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[표지] 1

연구 보고서 2

목차 3

요약문 14

Summary 18

제1장 서론 24

제2장 위험기상 분석 및 예보기술 고도화 26

제1절 여름철 2차 강수량 극대기의 장기 변화 분석 26

1. 서론 26

2. 자료 및 연구방법 27

3. 결과 31

제2절 우리나라 대설 유형 분류 및 특성 분석 37

1. 연구배경 37

2. 우리나라 대설 사례의 객관적 유형 분류 39

3. 영동 대설 사례의 객관적 유형 분류 49

제3절 장마철 강수 예보 지원을 위한 진단인자의 현업활용체계 강화 55

1. 개요 55

2. 장마진단 인자의 개선 56

3. 사례 분석 및 예보 지원 62

제3장 수도권 위험기상 입체관측 및 예보활용 기술 개발 70

제1절 수치모델 기반 2022년 여름철 고층집중관측 영향평가 실험 70

1. 실험 개요 70

2. 실험 결과 75

제2절 수도권 집중관측자료 보정 및 품질관리기술 개선 95

1. 연직강우레이더 관측자료 품질관리기법 개발 95

2. 광학우적계 관측자료 품질관리기법 개발 104

3. 윈드라이다 관측자료 품질관리 기술 적용 108

제4장 요약 및 결론 113

참고 문헌 116

List of Tables 5

Table 2.1.1. Variable, time, and spatial resolution of the data 28

Table 2.1.2. Comparison table of rainy periods 33

Table 3.1.1. The configuration of numerical model 71

Table 3.1.2. Data assimilation experiment method and its input observations 72

Table 3.1.3. Contingency table for verification of the precipitation 81

Table 3.1.4. 24 hours accumulated precipitation BIAS, root mean square error... 87

Table 3.1.5. Improvement rate of BIAS, root mean square error(RMSE),... 94

Table 3.2.1. FUZZY membership function 97

Table 3.2.2. a and b values for each membership variable 98

Table 3.2.3. The weight for each characteristic variable 99

List of Figures 6

Fig. 2.1.1. The location of (a) 15 and (b) 63 ASOS stations during the... 27

Fig. 2.1.2. Annual distribution of the climatological daily rainfall(mm... 29

Fig. 2.1.3. Example of typhoon influence radius and ASOS stations... 31

Fig. 2.1.4. (a) Annual distribution of the climatological daily... 32

Fig. 2.1.5. (a,c,e) Annual time series of daily typhoon rainfall(mm... 34

Fig. 2.1.6. (a) Frequency of typhoon track and (b) their trends(number year-1)... 35

Fig. 2.2.1. Monthly frequencies(left) and definitions(right) by 24-types 37

Fig. 2.2.2. Type classification of heavy snowfall by pressure systems 39

Fig. 2.2.3. Locations of ASOS stations and the percentage of cases satisfying... 40

Fig. 2.2.4. Averaged sea-level pressure anomaly patterns of heavy snowfall... 42

Fig. 2.2.5. Percentage of cases satisfying the heavy snowfall criteria for ASOSs... 44

Fig. 2.2.6. Averaged 925hPa geopotential heights(blue lines), temperatures... 46

Fig. 2.2.7. Averaged 500hPa geopotential height anomaly patterns in each... 48

Fig. 2.2.8. Averaged sea-level pressure anomaly patterns of heavy snowfall... 50

Fig. 2.2.9. Temporal changes for ±48 hr snowfall at Gangneung(red) and... 51

Fig. 2.2.10. Averaged 925hPa geopotential heights(blue lines), temperatures... 52

Fig. 2.2.11. Averaged 850hPa geopotential heights(blue lines), temperatures... 53

Fig. 2.2.12. Averaged 850hPa geopotential heights(blue lines), temperatures... 54

Fig. 2.3.1. Cartesian coordinate system(x, y) and... 57

Fig. 2.3.2. Schematic diagram when stretching deformation becomes negative... 58

Fig. 2.3.3. Spatial distribution example and information of stretching deformation 58

Fig. 2.3.4. Spatial distribution example of IVT(left) and IWV(right) 60

Fig. 2.3.5. Averaged weather information on the onset date of Changma season... 61

Fig. 2.3.6. Temperal change of 5-day moving averaged 500hPa geopotential... 61

Fig. 2.3.7. Weather charts for case study at 09KST 2 July 2021. Composite... 63

Fig. 2.3.8. Same as in Fig. 2.3.7 but at 09 KST 03 July 2021 64

Fig. 2.3.9. Weather charts for case study at 09KST 2 July 2021. Composite... 65

Fig. 2.3.10. Various uses of stretching deformation and IVT charts 67

Fig. 2.3.11. IVT charts predicted 72 hours ago(3 days before onset and retreat... 69

Fig. 3.1.1. The model domain for numerical... 70

Fig. 3.1.2. Radiosonde stations for the intensive... 72

Fig. 3.1.3. (a) 00 UTC, August 9, 2022 weather chart, (b) AWS 48 hours... 73

Fig. 3.1.4. Time series of precipitation, wind and temperature... 74

Fig. 3.1.5. Data assimilation time and period of control... 74

Fig. 3.1.6. 18 UTC, August 7, 2022, 850 hPa wind and difference[ms⁻¹] of... 76

Fig. 3.1.7. 18 UTC, August 7, 2022, 850 hPa temperature and difference[℃] of... 77

Fig. 3.1.8. 18 UTC, August 7, 2022, 850 hPa water vapor mixing ratio and... 78

Fig. 3.1.9. 18 UTC, August 7, 2022, 850 hPa rain mixing ratio and difference... 79

Fig. 3.1.10. Elevation and AWS station location in... 80

Fig. 3.1.11. Averaged precipitation of AWS from 19 UTC, August 9, 2022... 82

Fig. 3.1.12. (a-k) 6 hour accumulative precipitation distribution map of AWS... 84

Fig. 3.1.13. 24 hours(01 UTC, August 8, 2022 - 00 UTC, August 9,... 85

Fig. 3.1.14. Scatter plot of AWS and experiments for 24 hours(01 UTC,... 86

Fig. 3.1.15. 6 hours(01 UTC, August 8, 2022 - 06 UTC, August 8, 2022)... 88

Fig. 3.1.16. 6 hours(07 UTC, August 8, 2022 - 12 UTC, August 8, 2022)... 89

Fig. 3.1.17. 6 hours(13 UTC, August 8, 2022 - 18 UTC, August 8, 2022)... 90

Fig. 3.1.18. 6 hours(19 UTC, August 8, 2022 - 00 UTC, August 9, 2022)... 91

Fig. 3.1.19. Scatter plot of AWS and experiments for 6 hours(01 UTC,... 92

Fig. 3.1.20. 1 hour precipitation(01 UTC, August 8, 2022 - 00... 93

Fig. 3.2.1. Example of MK12 algorithm application 96

Fig. 3.2.2. Flow chart of Quality Control(QC) algorithm used on the MRR dataset 96

Fig. 3.2.3. Initial membership function for each membership variable 99

Fig. 3.2.4. The values of membership function for each characteristic variable... 100

Fig. 3.2.5. Before and after the adaptive update of membership function for reflectivity 101

Fig. 3.2.6. Before fuzzy(Static) and after adaptive update fuzzy(Static+Adaptive)... 102

Fig. 3.2.7. Examples of applying the QC for mixed cases of noise and precipitation... 103

Fig. 3.2.8. Examples of applying the QC for precipitation cases at SWN and SEL 104

Fig. 3.2.9. PARSIVEL data in MSC format 105

Fig. 3.2.10. Frequencies of precipitation particles in fall velocity-diameter before and... 106

Fig. 3.2.11. Same as Fig. 3.2.10, except for SWN 107

Fig. 3.2.12. Same as Fig. 3.2.10, except for SEL 107

Fig. 3.2.13. Flow chart of QC algorithm used on the Wind lidar dataset 108

Fig. 3.2.14. Texture(Radial/azimuthal)-SNR Frequency distribution of Wind3D6000 Wind lidar 109

Fig. 3.2.15. PPI images before applying the QC algorithm at an elevation angle of 1.0° at... 110

Fig. 3.2.16. Same as Fig.3.2.15, except after applying the QC algorithm 110

Fig. 3.2.17. PPI images before applying the QC algorithm at an elevation angle of 6.0° at... 111

Fig. 3.2.18. Same as Fig.3.2.17, except after applying the QC algorithm 111

Fig. 3.2.19. RHI images before applying the QC algorithm at an azimuth angle of 270° at... 112

Fig. 3.2.20. Same as Fig.3.2.19, except after applying the QC algorithm 112

이용현황보기

위험기상분석 및 예보기술 고도화 = Advancing severe weather analysis and forecast technology ; 수도권 위험기상 입체관측 및 예보활용 기술 개발 = Observing severe weather in Seoul metropolitan area and developing its application technology for forecasts 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
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