[보고서]동아시아 지역의 중규모 대류계/구름 규모 집중강수 메커니즘 및 모델링 연구

이동규

동아시아 지역의 중규모 대류계/구름 규모 집중강수 메커니즘 및 모델링 연구
Research on Mechanism and Modeling of Mesoscale Convective System and Cloud-Scale Heavy Precipitation over East Asia 원문보기

보고서 정보
주관연구기관	서울대학교 Seoul National University
연구책임자	이동규
참여연구자	차동현 , 김주완 , 최석진 , 하지현 , 최용한 , 진천실 , 엄대용 , 김경나 , 한경동 , 이재복
보고서유형	최종보고서
발행국가	대한민국
언어	한국어
발행년월	2010-04
과제시작연도	2009
주관부처	기상청
사업 관리 기관	기상청 Korea Meteorological Administration
등록번호	TRKO201000000725
과제고유번호	1365000781
DB 구축일자	2013-04-18
키워드	실시간 예측 시스템,예측성,집중강수,자료동화,3차원 변분 자료 동화,4차원 변분 자료 동화Real-time forecasting system,Predictability,Heavy rainfall,Data assimilation,3DV AR,4DV AR

초록 ▼

1. 1차년도
가. 실시간 예측 시스템의 동아시아 및 한반도에서 발생하는 집중강수에 대한 예측성 규명
- 실시간 예측 시스템의 예측성 검증: 계절별, 모의 영역별
- 2007년과 2008년 여름철 집중강수에 대한 강수 모의 예측성 검증
나. 자료 동화 시스템의 구축을 통한 실시간 예측 능력의 향상
- 3차원 변분 자료 동화 방법 (3DVAR)에서 사용되는 규모 길이 (scale length)의 조절: 레이더 자료 동화, AWS 자료 동화
- RUC 혹은 IAU를 이용한 3차원 변분 자료 동화 cycling 시 최적의 실험 설계 결정: assimilation window (동화 창)와 update frequency (갱신 간격) 결정
- 3차원 변분 자료 동화 cycling에 사용되는 초기화 방법의 비교: RUC, IAU
- 실시간 예측 시스템의 모의 영역 별 최적의 관측 자료와 자료 동화 기법 결정: GTS 자료, 윈드 프로파일러 자료, AWS 자료, 레이더 자료/3DVAR RUC, IAU
- 자료 동화 시스템 구축 후 향상된 예측성에 대한 정량적 분석: 2007년 여름 집중강수 사례에 대한 강수 예측성 검증
- 집중 강수 사례 모의에 있어 4차원 변분 자료 동화와 3차원 변분 자료 동화의 비교

Abstract ▼

A real-time forecasting system based a high-resolution mesoscale model, WRF (Weather Research and Forecasting) was established in October 2006. It is very important to quantify predictability of a real-time forecasting system in simulating heavy precipitation by using high-resolution observational data. Outputs from the real-time forecasting system were verified against high-resolution observational data for heavy precipitation cases that occurred in 2007/2008 summer. Quantitative results on predictability of the real-time forecasting system such as precipitation amount error and distance error can be used for reducing systematic errors of the system or developing advanced dynamical/physical schemes of the system.
Data assimilation is essential to improve quality of an initial condition of a high-resolution mesoscale model. Sensitivity experiments were conducted to determine optimal observational data and data assimilation method that enhance predictability of the real-time forecasting system. A real-time forecasting system with a data assimilation system showed improved predictability compared to the forecasting system without data assimilation, especially for heavy precipitation cases. Three-Dimensional Variational Data Assimilation (3DVAR) method should be modified to assimilate high-resolution observational data like radar data and AWS data in simulating heavy precipitation over the Korean Peninsula. In detail, length scales in 3DVAR method was tuned to reflect characteristics of the observational data and flow-dependent background error covariance was used implicitly in the Four-dimensional variational data assimilation (4DVAR) method.

목차 Contents

표지...1
제출문...2
보고서 요약서...3
요약문...4
SUMMARY...6
CONTENTS...7
목차...8
제 1 장 연구개발과제의 개요 ...9
제 1 절 연구개발의 목적 ...9
제 2 절 연구개발의 필요성 ...9
제 3 절 연구범위 및 연구 수행 방법 ...11
제 2 장 국내외 기술개발 현황 ...12
제 1 절 국내외 관련분야의 기술개발 현황 ...12
제 2 절 국내외 기술개발 현황에서 차지하는 위치 ...13
제 3 장 연구개발수행 내용 및 결과 ...14
제 1 절 1차년도 연구내용 및 결과 ...14
제 2 절 요약 및 결론 ...87
제 4 장 목표달성도 및 관련분야에의 기여도 ...89
제 1 절 연구개발목표의 달성도 ...89
제 2 절 평가의 착안점에 따른 목표달성도에 대한 자체 평가...89
제 3 절 관련분야 기술발전에의 기여도 ...90
제 5 장 연구개발결과의 활용계획 ...92
제 1 절 연구개발결과의 활용방안 ...92
제 2 절 연구개발의 기대성과 ...93
제 6 장 연구개발과정에서 수집한 해외과학기술정보 ...94
제 7 장 참고문헌 ...95

표/그림 (57)

표 Vertical distribution of temperature bias in DM1: (a) MAM, (b) JJA, (c) SON, and (d) DJF. Vertical distribution of temperature bias in DM1: (a) MAM, (b) JJA, (c) SON, and (d) DJF.
표 Vertical distribution of relative humidity bias in DM1: (a) MAM, (b) JJA, (c) SON, and (d) DJF.
표 RMSE and anomaly correlation of temperature and water vapormixing ratio in DM1.
표 Horizontal distribution of difference between 48-h forecast and FNLdata during summer season in DM1.
표 Spatial distribution of water vapor mixing ratio bias (differencebetween 36-h forecast and radiosonde data) during summer season in DM1.
표 ETS, TS, BS of forecast rainfall during 06h∼30h for the five thresholdvalues over the Korean peninsular in DM2.
표 Vertical distribution of temperature and water vapor mixingratio bias in DM2.
표 Spatial distribution of temperature and water vapor mixing ratio biasat 850 hPa and 500 hPa level in DM2.
표 ETS, TS, BS of forecast rainfall during 01h∼24h for the five thresholdvalues over the Korean Peninsular in DM3.
표 29 heavy rainfall cases of 2007/2008 summer.
표 Maximum rainfall amount error (mm) and maximum rainfall positionerror (km) of real-time forecasting system for 29 heavy rainfall cases in 2007/2008
표 Equitable threat scores (ETSs) for the four threshold values (5.0, 10.0, 25.0,50.0 mm) as a function of forecast time.
표 Bias scores (BSs) for the four threshold values (5.0, 10.0,25.0, 50.0 mm) as a function of forecast time.
표 O-B correlation from O-B statistics (solidline with filled circles) and from recursive filter with a length scale of 6 (2) km (dash-dotted line), 9 (4) km (dotted line), 18 (8) km (dashed line) for (a) radar radial velocity and (b) reflectivit
표 Simulated 6-hour accumulated rainfall distribution from21 UTC 24 to 03 UTC 25 July for (a) observation, (b) CON, (c) RUC, and (d) IAU experiments. Maximum rainfall point and amount (in mm) are also shown.
표 Time series of hourly rainfall amount from 21 UTC 24to 03 UTC 25 July for observation (black), IAU (red), RUC (green), and CON (blue) experiments.
표 Root Mean Squared Errors (RMSEs) of simulated radialvelocity for IAU (blue), RUC (green), and CON (red) experiments.
표 First eigenvector of the verticalcomponent of time-domain-averaged background
표 Scale length at the lowest model level of controlvariables.
표 Vertical zonal wind response for single AWS observation. (a), (b), (c)and (d) are the responses by NMC BE, NMC BE 0.5, NMC BE 0.7_0.3 and NMC BE 0.7_0.5_0.3 experiment.
표 Total precipitation between 2100 UTC 11 July and 0900 UTC 12 July 2006 in(a)observation, (b) CNTL, (c) NMC BE, (d) NMC BE 0.7, (e) NMC BE 0.5, (f) NMC BE 0.3 and (g) NMC BE 0.7_0.3 experiment.
표 Root Mean Squared Errors (RMSEs) of simulated radial velocity forfive sensitivity experiments, 1H_12MIN (magenta), 1H_30MIN (sky blue), 3H_12MIN (red), 3H_30MIN (blue), and 3H_60MIN (green).
표 Simulated 6-hour accumulated rainfall distributionfrom 21 UTC 24 to 03 UTC 25 July for (a) 3H_12MIN, (b) 3H_30MIN, (c) 1H_12MIN, and (d) 1H_30MIN. The contours starts from 10 mm and the interval is 20 mm. The areas where the rainfall amount exceeds 50 mm are shaded. The maximum rainfall point and amount (in mm) are also shown.
표 Time series of hourly rainfall amount from 21UTC 24 to 03 UTC 25 July for four sensitivity experiments, 1H_12MIN (magenta), 1H_30MIN (sky blue), 3H_12MIN (red), and 3H_30MIN (blue), and observation (black).
표 Simulated 6-hour accumulated rainfall distribution from21 UTC 24 to 03 UTC 25 July for (a) observation, (b) CON, (c) RUC, and (d) IAU experiments. Maximum rainfall point and amount (in mm) are also shown.
표 Time series of hourly rainfall amount from 21 UTC24 to 03 UTC 25 July for observation (black), IAU (red), RUC (green), and CON (blue) experiments.
표 Root Mean Squared Errors (RMSEs) of simulated radialvelocity for IAU (blue), RUC (green), and CON (red) experiments.
표 (a) Surface weather chart and (b) geopotential height (solid), equivalentpotential temperature (dashed), wind speed (shaded) and wind vectors for 850 hPa weather chart at 1800 UTC 11 July 2006.
표 Total precipitation (mm) between 2100 UTC 11 and 0900 UTC 12 July 2006 in(a) observation, (b) CNTL, (c) RADAR, (d) AWS and (e) RADAR+AWS experiment.
표 Comparison of threat scores for 12 h accumulatedprecipitation from 2100 UTC 11 to 0900 UTC 12 July in CNTL, RADAR, AWS and RADAR+AWS experiment.
표 (a) Surface, (b) 700, (c) 500 and (d) 200 hPa weather charts at 0000 UTC 4July 2007 from FNL analysis. (e) and (f) indicate local weather chart at 0300 UTC 04 July, and surface divergence and relative humidity at 0200 UTC 04 July, respectively.
표 Observed 3h accumulated rainfall, analysis field of sea level pressure and wind,and total precipitation between 0000 UTC and 0300 UTC 4 July and sea level pressure in CTRLD1, EXP1D1, EXP2D1 and EXP3D1.
표 Total precipitation between 0000 UTC and 0300 UTC 4 July in CTRLD2,EXP1D2, EXP2D2, EXP3D2, EXP4D2 and EXP5D2.
표 Total precipitation between 0000 UTC and 0300 UTC 4 July in observationfrom AWS, EXP1D3, EXP2D3 and EXP3D3.
표 Assimilation experiments using the RUC initialization and forecast periods.
표 WRF model description and Model domains.
표 Summary of experiments design in all domain.
표 Time-longitude (hovmueller) diagram of averagedsea-level-pressure and 850-hPa relative vorticity on the 32-35°N latitudinal band where the monsoon rainband was located (left), and the 3-h accumulated rainfall timeseries of station-averaged and area-averaged (34-39°N, 120-125°E) TRMM over Korea (right) during the one-month period.
표 Vertical distribution of mixing ratio and temperature biasesbetween radiosonde and the experiments at from 12-h (12 h forecast) to 36-h (36 h forcast) forecats per 6 hourly intervals in domain 1: (a, c) CTRLD1, (b, d) EXP1D1.
표 Horizontal distribution of daily mean rainfall duringKEOP-2007 in domain 2: (a) TRMM, (b) CTRLD2, (c) EXP1D2, (d) EXP2D2, and (e) EXP3D2.
표 ETSs from 01-h to 24-h rainfall forecast in domain 3 for rainfall ofthe entire period (ALL) and heavy rainfall cases (HR).
표 Same as in Table 2 except for BSs.
표 Diurnal variation of rainfall over southern Korea is analyzed usingthe model experiments in (a) domain 2 and (b) domain 3.
표 Total of 14 heavy precipitation cases that occurred during Changma period of2007.
표 Equitable threat scores (ETSs) for the four threshold values (5.0,10.0, 25.0, 50.0 mm) as a function of forecast time.
표 Bias scores (BSs) for the four threshold values (5.0, 10.0, 25.0, 50.0mm) as a function of forecast time.
표 Horizontal resolution, dimension, number of vertical levels, pressure at model top,and physical parameterization schemes for numerical experiments.
표 Assimilation window and update frequency for data assimilation experiments.
표 Initial time, forecast length, and summary of heavy rainfall cases occurred in 2006.
표 18-hour accumulated precipitation distribution from 18UTC 26 to 12 UTC 27 July 2006 for observations, NODA, 3DVAR, and 4DVAR experiments. Maximum precipitation point and its amount (in mm) are also shown.
표 Threat score (left panel) and bias score (right panel) of CONTROL, 3DVAR,and 4DVAR experiments for threshold values of 5, 10, 25, and 50 mm as a function of forecast time.
표 Statistics of 4DVAR and 3DVAR experiments.
표 Analysis increment of wind vector at 520 hPa level (left panel)and 940 hPa level (right panel) for 4DVAR experiment.
표 30-hour accumulated precipitation distribution from 12UTC 22 to 18 UTC 23 October 2006 for observations, NODA, 3DVAR, and 4DVAR experiments. Maximum precipitation point and its amount (in mm) are also shown.
표 Threat score (left panel) and bias score (right panel) of CONTROL, 3DVAR,and 4DVAR experiments for threshold values of 5, 10, 25, and 50 mm as a function of time.
표 Statistics of 4DVAR and 3DVAR experiments.
표 Analysis increment of temperature at 620 hPa level (left panel)and 880 hPa level (right panel) for 4DVAR experiment.

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연합인증

동아시아 지역의 중규모 대류계/구름 규모 집중강수 메커니즘 및 모델링 연구
Research on Mechanism and Modeling of Mesoscale Convective System and Cloud-Scale Heavy Precipitation over East Asia 원문보기