본문 바로가기 주메뉴 바로가기
국회도서관 홈으로 정보검색 소장정보 검색

목차보기

목차

표제지=0,1,1

제출문=0,2,1

요약문=i,3,7

SUMMARY=viii,10,9

목차=xvii,19,2

CONTENTS=xix,21,3

List of Figures=xxii,24,13

List of Tables=xxxv,37,2

제1장 서론=1,39,3

제2장 국내외 기술개발 현황=4,42,1

제1절 지역모델 초기 입력자료 산출=4,42,1

제2절 국지분석 및 예측시스템(KLAPS)을 이용한 관측자료의 분석ㆍ예측의 민감도 연구=5,43,1

제3절 지역 접합 모델의 기반 기술 개발=6,44,1

제4절 전지구 모형의 개선=6,44,2

제5절 한반도지역 고해상도 예측을 위한 병렬 기상모델의 개발=7,45,2

제6절 여름철 집중호우 예측체계 개선=8,46,2

제3장 연구개발수행 내용 및 결과=10,48,1

제1절 지역모델 초기 입력자료 산출=10,48,1

1. GMS-5자료를 이용한 해수면온도 산출=10,48,13

2. NOAA-16/ATOVS 자료를 이용한 연직 온습도 프로파일 산출=22,60,21

3. NOAA/AVHRR 자료를 이용한 snowcover 산출=43,81,21

제2절 국지분석 및 예측시스템(KLAPS)을 이용한 관측자료의 분석ㆍ예측의 민감도 연구=64,102,1

1. KORMEX 집중 관측 자료의 수집 및 KLAPS를 이용한 재분석 자료의 생산=64,102,8

2. 관측자료 종류별 분석ㆍ예측의 민감도 실험을 통한 관측자료의 민감도 평가=72,110,23

3. KLAPS 분석자료의 4차원 자료동화 기법에 적용=95,133,2

제3절 지역 접합 모델의 기반 기술 개발=97,135,1

1. 클러스터를 위한 혼합형 병렬화 기법 개발=97,135,13

2. 지표-수문과정의 기반기술 개발=110,148,47

3. 대기-파고 접합모델의 기반기술개발=157,195,10

제4절 전지구 모형의 개선=167,205,1

1. 전지구 모형의 분해능 민감도 실험=167,205,8

2. 수증기의 세미 라그랑지안 수송 기법 개발=174,212,16

3. 기압경도력 계산방법의 개선=190,228,8

4. 대류에 의한 운동량 수송 기법의 개발=198,236,7

제5절 한반도지역 고해상도 예측을 위한 병렬 기상모델의 개발=205,243,1

1. 개요=205,243,2

2. 병렬 GDAPS의 구현 체계=206,244,2

3. 병렬프로그래밍=208,246,4

4. GDAPS의 병렬화=212,250,10

5. 연구결과=222,260,1

제6절 여름철 집중호우 예측 체계 개선=223,261,1

1. 서론=223,261,2

2. 연구내용 및 결과=224,262,90

제4장 연구개발목표 달성도 및 대외기여도=314,352,6

제5장 연구개발결과의 활용계획=320,358,4

제6장 참고문헌=324,362,16

영문목차

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

Summary=i,3,7

Summary in English=viii,10,9

Contents=xvii,19,2

Contents in English=xix,21,3

List of Figures=xxii,24,13

List of Tables=xxxv,37,2

Chapter 1 Introduction=1,39,3

Chapter 2 The status of domestic and international research=4,42,1

3.1 Production of input data for regional model using satellite data.=4,42,1

3.2 The research for the sensitivity test of analysis and forecast using KLAPS=5,43,1

3.3 Development of base technology of regional coupled model=6,44,1

3.4 Improvement of GDAPS performance=6,44,2

3.5 Development of a parallel atmospheric model for hi-resolution prediction on the Korean Peninsular=7,45,2

3.6 Development of Forecasting System for Concentrated Heavy Rain in Summer.=8,46,2

Chapter 3 Methods and results of this research=10,48,1

3.1 Production of input data for regional model using satellite data=10,48,1

1. Production of sea surface temperature using GMS-5 data=10,48,13

2. Production of vertical profile of temperature and humidity using NOAA-16/ATOVS data=22,60,21

3. Production of snowcover using NOAA/AVHRR data=43,81,21

3.2 The research for the sensitivity test of analysis and forecast using KLAPS=64,102,1

1. The acquisition and production of assimilated high-resolution KORMEX-IOP data=64,102,8

2. The estimation of sensitivity of each observational data=72,110,23

3. The application of KLAPS for 4DDA=95,133,2

3.3 Development of base technology of regional coupled model=97,135,1

1. Development of hybrid parallelism based on cluster=97,135,13

2. Development of base technology of Land-Surface processor=110,148,47

3. Development of base technology of coupled atmosphere-wave model=157,195,10

3.4 Improvement of GDAPS performance=167,205,1

1. Sensitivity of GDAPS to increased vertical resolution=167,205,8

2. Development of semi-Lagrangian method for water vapor transport=174,212,16

3. Improvement of computation method for pressure gradient force=190,228,8

4. Development of Convective momentum transport procedure in GDAPS=198,236,7

3.5 Development of a parallel atmospheric model for hi-resolution prediction on the Korean Peninsular=205,243,1

1. Overview=205,243,2

2. GDAPS parallelizing structure=206,244,2

3. Parallel programming=208,246,4

4. Parallelization of GDAPS=212,250,10

5. The results of the research=222,260,1

3.6 Development of Forecasting System for Concentrated Heavy Rain in Summer=223,261,1

1. Introduction=223,261,2

2. Results=224,262,90

Chapter 4 Achievements and contributions=314,352,6

Chapter 5 Application plans=320,358,4

Chapter 6 References=324,362,16

그림목차

Fig. 1.1. Accuracy of weather prediction.=2,40,1

Fig. 2.1. The flow diagram of the KLAPS data ingest.=5,43,1

Fig. 3.1.1. The number of collocation data for different parameters (month,DT,buoy SST and SZA).=12,50,1

Fig. 3.1.2. Bias of GMCSST,RMCSST and RQSST as a function of month (a),DT (b),buoy 557 (c) and SZA (d).=14,52,1

Fig. 3.1.3. Similar to Fig.3.1.2 execpt for RMSE.=14,52,1

Fig. 3.1.4. DT as a function of water vapor amount (x:wintertime,*:summer-time).=15,53,1

Fig. 3.1.5. DT from GMS-5 IR as a function of month(time).=16,54,1

Fig. 3.1.6. Similar to Fig. 3.1.2 except for RPFSST.=18,56,1

Fig. 3.1.7. Similar to Fig. 3.1.3 except for RPFSST.=18,56,1

Fig. 3.1.8. Bias of GMCSST,RMCSST,RQSST and RPFSST as a function of month (a),DT (b),buoy SST (c) and SZA (d) for 2001.=21,59,1

Fig. 3.1.9. Similar to Fig. 7 except for RMSE.=21,59,1

Fig. 3.1.10. Overall procedure of ATOVS packages implemented in KMA.=30,68,1

Fig. 3.1.11. The morning orbit coverage of NOAA-16.=35,73,1

Fig. 3.1.12. The number of retrieved point from NOAA-16/ATOVS during August 2001.=35,73,1

Fig. 3.1.13. An example of the temperature distribution (upper) and dew point temperature distribution (lower) obtained from NOAA-16/ATOVS (Left:850 hPa,Right:700 hPa).=36,74,1

Fig. 3.1.14. The time series of the number of retrieved profiles from NOAA-16 data received at KMA.=37,75,1

Fig. 3.1.15. Vertical distribution of RMSE and Bias averaged for all stations for 06 UTC and 18 UTC for spatial domain of 1˚. Red,orange,and blue lines are for clear,cloudy,and all sky conditions,respectively.=38,76,1

Fig. 3.1.16. Time series of the monthly mean (box) and standard deviation (bar) of residual,radiosonde temperature-ATOVS temperature,at four different altitudes,300,500,850,and 950 hpa. Numbers denote the number of data points used.=39,77,1

Fig. 3.1.17. The validation result of water vapor mixing ratio.=40,78,1

Fig. 3.1.18. Divided Land and Ocean Radiosonde station.=41,79,1

Fig. 3.1.19. Vertical distribution of RMSE and Bias averaged for all stations for Land and Ocean for spatial domain of 1˚. Red and Blue lines are for Land,Ocean,respectively.=41,79,1

Fig. 3.1.20. VIS1(Channel 1) reflectance for NOAA-16 AVHRR on January 23,2002. The area snow covered includes most of Mongolia and Northern China.=45,83,1

Fig. 3.1.21. Calculated NDSI value for the same time of Figure 3.1.20.=46,84,1

Fig. 3.1.22. NDSI distribution for four selected region in Figure 3.1.21. AREA1 and AREA2 are snow covered area,on the contrary,AREA3 and AREA4 are cloudy area.=47,85,1

Fig. 3.1.23. VIS1 reflectance imagery after the first VIS1 reflectivity threshold test for eliminating non-snow covered and non-cloudy region from Figure 3.1.20.=48,86,1

Fig. 3.1.24. NIR (Channel 3a) reflectance for NOAA-16/AVHRR for the same time and region of Figure 3.1.20.=49,87,1

Fig. 3.1.25. NIR reflectance imagery after NIR threshold test for discrimination between snow covered and cloudy region.=50,88,1

Fig. 3.1.26. NDIFI imagery for the same time and region of Figure 1.=51,89,1

Fig. 3.1.27. The sequence of NDIF1 test for discrimination between bare soil,desert,forest and cloud,snow-cover. NDIF1 value is drawn only between -0.1 and 0.2.=52,90,1

Fig. 3.1.28. The procedure for snow detection algorithm.=53,91,1

Fig. 3.1.29. The final snow cover imagery on January 23,2002. The gray region is snow-coverd.=54,92,1

Fig. 3.1.30. Zoom in imagery for Korean Peninsula on Janaury 23,2002.=55,93,1

Fig. 3.1.31. NDSI imagery on January 1,2002. The value larger than 0.5 is gray colored.=57,95,1

Fig. 3.1.32. NDSI distribution of mid-west area (left) and Kangwon area (right) for the yellow boxed region in Fig. 3.1.31.=59,97,1

Fig. 3.1.33 Terra/MODIS RGB composite imagery for the same day of Fig. 3.1.31.=59,97,1

Fig. 3.1.34. Terra/MODIS RGB composite imagery and NOAA/AVHRR NDSI on Dec. 17,2001.=60,98,1

Fig. 3.1.35. Same as Figure 3.1.34 except for Jan. 31,2002.=61,99,1

Fig. 3.1.36. NOAA/AVHRR VIS-IR composite imagery and NDSI on Dec. 26,2002.=62,100,1

Fig. 3.2.1. The milestone of the GAME enhanced observing experiments on 1998.=65,103,1

Fig. 3.2.2. The locations of the special and existing radiosonde sites.=67,105,1

Fig. 3.2.3. The 200 hPa height and wind field at 0000 UTC Jun 1998 (a) weather map (b) the 1st guess field of JMA 2.5˚ reanalysis data (c) (b)+ radiosonde observational data and (d) the same as (b) except for the JMA 0.5625˚ reanalysis data.=69,107,1

Fig. 3.2.4. Same as Fig. 3.2.3 except for the surface pressure and wind field.=70,108,1

Fig. 3.2.5. (a) The 3-hourly accumulated precipitation amounts,(b) surface weather map and (c) the GMS IR image at 0900 UTC Jun. 30,1998.=70,108,1

Fig. 3.2.6. The 3 hourly accumulated rainfall and sea level pressure with respect to Table 3.2.2 valid on 0900 UTC Jun 30,1998.=71,109,1

Fig. 3.2.7. The wind vector distribution of the ACARS observation data on 13 August 2001. Asterisks represent radiosonde sites.=74,112,1

Fig. 3.2.8. The 500 hPa ATOVS temperature distribution at 0600 UTC 31 October 2001.=75,113,1

Fig. 3.2.9. Same as Fig. 3.2.8 except for CDW wind data.=76,114,1

Fig. 3.2.10. The 100 km searching radius of data pairs' between radiosonde sites and other observational data.=77,115,1

Fig. 3.2.11. The vertical distributions of RMSE,BIAS,and data pairs' number for ACARS,CDW,ATOVS data.=79,117,2

Fig. 3.2.12. (a) Scatter plot of each data (ACARS,CDW,ATOVS) vs. radiosonde data(left panels),(b) separation distance vs. difference of radiosonde and each data values according to altitude ranges (1:650~1000,2:450~600,3:150~400 hPa)(right panels).=82,120,4

Fig. 3.2.13. (a) Vertical,and (b) time series data verification graph operated on DSRPS homepage (Daejeon Short Range Prediction System,http://190.1.40.111/~expo/).=87,125,1

Fig. 3.2.14. Temperature and wind analysis process in KLAPS.=88,126,1

Fig. 3.2.15. The distribution of the asynoptic observational data.=90,128,1

Fig. 3.2.16. The 200 hPa temperature and height of the first guess field and ACARS observation data at 0000 UTC August 13,2001.=90,128,1

Fig. 3.2.17. (a) Weather map,(b) analyzed height and temperature field,and (c) analyzed wind vector and speed field at 0000 UTC Aug. 13,2001.=92,130,1

Fig. 3.2.18. (a) AWS 3-hour accumulated precipitation amounts,(b) the 850 hPa temperature difference field between 1st guess and analyzed field,simulated 3-hour accumulated precipitation amounts from (c) CNTL run and (d) E_ATOVS run valid on 1200 UTC Au=93,131,1

Fig. 3.2.19. (a) AWS 3-hourly accumulated precipitation amounts,(b) GMS IR image and (c) surface weather map valid on 1800 UTC Aug. 14 2001.=94,132,1

Fig. 3.2.20. Simulated 3-hour accumulated precipitation amounts from (a) E_ACARS run,(b) E_ATOVS run,(c) E_CDW run and (d) CNTL run valid on 1800 UTC August 14 2001.=94,132,1

Fig. 3.2.21. The flow diagram of pre and post processes of the KLAPS for the 4DDA (Four Dimensional Data Assimilation).=95,133,1

Fig. 3.2.22. The cycle of the SRAPS data assimilation.=96,134,1

Fig. 3.3.1. Structure diagram for the OpenMP.=99,137,1

Fig. 3.3.2. Structure diagram for the Hybrid.=100,138,1

Fig. 3.3.3. (a) Runtime results for IBM SP-1,Cray T3e and PC-cluster. (b) Load balance of 2X2 and 4X4 case for IBM SP-1,Cray T3e and PC-cluster.=101,139,1

Fig. 3.3.4. Structure diagram for the 3rd generation cluster.=103,141,1

Fig. 3.3.5. The 3rd generation cluster in Daejun Regional Meteorological Office. (a) Front of the cluster,(b) Backward of the cluster.=104,142,1

Fig. 3.3.6. Structure diagram for the hybrid.=105,143,1

Fig. 3.3.7. Structure diagram for the channel bonding.=105,143,1

Fig. 3.3.8. Compare with Fast Ethernet and Channel bonding.=107,145,1

Fig. 3.3.9. Compare with MPI and Hybrid.=108,146,1

Fig. 3.3.10. Compare with each hardware.=109,147,1

Fig. 3.3.11. Schematic representation of the processes in the land-surface model (Chen et al.,2001).=112,150,1

Fig. 3.3.12. Sensitivity of surface heat fluxes to the initial soil moisture for two dry point (Chen et al.,2001).=117,155,1

Fig. 3.3.13. The model domains and their topography. Contour interval is 200 m. The horizontal distance of grids is 45 km,15 km,5 km respectively.=119,157,1

Fig. 3.3.14. The distribution of soil category in the finest domain.=120,158,1

Fig. 3.3.15. Geopotential height at 500 hPa and sea level press in (a) 12 UTC 15 (b) 00 UTC 16 (c) 12 UTC 16,and (d) 00 UTC 17 June 1999.=125,163,2

Fig. 3.3.16. The distribution of 3 hourly accumulated rainfall observed by AWS in (a) 00 UTC 16 (b) 06 UTC 16 (c) 12 UTC 16 (d) 18 UTC 16 (e) 00 UTC 17 and (f) 06 UTC 17 June 1999. Contour interval is 5 mm.=127,165,1

Fig. 3.3.17. Same as Fig. 3.3.16 except for by simulation of LSM experiment.=128,166,1

Fig. 3.3.18. Same as Fig. 3.3.16 except for by simulation of SLAB experiment.=129,167,1

Fig. 3.3.19. Difference between the temperature observed by AWS and 2 m temperature simulated by LSM experiment in (a) 00 UTC 16 (b) 06 UTC 16 (c) 12 UTC 16 (d) 18 UTC 16 (e) 00 UTC 17 and (f) 06 UTC 17 June 1999. Contour interval is 1℃. Dotted line mean=130,168,1

Fig. 3.3.20. Same as Fig. 3.3.19 except for SLAB.=131,169,1

Fig. 3.3.21. Global solar radiation by Pyranometer at 22 conventional station in (a) 06 UTC 16 and (b) 06 UTC 17 June 1999. Contour interval is 100 W/M.=132,170,1

Fig. 3.3.22. Same as Fig. 3.3.21 except for simulation of LSM..=133,171,1

Fig. 3.3.23. Same as Fig. 3.3.21 except for simulation of SLAB.=134,172,1

Fig. 3.3.24. Geopotential height at 500 hPa and sea level press in (a) 00 UTC 8 (b) 12 UTC 8 (c) 00 UTC 9 and (d) 12 UTC 9 August 2000.=137,175,2

Fig. 3.3.25. Geopotential height at 500 hPa and sea level press simulated by LSM experiment in (a) 00 UTC 8 (b) 12 UTC 8 (c) 00 UTC 9 and (d) 12 UTC 9 August 2000.=139,177,2

Fig. 3.3.26. Same as Fig. 3.3.25,except for SLAB experiment.=141,179,2

Fig. 3.3.27. Images of enhanced infrared images by GMS satellite in (a) 03 UTC (b) 06 UTC (c) 09 UTC and (d) 12 UTC 8 August 2000.=143,181,1

Fig. 3.3.28. The distribution of 3 hourly accumulated rainfall observed by AWS in (a) 00 UTC (b) 03 UTC (c) 06 UTC (d) 09 UTC (e) 12 UTC and (f) 15 UTC 8 August 2000. Contour interval is 5 mm.=144,182,1

Fig. 3.3.29. Same as Fig. 3.3.28,except for simulation by LSM experiment.=145,183,1

Fig. 3.3.30. Same as Fig. 3.3.28,except for simulation by SLAB experiment.=146,184,1

Fig. 3.3.31. Surface latent heat flux from simulation of LSM in (a) 06 UTC and (b) 09 UTC 8 August 2000. Contour interval is 50 WM².=147,185,1

Fig. 3.3.32. Same as Fig. 3.3.31,except for surface sensible heat flux.=148,186,1

Fig. 3.3.33. Same as Fig. 3.3.32,except for simulation of SLAB experiment=149,187,1

Fig. 3.3.34. Same as Fig. 3.3.33,except for surface sensible heat flux.=150,188,1

Fig. 3.3.35. Difference between the temperature observed by AWS and 2 m temperature simulated by LSM experiment in (a) 00 UTC 8 (b) 06 UTC 8 (c) 12 UTC 8 (d) 18 UTC 8 (e) 00 UTC 9 and (f) 06 UTC 9 August 2000. Contour interval is 1℃. Dotted line means ne=151,189,1

Fig. 3.3.36. Same as Fig. 3.3.35 except for simulation of SLAB experiment.=152,190,1

Fig. 3.3.37. The RMSE and BIAS of surface temperature between simulation and observation on case of 16 June 1999. The value are meaned over 79 station under KMA. LSM and SLAB are the result from LSM experiment and SLAB experiment respectively.=154,192,1

Fig. 3.3.38. Same as Fig. 3.3.37 except for case of 8 August 2000.=155,193,1

Fig. 3.3.39. Same as Fig. 3.3.37 except for case of 6 October 1999.=155,193,1

Fig. 3.3.40. The multi-processor loading program of MM5 (ymm) and WW3 (w3shel) using single executable.=158,196,1

Fig. 3.3.41. The procedures of sending wind (10 m) from MM3 to WW3=160,198,1

Fig. 3.3.42. The receiving procedures of wind (10 m) from MM5=160,198,1

Fig. 3.3.43. The procedures of sending roughness length (mm5z0tmp) from WW3 to MM5.=161,199,1

Fig. 3.3.44. The procedures of receiving roughness length (mm5z0tmp) from WW3 in MRF PBL.=161,199,1

Fig. 3.3.45. Domains of Coupled MM5/WW3. Dot points denote WW3 grid (0.25˚X0.25˚).=162,200,1

Fig. 3.3.46. Rainfall and sea level pressure of (a) uncoupled MM5 and (b) Coupled MM5/WW3.=164,202,1

Fig. 3.3.47. (a) Sensible heat flux (Wm²) of the 2nd domain of uncoupled MM5 and (b) the difference of sensible heat flux between MM5 and Coupled MM5/WW3.=165,203,1

Fig. 3.3.48. Same as in Fig. 3.3.47 except latent heat flux.=166,204,1

Fig. 3.4.1. Model levels of GDAPS_T106L30 (left) and GDAPS_T106L40 (right).=169,207,1

Fig. 3.4.2. ECMWF reanalysis and difference fields of 500 hPa geopotential height (CNTL:control run with L30,L40M:test run with increased vertical resolution).=170,208,1

Fig. 3.4.3. ECMWF reanalysis and difference fields of zonal mean u wind (CNTL:control run with L30,L40M:test run with increased vertical resolution).=171,209,1

Fig. 3.4.4. Time series of RMSEs (CNTL:control run with L30,L40M:test run with increased vertical resolution).=173,211,1

Fig. 3.4.5. Schematic diagram of moisture circulation.=175,213,1

Fig. 3.4.6. Schematic diagram of Semi-Lagrangian method in spectral model.=180,218,1

Fig. 3.4.7. Temperature field at 500 hPa in JJA(1999) of (a) ECMWF,(b) difference of control run from ECMWF,(c) same as (b) except for SLTQ,and (d) same as (b) except for SLTQ and control run.=181,219,1

Fig. 3.4.8. Same as Fig. 3.4.7 except for Geopotential Height at 500 hPa.=182,220,1

Fig. 3.4.9. Zonal mean temperature in JJA of (a) difference of control run from ECMWF,(b) Same as (a) except for SLTQ,and (c) same as (a) except for SLTQ and control run.=183,221,1

Fig. 3.4.10. Same as Fig. 3.4.9 except for zonal mean wind.=184,222,1

Fig. 3.4.11. Same as Fig 3.4.9 except for mixing ratio.=185,223,1

Fig. 3.4.12. Comparizon of precipitation between (a) CMAP,(b) control,(c) semi-Lagrangian,and (d) difference of control and semi-Lagrangian.=186,224,1

Fig. 3.4.13. RMSE (Root mean square error) of temperature at 500 hPa of (a) Global,(b) northern hemisphere,(c) equator,and (d) southern hemisphere.=187,225,1

Fig. 3.4.14. Same as Fig. 3.4.13 except for mixing ratio at 850 hPa.=188,226,1

Fig. 3.4.15. Temperature field at 500 hPa in JJA(1999) of (a) ECMWF,(b) difference of ECMWF from control run,(c) same as (b) except for PGFX,and (d) same as (b) except for PGFX and control run.=193,231,1

Fig. 3.4.16. Same as Fig. 3.4.15 except for geopotential height at 500 hPa.=194,232,1

Fig. 3.4.17. Same as in Fig. 3.4.15 except for zonal mean temperature.=195,233,1

Fig. 3.4.18. Same as Fig 3.4.15 except for zonal mean wind.=195,233,1

Fig. 3.4.19. RMSE(Root Mean Square Error) of geopotential height at 500 hPa of (a) Global,(b) northern hemisphere,(c) equator,and (d) southern hemisphere.=196,234,1

Fig. 3.4.20. Schematic procedure for adjustment of momentum transport to GDAPS.=202,240,1

Fig. 3.4.21. 72 hour forecast fields of (a) precipitation rate (mm/day),(b) difference of 200hPa u wind (m/s) from that without momentum transport,and (c) same with (b) except for 850hPa.=203,241,1

Fig. 3.5.1. GDAPS parallelizing structure=207,245,1

Fig. 3.5.2. Shared memory parallel computer=209,247,1

Fig. 3.5.3. Distributed memory computer=209,247,1

Fig. 3.5.4. Function of HPCL=213,251,1

Fig. 3.5.5. GDAPS program structure=213,251,1

Fig. 3.5.6. GDAPS execution profile=214,252,1

Fig. 3.5.7. HPC_READ() function=216,254,1

Fig. 3.5.8. HPC_WRITE() function=217,255,1

Fig. 3.5.9. 1-dimensional data decomposition along j-direction=218,256,1

Fig. 3.5.10. Global indices and local indices=219,257,2

Fig. 3.5.11. HPC_COMM_PATTERN() function=221,259,1

Fig. 3.6.1. The interannual variation of heavy rain frequency over 1981~2000.=225,263,1

Fig. 3.6.2. The interannual variation of heavy rain frequency over 1981~2000.=225,263,1

Fig. 3.6.3. The distribution of heavy rain frequency over 1981~2000.=227,265,1

Fig. 3.6.4. The frequency of heavy rain at Jun~Sep. over 1981~2000.=228,266,1

Fig. 3.6.5. The frequency of heavy rain at Jun~Sep. over 1981~2000.=228,266,1

Fig. 3.6.6. The distribution types of heavy rain.=230,268,1

Fig. 3.6.7.(a). The distribution types of heavy rain (all station).=236,274,7

Fig. 3.6.7.(b). The distribution types of heavy rain (mid-land).=243,281,2

Fig. 3.6.8.(a). The distribution types of heavy rain (mid west coastal types).=245,283,4

Fig. 3.6.8.(b). The distribution types of heavy rain (Kyoungki inland).=249,287,4

Fig. 3.6.8.(c). The distribution types of heavy rain (North Kyoungki).=253,291,1

Fig. 3.6.9.(a). The distribution types of heavy rain (southwest coastal types).=254,292,3

Fig. 3.6.9.(b). The distribution types of heavy rain (southwest coastal types).=257,295,2

Fig. 3.6.9.(c). The distribution types of heavy rain (southeast coastal types).=259,297,3

Fig. 3.6.10.(a). The distribution types of heavy rain (Gangwon inland types).=262,300,1

Fig. 3.6.10.(b). The distribution types of heavy rain (east sea coastal types)=263,301,1

Fig. 3.6.11. The heights of correlation analysis.=266,304,1

Fig. 3.6.12. The area of correlation analysis.=267,305,1

Fig. 3.6.13. Correlation map of the meteorological elements and heavy in all station type.=269,307,1

Fig. 3.6.14. Same as Fig. 3.6.13 except for Kyungki inland type.=270,308,1

Fig. 3.6.15. Same as Fig. 3.6.13 except for North Kyungki type.=271,309,1

Fig. 3.6.16. Same as Fig. 3.6.13 except for Mid west type.=272,310,1

Fig. 3.6.17. Same as Fig. 3.6.13 except for South-west coastal type.=273,311,1

Fig. 3.6.18. Same as Fig. 3.6.13 except for Honam inland type.=274,312,1

Fig. 3.6.19. Same as Fig. 3.6.13 except for East sea type.=275,313,1

Fig. 3.6.20. Same as Fig. 3.6.13 except for Kangwon inland type.=276,314,1

Fig. 3.6.21. Five-day averaged precipitation (mm/day) by CMAP data on early June (4~8 days) 1998.=286,324,1

Fig. 3.6.22. Five-day averaged precipitation (mm/day) and moisture flux (kg/m²/s) by RAMS model on early June (4~8 days) 1998.=287,325,1

Fig. 3.6.23. Geopotential height at 850 hPa on June 10 (12Z),1998.=288,326,1

Fig. 3.6.24. Accumulated precipitation (mm/day) during 10 days on early June (1~10 days) 1998.=288,326,1

Fig. 3.6.25. Wind fields (m/s) and water vapor (g/kg) at 850 hPa on June 10 (12Z),1998.=289,327,1

Fig. 3.6.26. Moisture flux (kg/m²/s) at 850 hPa on June 10 (12Z),1998.=290,328,1

Fig. 3.6.27. Isosurface of zonal flow (jet stream,m/s) on June 10 (12Z),1998=291,329,1

Fig. 3.6.28. Vertical distributions of water vapor (kg/kg) and zonal flow (m/s) on June 10 (12Z),1998.=291,329,1

Fig. 3.6.29. Five-day averaged TBB(℃) by infrared 1 on early June (4~8 days) 1998.=292,330,1

Fig. 3.6.30. Five-day averaged precipitation(mm/day) by CMAP data on late June (24~28 days) 1998.=293,331,1

Fig. 3.6.31. Five-day averaged precipitation (mm/day) and moisture flux (kg/m²/s) by RAMS model on late June (24~28 days) 1998.=293,331,1

Fig. 3.6.32. Geopotential height at 850 hPa on June 30 (12Z),1998.=294,332,1

Fig. 3.6.33. Accumulated precipitation (mm/day) during 10 days on late June (20~30 days) 1998.=295,333,1

Fig. 3.6.34. Wind fields (m/s) and water vapor (g/kg) at 850 hPa on June 30 (12Z),1998.=296,334,1

Fig. 3.6.35. Moisture flux (kg/m²/s) at 850 hPa on June 30 (12Z),1998.=296,334,1

Fig. 3.6.36. Isosurface of zonal flow (jet stream,m/s) on June 30 (12Z),1998.=298,336,1

Fig. 3.6.37. Vertical distributions of water vapor (kg/kg) and zonal flow (m/s) on June 30 (12Z),1998.=298,336,1

Fig. 3.6.38. Five-day averaged TBB (℃) by infrared 1 on late June (24~28 days) 1998.=299,337,1

Fig. 3.6.39. Five-day averaged precipitation (mm/day) by CMAP data on late July (24~28 days) 1993.=299,337,1

Fig. 3.6.40. Five-day averaged precipitation (mm/day) and moisture flux (kg/m²/s) by RAMS model on late July (24~28 days) 1993.=300,338,1

Fig. 3.6.41. Geopotential height at 850 hPa on July 30 (12Z),1993.=301,339,1

Fig. 3.6.42. Accumulated precipitation (mm/day) during 10 days on late July (20~30 days) 1993.=301,339,1

Fig. 3.6.43. Wind fields (m/s) and water vapor (g/kg) at 850 hPa on July 30 (12Z),1993.=302,340,1

Fig. 3.6.44. Moisture flux (kg/m²/s) at 850 hPa on July 30 (12Z),1993.=303,341,1

Fig. 3.6.45. Isosurface of zonal flow (jet stream,m/s) on July 30 (12Z),1993.=303,341,1

Fig. 3.6.46. Vertical distributions of water vapor (kg/kg) and zonal flow (m/s) on July 30 (12Z),1993.=304,342,1

Fig. 3.6.47. Five-day averaged TBB (℃) by infrared 1 on late July (24~28 days) 1993.=304,342,1

Fig. 3.6.48. Spatial distribution of the variance contained in percentage by the 10-20 day period oscillations. A36 average for all the 23 years;D36 average for the 4 deficient monsoon years;E36 average for the 4 excess monsoon years.=309,347,1

Fig. 3.6.49. Same as Fig. 3.6.48 but for the 30-60 day period oscillations. Relation with the extreme phases of the ENSO=309,347,1

Fig. 3.6.50. Percentage variance contained in the 10-20 day band for the 3 El-Nino (Nino12) and 3 La-Nina (Nina12) cases;30-60 day band for El-Nino (Nino36) and La-Nina (Nina36) cases (Nino12,Nina12,Nino36,Nina36).=311,349,1

Fig. 3.6.51. Interannual variability of the standardized variance in 10-20 day band (P12),variance in 30~60 day band (P36) and the Korean summer monsoon rainfall (KMR).=312,350,1

칼라목차

jpg

Fig. 3.1.11. The morning orbit coverage of NOAA-16.=35,73,1

Fig. 3.1.12. The number of retrieved point from NOAA-16/ATOVS during August 2001.=35,73,1

Fig. 3.1.13. An example of the temperature distribution (upper) and dew point temperature distribution (lower) obtained from NOAA-16/ATOVS (Left:850 hPa,Right:700 hPa).=36,74,1

Fig. 3.1.14. The time series of the number of retrieved profiles from NOAA-16 data received at KMA.=37,75,1

Fig. 3.1.20. VIS1(Channel 1) reflectance for NOAA-16 AVHRR on January 23,2002. The area snow covered includes most of Mongolia and Northern China.=45,83,1

Fig. 3.1.21. Calculated NDSI value for the same time of Figure 3.1.20.=46,84,1

Fig. 3.1.23. VIS1 reflectance imagery after the first VIS1 reflectivity threshold test for eliminating non-snow covered and non-cloudy region from Figure 3.1.20.=48,86,1

Fig. 3.1.24. NIR (Channel 3a) reflectance for NOAA-16/AVHRR for the same time and region of Figure 3.1.20.=49,87,1

Fig. 3.1.25. NIR reflectance imagery after NIR threshold test for discrimination between snow covered and cloudy region.=50,88,1

Fig. 3.1.26. NDIFI imagery for the same time and region of Fig. 3.1.20.=51,89,1

Fig. 3.1.27. The sequence of NDIF1 test for discrimination between bare soil,desert,forest and cloud,snow-cover. NDIF1 value is drawn only between -0.1 and 0.2.=52,90,1

Fig. 3.1.29. The final snow cover imagery on January 23,2002. The gray region is snow-coverd.=54,92,1

Fig. 3.1.30. Zoom in imagery for Korean Peninsula on Janaury 23,2002.=55,93,1

Fig. 3.1.31. NDSI imagery on January 1,2002. The value larger than 0.5 is gray colored.=57,95,1

Fig. 3.1.32. NDSI distribution of mid-west area (left) and Kangwon area (right) for the yellow boxed region in Fig. 3.1.31.=59,97,1

Fig. 3.1.33. Terra/MODIS RGB composite imagery for the same day of Fig. 3.1.31.=59,97,1

Fig. 3.1.34. Terra/MODIS RGB composite imagery and NOAA/AVHRR NDSI on Dec. 17,2001.=60,98,1

Fig. 3.1.35. Same as Figure 3.1.34 except for Jan. 31,2002.=61,99,1

Fig. 3.1.36. NOAA/AVHRR VIS-IR composite imagery and NDSI on Dec. 26,2002.=62,100,1

Fig. 3.4.9. Zonal mean temperature in JJA of (a) difference of control run from ECMWF,(b) Same as (a) except for SLTQ,and (c) same as (a) except for SLTQ and control run.=183,221,1

Fig. 3.4.10. Same as Fig. 3.4.9 except for zonal mean wind.=184,222,1

Fig. 3.4.12. Comparizon of precipitation between (a) CMAP,(b) control,(c) semi-Lagrangian,and (d) difference of control and semi-Lagrangian.=186,224,1

표목차

Table 3.1.1. Coeffiecients and accuracies for each SST algorithms.=13,51,1

Table 3.1.2. Regression coefficients of RPFSST for two different time periods and accuracies of RPFSST (RPFSST1:Jan.~May,RPFSST2:Aug.~Oct.)=17,55,1

Table 3.1.3. Monthly accuracies of different SST algorithms for 2001.=19,57,1

Table 3.1.4. The serise of NOAA satellites.=23,61,1

Table 3.1.5. The 3 times of instrument noise for each channels of HIRS/3 and AMSU.=33,71,1

Table 3.1.6. Snow depth ground station observation data in South Korea on January 23,2002.=56,94,1

Table 3.1.7. Snow depth ground station observation data in South Korea at 15 UTC on January 1,2002.=58,96,1

Table 3.1.8. Snow depth ground station observation data in South Korea on January 31,2002.=62,100,1

Table 3.2.1. The list of KORMEX data on 1998.=66,104,1

Table 3.2.2. The experimental design for the sensitivity test of the KORMEX data.=67,105,1

Table 3.2.3. The number of surface and upper level observational data for the SRAPS domain in 3 hour interval.=72,110,1

Table 3.2.4. The accuracy comparisons between radiosonde data and other data (ACARS,ATOVS and CDW) for 3 months.=78,116,1

Table 3.2.5. The experimental design for the sensitivity test of each asynoptic data.=89,127,1

Table 3.3.1. Compare with serial source and OpenMP source.=99,137,1

Table 3.3.2. Hardware and software for FRL´s Linux cluster.=101,139,1

Table 3.3.3. Characteristics of 2nd generation cluster.=102,140,1

Table 3.3.4. Characteristics of 3rd generation cluster.=103,141,1

Table 3.3.5. The configuration of MM5.=121,159,1

Table 3.5.1. Relationship between parallel computers and parallel programs.=211,249,1

Table 3.6.1. The frequency of heavy rain over 1981~2000.=224,262,1

Table 3.6.2. The frequency of heavy rain over 1981~2000.=226,264,1

Table 3.6.3. The variability and frequency no. of heavy rain over 1981~2000 (unit:station,mm/day).=227,265,1

Table 3.6.4. The cell types of heavy rain over 1981~2000.=229,267,1

Table 3.6.5. The heavy rain day (all station type).=231,269,1

Table 3.6.6. The heavy rain day (Mid-inland type).=232,270,1

Table 3.6.7. The heavy rain day (Mid west coastal type)=232,270,1

Table 3.6.8. The heavy rain day (Kyungki inland type).=233,271,1

Table 3.6.9. The heavy rain day (North Kyungki type).=233,271,1

Table 3.6.10. The heavy rain day (Southwest coastal type).=234,272,1

Table 3.6.11. The heavy rain day (Honam inland type).=234,272,1

Table 3.6.12. The heavy rain day (Southeast coastal type).=235,273,1

Table 3.6.13. The heavy rain day (East coastal type).=235,273,1