표제지
연구보고서
목차
Contents 6
국문요약 15
Summary 17
제1장 서론 19
제2장 3차원 레이더 합성 바람장 산출 21
2.1. 서론 21
2.2. 기상 레이더 관측 23
2.2.1. 반사도와 시선속도 자료의 관측 영역 26
2.2.2. 시선속도의 접힘 현상(Folding) 29
2.3. 시선속도의 보정 알고리즘 31
2.4. 레이더 자료의 좌표계 변환 38
2.5. 이중 도플러 레이더 바람장 산출 방법 39
2.6. 3차원 레이더 바람장 합성 44
2.7. 요약 및 결론 48
제3장 레이더-AWS 강우강도 산출시스템 개발 49
3.1. 서론 49
3.2. 자료 처리 51
3.2.1. 입력 자료 51
3.2.2. 알고리즘 54
3.3. 실시간 홈페이지 구축 56
3.3.1. 강우강도 합성장 56
3.3.2. 강우강도 사이트 영상 56
3.3.3. 시ㆍ공간분포검증 58
3.4. 사례분석 59
3.4.1. 2006년 여름철 검증 59
3.4.2. 태풍 '에위니아' 61
3.4.3. 고양시 집중호우 67
3.5. 요약 및 결론 73
제4장 초단시간 강수예측 시스템 개발 75
4.1. 서론 75
4.2. 초단시간 강수예측 시스템 알고리즘 76
4.2.1. 입력자료 76
4.2.2. 외삽 알고리즘 78
4.2.3. 병합 알고리즘 82
4.3. 실시간 검증 시스템 구축 88
4.4. 사례분석 92
4.4.1. 2006년 여름철 검증 92
4.4.2. 고양시 집중호우 94
4.4.3. 태풍 에위니아 96
4.5. 요약 및 결론 98
제5장 집중관측 시스템 구축 99
5.1. 서론 99
5.2. 집중관측 시스템 101
5.3. 관측 및 분석 105
5.3.1. 연구용 레이더 105
5.3.2. 마이크로강우레이더 106
5.3.3. 광학강우강도계 108
5.3.4. 광학디스트로메터 110
5.4. 요약 및 결론 117
제6장 요약 및 향후계획 118
참고문헌 119
부록 A. 레이더 운영일지 124
A.1. 2006년 연구용 기상레이더 주요 작업일지 124
A.2. 2006년 기상개황 131
A.3. 레이더 정기정검 일지 139
부록 B. 제7회 기상레이더 워크숍 167
부록 C. 3차원 레이더 바람장 산출 알고리즘 168
부록 D. 2006년 연구성과 218
Table 2.1. The characteristics for each of radar observation is operated by KMA. 25
Table 3.1. The maximum ranges and the number of corresponding included rain gauges over each operational radar coverage of Korea Meteorological Administration. 52
Table 3.2. The verification score of RAR and M-P relationship in summer 2006(threshold 0.1 mmh-¹).(이미지참조) 61
Table 3.3. The verification score of RAR and M-P relationship on Jul. 10, 2006(threshold 0.1 mmh-¹).(이미지참조) 67
Table 3.4. The verification score of RAR and M-P relationship on Jul. 12, 2006(threshold 0.1 mmh-¹).(이미지참조) 73
Table 4.1. Description of VSRF initial data. 76
Table 4.2. The condition of calculating pattern distance. 86
Table 5.1. Classification of precipitation type and accumulated surface rainfall at each ground site. 100
Table 5.2. Characteristics of instruments for the KEOP (Korea Enhanced Observing Program). 103
Table 5.3. Characteristics of ORG measurement 108
Fig. 2.1. The location and its name of radar site is installed by KMA and USA air force. 24
Fig. 2.2. Comparison between observation area of reflectivity and velocity field at 0900 LST on 18 Jul. 2006. Reflectivity and velocity field for Gwangduk[(a),(b)], Kwanak[(c),(d)] and PuSan[(e),(f)] radar respectively. 27
Fig. 2.3. The same as Fig. 2.2 except for JinDo[(a),(b)], GoSan[(c),(d)] and SungSanPo[(e),(f)] radar site. 28
Fig. 2.4. The same as Fig. 2.2 except for JinDo[(a),(b)], GoSan[(c),(d)] and PuSan[(e),(f)] radar site. 30
Fig. 2.5. An overview flowchart of the automated 2D multi-pass velocity dealiasing algorithm. 34
Fig. 2.6. PPI of velocity field before[(a),(c)] and after[(b),(d)] dealiasing process for JinDo radar at 0830 LST on 10 Jul. and 1420 LST on 4 Dec. 2006 respectively. 36
Fig. 2.7. PPI of velocity field before(a) and after(b) dealiasing process for JinDo radar at 0700 LST on 10 Jul. 2006. (c),(d) is the same images as (a),(b) except for PuSan radar at 07000 LST on 10 Jul. 2006. 37
Fig. 2.8. Schematic diagram for the dual Doppler analysis method 39
Fig. 2.9. The relationship between β and error variances(cosec2β). 43
Fig. 2.10. The area S1(β) denoted by stippling. This is the locus of points which subtend between-beam angles in the range [β, π-β], and is outlined by two circles with centers at (0,±dcotβ) and radius, d cosecβ. The radars are located at (±d,0). (a) The area S2(R), denotes by stippling, of points which lie within distance R of both radars (b) (from Kim et al., 1988). 43
Fig. 2.11. Flow chart of 3D composite wind retrieval using dual Doppler radar analysis. 44
Fig. 2.12. Radar observation range for GwangDuk and KunSan radar and overlap area of them. 45
Fig. 2.13. The weather chart and IR image of MTSAT at 0600 LST on 27 Aug. 2006. 46
Fig. 2.14. Web image of dual Doppler radar wind field at 0630 LST on 27 Aug. 2006. 47
Fig. 3.1. Location of the rain gauge (small circle), the radar sites (triangle), and the coverage of the radar composite (inner of solid line). 51
Fig. 3.2. Schematic diagram of the RAR system. 55
Fig. 3.3. The composite radar precipitation of RAR system web page. 57
Fig. 3.4. The radar precipitation of RAR system web page. 57
Fig. 3.5. The spatial distribution verification of RAR system web page. 58
Fig. 3.6. The temporal distribution verification of RAR system web page. 59
Fig. 3.7. (a) Surface weather charts at 0900 LST, (b) MTSAT 1R composite image at 1000 LST, and (c) daily accumulated rainfall on 10 Jul. 2006. 63
Fig. 3.8. Spatial distribution of the observed (upper) and estimated (lower) rainrate on (a), (b), 6, (c), (d), 8, (e), (f), 10, (g), (h), 12 LST July 10, 2006. 64
Fig. 3.9. Scatter diagram of daily rainfall measured at gauges versus daily rainfall estimated by RAR (filled circle) and M-P relationship (hollow circle) of (a) lyang and (b) Saryangdo on July 10, 2006. 66
Fig. 3.10. Same as Fig. 3.7 except on 12 July, 2006. 69
Fig. 3.11. Same as Fig. 3.8 except on (a), (b), 7, (c), (d), 8, (e), (f), 9, (g), (h), 10 LST 12 July, 2006. 71
Fig. 3.12. Same as Fig. 3.9 except (a) Dobong and (b) Goyang on July 12, 2006. 72
Fig. 4.1. Flowchart of the forecasting, merging, and verification process of VSRF. 79
Fig. 4.2. Mechanism of enhancement and dissipation of precipitation by the orographic effect. 81
Fig. 4.3. Schematic diagram of extrapolation with displacement vector and orographic effect. 82
Fig. 4.4. Accuracy of forecast as a function of forecast time. 83
Fig. 4.5. The flowchart of blending process. 85
Fig. 4.6. The schematic diagram for the concept of pattern distance. 85
Fig. 4.7. The temporal weighting function for appling to the blending process. 87
Fig. 4.8. The main page of VSRF sistem web page serviced via http://mis.kma.go.kr. 88
Fig. 4.9. The spatial distribution verification of VSRF system web page serviced via http://mis.kma.go.kr. 89
Fig. 4.10. The temporal distribution verification of VSRF system web page Serviced via http://mis.kma.go.kr. 89
Fig. 4.11. The zoom function of VSRF system web page. 90
Fig. 4.12. The zoom image of VSRF system web page. 91
Fig. 4.13. The method of time delay reduction. 91
Fig. 4.14. CSI of the forecasted (solid), merged (dash), and RDAPS 10 km (dash-dot) precipitation versus the observed (AWS) one during the summer period from June to August in 2006. 93
Fig. 4.15. RMSE of the forecasted (solid), merged (dash) precipitation versus the observed (AWS) one during the summer period from June to August in 2006. 94
Fig. 4.16. Spatial distribution of the (a) observed, (b) VSRF 1hr forecasted, (c) RDAPS 6hr forecasted precipitation at Goyang city (square box) on 09 KST July 12 in 2006. 95
Fig. 4.17. Time series of the 1hr accumulated rainfall (mm/hr) of AWS (box), RAR (line), VSRF 1hr forecasted (dot) and RDAPS 21KST July 11 (dash) and 03KST (line-dot) July 12 on July 12, 2006 at Goyang. 96
Fig. 4.18. Spatial distribution of the (a) AWS observed, (b) MTSAT-1R observed, (c) RDAPS 9hr forecasted precipitation on 12 KST July 10 in 2006. 97
Fig. 4.19. Spatial distribution of the (a) VSRF 1hr, (b) VSRF 2hr, (c) VSRF 3hr forecasted precipitation on 12 KST July 10 in 2006. 97
Fig. 5.1. Location of measurement site for the KEOP. 102
Fig. 5.2. Equipments located on Mokpo:(a) 0.2 mm tipping bucket raingauges, (b) optical disdrometer (PARSIVEL), (c) 0.1, 0.5 mm tipping bucker raingauges, (d) MRR, and (e) ORG. 104
Fig. 5.3. Equipments located on Unnam:(a) MRR and (b) 0.1 mm tipping bucket raingauge. 104
Fig. 5.4. RHI reflectivity images scanned by METRI radar on 22 June (left), 27 June (center), and 1 July (right). 105
Fig. 5.5. (a) Radar reflectivity, (b) rain-rate, (c) liquid water contents, and (d) falling velocity of Drops measured by MRR from 03LST to 09LST 1 July 2006. 107
Fig. 5.6. (a) Rainfall intensity and the number of tips of raingauge, (b) moving hourly accumulated rainfall, (c) temperature, and (d) mean level presure and relative humidity, and (e) horizontal wind of AWS measured by ORG and AWS on 1 July 2006. 109
Fig. 5.7. Side views of optical disdrometer in tunnel housing. 110
Fig. 5.8. Signals of particles falling through the light sheet. (a) small and large particles, (b) raw signal from the sensor, and (c) inverted and amplified signal after thresholding for measuring purposes. 111
Fig. 5.9. Schematic concept for using velocity and size information to detect different types of hydrometeors. 113
Fig. 5.10. PASIVEL products display window:(a) current weather, (b) intensity, weather code, radar reflectivity, MOR visibility, temperature and etc, (c) size-velocity distribution, and (d) time series of product at Mokpo on 26 May 2006. 115
Fig. 5.11. Same as Figure 5.8 except for 13 December 2005. 116