본문 바로가기 주메뉴 바로가기
22대 대한민국 국회 국회정보길라잡이 국회의원검색
로그인 회원가입 English 국회부산도서관

전체메뉴

HOME

정보검색

소장정보 검색

국회도서관 홈으로 정보검색 소장정보 검색

결과 내 검색

동의어 포함

고급검색

인명/단체명

저자정보 상세정보
인명/단체명을 입력하세요.

키워드

  • 대표어

  • 외국어

-

목차보기

[표지] 1

연구 보고서 2

목차 3

요약문 12

Summary 16

제1장 서론 22

제2장 재해기상 추적·목표관측체계 구축 및 활용 연구 24

제1절 재해기상 추적·목표관측체계 구축 및 운영 24

1. 2022년 기상관측차량운영 24

2. 2022년 재해·재난현장 관측 지원 26

제2절 추적·목표관측자료를 활용한 재해기상 연구 37

1. 강원 영동지역 재해기상 사례 분석 연구(대설) 37

2. 강원 영동지역 재해기상 사례 분석 연구(강풍) 45

3. 재해기상 사례 분석 연구(호우) 52

4. 해무다발지역 종합관측망 운영 및 기상드론 집중관측 수행 58

5. 이중카메라(가시 및 적외) 탑재 기상드론의 안개 현장 활용 기술 개발 70

제3장 재해기상 메커니즘 분석 및 예측성 향상 연구 76

제1절 도로위험 기상정보에 대한 관측 체계 구축 및 예측정보 개선 76

1. 일반자동차에서 관측되는 도로기상정보의 자료수집 및 활용체계 구축 76

2. 도로살얼음 발생 가능성 예측모형 개선 및 검증 85

제2절 겨울철 강설에 의한 교통분야 영향 분석 100

1. 지역별 교통사고 발생에 대한 강설의 영향 100

2. 강설시 교통량 패턴 분석 111

제3절 비정형 기상재해 자료 활용가능성 연구 120

제4절 특보체계 개선을 위한 기준 및 구역 세분화 연구 127

1. 강수 관측자료를 이용한 강수분포 및 변화 경향 분석 127

2. 부산·울산 지역 특보구역 세분화(안) 산출 133

제4장 요약 및 결론 138

참고 문헌 142

List of Tables 5

Table 2.1.2.1. Summary of high impact weather observation support(forest... 26

Table 2.1.2.2. Summary of Gangwon Yeongdong Observations in 2022 28

Table 2.1.2.3. Period of observation by observation number for day and night 32

Table 2.1.2.4. Summary of heavy rainfall observation in metropolitan area in 2022 34

Table 2.2.1.1. Snow depth and precipitation of the 4 weather stations in the... 38

Table 2.2.2.1. Wind speed and direction at maximum and strong wind... 47

Table 2.2.3.1. Overview of the observational instruments used 54

Table 2.2.4.1. Specification of weather observation equipments installed for... 59

Table 2.2.4.2. Specification of weather drone used for sea fog observation 60

Table 2.2.4.3. Specification of observation sensor installed on the weather drone 61

Table 2.2.4.4. Weather drone observation summary 61

Table 2.2.4.5. Monthly occurrence of sea fog in integrated observation network 62

Table 2.2.4.6. Classification results of sea fog and mist cases 64

Table 2.2.5.1. Standard deviation, Skewness and Kurtosis of frequency... 73

Table 3.1.1.1. Deviation by observation equiment according to the test observation 82

Table 3.1.2.1. Improvement Information of Black Ice Prediction Program 86

Table 3.1.2.2. Stationary observation station... 88

Table 3.1.2.3. Information for Bias analysis of Black Ice... 90

Table 3.1.2.4. Statistical analysis of bias between Black Ice Predictive Program... 91

Table 3.1.2.5. Correlation between Black Ice Predictive Program and observation... 91

Table 3.1.2.6. Statistical results of risk calculated from Black Ice Predictive... 91

Table 3.1.2.7. Details information of Fig 3.1.2.4 92

Table 3.1.2.8. Statistical results of risk calculated from Black Ice Prediction... 93

Table 3.1.2.9. Bias of tendency of Black ice predict model 95

Table 3.1.2.10. Average of Bias and standard deviations from October to... 95

Table 3.1.2.11. Average of RMSE by forecast Time 96

Table 3.1.2.12. Standard deviation of RMSE by forecast Time 96

Table 3.1.2.13. Average of Correlation Coefficients by forecast Time 96

Table 3.1.2.14. Average of Bias and standard deviation of stationary observation... 97

Table 3.1.2.15. Average of RMSE of stationary observation and predictive... 98

Table 3.1.2.16. Average of Correlation coefficient and Coefficient of... 98

Table 3.2.1.1. Variables of traffic accidents data 102

Table 3.2.1.2. Traffic Accident data used in the study 103

Table 3.2.1.3. Effects on traffic accidents by cumulative snowfall 108

Table 3.2.1.4. Effects of snowfall on traffic accidents in Seoul and Gwangju 108

Table 3.2.1.5. Logistic regression classification accuracy by region 109

Table 3.2.2.1. An index for determining the optimal number of clusters... 115

Table 3.3.1.1. The number of weather disasters inputted in Weather Impact DB... 121

Table 3.3.1.2. The order of weather disaster by weather types for 2021, 2022... 125

List of Figures 7

Fig. 2.1.1.1. Improvement of charging system of MOVE 1 and 2 24

Fig. 2.1.2.1. Photos taken at the forest fire support sites (a) 24th February, (b)... 27

Fig. 2.1.2.2. Time series of (a-b) AWS, (c-d) vertical profile of relative... 29

Fig. 2.1.2.3. Time series of (a, c) AWS, (b, d) Skew T-log P chart, (a-b) 8-9,... 30

Fig. 2.1.2.4. Photos of the strong wind observation (a-b) 9th April in Sokcho... 30

Fig. 2.1.2.5. Observation route of road weather... 31

Fig. 2.1.2.6. Thermal mapping of (a) air temperature and (b) road surface... 32

Fig. 2.1.2.7. Thermal mapping of (a) air temperature and (b) Road surface... 33

Fig. 2.1.2.8. Time series of AWS 8-11, August. The precipitation range is from... 35

Fig. 2.1.2.9. Time series of (a) vertical profile of relative humidity(shading)... 36

Fig. 2.1.2.10. Photos of heavy rainfall observation in Icheon-si (a) 23th June,... 36

Fig. 2.2.1.1. Distribution of 24 hr accumulated precipitation over the... 39

Fig. 2.2.1.2. Surface weather charts for (a) 0000UTC 1, (b) 0600UTC 1,... 41

Fig. 2.2.1.3. Vertical profiles for (a) equivalent potential... 43

Fig. 2.2.2.1. Time (LST)-height cross section of horizontal... 49

Fig. 2.2.2.2. Skew T-log P chart from Bukgangneung and Gangneung-Wonju... 50

Fig. 2.2.3.1. Map of the study area. The right magnified map shows the... 53

Fig. 2.2.3.2. (a, b) Surface weather chart at (upper left) 2100UTC 23 June and... 55

Fig. 2.2.3.3. Time series of the precipitable water vapor (PWV) at the Geochang... 56

Fig. 2.2.3.4. Level-averaged vertical profiles of (a,d) specific humidity and (b,e)... 57

Fig. 2.2.4.1. Location of integrated observation network for the sea fog of the... 59

Fig. 2.2.4.2. Monthly average meteorological characteristics of sea fog occurrence... 62

Fig. 2.2.4.3. Sea fog vertical observation results on June 22. (a)... 65

Fig. 2.2.4.4. Same as Fig. 2.2.4.3 except on June 23 66

Fig. 2.2.4.5. Same as Fig. 2.2.4.3 except on May 17 67

Fig. 2.2.4.6. Same as Fig. 2.2.4.3 except on May 25 68

Fig. 2.2.5.1. Dual camera-attached weather drone 71

Fig. 2.2.5.2. RGB DN frequency distribution for visible images taken by weather... 72

Fig. 2.2.5.3. Flow chart for the development of fog emphasis images using... 74

Fig. 2.2.5.4. Results of fog pixels emphasis according to flow chart for the... 74

Fig. 2.2.5.5. Visible and infrared images of horizontal and vertical observation... 75

Fig. 3.1.1.1. The state of the AWS near the road in... 77

Fig. 3.1.1.2. Road surface temperature sensor... 78

Fig. 3.1.1.3. OBD and the temperature sensor of the vehicle 79

Fig. 3.1.1.4. The road weather observation data collection system of the OBD 79

Fig. 3.1.1.5. Comparison of altitude and road weather observations according to... 80

Fig. 3.1.1.6. Correlation of observation sensors 81

Fig. 3.1.1.7. Example of black ice risk area notification using road... 83

Fig. 3.1.1.8. Provide weather information using a road surface temperature 84

Fig. 3.1.2.1. Location of road weather... 88

Fig. 3.1.2.2. Data matching method of Mobile observation and Black... 89

Fig. 3.1.2.3. Matching information of Mobile observation and prediction model by... 89

Fig. 3.1.2.4. Observation and black ice prediction route information,... 92

Fig. 3.1.2.5. AI method information of before and after improvement that road... 93

Fig. 3.1.2.6. Risk level map of Black Ice... 94

Fig. 3.2.1.1. Study areas and... 102

Fig. 3.2.1.2. Trends in the rates of accident and injury during snowfall (left... 105

Fig. 3.2.2.1. A map showing the study areas, roads, traffic volume and... 112

Fig. 3.2.2.2. Structure of self-organizing map... 113

Fig. 3.2.2.3. Annual trend of snowfall and traffic volume during snowfall... 116

Fig. 3.2.2.4. Correlation between snowfall intensity and traffic volume... 117

Fig. 3.2.2.5. Correlation between snowfall duration and traffic volume... 118

Fig. 3.2.2.6. Traffic volume pattern during snowfall by study area. Gray dash... 118

Fig. 3.3.1.1. The main page of Impact-based forecast system(left) and the list... 121

Fig. 3.3.1.2. Monthly number of weather disaster for each year... 122

Fig. 3.3.1.3. Time series of weather disaster from June to October... 124

Fig. 3.3.1.4. The number of weather disaster by regions for entire... 125

Fig. 3.4.1.1. The distribution of yearly averaged precipitation for South... 128

Fig. 3.4.1.2. The time series of averaged precipitation for South Korea... 129

Fig. 3.4.1.3. The time series of monthly averaged precipitation for each... 130

Fig. 3.4.1.4. The distribution of annual heavy rainfall damage except for typhoon... 131

Fig. 3.4.2.1. Proposal of special warning zone segmentation based on grouping... 135

이용현황보기

재해기상 목표관측·분석·활용기술 개발 = Developing target observation, analysis and application technology for high impact weather 이용현황 표 - 등록번호, 청구기호, 권별정보, 자료실, 이용여부로 구성 되어있습니다.
등록번호 청구기호 권별정보 자료실 이용여부
0003004263 551.64 -23-7 서울관 서고(열람신청 후 1층 대출대) 이용가능
0003004264 551.64 -23-7 서울관 서고(열람신청 후 1층 대출대) 이용가능

추천서가 (다양한 추천 자료를 만나보세요)

권호기사보기

권호기사 목록 테이블로 기사명, 저자명, 페이지, 원문, 기사목차 순으로 되어있습니다.
기사명 저자명 페이지 원문 기사목차

권호

기사명 원문보기 기사목차
닫기