[보고서]동아시아 악기상의 관측 및 모델링 연구

이동규

동아시아 악기상의 관측 및 모델링 연구
Observation and Modeling Studies of High-impact Weather in East Asia 원문보기

보고서 정보
주관연구기관	서울대학교 산학협력단
연구책임자	이동규
참여연구자	임규호 , 이태영 , 홍성유 , 김현미 , 박록진
보고서유형	1단계보고서
발행국가	대한민국
언어	한국어
발행년월	2012-10
과제시작연도	2011
주관부처	교육과학기술부
사업 관리 기관	한국연구재단
등록번호	TRKO201300023461
과제고유번호	1345158765
DB 구축일자	2013-08-26
키워드	악기상/기후,악기상 관측 및 분석,악기상/기후예측모형(WRF,MPAS) 시스템 개발 및 활용,악기상/기후 전문가교육,아시아 허브High-impact weather and climate,Observation and analysis,Prediction model(WRF,MPAS) system development and application,Education of high-impact weather and climate,Asian hub

초록 ▼

2. 연구개발 내용 및 범위
수치모델을 이용한 악기상 예측을 향상시키기 위하여 NCAR의 차세대 기상예측 모델인 WRF와 MPAS 개발에 참여하여 역학·물리과정·자료동화·활용 과정을 공동 개발한다. 개발한 모델의 실시간 예측을 통해 동아시아, 한반도 악기상 예측의 test-bed로 활용하고 모델시스템의 실용화를 추진한다. 악기상 관측을 위한 국제 컨소시엄을 구성하여 동아시아 악기상 집중관측을 수행을 위한 기반을 조성한다. WRF 모델 기반의 지역기후모델을 구축하고 국가 미래기후변화 시나리오를 생산한다. 국제공동 워크숍, 모델링 및 관측 튜토리얼, 온라인 강의, 전문가 초청 강연 등을 개최하여 악기상 전문가를 양성한다. 이와 같은 공동연구를 통하여 한미기상기후센터(KUWCC)를 설립하고, MOU를 협정하여 국제협력을 강화하여 악기상 연구분야의 아시아 허브로 역할을 한다.

Abstract ▼

2. Contents and scope of the research
To improve the predictions of high-impact weather and climate using numerical models, KUWCC jointly participates in the development of Weather Research and Forecasting (WRF) model and global-mesoscale Model for Prediction Across Scales (MPAS), and develops new dynamic core, physical schemes, and data assimilation techniques of WRF model and MPAS. KUWCC builds a real-time forecasting system based on WRF model, and the outcomes are used for test-beds in the studies on high-impact weather and climate prediction. The center prepares an observation program of high-impact weathers in order to better understand and predict them. It develops a WRF-based regional climate model by configuring the best physics parameterization set. It holds international workshops, tutorials, online lectures and seminars to train modeling and observation experts for high-impact weather and climate research. KUWCC exchanges MOU to conduct joint research and plays a role in an East Asian hub of high-impact weather and climate researche.

목차 Contents

표지 ... 1
제출문 ... 2
보고서 요약서 ... 3
요약문 ... 4
SUMMARY ... 10
CONTENTS ... 17
목차 ... 19
List of Figures ... 21
List of Tables ... 27
제1장 연구개발 ... 28
제1절 : 연구개발의 목적 및 필요성 ... 28
제2절 : 국내외 기술개발 현황 ... 29
1. 국내 기술 개발 현황 ... 29
2. 국외 기술 개발 현황 ... 29
제3절 : 연구개발 내용 및 결과 ... 31
1. WRF 모형 기반의 예측 시스템 실시간 운영과 검증 ... 31
2. WRF 모델 및 3DVAR 자료동화 시스템을 이용한 중규모 예측 시스템 구축 ... 39
3. 고해상도 중규모 수치모형을 위한 독자적 수치기법 개발 ... 43
4. 중규모 수치모형에서 둥지격자의 영향에 관한 연구 ... 53
5. 악기상 예측에 있어 4차원 변분 자료 동화 방법의 효과 분석 ... 60
6. 앙상블 자료 동화 기법의 향상과 중규모 수치 모델을 이용한 검증 ... 65
7. 회귀적 최적 내삽법 ... 70
8. 최적 관측 전략을 이용한 악기상 역학 연구 ... 73
9. WRF 모델을 이용한 예보 관측 민감도 (FSO) 계산 ... 76
10. 미세물리과정 개선 ... 82
11. 행성경계층 과정 개선 ... 97
12. 얕은 대류 모수화 방안 개선 ... 103
13. 강수 과정에 미치는 에어로졸의 간접 효과 ... 109
14. 악기상 관측 컨소시엄 구성을 위한 워크숍 개최 ... 122
15. CORDEX 동아시아 지역 기후특성에 적합한 지역기후모델 환경구축 ... 123
16. 새로운 기후변화 시나리오를 이용한 현재 및 미래 지역기후모의 ... 129
17. 악기상 전문가 양성 및 국제 협력 ... 135
제4절 : 연구개발 목표달성도 및 관련분야에의 기여도 ... 136
1. 1차년도 연구 개발 목표달성도 및 관련분야에의 기여도 ... 136
2. 2차년도 연구 개발 목표달성도 및 관련분야에의 기여도 ... 137
제5절 : 연구개발결과의 활용 계획 ... 140
제2장 기관유치 및 운영 ... 142
제1절 : 해외연구기관 유치 목적 및 필요성 ... 142
제2절 : 공동연구센터 운영 현황 ... 143
제3절 : 해외연구기관의 협력 성과 ... 144
제4절 : 해외연구기관 유치 노력 및 성과 ... 147
제5절 : 공동연구센터 자립 및 활용계획 ... 147
제3장 기타 사항 ... 148
제1절 : 사업추진과정에서 수집한 해외과학기술정보 ... 148
제2절 : 연구시설 장비 현황 ... 148
1. 연구시설 현황 ... 148
2. 연구기자재 현황 (3천만원 이상) ... 149
3. 연구시설 및 장비 운영 현황 및 계획 ... 149
제3절 : 기타 사업성과 (붙임 3 참조) ... 149
제4절 : 참고문헌 ... 150
[붙임 1] 연차별 목표 및 달성도 현황 ... 157
[붙임 2] 예산 확보 및 활용 실적 ... 160
[붙임 3] 기타사업 성과 ... 164
[붙임 4] 지원조건 실적 ... 178

표/그림 (71)

표 Fig. 1.1 Averaged 24-h accumulated rainfall distribution of the observations, real-time forecast, and their difference for rainfall and heavy rainfall cases
표 Fig. 1.2 Probability density function for rainfall amount and heavy rainfall amount of theobservations, domain 1, and domain 3.
표 Fig. 1.4 Anomaly correlation coefficient of simulated 24-h accumulatedrainfall in domain 3 as a function of forecast time for rainfall and heavyrainfall cases.
표 Fig. 1.3 Scatter diagram between simulated rainfall amount and observed rainfallamount for area-averaged 24-h accumulated rainfall and maximum-point 24-haccumulated rainfall
표 Fig. 1.5 Diurnal variation of rainfall for the observed rainfall, total simulatedrainfall, convective simulated rainfall, and nonconvective simulated rainfall.
표 Fig. 1.6 Difference of horizontal wind and its speed at 850 hPa between FNL dataand real-time forecasts.
표 Fig. 1.7 Difference of Convective Available Potential Energy (CAPE) betweenFNL data and real-time forecasts.
표 Fig. 2.1 Scatter plot of background and observation (a) with and (b) without bias correction forchannel 7 of AMSU-A from METOP-2 satellite at 00 UTC 11 September, 2008. Time-series ofobservation-minus-background with/without bias correction is presented in (c).
표 Fig. 3.1 The stability area for the third-order Runge-Kutta (gray-dash),forth-order Runge-Kutta (blue-sash), (a) R21 and (b) L21 scheme
표 Fig. 3.2 Amplification factor for R21 scheme with (a) third-order (b) fourthorder(c) fifth-order and (d) sixth-order spatial differencing discretization.Contour interval is 0.05. The shaded region indicates where the scheme isunstable.
표 Fig. 3.3 The same as Fig. 3.2 except for L21
표 Fig. 3.4 (a) Accumulated rainfall and (b) surface weather chart and (c) satellite image for10 UTC July 2004
표 Fig. 3.5 Weather map charts (left panel) and numerical simulated results (right panel)for 12 UTC 10 (top panel), 00 UTC 11 (middle panel) and 12 UTC 11 (bottom panel)July 2004
표 Fig. 3.6 Simulated geopotential height (solid contour) and wind speed (shading) for 850hPa (left panel) and 200 hPa (right panel) at 12 UTC 10 (top panel), 00 UTC 11 (middlepannel) and 12 UTC 11 (bottom panel) July 2004. Marked circles in 200 hPa are locatedat the center of surface minimum pressure
표 Fig. 4.1 Domain settings for (a) EXP01, and (b) EXP02, EXP03.
표 Fig. 4.3 (a) Kinetic energy spectra of horizontal velocity. The red, green solidlines indicate spectra of one-way nested- and two-way nested simulation resultsfor EXP01. The Lindborg (1999) functional fit to MOZAIC aircraft observations ifalso plotted. (b) Kinetic energy spectra of vertical velocity for EXP01 simulation.The dashed and solid line indicate one-way nested and two-way nestedsimulation results.
표 Fig. 4.2 Track of Typhoon Muifa (2011) for RSMC best track data (black), one-waynested simulation result (red), and two-way nested simulation result (green) for EXP01
표 Fig. 4.4 Simulated radar reflectivity for one-way nested (left panel) and two-way nested(right panel) simulation results at T+40h for EXP01 (top panel) and EXP02 (bottompanel).
표 Fig. 4.5 Track of Typhoon Muifa for RSMC best track data (black solid line),one-way nested (colored dashed line) and two-way nested (colored solid line)simulation results for (a) domain 2 and (b) domain 3. Spectra of verticalvelocity from (a) domain 2 and (b) domain 3. The colored solid lines indicatetwo-way nested simulation results and the colored dashed lines indicateone-way nested simulation results
표 Fig. 5.1 18-h accumulated rainfall distribution from 18UTC 26 to 12 UTC 27 July 2006 for the observations,CONTROL, 3DVAR, FGAT, 4DVAR, and RUC experiments.
표 Fig. 5.2 Time series of hourly rainfall at Seoul for the observations, CONTROL, 3DVAR, FGAT, and 4DVAR experiments.
표 Fig. 5.3 Analysis increments of horizontal wind and equivalent potential temperature at 850hPa for 3DVAR, FGAT, and 4DVAR experiments
표 Fig. 5.4 SLP tendency as a function of time for 4DVAR andRUC experiments
표 Fig. 6.1 Lorentz 단순 모형을 통해 시간 적분한 모델의 결과 모식도
표 Fig. 6.2 Lorentz 단순 모형의 결과
표 Fig. 6.3 실험 도메인 및 설계
표 Fig. 6.4 강수 사례 모의 결과
표 Fig. 7.1 2003년 8월 6일 13UTC,GOES-9 합성 영상
표 Fig. 7.2 2003SUS 8월 6일 13UTC, 기상청 레이더 rain rate 영상
표 Fig. 7.3 회귀적 최적 내삽법을 이용한 분석 자료를 이용한 예측 오차의 변화. 음의 값은 대조군 실험에 비해 예측 오차 감소를 의미
표 Fig. 8.1 scatter plot
표 Fig. 8.2 태풍 루사에 의한 누적 강수 및 강수 시계열
표 Fig. 8.3 반응 함수(강릉 지역 강수)에 대한수증기장의 500hpa 민감도
$Fig. 9.1 Time-averaged statistics aggregated with each observation variable for (a) total observationimpact, (b) observation number, (c) normalized observation impact, and (d) fraction of beneficialobservation.$ 표 Fig. 9.1 Time-averaged statistics aggregated with each observation variable for (a) total observationimpact, (b) observation number, (c) normalized observation impact, and (d) fraction of beneficialobservation.
표 Fig. 9.2 Same as in figure 1 but for time-averaged statistics aggregated with each observation type.
표 Fig. 9.3 Same as in figure 1 but for time-averaged statistics aggregated with each AMSU-A channelfrom four satellites
표 Fig. 9.4 Vertical distribution of (a, d) time-averaged total observation impact, (b, e) observationnumber, and (c, f) normalized observation impact for SOUND (upper panel) and GEOAMV
표 Fig. 9.5 (a) Horizontal distribution of targeted region, (b) observation impact for observationvariable, (c) observation impact for observation type, and (d) horizontal distribution of normalizedobservation impact for SOUND at 12 UTC 11 September 2008
표 Fig. 10.1 (a) Observed 24-h accumulated precipitation (mm) valid at 00 UTC 15 July 2001 andsimulate results from the (b) WSM6 and (c) PLIN microphysics schemes.
표 Fig. 10.2 (a) Observed 36-hr accumulated precipitation (mm) ending at 1200 UTC 05 March 2004and simulate results from the (b) WSM6 and (c) WSM6_NEW microphysics schemes. Contour linesin (c) and (d) indicate the percentageof precipitation due to snow.
표 Fig. 10.3 Chart for the developmental strategy of the WDM6 microphysics scheme
표 Fig. 10.4 Flowchart of the microphysics processes for the prediction of (a) the mixing ratios and(b) the number concentrations in the WDM6 scheme. The terms with red (blue) colors are activatedwhen the temperature is above (below) 0℃, whereas the terms with black color are in the entireregime of temperature. The added term compared with the WSM6 scheme is circled in green inFigure 10.1a. Number concentrations of the species in the green box in Figure 10.1b are onlypredicted in the current scheme.
표 Fig. 10.5 Time series of a) maximum vertical velocity, b) minimum vertical velocity, and c) sum ofdomain-average rain rate for the WDM (solid) and WSM (dotted) experiments.
표 Fig. 10.6 Hovmoller plots of the surface rainfall rate for the (a) WDM and (b) WSM simulations.The contour interval is every 1 mm/10 min for rates between 0 and 4 mm/10 min and every 3mm/10 min for rates greater than 4 mm/10 min. To highlight the stratiform rain region,precipitation rates between 0.05 and 4 mm/10 min are gray-shaded. (c) and (d) are the maximumreflectivity from the WDM and WSM, respectively
표 Fig. 10.7 Storm-relative 2-D wind vectors (arrows), cold pool defined by the -2 K isotherm ofpotential temperature perturbation (thick solid line), and boundary of hydrometeors with mixing ratiogreater than 0.01 gkg-1 (thin solid line) at 4 hours are shown in (a) and (b). For clarity, thevertical component of wind vectors is exaggerated by a factor of 5. Only the lowest 13 km of thedomain are shown. Simulated reflectivity (dBZ) at 4 hours for the two experiments are alsorepresented in (c) and (d). The dashed line represents the level of 0℃ temperature. Left (Right)panels show the WDM (WSM) simulations
표 Fig. 10.8 Vertical distribution of the domain-averaged water species between 5 and 6 hours fromthe (a) WDM and (b) WSM experiments, and (c) their differences (WDM-WSM). Units are ? ?? for all species.
표 Fig. 10.9 Rain mixing ratio (gkg-1) averaged between 5 and 6 hours as a function of the relativedistance from the leading edge of precipitation from the (a) WDM and (b) WSM experiments andrain number concentration (m-3) averaged between 5 and 6 hours as a prognostic variable in the(c) WDM experiment and as a diagnosed variable, which is calculated using the slope of rain sizedistribution and intercept parameter of rain in the WSM6 microphysics scheme, in the (d) WSMexperiment.
표 Fig. 10.10 Simulated number concentrations (cm -3) of the (a) CCN and (b) cloud water, obtainedfrom the WDM experiment at 6 hours. Cloud water mixing ratio corresponding to 0.01 is gkg-1 isalso represented in Figure 10.10b with a black solid line.
표 Fig. 10.11 Modeled number concentration and effective mean volume radius of cloud droplets,which are obtained from whole domain-averaged values (Solid) and cloud area-averaged ones(Dashed) during the first 4-hour integration time period
표 Fig. 10.12 Total surface precipitation with respect to the initial CCN number concentration with theWDM6 microphysics scheme
표 Fig. 11.1. Vertical profiles of the (a) simulated potential temperature (K) at 1900 UTC (1400 LST) 23October 1999 and (b) wind speed (m s-1) at 0700 UTC (0200 LST) 24 October 1999 with correspondingradiosonde soundings (gray lines with cross marks). The simulated results are from the YSU (black), ACM2(red), MYJ (green), QNSE (blue), and BouLac (light blue) experiments. In (a), the insert provides a closerlook at the temperature profiles in the lowest 1000 m.
표 Fig. 11.2. Vertical profiles of the (a) simulated potential temperature (K) and (b) wind speed (m s-1) at 1900UTC (1400 LST) 23 October 1999 with corresponding radiosonde soundings (gray lines with cross marks).The simulated results are from the CTL (black), PR (green), WS (blue), and PRWS (red) experiments. In (a),the insert provides a closer look at the temperature profiles in the lowest 1000 m.
표 Fig. 11.3. Vertical profiles of the (a) simulated potential temperature (K) and (b) wind speed (m s-1) at 0700UTC (0200 LST) 24 October 1999 with corresponding radiosonde soundings (gray lines with cross marks).The simulated results are from the CTL (black), PR (green), WS (blue), and PRWS (red) experiments
표 Fig. 12.1 꼭대기에 층적운이 위치한 경계층의 모식도
표 Fig. 12.2. 온도 (K)와 습도 (%)의 1996, 1997, 1999년에 대한 3년 평균의 연직 분포.
표 Fig. 12.3. Fig. 12.2와 동일하나 GFS SAS 적운 대류 모수화 방안을 사용함.
$Fig. 13.1 Sensitivity analysis of aerosol activation fraction to CCN forsupersaturation for (a) Aitken (top) and (b) accumulation (bottom)modes. Here, WRF (red diamond) is the computation with Twomeyequation in original WRF. Case 1 (black circle) is sectional computationwith 100 bins for aerosol size distribution. Case 2 (empty circle) iscalculated using the equation of Khvorostyanov and Curry$ 표 Fig. 13.1 Sensitivity analysis of aerosol activation fraction to CCN forsupersaturation for (a) Aitken (top) and (b) accumulation (bottom)modes. Here, WRF (red diamond) is the computation with Twomeyequation in original WRF. Case 1 (black circle) is sectional computationwith 100 bins for aerosol size distribution. Case 2 (empty circle) iscalculated using the equation of Khvorostyanov and Curry
표 Fig. 13.2 Seasonal total precipitation of simulations and observation fordomain 1 (45 km x 45 km) over East Asia for summer, 2004. Observationis Global Precipitation Climatology Project (GPCP) with 1o x 1o spatialresolution. Simulations are baseline with original WRF code and sensitivitywith modified WRF code and aerosol data simulated with CMAQ
표 Fig. 13.3 Seasonal total precipitation of baseline, baseline_bdy_sen, andsensitivity simulations for domain 2 (15 km x 15 km) for summer, 2004.Baseline_bdy_sen is baseline simulated with boundary obtained fromsensitivity simulation of domain 1.
표 Fig. 13.4 Seasonal water vapor contents at boundary for southern (top),western (middle), and eastern (bottom) boundaries from baseline (left) andsensitivity (middle) simulations for domain 2 (15 km x 15 km) for summer,2004. Right column is the difference of water vapour contents betweenbaseline and sensitivity simulations.
표 Fig. 13.5 Seasonal total precipitation of simulations and observation fordomain 3 (5 km x 5 km) in South Korea for summer, 2004. Observation isAutomatic Weather System (AWS) of Korea Meteorological Administration.Simulations are baseline with original WRF code and sensitivity withCMAQ aerosol data
표 Fig. 13.6 Anomaly of weekly seasonal precipitation rate simulated for (a) baselineand (b) sensitivity in South Korea for summer, 2004. (c) Anomaly of observedprecipitation frequency in South Korea during 1955 ~ 2005 from Kim et al.
표 Fig. 13.7 Weekly variations of simulated cloud water content for (a) baseline and (b)sensitivity. Black circle and solid line are the results at surface and empty circle anddashed line.
표 Fig. 13.8 Seasonal mean weekly simulated rainwater content (top), raindropnumber concentration (middle), and raindrop diameter (bottom) for baseline (leftcolumn) and sensitivity (right column). Black circle and solid line are the results atsurface and empty circle and dashed line are at 1.5 km altitude
표 SNURCM configurations for the present climate simulation using the reanalysis data
표 Fig. 15.1 Taylor Diagram for (1) precipitation and (2) surface temperature simulated by WRF andSNURCM physic sensitivity experiments
표 Fig. 15.4. Time-latitude cross section of 20-year mean precipitation averaged over 110-130°E
표 Fig. 16.1. Radiative forcing for the RCP scenarios
표 Fig. 16.2 Spatial distribution of annual surface air temperature (℃) and its differences(℃) between (a) WRF and OBS (b) SNURCM and OBS for 26 years
표 Fig. 16.3 The 850hPa winds (m/s; vector) and geopotential height (m; contour)of the 26-year (1980-2005) summer (JJA) season, obtained from (a) RA2, (b)HedGEM2-AO, (c) WRF (d) SNURCM
표 Fig. 16.4 Spatial distribution of annual precipitation (mm/day) and its differences(mm/day) between (a) WRF and OBS (b) SNURCM and OBS for 26 years

과제명(ProjectTitle) :	-
연구책임자(Manager) :	-
과제기간(DetailSeriesProject) :	-
총연구비 (DetailSeriesProject) :	-
키워드(keyword) :	-
과제수행기간(LeadAgency) :	-
연구목표(Goal) :	-
연구내용(Abstract) :	-
기대효과(Effect) :	-

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 제목(한글), 저자명(한글), 발행일자, 전자원문, 초록(한글), 초록(영문) 관리번호, 제목(한글), 제목(영문), 저자명(한글), 저자명(영문), 주관연구기관(한글), 주관연구기관(영문), 발행일자, 총페이지수, 주관부처명, 과제시작일, 보고서번호, 과제종료일, 주제분류, 키워드(한글), 전자원문, 키워드(영문), 입수제어번호, 초록(한글), 초록(영문), 목차
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

연합인증

동아시아 악기상의 관측 및 모델링 연구
Observation and Modeling Studies of High-impact Weather in East Asia 원문보기