슈퍼앙상블 계절예측시스템 연구. 1 / 기상연구소

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소장자료
공공정책정보
외부기관 자료

일반도서 슈퍼앙상블 계절예측시스템 연구. 1

저자사항
기상연구소
발행사항
[서울] : 기상연구소, 2004
청구기호
전자형태로만 열람가능함
자료실 서울관 해당자료 없음
형태사항
xii, 223 p. : 도표 ; 30 cm
제어번호
MONO1200514025
주기사항
연구부서: 기상연구소 기후연구실
연구책임자: 강성대
연계정보
원문
외부기관 원문

목차보기

표제지=0,1,1

연구보고서=0,2,1

목차=i,3,2

CONTENTS=iii,5,2

List of Figures=v,7,3

List of Tables=viii,10,1

요약문=ix,11,2

SUMMARY=xi,13,2

제1장 서론=1,15,3

제2장 기상연구소 계절 예측 시스템 구축 및 시험 운영=4,18,1

제1절 기상연구소 계절 예측 시스템 구축=4,18,2

1. 기상연구소 자동 계절 예측 시스템 (MSPS) 구성=5,19,2

2. MSPS의 자동 계절 예측 수행 절차=6,20,3

제2절 계절 예측 시스템의 시험 운영=9,23,1

1. 가을철 계절 예보=9,23,3

2. 겨울철 계절 예보=11,25,5

제3절 잠재와도 (Potential Vorticity) 분석=16,30,8

제4절 소결론=24,38,2

제3장 최적 슈퍼앙상블 기법 선정=26,40,1

제1절 서론=26,40,1

1. 멀티모델 앙상블 개요=26,40,2

2. 참여 모델 및 Hindcast 자료=27,41,4

3. 기상연구소 멀티모델 앙상블 계절 예측 기법의 종류=30,44,2

제2절 인공신경망 모델을 이용한 멀티모델 앙상블 기법 개발=32,46,1

1. 인공신경망의 개요=32,46,2

2. 인공신경망의 모델을 이용한 멀티모델 앙상블 기법=33,47,4

3. 민감도 실험=37,51,9

제3절 멀티모델 앙상블 기법의 예측성의 검증=46,60,1

1. 예측성 검증 방법=46,60,4

2. 예측성 검증=50,64,44

제4절 기상연구소 멀티모델 앙상블 시스템을 위한 최적 기법의 선정=94,108,4

제5절 소결론=98,112,1

제4장 결론=99,113,2

참고 문헌=101,115,4

부록 1. 기상연구소 계절 예측 시스템 자동화 프로그램=105,119,28

2004 학술용역과제:Multi-model 슈퍼앙상블 장기예보 시스템 구축을 위한 hindcast 앙상블 실험 및 검증-II=133,147,2

연구보고서=135,149,2

차례=137,151,1

CONTENTS=138,152,1

LIST OF FIGURES=139,153,6

LIST OF TABLES=145,159,2

요약문=147,161,1

SUMMARY=148,162,1

제1장 서론=149,163,2

제2장 Hindcast Ensemble Forecast=151,165,1

제1절 참여 모델=151,165,3

제2절 실험 방법=154,168,2

제3장 가을,봄철 예측성 평가=156,170,1

제1절 평균기후값=156,170,1

제2절 경년 변동성=156,170,2

제3절 단순 예측성 평가=157,171,60

제4장 지표면 초기화 방안=217,231,3

제5장 요약 및 결론=220,234,1

참고문헌=221,235,3

영문목차

[title page etc.]=0,1,2

Contents(Korean)=i,3,2

Contents=iii,5,2

List of Figures=v,7,3

List of Tables=viii,10,1

Summary(Korean)=ix,11,2

Summary=xi,13,2

Chapter 1 Introduction=1,15,3

Chapter 2 Development and Operational Test of METRI Seasonal Prediction System=4,18,1

2.1 Developement of METRI Seasonal Prediction System=4,18,2

1. Configuration of METRI Seasonal Prediction System=5,19,2

2. Operational Procedure of METRI Seasonal Prediction System=6,20,3

2.2 Operational Test of METRI Seasonal Prediction System=9,23,1

1. Seasonal Prediction for Fall=9,23,3

2. Seasonal Prediction for Winter=11,25,5

2.3 Analysis of Potential Vorticity=16,30,8

2.4 Conclusions=24,38,2

Chapter 3 Selection of Optimum Multimodel Ensemble Technique=26,40,1

3.1 Introduction=26,40,1

1. Introduction of Multimodel Ensemble=26,40,2

2. Participating Models and Hindcast data=27,41,4

3. Description of METRI Multimodel Ensemble Techniques=30,44,2

3.2 Developement of Multimodel Ensemble Technique using Artificial Neural Network Model=32,46,1

1. Introduction of Artificial Neural Networks=32,46,2

2. Multimodel Ensemble Technique using Artificial Neural Network=33,47,4

3. Experiments of Sensitivity=37,51,9

3.3 Evaluation of Predictability of Multimodel Ensemble Techniques=46,60,1

1. Method of Evaluation=46,60,4

2. Evaluation of Seasonal Predictability=50,64,44

3.4 Selection of Optimum Technique for METRI Multimodel Ensemble System=94,108,4

3.5 Conclusions=98,112,1

Chapter 4 Conclusions=99,113,2

References=101,115,4

Appendix 1. Description of Automatic Program of METRI Seasonal Prediction System=105,119,28

Hindcast Ensemble Experiment and Verification for Construction of Multi-model Super-ensemble Long-range Forecasting System-II=133,147,5

CONTENTS=138,152,11

I Introduction=149,163,2

II Hindcast Ensemble Forecast=151,165,1

1 Participating Model=151,165,3

2 Experimental Design=154,168,2

III Evaluation of forecast=156,170,1

1 Climatology=156,170,1

2 Interannual Variability=156,170,2

3 Simple evaluation of predictability=157,171,60

IV Land Surface Initialization Methods=217,231,3

V Summary and Conclusion=220,234,1

References=221,235,3

그림목차

Fig. 2.1.1. Schematic Diagram of Ensemble Prediction System at METRI=5,19,1

Fig. 2.2.1. Mean and anomalous geopotential height chart at 500 hPa level (a) and anomalous temperature chart at 850 hPa level (b) for fall season,respectively=9,23,2

Fig. 2.2.2. Anomalous precipitation chart for fall season=10,24,1

Fig. 2.2.3. Mean and anomalous geopotential height chart at 500 hPa level (a) and anomalous temperature chart at 850 hPa level (b) for winter season,respectively=12,26,1

Fig. 2.2.4. Anomalous precipitation chart for winter season=13,27,1

Fig. 2.2.5. Multimodel ensemble produced mean and anomalous geopotential height chart at 500 hPa level (a) and anomalous temperature chart at 850 hPa level (b) for winter season,respectively=14,28,1

Fig. 2.2.6. Multimodel ensemble produced anomalous precipitation chart for winter season=15,29,1

Fig. 2.3.1. Schematic diagram of Potential Vorticity (PV) analysis for tracing airmass in the wet atmosphere=16,30,1

Fig. 2.3.2. PV analysis chart at 20 hPa (a) and 150 hPa (b) for fall season=19,33,1

Fig. 2.3.3. PV analysis chart at 500 hPa (a) and 1000 hPa (b) for fall season=21,35,1

Fig. 2.3.4. Vertical profile of PV along meridionally=22,36,1

Fig. 2.3.5. Vertical profile of PV & Chi along zonally=23,37,1

Fig. 3.2.1. Configuration of the artificial neural network model for multimodel ensemble=34,48,1

Fig. 3.2.2. Flow chart of error back-propagation=35,49,1

Fig. 3.2.3. In order to minimise E²the delta rule gives the direction of weight change required=36,50,1

Fig. 3.2.4. Mean square errors over globe of sensitivity experiments=38,52,1

Fig. 3.2.5. Mean square errors over East Asian region (30(이미지참조)-50(이미지참조),90(이미지참조)-150(이미지참조)) of sensitivity experiments=39,53,1

Fig. 3.2.6. The sensitivity of ALR on the predicted precipitation by neural network model=41,55,1

Fig. 3.2.7. The sensitivity of BLR on the predicted precipitation by neural network model=42,56,1

Fig. 3.2.8. The regional dependence of the forecast of precipitation in neural network. (a) is MSE of predicted precipitation,(c) is MSE of output of training periods,and (e) is MSE of output of training periods in neural network. (b),(d),and (f) are same=44,58,1

Fig. 3.2.9. The regional dependence of the forecast of precipitation in neural network. (a) is ACC of predicted precipitation,(c) is ACC of output of training periods,and (e) is MSE of output of training periods in neural network. (b),(d),and (f) are same=45,59,1

Fig. 3.3.1. Mean squared errors of the precipitation produced by participating models,SAM,SLR,MLR and ANN=54,68,1

Fig. 3.3.2. Same as Fig. 2.3.1,but for anomaly correlation coefficients (ACC) between forecasts and observations=55,69,1

Fig. 3.3.3. Mean squared skill scores (MSSS) of the precipitation for winter (Dec-Jan-Feb) produced by participating models,SAM,SLR,MLR,and ANN=56,70,1

Fig. 3.3.4. Same as Fig. 2.3.3,but for Temporal Correlation coefficients (COR) between forecasts and observations=57,71,1

Fig. 3.3.5. Same as Fig. 2.3.3,but for the ratio of the forecasts to observed variances=58,72,1

Fig. 3.3.6. Same as Fig. 2.3.3,but for summer (Jun-Jul-Aug)=59,73,1

Fig. 3.3.7. Same as Fig. 2.3.4,but for summer (Jun-Jul-Aug)=60,74,1

Fig. 3.3.8. Same as Fig. 2.3.5,but for summer (Jun-Jul-Aug)=61,75,1

Fig. 3.3.9. Zonal averaged (a) mean squared skill score,(b) the temporal correlation between forecasts and observations,and (c) the ratio of the forecast to observed variances of the precipitation produced by participating models,SAM,SLR,MLR and ANN=62,76,1

Fig. 3.3.10. Heidke skill scores (HSS) of the precipitation for winter (Dec-Jan-Feb) produced by participating models,SAM,SLR,MLR,and ANN=63,77,1

Fig. 3.3.11. Same as Fig. 2.3.10,but for summer (Jun-Jul-Aug)=64,78,1

Fig. 3.3.12. Zonal averaged Heidke skill scores of the precipitation produced by participating models,SAM,SLR,MLR and ANN=65,79,1

Fig. 3.3.13. Same as Fig. 3.3.1,but for 850 hPa air temperature=68,82,1

Fig. 3.3.14. Same as Fig. 3.3.2,but for 850 hPa air temperature=69,83,1

Fig. 3.3.15. Same as Fig. 3.3.3,but for 850 hPa air temperature=70,84,1

Fig. 3.3.16. Same as Fig. 3.3.4,but for 850 hPa air temperature=71,85,1

Fig. 3.3.17. Same as Fig. 3.3.5,but for 850 hPa air temperature=72,86,1

Fig. 3.3.18. Same as Fig. 3.3.6,but for 850 hPa air temperature=73,87,1

Fig. 3.3.19. Same as Fig. 3.3.7,but for 850 hPa air temperature=74,88,1

Fig. 3.3.20. Same as Fig. 3.3.8,but for 850 hPa air temperature=75,89,1

Fig. 3.3.21. Same as Fig. 3.3.9,but for 850 hPa air temperature=76,90,1

Fig. 3.3.22. Same as Fig. 3.3.10,but for 850 hPa air temperature=77,91,1

Fig. 3.3.23. Same as Fig. 3.3.11,but for 850 hPa air temperature=78,92,1

Fig. 3.3.24. Same as Fig. 3.3.12,but for 850 hPa air temperature=79,93,1

Fig. 3.3.25. Same as Fig. 3.3.1,but for 500 hPa geopotential height=82,96,1

Fig. 3.3.26. Same as Fig. 3.3.2,but for 500 hPa geopotential height=83,97,1

Fig. 3.3.27. Same as Fig. 3.3.3,but for 500 hPa geopotential height=84,98,1

Fig. 3.3.28. Same as Fig. 3.3.4,but for 500 hPa geopotential height=85,99,1

Fig. 3.3.29. Same as Fig. 3.3.5,but for 500 hPa geopotential height=86,100,1

Fig. 3.3.30. Same as Fig. 3.3.6,but for 500 hPa geopotential height=87,101,1

Fig. 3.3.31. Same as Fig. 3.3.7,but for 500 hPa geopotential height=88,102,1

Fig. 3.3.32. Same as Fig. 3.3.8,but for 500 hPa geopotential height=89,103,1

Fig. 3.3.33. Same as Fig. 3.3.9,but for 500 hPa geopotential height=90,104,1

Fig. 3.3.34. Same as Fig. 3.3.10,but for 500 hPa geopotential height=91,105,1

Fig. 3.3.35. Same as Fig. 3.3.11,but for 500 hPa geopotential height=92,106,1

Fig. 3.3.36. Same as Fig. 3.3.12,but for 500 hPa geopotential height=93,107,1

Fig. 3.4.1. Heidke skill scores (HSS) averaged over (a) Equatorial Pacific region and (b) East Asian region of the precipitation for winter (Dec-Jan-Feb) produced by various multimodel ensemble techniques=96,110,1

Fig. 3.4.2. Same as Fig. 3.4.1,but for summer (Jun-Jul-Aug)=97,111,1

Fig. 2.2.1. Schematic diagram of land surface initialization=155,169,1

Fig. 3.1.1. Climatology for fall(SON) of 1979-2003,geopontential height at 500hPa=159,173,1

Fig. 3.1.2. Difference of model data and observation data for fall(SON) of 1979-2003,geopontential height at 500hPa=160,174,1

Fig. 3.1.3. Climatology for spring(MAM) of 1979-2003,geopontential height at 500hPa=161,175,1

Fig. 3.1.4. Difference of model data and observation data for spring(MAM) of 1979-2003,geopontential height at 500hPa=162,176,1

Fig. 3.1.5. Climatology for fall(SON) of 1979-2003,zonal wind at 200hPa=163,177,1

Fig. 3.1.6. Difference of model data and observation data for fall(SON) of 1979-2003,zonal wind at 200hPa=164,178,1

Fig. 3.1.7. Climatology for spring(MAM) of 1979-2003,zonal wind at 200hPa=165,179,1

Fig. 3.1.8. Difference of model data and observation data for spring(MAM) of 1979-2003,zonal wind at 200hPa=166,180,1

Fig. 3.1.9. Climatology for fall(SON) of 1979-2003,precipitation=167,181,1

Fig. 3.1.10. Difference of model data and observation data for fall(SON) of 1979-2003,precipitation=168,182,1

Fig. 3.1.11. Climatology for spling(MAM) of 1979-2003,precipitation=169,183,1

Fig. 3.1.12. Difference of model data and observation data for spring(MAM) of 1979-2003,precipitation=170,184,1

Fig. 3.1.13. Climatology for fall(SON) of 1979-2003,surface temperature=171,185,1

Fig. 3.1.14. Difference of model data and observation data for fall(SON) of 1979-2003,surface temperature=172,186,1

Fig. 3.1.15. Climatology for fall(MAM) of 1979-2003,surface temperature=173,187,1

Fig. 3.1.16. Difference of model data and observation data for fall(MAM) of 1979-2003,surface temperature=174,188,1

Fig. 3.2.1. Surface temperature anomaly for fall(SON) in Elnino(1982)=175,189,1

Fig. 3.2.2. Surface temperature anomaly for spring(MAM) in Elnino(1983)=176,190,1

Fig. 3.2.3. Surface temperature anomaly for fall(SON) in Elnino(1997)=177,191,1

Fig. 3.2.4. Surface temperature anomaly for spring(MAM) in Elnino(1998)=178,192,1

Fig. 3.2.5. Surface temperature anomaly for fall(SON) in Lanina(1988)=179,193,1

Fig. 3.2.6. Surface temperature anomaly for spring(MAM) in Lanina(1989)=180,194,1

Fig. 3.2.7. Surface temperature anomaly for fall(SON) in Lanina(1999)=181,195,1

Fig. 3.2.8. Surface temperature anomaly for spring(MAM) in Lanina(2000)=182,196,1

Fig. 3.2.9. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in SON 1982=183,197,1

Fig. 3.2.10. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in MAM 1983=184,198,1

Fig. 3.2.11. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCM3 (a,c,e) and observation (b,d,f) in SON 1982=185,199,1

Fig. 3.2.12. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCSR (a,c,e) and observation (b,d,f) in SON 1982=186,200,1

Fig. 3.2.13. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in SON 1997=187,201,1

Fig. 3.2.14. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in MAM 1998=188,202,1

Fig. 3.2.15. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCM3 (a,c,e) and observation (b,d,f) in SON 1997=189,203,1

Fig. 3.2.16. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCSR (a,c,e) and observation (b,d,f) in SON 1997=190,204,1

Fig. 3.2.17. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in SON 1988=191,205,1

Fig. 3.2.18. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in MAM 1989=192,206,1

Fig. 3.2.19. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCM3 (a,c,e) and observation (b,d,f) in SON 1988=193,207,1

Fig. 3.2.20. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCSR (a,c,e) and observation (b,d,f) in SON 1988=194,208,1

Fig. 3.2.21. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in SON 1999=195,209,1

Fig. 3.2.22. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CAM2 (a,c,e) and observation (b,d,f) in MAM 2000=196,210,1

Fig. 3.2.23. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCM3 (a,c,e) and observation (b,d,f) in SON 1999=197,211,1

Fig. 3.2.24. Comparison of anomalous precipitation (a,b),mean sea level pressure (c,d),and geopotential height at 500hPa (e,f) between forecast of CCSR (a,c,e) and observation (b,d,f) in SON 1999=198,212,1

Fig. 3.3.1. Scatter diagram of area averaged surface temperature (left) and precipitation (right) anomaly over the Korean Peninsula(120~130E,30~45N) for fall(SON). Abscissa is forecast (CAM2),ordinate is observation. Empty spots are ensemble members,full=199,213,1

Fig. 3.3.2. Scatter diagram of area averaged surface temperature (left) and precipitation (right) anomaly over the Korean Peninsula(120~130E,30~45N) for spring(MAM). Abscissa is forecast (CAM2),ordinate is observation. Empty spots are ensemble members,ful=200,214,1

Fig. 3.3.3. Scatter diagram of area averaged surface temperature (left) and precipitation (right) anomaly over the Korean Peninsula(120~130E,30~45N) for fall(SON). Abscissa is forecast (CCM3),ordinate is observation. Empty spots are ensemble members,full=201,215,1

Fig. 3.3.4. Scatter diagram of area averaged surface temperature (left) and precipitation (right) anomaly over the Korean Peninsula(120~130E,30~45N) for fall(SON). Abscissa is forecast (CCSR),ordinate is observation. Empty spots are ensemble members,full=202,216,1

Fig. 3.3.5. Anomaly correlation coefficient of zonal wind at 200hPa between observation and forecast for fall=203,217,1

Fig. 3.3.6. Anomaly correlation coefficient of zonal wind at 200hPa between observation and forecast for spring=204,218,1

Fig. 3.3.7. Anomaly correlation coefficient of geopotential height at 500hPa between observation and forecast for fall=205,219,1

Fig. 3.3.8. Anomaly correlation coefficient of geopotential height at 500hPa between observation and forecast for spring=206,220,1

Fig. 3.3.9. root mean squared error=207,221,1

Fig. 3.3.10. ensemble spread=208,222,1

Fig. 3.3.11. Anomaly cohenrence index of precipitation between ensemble members for fall=209,223,1

Fig. 3.3.12. Anomaly cohenrence index of precipitation between ensemble members for spring=210,224,1

Fig. 3.3.13. Anomaly cohenrence index of geopotential height at 500hPa between ensemble members for fall=211,225,1

Fig. 3.3.14. Anomaly cohenrence index of geopotential height at 500hPa between ensemble members for spring=212,226,1

Fig. 3.3.15. Anomaly cohenrence index of zonal wind at 200hPa between ensemble members for fall=213,227,1

Fig. 3.3.16. Anomaly cohenrence index of zonal wind at 200hPa between ensemble members for spring=214,228,1

Fig. 3.3.17. Anomaly cohenrence index of surface temperature between ensemble members for fall=215,229,1

Fig. 3.3.18. Anomaly cohenrence index of surface temperature between ensemble members for spring=216,230,1

표목차

Table 3.2.1. The lists of parameters to be tuned=37,51,1

Table 3.2.2. The lists of sensitivity experiments=37,51,1

Table 3.3.1. The three-by-three contingency table for calculation of Heidke skill score=49,63,1

Table 3.4.1. HSS of various multimodel ensemble techniques for winter. PRCP means precipitation,T850 means 850 hPa air temperature,and H500 means 500 hPa geopotential height. EP means HSS averaged over Equatorial Pacific region and EA means HSS averaged o=95,109,1

Table 3.4.2. Same as Table 3.4.1,but for summer=95,109,1

Table. 2.1.1 Major physical processes used in CAM2=151,165,2

Table. 2.1.2 Major physical processes used in CCM3=152,166,2

Table. 2.1.3 Major physical processes used in CCSR=153,167,1

Table. 2.2.1 Dataset used for atmospheric initial condition=154,168,2

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