[보고서]동지나해 공동해역에서의 대기 강수시스템 및 호우 발달에 관한 연구

이동인

동지나해 공동해역에서의 대기 강수시스템 및 호우 발달에 관한 연구
Investigation of heavy precipitation systems around the East China Sea 원문보기

보고서 정보
주관연구기관	부경대학교 Pukyong National University
연구책임자	이동인
참여연구자	김경식 , 이병걸 , 유철환 , 김부경 , 방영수 , 강미영 , 장상민 , 박성화 , 정성아 , 정종훈 , 정운선 , 백운학 , 김주연 , 김수경 , 손승우 , 강덕두 , 이정태 , 강윤희 , 김현준 , 서성호 , 박소연 , 고경연 , 김유라 , 이승윤 , 김여리 , 류경태 , 조대관 , 민승식 , 이민혁 , 임규성 , 이근옥 , 변지선 , 윤종현 , 이슬기 , 고정우 , Loh Jui Le , Paydary Maryam , Ramiz Mahdi Jawad Shubbar
보고서유형	최종보고서
발행국가	대한민국
언어	한국어
발행년월	2015-03
과제시작연도	2014
주관부처	미래창조과학부 Ministry of Science, ICT and Future Planning
등록번호	TRKO201700009002
과제고유번호	1711011253
사업명	글로벌연구실사업
DB 구축일자	2017-11-04
키워드	집중관측.강수량예측.레이더 자료품질 관리.이중편파레이더.이중도플러분석.구름분해모델.자료동화.3차원 변분법.에어로솔.모수화 알고리즘.민감도 분석.정확도 평가.초단기 예보모델.장마.태풍.집중호우.Intensive observation.Precipitation forecast.Radar data quality control.Dual polarization radar.Dual Doppler analysis.Cloud resolving model.Data assimilation.3D-Var.Aerosol.Parameterization algorithm.Sensitivity analysis.Accuracy analysis.Nowcasting model.Changma.Typhoon.Heavy rainfall.
DOI	https://doi.org/10.23000/TRKO201700009002

초록 ▼

1. 동지나 해역에서의 집중관측 및 자료 수집
1) 동지나 해역(제주도, 마라도, 추자도, 이어도, 오키나와, 대만 등)에서의 국내집중관측 및 국제공동관측
2) Parsivel, POSS, 우량계, 3차원 풍향풍속계, 라디오존데, AWS, C밴드 및 X밴드 이중편 파레이더, S밴드 기상레이더 등을 이용한 대기관측 및 관측 자료의 수집
3) 상시관측 및 집중관측 자료의 QC

2. 고해상도 구름분해모델을 이용한 정량적 강수량 예측 정확도 향상 연구
1) 구름모델의 레이더 자료동화 기법개선
2) 에어로솔의 물리·화학적 특성을 고려한 모수화 알고리즘 개발
3) 구름모델의 재해기상 모의에 대한 민감도 분석
4) 구름모델의 강수량 추정 정확도 및 3차원 대기모의 정확도 평가

3. 레이더 기반의 초단기 강수예측 모델 개발
1) 시간고해상도 레이더 자료 재생산 기법 개발
2) 레이더 기반의 초단기 강수예측 모델 개발
3) 초단기 예측 모델의 정확도 평가

(출처 : 보고서 요약서 3p)

Abstract ▼

Ⅳ. Research Results

1. Intensive observation and data acquisition on East China Sea
1) Domestic field observation in East China Sea (Jeju island and Ieodo)
2) Accumulation of observed meteorological data such as radar, radio sonde, disdrometer (Parsivel, POSS, JWD and 2DVD), rain gauge, AWS, LPC, rain sampler, air sampler and 3D ultrasonic anemometer etc
3) Intensive observation and data quality control
① Rain gauges data processing (Program registration)
② DISDROMETER RD-80 observational data processing (Program registration)
③ Dual-Doppler radar wind field analysis data extraction at the fixed point (Program registration)
④ Rearranged according to the point of wind profiler data (Program registration)
⑤ Dual-Doppler radar wind field analysis accuracy verification (Program registration)
⑥ Estimate terminal veloicity of the raindrop using temperature data (Program registration)
⑦ Data Control of Parsivel (Program registration)
⑧ Data Control of Raingauge (Program registration)
⑨ Displaying the Precipitable water graph image (Program registration)
⑩ Maximum accumulated rainfall amount expression using radar data (Program registration)
⑪ Probability density function and cumulative density function calculation program using observed raindrop size distribution. (Program registration)
⑫ Climatological characteristics analysis of rainfall system program using observed raindrop size distribution. (Program registration)
⑬ Convergence pattern detection using radar radial velocity (Program registration)
⑭ Expressing concentration change over time using Parsivel, the instrument measuring distrometer of precipitation. (Program registration)
⑮ Comparing variable values of precipitation distrometer distribution in every 10 minutes by using Parsivel data (Program registration)
⑯ Expressing precipitation distrometer distribution in every 10 minutes by using Parsivel data (Program registration)
⑰ Calculation of Froude number with upper air sounding data (Program registration)
⑱ Downburst detection algorithm by Doppler weather radar network (Patent application/registration)
⑲ Method of mitigation in second-trip echo for exact rainfall estimation (Patent registration)
⑳ Downburst detection system and method using the radar radial velocity (Patent registration)

2. Improvement of forecasting capacities of precipitation using cloud resolving model
1) Development of data assimilation techniques using radar data
① Method of revising the differential phase shift for exact rainfall estimation (patent and registered)
② Creating method of observation data concerning beam spread of radar (patent and registered)
③ Assimilation techniques for building Newtonian relaxtion using local radar data
④ Three-dimensional variational data assimilation techniques using radial velocity data from local radar
2) Parameterization algorithm developed considering the physical and chemical properties of aerosols
① Statistical characterization of aerosols in the Korea peninsula (SCI papers)
② Study of aerosol effects on clouds and precipitation during the rainy season through the CRL scheme
③ Displaying the aerosol optical depth image by satellite data (software release)
3) The sensitivity analysis of the severe weather simulation
① Meteorological disaster simulation sensitivity analysis based on the spatial resolution change
② Meteorological disaster simulation sensitivity analysis based on cloud thermodynamic process change
③ Meteorological disaster simulation sensitivity analysis based on the terrain elevation changes (SCI papers)
4) The analysis of accuracy of the severe weather simulation
① Comparison of accumulation of rainfall from rain gauge observations and simulated result
② Assessment of accuracy of precipitation presence in Jeju island area through applying Skill score
③ Assessment of accuracy of specific vertical environment variables simulation on the elevation change through applying ME, MAE, RMSE, RMSF
④ Varification of comparing vertical environmental variables (temperature, humidity, dew point temperature, etc.) between sounding observation data and simulated data
⑤ Assessment of accuracy of simulated vertical environmental variables through applying ME, MAE, RMSE, RMSF
⑥ Model simulations vector field comparison validation based on the calculated three-dimensional wind field from radar

3. The development of radar-based short term rainfall forecasting model
1) Development of high-temporal resolution radar data generating system for short term precipitation prediction
2) Development of real time numerical weather prediction for input data of short term precipitation prediction model
3) Development of radar-based nowcasting model
① Method for gaining information about the convection precipitation in use of the weather radar (Patent and Registration)
② Method for estimating the convection precipitation in use of the weather radar(patent applied)
4) Assessment of accuracy of the nowcasting model
① quantitative assessment
② qualitative assessment

(출처 : SUMMARY 13~15p)

목차 Contents

표지 ... 1제 출 문 ... 2보고서 요약서 ... 3요 약 문 ... 5SUMMARY ... 10CONTENTS ... 18목차 ... 19제1장 연구개발과제의 개요 ... 20 1.1절 연구개발의 필요성 ... 20 1.2절 연구개발 목표 ... 22제2장 국내외 기술개발 현황 ... 26 2.1절 국내의 기술개발 현황 ... 26 2.2절 국외의 기술개발 현황 ... 28 2.3절 현기술상태의 취약성 ... 32제3장 연구개발수행 내용 및 결과 ... 34 List of Tables ... 36 List of Figures ... 39 3-1절 동중국 해역에서의 강수시스템에 관한 연구 ... 58 3-1.1 동중국해 북부해역에서의 집중관측 ... 58 3-2절 고해상도 구름모델을 이용한 정량적 강수량 예측 정확도 향상 연구 ... 111 3-2.1 구름모델의 레이더 자료 동화기법 개선 ... 111 3-2.2 에어로솔의 물리·화학적 특성을 고려한 모수화 알고리즘 개발 ... 156 3-2.3재해기상 모의에 대한 구름모델 민감도 분석 ... 246 3-2.4구름분해모델의 정확도 평가 ... 321 3-3절레이더 기반의 초단기 강수예측 모델 개발 ... 363 3-3.1서론 ... 363 3-3.2레이더 기반의 초단기 강수예측 향상을 위한 고해상도 레이더 자료 재생산 기법 개발 ... 364 3-3.3 부경대학교 CReSS모델을 이용한 실시간 예측 시스템 구축(PKNU CReSS real-time forecasting system) ... 393 3-3.4.레이더 기반의 초단기 강수예측 모델 개발 ... 404 3-3.5.초단기 예측 모델의 정확도 평가 ... 431 3-3.6 활용방안 및 기대효과 ... 443제4장 목표달성도 및 관련분야에의 기여도 ... 444 4.1절 연구개발 목표의 달성도 ... 444 4.2절 관련분야에의 기여도 ... 447제5장 연구개발결과의 활용계획 ... 450 5.1절 기대성과 ... 450 5.2절 활용방안 ... 452제6장 연구개발과정에서 수집한 해외과학기술정보 ... 453제7장 참고문헌 ... 454Final report for the GRL ... 473끝페이지 ... 482

표/그림 (300)

표 The schedule of intensive observation at Jeju island and Ieodo.
표 Characteristics of Parsivel disdrometer: wavelength and measuring area of parsivel disdrometer, effective particle size and velocity, observation channel, effective precipitation intensity, observed data interval set.
표 Intensive observation network in Jeju Island.
표 Information of observation sites in Jeju island.
표 The installed instruments of Parsivel, Raingauges in Jeju island.
표 (continued).
표 Photographs of radio sonde observation in Jeju island.
표 The installation view of AWS, raingauge and LPC in Ieodo.
표 (Continued.)
표 The map of RADAR and radio sonde observation sites in southern part of Korea.
표 Intensive observation network in Jeju Island.
표 Specification of S-Band Doppler radars of GSN, SSP in Jeju Island.
표 Summarized specifications of 2DVD.
표 Specifications of the sensor of radiosonde (M10).
표 (Continued).
표 Specifications of the ground station of radiosonde (M10).
표 The installation view of 2DVD in Jeju Island.
표 The installation view of Parsivel, Rainguage and UVW in Jeju Island.
표 The photographs of radio sonde observation in Jeju island.
표 Specification of Auto Weather System (AWS).
표 Specification of an ultrasonic anemometer (Model 81000).
표 The installation view of AWS, raingauge and LPC in Ieodo.
표 (Continue).
표 Specification of observation devices installed at Gisang 1
표 The photographs of Ship, AWS, ASAP, Particulate sampler in Gisang
표 (Continued).
표 The moving route of Gisang 1 for Ieodo from Jeju island.
표 The rainfall amount distribution during observation period (29 June ∼ 15 July) in Jeju university site, 2012.
표 Surface weather charts at 1500 LST on 01 July and 0000 LST on 02 July 2012.
표 (a) Pressure and wind vector at the surface, (b) equivalent potential temperature and wind vector at the level of 850 hPa, (c) relative vorticity and wind vector at the level of 500 hPa, (d) geopotential height and wind vector at the level of 300 hPa at 0300 LST on 3 July 2012.
표 (a) daily accumulation rainfall amount, (b) daily accumulation rainfall amount at jeju island on 01 and 02 July 2012.
표 Time series of number density of raindrops with rainfall rate (black line) measured by Parsivel on 1 and 2 July 2012.
표 The vertical profiles of potential temperature (θ), equivalent potential temperature (θe), saturated equivalent potential temperature (θe *), wind direction and wind speed at 0900 LST 1 (before precipitation), 1500 LST 1 (during precipitation), 2100 LST 2 July (after precipitation), 2012 by upper-air sounding observation at Jeju island.
표 Surface weather charts at 1500 LST on 10 July and 0600 LST on 11 July 2012.
표 daily accumulation rainfall amount and daily accumulation rainfall amount at jeju island on 11 July 2012.
표 Same as Fig. 3-1.1.18 but for Parsivel, PR4 on 11 July 2012.
표 Same as Fig. 3-1.1.19 but for at 2100 LST 10 (before precipitation), 0300 LST 11 (during precipitation), 0900 LST 11 July (after precipitation), 2012.
표 Surface weather chart at 0900 LST on 14 July 2012.
표 daily accumulation rainfall amount and daily accumulation rainfall amount at jeju island on 14 July 2012.
표 Same as Fig. 3-1.1.18 but for Parsivel, PR3 on 11 July 2012.
표 Same as Fig. 3-1.1.19 but for at 0300 LST 14 (before precipitation), 1500 LST 14 (during precipitation), 2100 LST 14 July (after precipitation), 2012.
표 Surface weather chart at (a) 0300 LST on 3 July and (b) 1200 LST on 3 July 2012.
표 Same as Fig. 3-1.1.16 but at 0300 LST on 3 July 2012.
표 The variation of humidity, temperature, wind direction, wind speed rain amount and pressure from 1700 LST on 30 June to 2300 LST on 5 July 2012 at Ieodo.
표 The variation of (a) zonal wind, (b) meridional wind and (c) vertical wind from 1 to 6 July 2012.
표 Surface weather chart at 0900 LST and satellite image at 0700 LST on 19 June 2013.
표 12 hours accumulation rainfall amount and daily accumulation rainfall amount at Jeju island on 19 June 2013.
표 Same as Fig. 3-1.1.18 but for Parsivel, PR2 on 19 June 2013.
표 Same as Fig. 3-1.1.19 but for at 2100 LST 19 June (After precipitation), 2013 by upper-air sounding observation at Jeju island.
표 Surface weather chart at 0900 LST and satellite image at 1533 LST on 25 June 2013.
표 12 hours accumulation rainfall amount on 25 June 2013.
표 Same as Fig. 3-1.1.18 but for Parsivel, PR3 on 25 June 2013.
표 Same as Fig. 3-1.1.19 but for at 1500 LST 25 June 2013 at (a) Gosan, (b) Seongsan, (c) Ieodo.
표 Surface weather chart at 0900 LST and satellite image at 1400 LST on 6 July 2013.
표 Daily accumulation rainfall amount on 6 and 8 July 2013.
표 Surface weather chart at 0300 LST on 15 and on 18 November 2013.
표 The variation of humidity, temperature, wind direction, wind speed rain amount and pressure from 0000 LST on 15 November to 2300 LST on 19 November 2013 at Ieodo.
표 The variation of zonal wind, meridional wind, vertical wind and rain amount from 15 to 20 November 2013.
표 Flow charts of quality control process
표 Radar PPI images; results conducted by quality control process
표 Flow charts of quality control to calculate KDP
표 Radar PPI images; results from process to remove noise and recover data.
표 Description of model error with time. Red line is assimilated forecasting and Blue line is nonassimilated forecasting.
표 Schematic diagram to explain averaged wind and potential temperature profiles at unstable boundary
표 Configuration for Nudging experiments in CReSS.
표 Nudging coefficient as different experiments.
표 Model domains of case 1 (9 Aug. 2011; Domain A) and case 2 (10 Jun. 2014; Domain B)
표 Daily accumulated rainfall distribution on 9 Aug. 2011 (left) and composited radar image at 13 LST (right).
표 Surface weather chart at 09 LST on 9 Aug. 2011.
표 Accumulated rainfall distribution (07-08LST, 08-09 LST, 09-10LST) with model experiments (CNTL, N1, and N2)
표 Time series of AWS (Jeongup) recorded maximum rainfall on 9 Aug.
표 Accumulated rainfall at Jeongup point (Black: rain gauge, Grey: CNTL, Red: N1, Blue: N2).
표 Bias of mixing ratio between CNTL and Nudging experiments (N1 and N2)
표 Sketch showing the relationship between datasets (circles), and algorithms (rectangles) of the ARW system (WRF manual vr.3).
표 Surface weather chart at 09 LST on 25 Aug. 2014
표 Daily rainfall distribution on 25 Aug 2014.
표 Experimental large domain (D1) and nested domain (D2)
표 3D-Var 실험을 위한 CReSS 모델 환경설정
표 A comparison analysis between model (CNTL, 3DVAR) and observation (Radar).
표 Same as Fig 3-2.1.18., but for comparison analysis between model and observation in small region at 1420 LST on 25 Aug. 2014.
표 Relative humidity distribution of model simulation (CNTL, 3DVAR) at 1420 LST 25 Aug 2014. Solid lines to show vertical distribution of relative humidity are shown in Fig. 3-2.1.17
표 Vertical cross section along the solid lines (A-A’ and B-B’) presented in Fig. 3-2.1.16.
표 Zonal wind component of model simulation (CNTL, 3DVAR) at 1420 LST on 25 Aug. 2014.
표 Location of observed cities in the Korean peninsula.
표 The DCCA cross-correlation coefficient PDCCA between PM10 and temperature for 8 cities during the non-Asian dust days.
표 The DCCA cross-correlation coefficient PDCCA between PM10 and wind speed.
표 The DCCA cross-correlation coefficient PDCCA between PM10 and humidity.
표 PDF of critical points ρrc(42690,n) for the statistical test.
표 The critical value ρrc for the DCCA cross-correlation coefficient when for a given couple of time series each series is Gaussian i.i.d. in 90% confidence level with zero mean and unit variance
표 PDFs of critical points ρsc(n) for the statistical test between PM10 and temperature in eight cities during non-Asian dust events.
표 PDFs of critical points ρsc(n) for the statistical test between PM10 and wind speed.
표 PDFs of critical points ρsc(n) for the statistical test between PM10 and humidity.
표 Histograms of shuffled original data in Daejeon during non-Asian dust, where the value of Kurtosis and Skewness also judge normality.
표 Histograms of shuffled original data in Incheon.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and temperature during non-Asian dust in coastal area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and temperature in inland area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and humidity in coastal area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and humidity in inland area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and wind speed in coastal area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and wind speed in inland area.
표 Statistical test ρDCCA of Eq.
표 Plots of ρDCCA between PM10 and humidity.
표 Plots of ρDCCA between PM10 and wind speed.
표 The DCCA cross-correlation coefficient ρDCCA between PM10 and temperature for different values of box size n in 8 cities during the Asian dust.
표 The DCCA cross-correlation coefficient between PM10 and wind speed.
표 The DCCA cross-correlation coefficient between PM10 and humidity.
표 PDF of critical points ρrc(1134,n) for the statistical test.
표 Critical value ρrc(1134,n) for the DCCA cross-correlation coefficient when for a given couple of time series. Each series is Gaussian i.i.d. in 90% confidence level with zero mean and unit variance
표 PDFs of critical points ρsc(n) for the statistical test between PM10 and temperature in eight cities during Asian dust events.
표 PDFs of critical points ρsc(n) between PM10 and humidity.
표 PDFs of critical points ρsc(n) between PM10 and wind speed.
표 Histograms of shuffled original data in Daejeon during Asian dust.
표 Histograms of shuffled original data in Incheon.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and temperature in coastal area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and temperature but in inland area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and humidity in coastal area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and humidity in inland area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and wind speed in coastal area.
표 The critical value ρsc for the DCCA cross-correlation coefficient between PM10 and wind speed in inland area.
표 Statistical test ρDCCA of Eq.
표 Statistical test ρDCCA between PM10 and humidity.
표 Statistical test ρDCCA between PM10 and wind speed.
표 Mean, maximum, and minimum times of Asian dust in 8 cities.
표 Flow chart of the idealized experiment.
표 Three-nested domain configuration.
표 Summary of WRF model and design for the experiments configuration of basic step and physics were used
표 Aerosol size distribution by Whitby (1978). Red, orange, yellow, and green lines are the aerosol size distribution in cases of MR, CC, AC, and DC.
표 The surface weather chart at (a) 0000 LST, (b) 0600 LST, (c) 1200 LST, and (d) 1800 LST on 13 July 2012.
표 The distribution of accumulated rain amount using model in cases of (a) MR, (b) CC, (c) AC, (d) DC of CLR scheme, (e) WDM6 scheme, (f) WSM6 scheme, and observation instrument in (g) rain gauges result from 2100 LST on 12 to 2100 LST on 13 July 2012.
표 The time series of rain rate using model in cases of (a) MR, (b) CC, (c) AC, (d) DC of CLR scheme, and observation instrument in (e) rain gauges result from 2100 LST on 12 to 2100 LST on 13 July 2012.
표 The time series of rain diameter (mm) in (a) model and (b) observation instrument in Parsivel result from 2100 LST on 12 to 2100 LST on 13 July 2012.
표 Aerosol size distribution in cases of MR, CC, AC, DC, and observation result.
표 Aerosol number concentration in cases of MR, CC, AC, DC, and observation result by CPC from 0.01 to 3.0 μm size.
표 The horizontal distribution of reflectivity (dBZ, color shading) using model in cases of (a) MR, (b) CC, (c) AC, (d) DC, and observation instrument in (e) composite radar at 2 km elevation at 1230 LST on 13 July 2012.
표 The same as Fig. 3-2.2.29 but for the time-height distribution of domain-averaged reflectivity (dBZ) from 2100 LST on 12 to 2100 LST on 13 July 2012.
표 The same as Fig. 3-2.2.29 but for the reflectivity-height distribution of domain-percentage reflectivity
표 The time-height distribution of domain-averaged relative humidity (%, color shading) using model in cases of (a) MR, (b) CC, (c) AC, and (d) DC from 2100 LST on 12 to 2100 LST on 13 July 2012.
$The same as Fig. 3-2.2.32 but for the cloud fraction.$ 표 The same as Fig. 3-2.2.32 but for the cloud fraction.
표 The same as Fig. 3-2.2.32 but for the mixing ratio of vapor
표 The same as Fig. 3-2.2.32 but for the mixing ratio of cloud
표 The same as Fig. 3-2.2.32 but for the number concentration of cloud
표 The same as Fig. 3-2.2.32 but for the cloud mean volume radius
표 The same as Fig. 3-2.2.32 but for the mixing ratio of rain
표 The same as Fig. 3-2.2.32 but for the number concentration of rain
표 The same as Fig. 3-2.2.32 but for the rain mean volume radius
표 The same as Fig. 3-2.2.32 but for the mixing ratio of ice
표 The same as Fig. 3-2.2.32 but for the number concentration of ice
표 The same as Fig. 3-2.2.32 but for the mixing ratio of snow
표 The same as Fig. 3-2.2.32 but for the number concentration of snow
표 The same as Fig. 3-2.2.32 but for the mixing ratio of graupel
표 The same as Fig. 3-2.2.32 but for the number concentration of graupel
표 The time series of column-integrated and domain-averaged path of (a) vapor, (b) cloud, (c) rain, (d) ice, (e) snow, and (f) graupel from 2100 LST on 12 to 2100 LST on 13 July 2012.
표 The same as Fig. 3-2.2.47 but for the number concentration (#/kg) of (a) cloud, (b) rain, (c) ice, (d) snow, and (e) graupel.
$The same as Fig. 3-2.2.47 but for the (a) cloud mean volume radius (μm), (b) rain mean volume radius (mm), and (c) cloud fraction.$ 표 The same as Fig. 3-2.2.47 but for the (a) cloud mean volume radius (μm), (b) rain mean volume radius (mm), and (c) cloud fraction.
$The column-integrated and domain-averaged value during model simulation of (a) path of vapor, cloud, rain, ice, snow, graupel, (b) number concentration (#/kg) of cloud, rain, ice, snow, graupel, (c) CCN mass concentration (kg/kg) of cloud, rain, dry, total, (d) mean volume radius of cloud (μm), rain (mm), and (e) cloud fraction in cases of MR, CC, AC, and DC.$ 표 The column-integrated and domain-averaged value during model simulation of (a) path of vapor, cloud, rain, ice, snow, graupel, (b) number concentration (#/kg) of cloud, rain, ice, snow, graupel, (c) CCN mass concentration (kg/kg) of cloud, rain, dry, total, (d) mean volume radius of cloud (μm), rain (mm), and (e) cloud fraction in cases of MR, CC, AC, and DC.
표 Schematic diagram of the variations of hydrometeor parameters in idealized experiment.
표 Variation of hydrometeor parameters in low and high CCN
표 This study (orange color box) is included and ranged in the classification scheme of aerosol effects on precipitation (Khain, 2009).
표 CReSS model configuration for sensitivity test.
표 Grid information for sensitivity test according to horizontal and vertical resolution.
표 Simulation domains of case 1 (9 Aug. 2011; left), and case 2 (10 June 2014; right).
표 Daily rainfall distribution (left) and radar image at 20 LST on 10 June 2014.
표 Surface weather chart on 10 June 2014.
표 Total accumulated rainfall distribution (1, 2, 3 km) calculated of horizontal resolution sensitivity test about on 9 Aug. 2011.
표 Rainfall comparisons between AWS (gray thick) and simulated rainfall (red: 1km, blue: 2km, black: 3km) in Jeongup (245).
표 Time series of maximum value (potential temperature, mixing ratio, sensible heat, latent heat) simulated with horizontal resolutions on 9 Aug. 2011.
표 Same as Fig. 3-2.3.6 but for CAPE and PW
표 Total accumulated rainfall distribution (100, 300, 500 m) by vertical resolution sensitivity test about case on 9 Aug. 2011.
표 Simulated accumulated rainfall with 10 minutes in Jeongup.
표 Time series of maximum value (potential temperature, mixing ratio, sensible heat, latent heat) simulated with vetical resolutions on 9 Aug. 2011.
표 Same as Fig. 3-2.3.10 but for CAPE and PW
표 Total accumulated rainfall distribution (1, 2, 3 km) calculated by horizontal resolution sensitivity test about on 10 Jun. 2014.
표 Time series of maximum value (potential temperature, mixing ratio, sensible heat, latent heat) about simulation with horizontal resolutions on 10 Jun. 2014.
표 Same as Fig. 3-2.3.13 but for CAPE and PW
표 Total accumulated rainfall distribution (100, 300, and 500 m) calculated by vertical resolution sensitivity test about case on 10 Jun. 2014.
표 Time series of maximum value (potential temperature, mixing ratio, sensible heat, latent heat) about simulation with vetical resolutions on 10 Jun. 2014.
표 Same as Fig. 3-2.3.16 but for CAPE and PW
표 Maximum accumulated rainfall with experiments
표 Geography and topography (m, shading) of Korea and surrounding areas.
표 NCEP FNL (1° × 1°) daily mean analysis of geopotential height (gpm, contour) and horizontal wind vectors (m s-1, speed also shaded) at (a) 1000, (b) 850, (c) 500, and (d) 300 hPa on 7 July 2009.
표 Horizontal section of mosaic radar reflectivity (dBZ, color shading) over Korean Peninsula observed at 2 h intervals from (a) 0600 to (c) 1000 LST 7 July 2009.
표 Horizontal section of radar reflectivity (dBZ, color shading) at a height of 2 km observed by PSN radar and surface winds [m s-1, barbs, full (half) barbs denote 5 (2.5) m s-1] and cold pool outline (temperature depression of 4 °C, solid pink line) using automatic weather system from 0600 to 0800 LST 7 July 2009.
표 Summary of CReSS model and design for the two experiments configuration of basic setup, physics, and numerical methods were used.
표 Comparison of the 24-h accumulated rainfall (mm) on 7 July 2009 between (a) observation and (b) control experiment (CNTL).
표 Time series of houly rainfall (mm hr-1) at the cross symbol in Fig.
표 Same as in Fig. 3-2.3.20, but from model-simulated reflectivity (dBZ, color shading) over Korean Peninsula in CNTL run.
표 Model-simulated reflectivity (dBZ, contour, contours at intervals of 5 dBZ), vertical velocity (m s-1, color shading), and horizontal wind field (m s-1, vectors) at 585 m in CNTL run at 1-h intervals from (a) 0600 to (d) 0900 LST 7 July 2009.
표 Same as in Fig. 3-2.3.25, except for perturbation potential temperature (′, K, color shading). The black dashed lines indicate the cold pool outlines (isopleth of ′= 2 K). The thin gray lines indicate the topography of Korea at intervals of 500 m. In (d) the line A-A’ shows the location of the vertical cross section used in Fig. 3-2.3.28.
표 Model-simulated integrated vapor transport (kg m-1 s-1, color shading with vectors overlaid; vector scale is shown at upper right) for (a) 0600 and (b) 0900 LST 7 July 2009.
표 Vertical cross section (transect A-A’ in Fig. 3-2.3.26d) of model-simulated ′ (K, color shading), vertical velocity (m s-1, dotted for negative values), and wind vectors (m s-1) along the section plane in CNTL run at 1-h intervals from (a) 0400 to (d) 0900 LST 7 July 2009.
표 Same as in Fig. 3-2.3.25, except for results in the experiment without evaporative cooling of rain water (NOEVA).
표 Model-simulated 24-h accumulated rainfall (mm) on 7 July 2009 for (a) NOEVA run and (b) difference between two experiments (NOEVA minus CNTL) on 7 July 2009.
표 Same as in Fig. 3-2.3.26, except for results in the NOEVA run.
표 Same as in Fig. 3-2.3.28, except for results in the NOEVA run from (a) 0600 to (d) 0900 LST 7 July 2009.
표 Vertically averaged (a) (K, black color, scale of the left) and mixing ratio of water vapor (g kg-1, gray color, scale of the right), onshore/offshore flow boundary (dotted line) below 1 km, and (b) 24-h accumulated rainfall (mm) comparing between the CNTL (solid line) and NOEVA (dashed line) run.
표 Schematic illustration of cold pool effects, development of convection, and rainfall distribution near Busan as simulated by the CReSS model using (a) the CNTL run and (b) the NOEVA run.
표 Distribution of the peaking time (LST, colors) for the diurnal harmonic of cold cloud percentage (%) obtained from IR1 brightness temperature ( > -32 °C) for May of 1998-2001, with smoothed topography (km) plotted (adapted from Wang et al. 2005a).
표 The study area and its topography (m, shading, scale at bottom).
표 (a) Hovmöller diagram of TRMM rain rates (mm h-1, color, scales at bottom) overlaid with 850-hPa wind speed (m s-1, isotachs) in NCEP analysis during the case period from 0000 UTC 12 Jun to 0000 UTC 18 Jun 2008. (b) As in (a), but from model-simulated rain rates aggregated into resolutions of 25 km and 3 h (from 2.5 km and 15 min), overlaid with wind speed at 1594 m every 6 h in CTL.
표 Position of mei-yu front at 1000 hPa (thick solid line), wind-shift line at 850 (thick ling-dashed line) and 700 hPa (thick short-dashed line), trough at 500 hPa (thick dotted line), and regions of 850-hPa LLJ (≥12 m s-1, thick gray lines) in the NCEP FNL (1°×1°) analyses every 24h at 0000 UTC (a) 12, (b) 13, (c) 14, (d) 15, (e) 16, and (f) 17 Jun 2008, overlaid with MTSAT IR brightness temperatures
표 Regions of prefrontal low-level jet (wind speed ≥ 12 m s-1) at 850 hPa in the NCEP FNL (1°×1°) analyses every 24 h at 0000 UTC (a) 12-15 and (b) 16-18 Jun 2008. Those on 12-17 Jun 2008 are as in Fig. 3-2.3.38.
표 NCEP FNL (1°×1°) analyses of geopotential height (gpm, solid contours at intervals of 10 gpm) and horizontal winds [full (half) barb = 10 (5) m s-1] at (a) 1000, (b) 850, (c) 700, and (d) 500 hPa at 1200 UTC 16 Jun 2008.
표 Observed total daily rainfall distributions (mm, shading, 0000-2400 UTC) over Taiwan on (a) 13, (b) 14, (c ) 15, and (d) Jun 2008. (e)-(h), (i)-(l) As in (a)-(d), but model-simulated distributions in CTL and SNP experiments, respectively.
표 CReSS model domain configuration used in this study, common to all experiments except alterations in the IC/BCs as specified in Table 3-2.3.6. An asterisk denotes that the vertical grid spacing (Δz) of the CReSS model is stretched (samllest at the bottom), and the veraged spacing is given in the parentheses.
표 Design of control experiment (CTL) and sensitivity tests in this study, including their purpose, IC/LBCs used, and further remarks.
표 Distribution of the 1°×1° weekly mean SST (K) during IOP-8 used at the lower boundary in CTL, DIU, and DNT experiments.
표 As in Fig. 3-2.3.40, but for the time-averaging fields over the 6-day period from 0000 UTC 12 Jun to 0000 UTC 12 Jun to 0000 UTC 18 Jun 2008.
표 The averaged deviations of geopotential height (gpm, contours at intervals of 2 gpm, dashed for negative values), horizontal wind [ms-1, full (half) barbs denote 2(1) m s-1], and temperature (°C, dashed, shading, scale at bottom) at 850 hPa during the period of 0000 UTC 12-18 Jun 2008.
표 (a) As in Fig. 3-2.3.37b, but the rain rates are at the original resolutions of 2.5 km and 15 min from CTL. (b)-(d) As in (a), but from (b) SNP, (c) DIU, and (d) DNT experiments. Dotted lines with arrows (pink dashed ovals) mark the streaks (areas) of interest, and brown circles label common regions of convection development in different experiments (see text for details).
표 Model-simulated surface winds (m s-1, barbs) and convergence (10-4s-1, purple lines), rain rates(mm h-1, color), and the elevation of 500 m (gray lines) in CTL at (a) 0600 UTC 12 Jun and (b) 0600 UTC 17 Jun 2008. (c), (d) As in (a), (b), but in SNP experiment. For winds, full (half) barbs denote 10 (5) m s-1 and only positive values are plotted at intervals of 2 × 10-4s-1 for convergence (0-line omitted).
표 Model-simulated surface  (K, shading) and winds [ms-1, barbs, full (half) barbs denote 10 (5) m s-1] and vertical velocity (m s-1, contours at intervals of 0.5 m s-1, positive only, 0-line omitted) at 1594 m near Taiwan in CTL every 3 h at (a) 2100 UTC 15 Jun, (b) 0000 UTC 16 Jun, (c) 0300 UTC 16 Jun, (d) 0600 UTC 16 Jun. (e)-(h) As in (a)-(d), but in the SNP experiment. In (a), the line B-B’ marks the vertical cross section used in Fig. 3-2.3.48.
표 Vertical cross section of model-simulated wind vectors on the sections plane (m s-1, reference vector at bottom, vertical motion multiplied by 24)
표 Hovöller diagrams of the averaged diurnal cycle of model rain rates (mm h-1) over the period from 1200 UTC 12 Jun to 1200 UTC 17 Jun 2008 in (a) DIU and (b) DNT experiments.
표 Averaged model streamlines and convergence (10-4s-1, white contours at every 0.3 × 10-4s-1, positive only and 0-line omitted) at the surface (10-m height) and total daily rainfall distributions (mm, color, scale on right) near Taiwan from 1200 UTC 12 Jun to 1200 UTC 17 Jun 2008 in (a) DIU and (b) DNT experiments.
표 Time series of averaged rain rates (mm h-1) over the region of 22.5°-26.5°N, 113°-118°E in SEC (Fig. 3-2.3.36) on different dates from 12 to 17 Jun 2008 and the mean diurnal variations (thick dashed lines, 0000-1200 12 Jun and 1200-2400 17 Jun excluded from averaging) in (a) CTL, (b) SNP, (c) DIU, and (d) DNT experiments, respectively.
표 Variations of model rain rates (mm h-1) at 0000, 0600, 1200, and 1800 UTC (scale on left) and terrain height (m, scale on right) with longitude, averaged over 22.5°-26.5°N in SEC during the period from 1200 UTC 12 Jun to 1200 UTC 17 Jun 2008 in (a) CTL and (b) SNP experiments. Filled (open) arrows mark the location of major (minor) peaks in rain rates (where needed, see text for details).
표 The sites installed the raingauge
표 The sites information of the installed raingauge
표 The period, precipitation type and maximum accumulated rainfall of each case
표 (a)-(c) The surface weather map in each case
표 (a)-(b) The daily accumulated precipitation in each case
표 The information of CReSS model data
표 The quartile category about the qualitative verification
표 The type of the method about the qualitative verification
표 The accumulated rainfall distribution of (a-1) Raingauge and (a-2) CReSS model for case 1 (from 22 June to 13 July 2014 – Total measurement period)
표 The accumulated rainfall distribution of (b-1) Raingauge and (b-2) CReSS model for case 2 (9 July 2014 - Typhoon)
표 The accumulated rainfall distribution of (c-1) Raingauge and (c-2) CReSS model for case 3 (13 July 2014 – Convective rain band associated with stationary front)
표 (a) ACC (forecast accuracy) value of rainfall during the total analysis period from R1 to R14
표 (b) SEDS (symmetric extreme dependency score) value of rainfall during the total analysis period from R1 to R14
표 (c)BIAS (bias score) value of rainfall during the total analysis period from R1 to R14
표 (d) POD (probability of detection) value of rainfall during the total analysis period from R1 to R14
표 (e) FAR (false alarm ratio) value of rainfall during the total analysis period from R1 to R14
표 (f)HK (hanssen and kuipers score) value of rainfall during the total analysis period from R1 to R14
표 The observation sites of GPS-Sonde near Gosan(west)-Seongsan(east) S-Band Doppler radar sites in Jeju Island
표 The launching numbers, time and period of GPS-Sonde observation
표 The information of CReSS model data used to analyze the accuracy of vertical environmental parameters
표 The technical data of used GPS-Sonde
표 The comparative method of CReSS model data and GPS-Sonde data
표 The type of quantity verification method
표 The calculation method of temperature, dew point temperature and humidity using the CReSS model result
표 The comparison of vertical pressure profile between GPS-Sonde and CReSS model data
표 The comparison of vertical temperature profile between GPS-Sonde and CReSS model data
표 The comparison of vertical dew point temperature profile between GPS-Sonde and CReSS model data
표 The comparison of vertical humidity profile between GPS-Sonde and CReSS model data
표 The comparison of vertical potential temperature profile between GPS-Sonde and CReSS model data
표 The time series of PW (Precipitable Water) by GPS-Sonde and CReSS model and precipitation by the installed raingauge at R1 site
표 The analysis result for ME (Mean Error) of each parameter
표 The analysis result for MAE (Mean Absolute Error) of each parameter
표 The analysis result for RMSE (Root Mean Square Error) of each parameter
표 The analysis result for RMSF (Root Mean Squared Fraction) of each parameter
표 The used radar data information used at this analysis
표 The vertical profile of U, V obtained by GPS-Sonde and CReSS model at case 1(6 June 2014)
표 The vertical profile of U, V obtained by GPS-Sonde and CReSS model at case 2(13 July 2014)
표 Theoretical forecast skill as a function of lead time for several forecasting methods (adaped from Zipser 1990).
표 Geographic map of Kanto area in Japan. The mark (∎) indicates the radar sites.
표 Characteristics of X-band polarimetric radars.
표 Radar beam height according to range in each elevation angle.
표 Scan strategy of X-band polarimetric radar for (a) EBN and (b) SYK. One volume scan is composed of two RHI scan and twelve PPI scan for 5 minutes.
표 Flowchart of the algorithm for generating the higher spatio-temporal resolution radar data.
표 Diagram of advection vector calculation using the TREC method.
표 (a) Diagram of tendency about Vrg and Vaz. The red line is trend line about full time. (b) Diagram of correction value of Vrg and Vaz through tendency equation.
표 Conceptual model of linear interpolation method with time.
표 Conceptual model of linear interpolation method about elevation at the same time. k is defined by the increment from 0.2° to delta degree with interval of 0.2°.
표 PPI images of reflectivity about observed convective precipitation from EBN radar (around the height of 4.6km) and SYK radar (around the height of 5km).
표 continued.
표 PPI images of reflectivity about observation data at the height of 4.6 km from EBN radar
표 The tendency of (a) the Vrg, (b) corrected Vrg, and (c) the Vaz about the total precipitation time in the 5.6 elevation angle of EBN radar.
표 PPI images of reflectivity about observation data at the height of 4.6 km from EBN radar
표 PPI image of reflectivity about observation data at 0328 UTC from EBN radar
표 CAPPI image of reflectivity at 0333 UTC from EBN radar
표 CAPPI image of reflectivity at 2 km from EBN radar
표 The MDR according to range.
표 The relationships between the EBN radar and SYK radar.
표 (a) The result of the composition between the (b) CAPPI of EBN radar and (d) CAPPI of SYK corrected by equating with regression analysis, and (c) CAPPI of SYK radar.
표 Correlation of cloud microphysical processes in a bulk method.
표 Intensive observation site and information in Jeju Island, Korea.
표 PKNU CReSS forecast system strategy.
표 PKNU CReSS configuration
표 PKNU CReSS domain.
표 The type of images by observation and model.
표 The rainfall distribution by (a) AWS observation, (b) CReSS model over Korean peninsula and (c) AWS observation, and (d) CReSS model over Jeju Island.
표 The weather chart of (a) surface, (b) 900 hPa, (c) 850 hPa ver.1, (d) 850 hPa ver.2, (e) 500 hPa, and (f) 200 hPa over Korean peninsula.
표 The same as Fig 3-3.3.6. but over Jeju Island.
표 The visualization of CReSS real time forecast system on the web site.
표 The rainfall distribution by (a) observation and (b) model in 26 June 2013.
표 The same as Fig. 3-3.3.9 but in 18 June 2013.
표 The same as Fig. 3-3.3.9 but in 19 June 2013
표 The same as Fig. 3-3.3.9 but in 28 May 2014
표 Schematic diagram of how the ensemble samples the uncertainty in the forecast (adaped from Shakti, 2014).
표 Schematic showing the process of generating a STEPS forecast.
표 Rainfall fields are usually hierarchical in structure, with smaller areas of higher intensity rain embedded in larger of lower intensity rain(Courtesy of Dr. Alan Seed).
표 The lifetime of a storm increases as a power law of its size(Courtesy of Dr. Alan Seed).
표 General scheme of the S-PROG algorithm as it has been implemented by Berenguer et al.(2005).
표 In a simple downscaling approach, the coarse-scale grid box is split into 4 boxes, with the rain rate in each of the smaller boxes equal to a randomly chosen number (w ≥ 0) times the coarse-scale rain rate, R.
표 Schematic showing how the advection and NWP forecasts are blended for each forecast
표 Flowchart for generation of ensemble
표 (a) Surface weather map (b) Moisture Flux in 0900 LST 25 Aug. 2014.
표 (a) COMS EIR image in 1400 LST 25 Aug. 2014, (b) The CAPPI image in 1410 LST 25 Aug. 2014.
표 (a) Daily accumulated precipitation of AWS, (b) 12 hr Time series data of AWS at Busan in 25 Aug. 2014. sky color: precipitation detection time, blue
표 Rainfall amount on 25 Aug 2014 in Busan

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