 기반 정보 생산
- 여름철 계절내 진동 예측 고도화
◦ 기후서비스 대응 정보생산 체계 구축
- 고해상도 계절 예측 생산 기반 구축
- 기후예측정보 시공간 규모 축소 기술 고도화
- 통계-역학 계절예측 기술 개발(I)
◦ 극한기후 감시 및 예측기술 고도화
- 아태지역 홍수 예측 및 위험관리 기술개발(I)
- 아태지역 맞춤형 장기가뭄예측 기술개발(I)
- 아태지역 여름철 극한고온예측 기술개발(I)

나. 지역특성화 기반 아태지역 기후변화 대응역량 강화
◦ 기후융합 확률응용 예측 기술개발
- 유역관리 의사결정 지원을 위한 계절예측 활용 기술 개발
- 계절예측 기반 가뭄 대응 저수지 용수공급 관리 기술 개발
◦ 기후위험 관리 기술개발
- 수자원 예측 기반 기후위험 평가(I)
- 작물 생산성 예측 기반 기후위험 평가(I)
- 계절예측 기반 아태지역 병충해 조기경보 기술 개발(I)

다. 부가가치 창출을 위한 아태지역 기후정보서비스
◦ 기후정보서비스 플랫폼 구축
- 기후예측 정보서비스 개선 및 아태지역 수요분석
- 기후정보 생산을 위한 기후-응용 지식정보 플랫폼 개발
- 아태지역 재난재해연구 플랫폼 기반 기술 개발
◦ 아태지역 기후협력 네트워크 강화
- 개도국 젊은 과학자 방문연구 지원 사업 추진
- 기후예측 관련 개도국 교육프로그램 추진
- 기후정보 활용을 위한 APEC 기후심포지엄 개최
- 기후정보 생산 및 활용을 위한 전문가 회의 등 개최
- IPCC, GEO 등 기후분야 국제협력활동 강화 및 정책지원

라. 태평양 도서국 기후변화 대응 기술 개발
◦ 기후변화 공동대응 체계 구축
- 기후정보 이용 활성화를 위한 국가별 기후정보서비스 수요 분석 및 평가
◦ 이상기후 감시예측 능력 강화
- 남태평양 지역 기후예측정보 생산 및 제공
- 태도국 태풍 활동 예측 기반 기술개발
- 해수면 상승에 따른 농경지 염류피해평가 기반 기술개발
◦ 기후변화 복원력 향상 기술개발
- 계절예측정보 활용을 위한 작물생육모형 현지화
- 통가 농업기후정보서비스 시스템 구축
- 물부족 대응을 위한 기후정보 활용 수자원 관리조사 및 감시 기술개발
(출처 : 보고서 요약서)

Abstract ▼

Development results
□ Development Results

◦ Climate prediction system
- Operating and improving 6 month MME(Multi-Model Ensemble) prediction system
- Produced background information used for climate prediction
- Improved BSISO(Boreal Summer Intraseasonal Oscillation) forecast

◦ Climate information production system
- Established high-resolution seasonal prediction information
- Improved spatio-temporal scale reduction technology
- Developed statistical-dynamical seasonal prediction technology

◦ Extreme climate monitoring & prediction
- Developed flood prediction and risk management technology
- Developed long-term drought prediction technology
- Developed summer high temperature extremes prediction technology

◦ Climate probability information for application sector
- Developed technology to support decision making in river basin management
- Developed technology to manage water resource in reservoir responding to drought

◦ Climate risk management technology
- Evaluated climate risk based on water resources prediction
- Evaluated climate risk based on crop productivity prediction
- Developed application of rice disease model for a seasonal rice pest outlook in Philippines

◦ Climate information service platform
- Improved climate prediction information service and analyze demand
- Developed climate-application knowledge platform
- Developed disaster research support platform

◦ Climate cooperation network in Asia Pacific
- Hosted Young Scientist Support Program(YSSP) 2016
ㆍ 7 participants in YSSP
- Operated APCC training program
ㆍ 68 participants in training program
- Hosted APEC Climate Symposium(APCS)
- Hosted expert seminars
- Strengthened international cooperation in climate sector

◦ Joint climate change response system
- Analyzed demand and evaluate national climate information services

◦ Monitoring and prediction capabilities for abnormal climate
- Climate prediction information for the Pacific Island Countries
- Developed seasonal prediction technology for tropical cyclone activity
- Developed technology evaluating impact of sea-level rise on coastal agricultural land and water using saltwater intrusion modeling approach

◦ Resilience to climate change
- Localized crop model and establish agricultural decision making system
- Established Agro-meteorological system in Tonga
- Developed water resource management technology

(출처 : SUMMARY)

목차 Contents

표지 ... 1제 출 문 ... 2보고서 요약서 ... 3국문 요약문 ... 5SUMMARY ... 11CONTENTS ... 15목차 ... 16제 1 장 연구개발과제의 개요 ... 17제 2 장 국내외 기술개발 현황 ... 21제 3 장 연구수행 내용 및 결과 ... 29 제 1 절 기후예측 시스템 운영 및 품질개선 ... 29 제 2 절 기후서비스 대응 정보생산 체계 구축 ... 336 제 3 절 극한기후 감시 및 예측기술 고도화 ... 909 제 4 절 기후융합 확률응용 예측 기술개발 ... 1105 제 5 절 기후위험 관리 기술개발 ... 1177 제 6 절 기후예측 시스템 운영 및 개선 ... 1555 제 7 절 아태지역 기후협력 네트워크 강화 ... 1615 제 8 절 기후변화 공동대응 체계 구축 ... 1672 제 9 절 기후변화 공동대응 체계 구축 ... 1685 제 10 절 기후변화 복원력 향상 기술개발 ... 1861제 4 장 목표 달성도 및 관련분야 기여도 ... 1979제 5 장 연구결과의 활용계획 ... 1984제 6 장 국가과학기술종합정보시스템 에 등록한 연구 시설·장비 현황 ... 1988제 7 장 연구 개발과제의 대표적 연구실적 ... 1989제 8 장 참고문헌 ... 1990APPENDIX A. The rolling 3-month & 6-month MME Climate Outlook ... 2103APPENDIX B. Young Scientist Support Program Report ... 2215끝페이지 ... 2439

표/그림 (300)

표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 Temperature for boreal Spring 2016
표 Precipitation for boreal Spring 2016
표 Temperature for boreal Summer 2016
표 Precipitation for boreal Summer 2016
표 2m Temperature Anomaly (GAUS)
표 Precipitation and Wind Anomaly (GAUS)
표 Glosea5의 북반구 500hPa 지위고도에 대한 주별 hindcast 성능 (anomaly correlation)
표 예측 초기시점의 MJO phase에 따른 주별 예측 성능 평균값의 분포
표 예측 초기 MJO 위상이 3인 경우의 예측 1주부터 7주차까지 관측 (1, 3열) 및 예측(2, 4열) 500hPa 지위고도 편차의 합성장
표 예측성능이 우수한 6개 사례에 대한 예측 6주차 관측(좌 측), 예측(우측) 500hPa 지위고도 편차
표 데이터 구조 및 주별 예측성 평가 실험 정의
표 모델 내 weekly consistency 분석 결과(a) 및 모델의 실제 성능 분석 결과(b)
표 모델 내 consistency의 correlation 기준에 따른 weekly별 K-S test 결과
표 AO-NAO의 각 월 분포도.
표 1~3월 positive NAO의 case1에 대한 weekly별 K-S test 결과
표 Acronym names of institutions and models used in the text
표 Description of the eight coupled climate models used in this study.
표 Brief description of initialization strategies in the models in the study.
표 Classification of ENSO events.
표 (a) Time series of the monthly mean Niño 3.4 index from observations (red line), one-month (black) and four-month (grey) lead MME simulations with year-round 3-month running average taken, within the period of 1982 to 2006. Correlation coefficients between the observed and MME simulated indices are given in parentheses following the legends. (b) Time series of the observed amplitude (absolute value of Figure 1a) of Niño 3.4 (red dashed line) and anomaly pattern correlation (ACC) skill of global SST (60°S-80°N, 0°-330°E) prediction (blue dashed line). Solid lines show the 2-year lowpass filtered data of the former two time series.
표 Composite maps of anomalous SST (shading) during (a, b) El Niño and (c, d) La Niña phases corresponding to (a, c) strong and (b, d) weak ENSO events for boreal warm season.
표 Regression coefficients of SST onto the (a) Niño3 index and (b) El Niño Modoki index (EMI) based on observations for all seasons during 1982-2006.
표 Composite maps of anomalous SST (shading) during (a, b) El Niño and (c, d) La Niña phases for (a, c) strong and (b, d) weak ENSO events during the boreal cold season.
표 Composite maps of anomalous SST (shading) for weak El Niño events during the boreal warm season based on (a) MME mean and individual model simulations from (b) CMCC, (c) CANCM3, (d) CANCM4, (e) NASA and (f) PNU.
표 Composite maps of anomalous SST (shading) during weak El Niño in the boreal cold season based on (a) MME mean and simulations from (b) CMCC, (c) CANCM3, (d) CANCM4, (e) NASA and (f) PNU.
표 Skills of Niño3.4 index forecasts (in blue) and SST pattern forecasts over the globe (in red) during boreal cold (x axis) and warm (y axis) seasons for all years as well as for strong El Niño, weak El Niño, strong La Niña, and weak La Niña events (see legends in figures), based on (a) one-month lead and (b) August and November initiated MME simulations.
표 Evolution of the observed (left panels) and MME mean (right panels) forecasted Niño 3.4 index for (a, b) strong El Niño, (c, d) weak El Niño, (e, f) strong La Niña, and (g, h) weak La Niña events.
표 Mean (solid dot) and the range (bar) of anomaly pattern correlation coefficients (ACCs) between observed and model simulated SST patterns, computed based on data from (a) strong El Niño, (b) weak El Niño, (c) strong La Niña, and (d) weak La Niña years.
표 Time series of the monthly mean Nino 3.4 index from observations (red line), persistent (grey) and one-month lead MME (black) simulations with year-round, within the period of 1983 to 2005.
표 TCC skills of prediction of Nino 3.4 SST index as a function of forecast lead time for (a, d) all seasons combined and the seasons initiated from (b, d) May and (c, f) November.
표 Same as Figure 11 but for RMSD errors.
표 Time series of the monthly mean Nino 3.4 index from observations (bottom rows beneath the dotted lines) and one-to-six month lead model simulations (y-axis) as a function of targeted forecast time (x-axis).
표 TCC skills of prediction of Nino 3.4 index as a function of targeted forecast time and forecast lead time for (a) persistence, (b) MME, (c) APCC, (d) CMCC, (e) CANCM3, (f) CANCM4, (g) NASA, (f) NCEP, (i) PNU, and (i) POAMA.
표 Time series of the observed amplitude of the monthly mean Nino 3.4 index (red dashed line) and ACC skill of tropical Pacific SST (grey) and precipitation (black) MME predictions.
표 ACC skills of prediction of tropical Pacific SST anomalies as a function of forecast lead time for (a, d) all seasons combined and the seasons initiated from (b, d) May and (c, f) November.
표 Same as Figure 16 but for precipitation
표 Same as Figure 16 but for SST diversities.
표 ACC skills of SST prediction related with ENSO diversities as a function of targeted forecast time and forecast lead time for (a) persistence, (b) MME, (c) APCC, (d) CMCC, (e) CANCM3, (f) CANCM4, (g) NASA, (f) NCEP, (i) PNU, and (i) POAMA.
표 Same as Figure 18 but for precipitation diversities.
표 TCC skills between the observed and MME monthly mean (a, b, c) SST anomalies and (d, e, f) diversities taken from the whole period of 1983 to 2005 for (a, d) 1-, (b, e) 3-, and (c, f) 6-month lead times.
표 Same as Figure 21 but for precipitation.
표 Variance of (a, b, c) the observed and (d, e, f) MME monthly mean anomalies taken from the whole period of 1983 to 2005 for (a, d) ENSO-related variability and diversities for (b, e) 1-, and (c, f) 3-month lead times.
표 Same as Figure 23 but for precipitation.
표 Local maximum of variance of observed (black) and MME-simulated (blue) (a) SST and (b) precipitation variability related with ENSO (dashed lines) and diversities (solid line) as a function of forecast lead time.
표 PP of the model monthly mean SST diversities for (a) APCC, (b) CMCC, (c) MSC CANCM3, (d) MSC CANCM4, (e) NASA, (f) NCEP, (g) PNU, and (h) POAMA.
표 Area-averaged PP of SST variability related with ENSO diversities as a function lead time for (a) the averaged values and ranges of (b) individual models
표 Composite maps of anomalous precipitation (shading, see scale at bottom; units: mm/day) and 850 hPa wind (arrows, see scale at upper right of each panel; units: m/s) during (a, b) El Niño and (c, d) La Niña phases corresponding to (a, c) strong and (b, d) weak ENSO events for boreal warm season.
표 Same as Figure A1 but for boreal cold season.
표 11 model hindcasts of APCC-MME available for winter season analysis.
표 Three aspects and seven components measuring LFMs representing skill
표 Definitions of metrics quantifying biases of individual models in representing four major features of winter climatology:
표 Temporal variance (root mean square) of geopotential height anomalies at 500 hPa (Z500) at individual girds of NCEP-DOE Reanalysis (NDR) and 11 individual models.
표 Temporal correlation coefficients (TCC) of geopotential height anomalies at 500 hPa (Z500) between NCEP-DOE Reanalysis (NDR) and each of 11 individual models at individual grids.
표 Northern hemisphere (20ºN-90ºN) average of (top) absolute temporal variance biases (displayed in Figure 1) and (bottom) temporal correlation coefficients (displayed in Figure 2) of 11 individual models. Four model groups are colored by medium grey (Oceania), white (Europe), light grey (North America), and dark grey (Asia and Russia) with different continents where their institutes are located.
표 Loading patterns of top three rotated modes resolved in NCEP-NCAR Reanalysis dataset during boreal winter season (DJF) for two different time periods of (top) 1951/52 to 1999/2000 and (bottom) 1983/84 to 2004/05.
표 11 models average of MSSS and TCC of three indices for each of three lead months: (dark grey) Dec, (white) Jan, and (light grey) Feb.
표 Segment plots displaying WP representing skills of 7 metrics out of 3 verification scopes as introduced in Table 2.
표 WP representing skills in three verification scopes: IND, MODE, and EOF.
표 Same as in Figure 6 but for PNA.
표 Same as in Figure 7 but for PNA.
표 Same as in Figure 6 but for NAO.
표 Same as in Figure 7 but for NAO.
표 Frequency of each model counted as three (a) best and (b) worst performers based on the ranking for all of different three aspects of three modes.
표 Winter climatology of (a) SLP, (b) Z500, (c) U200, and (d) VP200 of NCEP-DOE Reanalysis (NDR).
표 Zonally/meridionally averaged pattern of (topleft) EAWM, (bottomleft) Atlantic dipole, (center) Pacific/Atlantic jet, and (right) Pacific/Atlantic Hadley circulation over the domains designated in Table 3 for each of NDR and 11 models.
표 Scatter plot between LFV representing skills (7 metrics in Table 2) and winter mean biases (20 metrics in Table 3) for (top) WP, (middle) PNA and (bottom) NAO.
표 Winter climatology of (a) total and (b) wave #1 of stationary waves averaged over the latitudinal domain of 45ºN to 60ºN for 11 models (grey dashes) and NCEP-DOE Reanalysis (NDR) (red solid). (c) Power of wave #1 (shown in (b)) out of total wave power (shown in (a)) is displayed.
표 Scatter plot between stationary wave #1 power shown in Figure 15(c) and LFV representing skill for 11 models.
표 Real-time BSISO forecast and its application
표 Mission of APCC in the view of real-time BSISO forecast and its application
표 Details of available models on BSISO operational forecast
표 Diagram on sample size applied by heavy rainfall definition
표 The monthly rainfall thresholds of heavy rainfall events.
표 Flow chart on how to research
표 Classification for calculating probability of heavy rainfall occurrence estimated by BSISO indices
표 Figure 5 from Weisheimer et al.(2014)
표 Historical flood affected area from
표 Flow chart for producing heavy rainfall probability forecast using the BSISO forecast indices
표 Web service on the probability of heavy rainfall determined by predicted BSISO indices.
표 Contingency Table for dichotomous forecasts
표 Contingency Table for multi-category forecasts
표 Contingency Table for multi-category forecasts
표 Probability of heavy rainfall occurrence according to each of BSISO1 phase during MAY.
표 Probability of heavy rainfall occurrence according to each of BSISO2 phase during JUL.
표 Probability of extreme rainfall occurrence for Oct. based on BSISO1 phases over Mekong River.
표 Probability of Heavy Rainfall occurrence over Mekong River based on BSISO1 index.
표 Same as in Fig. 3.4, but for BSISO2.
표 Topography map for Indochina
표 Topography map for Indochina from Tsai et al.(2015)
표 Climatology of precipitation for MJJASO during 1997-2010.
표 Correlation table from Tsai et al.(2015)
표 Domain of Indochina monsoon from Tsai et al.(2015).
표 Relationship between BSISO and ICMI 3 (box plot) and composite maps for OLR (shaded) and 850hPa wind (vector) anomalies when BSISO phase has a distinctive relationship (red box) with ICMI 3 during Jul.
표 Relationship between BSISO and ICMI 4-1 (box plot) and composite maps for OLR (shaded) and 850hPa wind (vector) anomalies when BSISO phase has a distinctive relationship (red box) with ICMI 4-1 during May.
표 Relationship between BSISO and ICMI 4-2 (box plot) and composite maps for OLR (shaded) and 850hPa wind (vector) anomalies when BSISO phase has a distinctive relationship (red box) with ICMI 4-2 during Oct.
표 Table for relationship between phase of BSISO1 and heavy rainfall probability as well as mechanism.
표 Table for relationship between phase of BSISO1 and heavy rainfall probability as well as mechanism.
표 Sample size for each forecast model
표 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using ECMWF for week1
표 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using ECMWF for week2
표 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using ECMWF for week1
표 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using ECMWF for week2
표 The Brier skill score for probability forecasts of heavy rainfall estimated by BSISO forecast indices using ECM.
표 The Brier skill score for probability forecasts of heavy rainfall from Jones et al.(2015).
표 The BSS for probability forecasts of heavy rainfall estimated by BSISO forecast indices using CFS.
표 BSISO phase diagram for forecasts initiated from 2 SEP, 2013.
표 Same as Fig. 3.23, but for 5 SEP, 2013.
표 Same as Fig. 3.23, but for 12 SEP, 2013.
표 Probability forecast of heavy rainfall determined by predicted BSISO indices for Sep. 2013.
표 Probability forecast of heavy rainfall determined by predicted BSISO Indices and observed precipitation anomalies for Sep. 2013.
표 BSISO phase diagram for observation during SEP, 2013.
표 Observed anomalies for precipitation and wind field at 850 hPa during case study period.
표 Climatological probability of heavy rainfall during summer.
표 Climatological probability of heavy rainfall during summer.
표 Forecast skills of heavy rainfall probability estimated by observed BSISO indices, which were considered as a perfect model.
표 Sensitivity analysis of BSISO strength for forecast skills of heavy rainfall probability
표 Probability of heavy rainfall occurrence according to each of BSISO1 phase during MAY.
표 Same as Fig.A1.1, but for JUN.
표 Same as Fig.A1.1, but for JUL.
표 Same as Fig.A1.1, but for AUG.
표 Same as Fig.A1.1, but for SEP.
표 Same as Fig.A1.1, but for OCT.
표 BSISO2 phase during MAY.
표 Same as Fig.A.1.7, but for JUN.
표 Same as Fig.A.1.7, but for JUL.
표 Same as Fig.A.1.7, but for AUG.
표 Same as Fig.A.1.7, but for SEP.
표 Same as Fig.A.1.7, but for OCT.
표 Relationship between BSISO and ICMI3 (box plot) and composite maps for OLR (shaded) and 850hPa wind (vector) anomalies when BSISO phase has a distinctive relationship (red box) with ICMI3 during May.
표 Same as in Fig. A2.1, but for Jun.
표 Same as in Fig. A2.1, but for Jul.
표 Same as in Fig. A2.1, but for Aug.
표 Same as in Fig. A2.1, but for Sep.
표 Relationship between BSISO and ICMI4-1 (box plot) and composite maps for OLR (shaded) and 850hPa wind (vector) anomalies when BSISO phase has a distinctive relationship (red box) with ICMI4-1 during May.
표 Same as in Fig. A2.6, but for Jun.
표 Same as in Fig. A2.6, but for Jul.
표 Same as in Fig. A2.6, but for Aug.
표 Same as in Fig. A2.6, but for Sep.
표 Same as in Fig. A2.6, but for Oct.
표 Relationship between BSISO and ICMI4-2 (box plot) and composite maps for OLR (shaded) and 850hPa wind (vector) anomalies when BSISO phase has a distinctive relationship (red box) with ICMI4-2 during Oct.
표 WEEK-1 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using BOM.
표 WEEK-2 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using BOM.
표 WEEK-1 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using BOM.
표 WEEK-2 Forecast skills of heavy rainfall amount estimated by BSISO forecast indices using BOM.
표 Same as in Fig. A3.1, but for CFS
표 Same as in Fig. A3.2, but for CFS
표 Same as in Fig. A3.3, but for CFS
표 Same as in Fig. A3.4, but for CFS
표 Same as in Fig. A3.1, but for GFS
표 Same as in Fig. A3.2, but for GFS
표 Same as in Fig. A3.3, but for GFS
표 Same as in Fig. A3.4, but for GFS
표 Same as in Fig. A3.1, but for CWB
표 Same as in Fig. A3.2, but for CWB
표 Same as in Fig. A3.3, but for CWB
표 Same as in Fig. A3.4, but for CWB
표 The Brier skill score for probability forecasts of heavy rainfall estimated by BSISO forecast indices using BOM.
표 Same as Figure A3.17 but for CFS.
표 Same as Figure A3.17 but for GFS.
표 Same as Figure A3.17 but for CWB.
표 The BSS for probability forecasts of heavy rainfall estimated by BSISO forecast indices using BOM.
표 Same as Figure A3.21 but for CFS.
표 Same as Figure A3.21 but for GFS.
표 BSISO1 지수의 강도가1.5를 초과한 날들만을 선택하여 구한제1 계절내 진동과 관련 한 일평균 상향 장파복사 아노말리 합성장 아노말리
표 BSISO2 지수의 강도가1.5를 초과한 날들만을 선택하여 구한 제2 계절내 진동과 관 련한 일평균 상향 장파복사 아노말리 합성장 아노말리
표 BSISO 지수의 강도가1 이상인 날들을 바탕으로 한 제1 계절내 진동과(좌) 제2 계절 내 진동과(우) 관련한 일평균 상향 장파복사 아노말리 합성장 아노말리
표 BSISO 지수의 강도가1 미만 날들을 바탕으로 한 제1 계절내 진동과(좌) 제2 계절내 진동과(우) 관련한 일평균 상향 장파복사 아노말리 합성장 아노말리
표 BSISO1 (a), BSISO2 (b) 지수를 바탕으로 한 APHRODITE 강수 자료 합성장의 동 아시아 영역 평균의 합성장의(위로부터) 상위10퍼센트, 상위15퍼센트, 중간값, 하위15퍼센트, 하 위 10퍼센트 값을 나타내는 상자-수염 그림. 각 도표의 붉은 점은 평균값을 나타낸다.
표 BSISO1 지수를 바탕으로 한 850 hPa의 바람장(vectors, m sec-1), 수분수렴 (shading, Kg/Kg sec-1), 지위고도(contour, m) 합성 분포도.
표 BSISO2 지수를 바탕으로 한 850 hPa의 바람장(vectors, m sec-1), 수분수렴 (shading, Kg/Kg sec-1), 지위고도(contour, m) 합성 분포도
표 BSISO1 지수를 바탕으로 한 동서 바람(contour), 연직속도(shading, Pa/sec), 위도 합성장을 동아시아 경도대(110°–135°E)에 대하여 평균한 기압-위도 분포도.
표 Figure 8과 유사하나 BSISO2 지수에 대한 합성분포도
표 BSISO1 지수를 바탕으로 한 500-hPa 지위고도 아노말리 합성장(m).
표 Figure 10과 유사하나 BSISO2 지수에 대한 합성분포도
표 10-60일 시간 필터링한 상향 장파복사 편차를 BSISO1지수를 바탕으로 한 합성분 석 결과 각 위상별 최소(a), 최대(b) 지점.
표 Figure 12와 유사하나 BSISO2의 경우
표 BSISO1 위상4의 상향장파복사(a) 최소값에 해당하는 지점에서30-60일 시간 필터링 된 상향장파복사 아노말리와 500-hPa 지위고도간의 지점 상관 계수(b) 최대값 지점과의 상관 계수. BSISO2 위상6의 상향장파복사(c) 최소값에 해당하는 지점에서10-25일 시간 필터링 된 상향장파복사 아노말리와 500-hPa 지위고도간의 지점 상관 계수(d) 최대값 지점과의 상관계수
표 열원을 중심으로 한 연직단면도.
표 1981년부터 2010년까지 5월에서부터 9월동안 BSISO 지수 각 위상 별 강도 (amplitude)의 통계치를 나타낸 상자-수염 그림.
표 과거 30년 여름철 BSISO1 지수 강도 크기 별 발생 빈도
표 과거 30년 여름철 BSISO2 지수 강도 크기 별 발생 빈도
표 Figure 16과 비슷하나 BSISO1의 각 위상 별 월별 통계치.
표 Figure 19와 비슷하나 BSISO2에 대한 위상 별 월별 통계치
표 BSISO1 각 위상의 강도가1.0 이상인 날 이후 다음 위상전이 확률.
표 Figure 21과 비슷하지만 BSISO2에 관한 결과
표 Figure 21과 유사하나 실시간 다변량 매든-줄리안 진동 지수(Real-time Multivariate MJO index: RMM index)를 바탕으로 한 결과
표 BSISO1과 BSISO2의 동시 발생 빈도. 각 축은 해당 모드의 위상을 나타냄
표 BSISO1의 (a) 이동하는 사례 (b) 정체하는 사례.
표 Figure 25와 동일하나 BSISO2에 해당하는 그림임.
표 BSISO1, BSISO2 사례 평균 지속 기간 및 사례당 포함되어 있는 위상의 개수
표 각 사례의 5-9월 동안 월별발생 횟수
표 1981-2016년 장마 시종일과 해당 일의BSISO 강도와 위상.
표 장마시작일BSISO 위상 별 발생 빈도수(1981-2016년)
표 Spatial patterns of temporal correlation coefficients between observations and initial conditions.
표 EOF patterns for (a) EP and (b) CP ENSO type from OISST, and time series of (c) EP and (d) CP indices represented by the principal components of each leading EOF pattern.
표 Approximate areas of four climatic regions (Murphy et al. 2014) to isolate the 15 Pacific Island Countries and East Timor
표 Lists of Pacific Island Countries.
표 Lists of 8 APCC MME Seasonal Forecast Models including APCC CCSM3 indicated by alphabet letters from B to I.
표 Spatial distribution of TCCs between 2m air temperatures predicted at 1-(upper panels), 3-(middle), and 6-(lower) month lead times in (a) CCSM3 and (b) SCOPS, and observations.
표 Same as Figure4 except for total precipitation.
표 (Left) TCCs and (Right) RMSEs for (a) NINO3.4, (b) NINO4, (c ) NINO3, and (d) NINO1+2 index between forecasts from 1-to 6-month lead times and OISST.
표 (left) auto correlations of NINO3.4 index (persistence forecast) and TCCs between model [(middle) CCSM3 and (right) SCoPS] forecasts and observations as a function of lead time and target month.
표 Time series of NINO3.4 index from observations (black solid line) over the period of April 1997 to April 1998 and from model forecasts stating from March (blue lines), May (red lines), July (green lines), and September (brown).
표 Model climate drift (bias) of SSTs averaged along the equatorial Pacific (2°S-2°N) for forecast from initial conditions of (a) March, (b) May, (c) July, and (d) October.
표 TCCs for (a) EP index and (b) CP index between forecasts from 1-to 6-month lead times and OISST.
표 Scatter plots of December-January-February (DJF) season averaged EP vs CP index from (a) observations and (b, c) 1-month and (d, e) 6-month lead forecasts of (b, d) SCoPS, and (c, e) CCSM3.
표 DJF-season averaged SST anomalies along the equator for (a) 1997/98 El Nino event and (b) 2009/2010 El Nino event.
표 Difference of climatological DJF season averaged SST between forecasts of (left panels) CCSM3 and (right panels) SCoPS at (upper) 1-month and (lower) 3-month lead times and observations.
표 TCCs area-averaged over the four climatic regions (see Figure 2) from 1-to 6-month lead times. Left panels are for 2m air temperature and right panels for precipitation.
표 Area-averaged TCCs between SCoPS forecasted and observed 2m air temperatures in four climatic regions, (a) NWP, (b) WP, (c) EQP, and (d) SWP, as a function of lead times and target months.
표 Same as Figure 15 expect for precipitation.
표 TCCs for (a) IOD, (b) EIO, and (c) WIO between forecasts from 1-to 6-month lead times and OISST.
표 Same as Figure 7 except for IOD index.
표 Same as Figure 17 except for (a) TIO, (b) NIO, and (c) SWIO.
표 Spatial distribution of correlation coefficients between December-January-February (DJF) season-averaged precipitation and NINO3.4 index from observations (shade) and forecasts (red contour) at 1-month lead (starting from November initial condition) from the SCoPS and CCSM3 Values (0.37) that are significant at 95% level (according to student’s t-test) are only contoured and shaded.
표 Same as Figure 20 except for June-July-August (JJA) season-averaged precipitation and NINO3.4 index.
표 Same as Figure 20 except for September-October-November (SON) season-averaged precipitation and IOD index.
표 TCCs as a function of forecast lead months and seasonal forecast models for (a) NINO4, (b) NINO34, (c) NINO3, and (d) NINO1+2 index.
표 Same as Figure 23 except for (a) IOD, (b) TIO, (c), NIO, and (d) SWIO indices.
표 TCCs area-averaged over the four climatic regions (see Figure 2), namely (a) NWP, (b) EQP, (c) WP, and (d) SWP, TCCs between observations and models’ forecasts from 1-to 6-month lead times.
표 Same as Figure 23 except for (a) IOD, (b) TIO, (c), NIO, and (d) SWIO indices.
표 Schematic diagram of coupling and exchanged field from each model component
표 Seasonal-mean SST in JJA (left) and DJF (right) from OISST observation (top), and hindcasts with 3 month lead time from the CCSM3 (middle), and SCoPS (bottom).
표 Climatological summer (left) and winter (right) bias (model minus observation) of the SST for (a), (c) CCSM3 and (b), (d) SCoPS.
표 Seasonal-mean 2m temperature in JJA (left) and DJF (right) from ECMWF interim reanalysis data (top), and hindcasts with 3 month lead time from the CCSM3 (middle), and SCoPS (bottom).
표 Climatological summer (left) and winter (right) bias (model minus observation) of the 2m temperature for (a), (c) CCSM3 and (b), (d) SCoPS.
표 Seasonal-mean precipitation in JJA (left) and DJF (right) from GPCP observation (top), and hindcasts with 3 month lead time from the CCSM3 (middle), and SCoPS (bottom).
표 Climatological summer (left) and winter (right) bias (model minus observation) of the precipitation for (a), (c) CCSM3 and (b), (d) SCoPS.
표 Monthly mean velocity potential and divergent wind at 200 hPa for the hindcast period (1982-2013) with 3 month lead time for JJA (left) and DJF (right).
표 Zonal mean of the velocity potential in Figure. 8 for the summer (left) and winter (right) of reanalysis data (black line), CCSM (blue line), and SCoPS (red line) hindcast with 3-month lead time.
표 Deviation from the zonal mean of the velocity potential for the summer (left) and winter (right) mean climatology from reanalysis data (top), CCSM3 (middle), and SCoPS (bottom) with 3-month lead time.
표 Annual mean (hindcast with 3-month lead time starting from Feb., May, Aug., Nov.,) of the velocity potential in Figure 10 for the climate.
표 Deviation from the annual mean of the velocity potential in Figure 10 for the summer (left) and winter (right) climatology.
표 Anomaly correlation coefficients with observations in the Nino4 (top), Nino 3.4 (middle), and Nino 3 (bottom) regions for CCSM3 (blue line) and SCoPS (red line) for varying lead times for stars in (a), (b), (c), May and (d), (e), (f) November.
표 Prediction skill of 2m temperature between reanalysis data and CCSM3 (left), and SCoPS (right) hindcast with starting May.
표 Same as Figure 14, but for precipitation. GPCP is used as observation.
표 Same as Figure 14, but for hindcast with starting November.
표 Same as Figure 15, but for hindcast with starting November.
표 Climatological mean precipitation (shaded) and zonal wind at 850hPa (contour) from (a) GPCP and ERA-interim, (b) APHRODITE precipitation, (c) CCSM3, and (d) SCoPS during June to August averaged over 32 years (1982-2013).
표 Latitude-time cross section of climatological mean precipitation and 850hPa zonal wind (a), (b), (c) over the Indian region (70-80E˚) and (d), (e), (f) over the East Asian region (120-130E˚) from GPCP and ERA-interim (top), CCSM3 (middle), and SCoPS (bottom).
표 Climatological mean precipitation (shaded) and zonal wind at 850hPa (contour) from individual model for MME prediction during June to August averaged over 23 years
표 Interannual variability of JJA 850hPa geopotential height for 1982-2013 from (a) NCEP reanalysis, (b) CCSM3, and (c) SCoPS. (d) is the WNPSH index from reanalysis data (black), CCSM3 (blue line), and SCoPS (red line).
표 Correlation between WNPSH index and JJA precipitation from (a) observations, (b) CCSM3, and (c) SCoPS. For the forecast systems, the correlation is taken between the ensemble mean WNSPH index and precipitation from individual hindcast members.
표 The summer (JJA) (a) WPEMI (Western Pacific East Asian Monsoon Index) and (b) EASM (East Asian Summer Monsoon) indices from observations (NCEP reanalysis data 2), CCSM3, and SCoPS.
표 The summer (JJA) EASM (East Asian Summer Monsoon) indices with correlation coefficients from observations (NCEP reanalysis data 2), CCSM3, SCoPS, individual models for MME prediction.
표 Climatological mean Sea Level Pressure (left; shaded), wind vector at 850hPa (left; contour), geopotential height (right; shaded), and zonal wind at 200hPa (right; contour) from reanalysis data (top), CCSM3 (middle), and SCoPS (bottom) during December to February averaged over 32 years (1982-2013).
표 Climatological mean biases of Sea Level Pressure (left; shaded), wind vector at 850hPa (left; contour), geopotential height (right; shaded), and zonal wind at 200hPa (right; contour) from (a), (c) reanalysis data minus CCSM3 (middle), and (b), (d) reanalysis data minus SCoPS during December to February averaged over 32 years (1982-2013).
표 Same as Figure 23, but for surface temperature (left), and precipitation(right).
표 Same as Figure 23, but for surface temperature (left), and precipitation(right).
표 The winter (DJF) EAWM (East Asian Winter Monsoon) indices from observations (ERA-Interim), CCSM3, and SCoPS.
표 The winter (DJF) EAWM (East Asian Winter Monsoon) indices from observations (ERA-Interim), CCSM3, SCoPS, and individual models for MME prediction.
표 DJF mean normalized AO index from reanalysis (black), CCSM3 (blue), and SCoPS (red). Percentages in left bottom string indicate explained variance (averaged explained variance from each ensemble member) from the pattern.
표 Steps for APCC-PRePS project
표 Experimental design
표 Experimental domain of (a) big domain and (b) small domain.
표 Monthly mean precipitation (mm) of observation and WRF simulations with big and small domain over the Philippines.
표 Time-series of 5-day running mean precipitation averaged over the Philippines.
표 Probability distribution function (PDF) for daily precipitation over the Philippines.
표 Root mean square error (RMSE) and correlation coefficients over the Philippines
표 Monthly mean precipitation (mm) of observation, hydrostatic, and non-hydrostatic WRF simulations.
표 Time-series of 5-day running mean precipitation averaged over (a) whole domain, and (b) the Philippines.
표 Probability distribution of daily precipitation over (a) whole domain, and (b) the Philippines.
표 Root mean square error (RMSE) and correlation coefficients over the whole domain.
표 Summary of RegCM4 experiments
표 MJJA precipitation (mm/day) of observation, hydrostatic and non-hydrostatic RegCM simulations.
표 Time-series of 5-day running mean precipitation (mm/day) obtained from observation and RegCM simulations.
표 Rainfall amount over land (yellow) and the oceans (blue) of observation and RegCM simulations.
표 Spatial and temporal correlation of MJJA precipitation.
표 Monthly precipitation (mm) of observation and GRIMs-RMP simulation.
표 Same as in Figure 25 except for over the Philippines
표 Time-series of 5-day running mean precipitation (mm/day) averaged over (a) whole domain, and (b) the Philippines.
표 Probability distribution of precipitation averaged over (a) domain and (b) the Philippines.
표 Root mean square error (RMSE) and correlation coefficients over the domain and the Philippines.
표 Flow chart to decide experimental design
표 기후변화를 고려한 수문시나리오 내 불확실성 평가를 위한 연구흐름도
표 CMIP5 격자자료 추출을 위한 한반도 주변 경계범위
표 본 연구에서 사용된 CMIP5 전지구모형
표 국내 ASOS 60개 관측지역
표 국내 7개 다목적 댐 위치.
표 선정된 7개 다목적댐 유역특성
표 월자료 BCSD 수행 모식도
표 BCSD에서 편이보정을 위한 QM 과정
표 BCSD에서 편이보정을 위한 QM 과정
표 BCCA과정 모식도

과제명(ProjectTitle) :	-
연구책임자(Manager) :	-
과제기간(DetailSeriesProject) :	-
총연구비 (DetailSeriesProject) :	-
키워드(keyword) :	-
과제수행기간(LeadAgency) :	-
연구목표(Goal) :	-
연구내용(Abstract) :	-
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저장형식	Text(ASCII format) Excel format
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

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