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Contents 8
제1장 서론 27
제2장 이중편파레이더 자료처리 기술 개발 29
제1절 레이더 사이트별 자료처리 최적화 기술 개발 29
1. 서론 29
2. 원거리(480 ㎞) 레이더자료 품질관리 기술 개발 30
3. 신규 이중편파레이더 강수· 비강수에코 정보 현업제공 39
4. 이중편파레이더 반사도, 차등반사도 보정오차 산출 45
5. 요약 및 결론 52
제2절 대기수상체분류 적용 및 온도자료에 따른 결과 분석 53
1. 서론 53
2. 신규 이중편파레이더 대기수상체분류 현업제공 53
3. 수치모델 온도자료에 따른 대기수상체분류 57
4. 요약 및 결론 60
제3절 부분 빔 차폐 영역의 반사도 보정을 통한 강우추정개선 61
1. 서론 61
2. 자료 63
3. 방법론 66
4. 결과 75
5. 요약 및 결론 81
제4절 무인 비행 장치를 이용한 레이더 관측환경조사 83
1. 서론 83
2. 자료 84
3. 연구방법 85
4. 연구결과 90
5. 요약 및 결론 98
제3장 예보지원을 위한 이중편파레이더 활용기술 개발 99
제1절 이중편파레이더 강우량 추정 산출 기술 개발·평가·검증 99
1. 서론 99
2. 자료 100
3. 이중편파레이더 강우량 추정 산출 기술 개선 101
4. 사례분석 121
5. 검증결과 125
6. 요약 및 결론 129
제2절 이중편파레이더를 이용한 스톰탐지· 예측 기반 기술 개발 130
1. 서론 130
2. 연구 자료 132
3. 연구 방법 133
4. 사례 분석 결과 140
5. 요약 및 결론 147
제3절 레이더 강설강도 산출 기술 개발 148
1. 서론 148
2. 자료 및 사례 150
3. 레이더 강설강도 추정 알고리즘 153
4. 결과 159
5. 요약 및 결론 163
제4절 다중레이더 3차원 바람장 적용 기반기술 개발 164
1. 서론 164
2. 배경바람장 166
3. 배경바람장을 이용한 시선속도 접힘풀기 173
4. 요약 및 결론 181
제5절 변분법 기반 다중레이더 바람장 산출 기술 개발 182
1. 서론 182
2. 변분법 기반 다중레이더의 3차원 바람장 산출 방법 183
3. 알고리즘 개발 및 검증 190
4. 요약 및 결론 201
제6절 이중편파레이더 강우량 예측 현업 활용 기반기술 개발 202
1. 서론 202
2. 레이더강수실황예측모델 소개 203
3. 레이더강수실황예측모델(3 종) 정확도 검증 및 개선 206
4. 레이더강수실황예측모델 이동벡터 산출 개선 216
5. 요약 및 결론 220
제4장 범부처 레이더 융합 활용기술 개발 221
제1절 레이더자료 품질감시기술 개발 및 분석 221
1. 서론 221
2. 이중편파 강우량 추정 산출식 개선 221
3. 용인테스트베드레이더 관측오차 분석 227
4. 이중편파레이더 특성지표를 이용한 자료품질 감시 231
5. 요약 및 결론 233
제2절 범부처 확대적용을 위한 이중편파레이더 강우량 추정 및 합성기술 개발 234
1. 서론 234
2. 다중고도각기반 레이더 추정 강우량 현업 제공 234
3. 다중고도각기반 레이더 추정 강우량 현업운영 239
4. 요약 및 결론 240
제3절 위험기상 입체분석을 위한 강수 연직구조 시계열 241
1. 서론 241
2. 사례분석 242
3. 요약 및 결론 247
제4절 레이더 기반 격자 강우량 산출기술 개발 248
1. 서론 248
2. 레이더 기반 격자 강우량 산출기술 기법 249
3. 레이더 기반 격자 강우량 의 우량계 자료처리 프로그램 개발 250
4. 레이더-우량계 평균 관계식 도출 및 적용 253
5. 레이더 강우량 오차 역가중 보정방법(Error Inversed Weighted Correction) 적용 265
6. 요약 및 결론 271
제5장 연구용 레이더 신기술 선행연구 및 현업활용기술 272
제1절 연구용 레이더 운영기술 개발 272
1. 서론 272
2. 운영현황 273
3. 정밀점검 및 결과 276
제2절 연구용 소형기상레이더 관측망 구축 278
1. 서론 278
2. 설치, 위치, 제원 279
3. 사례분석(대형기상레이더 VS 소형기상레이더) 284
4. 요약 및 결론 295
제3절 연구용소형기상레이더 관측망 자료분석 296
1. 서론 296
2. 하이브리드 펄스 송신 기법 관측자료 특성 분석 297
3. 하이브리드 펄스의 경계 영역 내 불연속 완화기법 개발 302
4. 요약 및 결론 306
제4절 위상배열레이더 기술개발현황 분석 307
1. 서론 307
2. 국외 위상배열레이더 기술개발 현황 307
3. 요약 및 결론 316
제6장 요약 및 향후계획 317
참고문헌 320
부록 331
부록 A. 연구용레이더(무안) 장애발생 및 대응 331
A.1. 연구용레이더(무안) 장애발생 331
A.2. 로터리 조인트 331
A.3. Dehydrator 메인보드 교체 및 압력 표출계 설치 332
A.4. 피드혼 334
A.5. RCM(Radar Control Monitor) 신규 설치 337
A.6. 모듈레이터 승압 339
A.7. 태양 보정 340
A.8. 기타 점검사항 341
A.9. 운영실 누수 344
부록 B. 연구용소형기상레이더 운영 매뉴얼 346
B.1. 안전예방 346
B.2. WeatherScout 346
B.3. IRIS 356
B.4. IRIS Focus 361
부록 C. 연구용 소형기상레이더 전자파 측정 364
C.1. 전자파 의미 364
C.2. 전자기파 종류 365
C.3. 소형기상레이더 전자파 366
C.4. 전자파 측정장비 368
C.5. 소형기상레이더 전자파 측정 및 결과 370
부록 D. 진천레이더비교관측소 관측일지 377
D.1. 서론 377
D.2. 2차원 영상 우적계 점검 및 운영 378
D.3. 주요 장애 사례 381
부록 E. 레이더자료 활용기술 확산 및 교육 383
E.1. 범부처 레이더자료 공동활용을 위한 기술공유 383
E.2. 2017년 국제 기상-수문레이더 컨퍼런스 개최 384
E.3. 2017년 "레이더영상 분석과정" 현장맞춤형 교육 실시 385
부록 F. 2017년 연구성과 386
Table 2.3.1. General characteristics of Jindo (JNI) S-band dual-polarization radar 64
Table 2.3.2. List of rainfall events used for verification. 65
Table 2.3.3. Condition of T-matrix algorithm. 73
Table 2.4.1. List of weather radars operated by KMA 83
Table 2.4.2. Specifications of Phantom 4 84
Table 2.4.3. Specifications of camera in Phantom 4 85
Table 2.4.4. Specifications of digital elevation model 85
Table 2.4.5. Date of photograph by location 86
Table 2.4.6. Authorization list of Radar site by location 87
Table 3.1.1. Threshold values of the identification parameters for ground clutter(GC)... 105
Table 3.1.2. Threshold values of the identification parameters for non-rain echo identification. 107
Table 3.1.3. Threshold values of the relations for rainfall estimation. 119
Table 3.2.1. Features of S-band weather radar located at Jindo 132
Table 3.2.2. Radar data used to develop the storm detection algorithm 132
Table 3.2.3. Definition of parameters used to identify the convective cell 135
Table 3.2.4. Algorithm of bright band identification 136
Table 3.2.5. Radar data used to analysis of parameter distribution in each storm 138
Table 3.2.6. Distribution of parameter in each classification 139
Table 3.3.1. Z-S relationships according to the type of snow 149
Table 3.3.2. Case studies of snowfall events in Korean Peninsula 152
Table 3.3.3. Description of steps for radar snowfall estimation using the web-bulb... 158
Table 3.4.1. Operational scan modes of S-band radars of KMA. 165
Table 3.4.2. Information of the wind observation data for Radial velocity unfolding. 166
Table 3.4.3. Information of the cases for the radial velocity unfolding. 173
Table 3.5.1. The modularization of the retrieval algorithm for 3-dimensional wind field... 194
Table 3.5.2. The basic configuration for retrieval of wind field from multiple variational... 196
Table 3.5.3. The validation score (root mean square error and correlation coefficient) of wind... 201
Table 3.6.1. Nowcasting systems used in operation for KMA 205
Table 3.6.2. A 2×2 contingency table in which the observation and model... 207
Table 4.1.1. Difference of raindrop axis ratio relations 223
Table 4.1.2. List of different polarimetric rainfall relations used for rainfall estimations... 224
Table 4.1.3. Different polarimetric rainfall relations 226
Table 4.1.4. Condition of T-matrix algorithm 228
Table 4.2.1. The elevation angles of each radar sites used HSR process. 235
Table 4.4.1. The list of 10 cases in 2016. 251
Table 4.4.2. The average verification scores during 10 rainfall cases in 2016. 253
Table 4.4.3. Coefficient A and B of site mean Z-R relationship 260
Table 4.4.4. The quantitative verification scores for case in 13~15th Aug 2017 270
Table 4.4.5. The quantitative verification scores for case in 25~16th Jun 2017 270
Table 5.1.1. Specifications of the ARC-250PM 274
Table 5.2.1. EWR 750DP Hardware Specifications 280
Table 5.2.2. Recommended Specifications for running WeatherScout 283
Table 5.3.1. Parameters and thresholds used to remove non-meteorological echoes 303
Table 5.4.1. Characteristics analysis of Radar of parabolic antenna and MPAR 311
Fig. 2.1.1. BRI 480㎞ radar data. 31
Fig. 2.1.2. Same as Fig. 2.1.1, except for KWK radar at 2130 KST 10 Oct 2017. 32
Fig. 2.1.3. Same as Fig. 2.1.1, except for MYN radar at 1330 KST 13 May 2017. 33
Fig. 2.1.4. Same as Fig. 2.1.1, except for PSN radar at 2100 KST 12 May 2017. 34
Fig. 2.1.5. Same as Fig. 2.1.1, except for JNI radar at 1200 KST 14 July 2017. 35
Fig. 2.1.6. Same as Fig. 2.1.1, except for GDK radar at 1630 KST 3 Nov 2017. 36
Fig. 2.1.7. Same as Fig. 2.1.1, except for GSN radar at 2030 KST 29 Nov 2017. 37
Fig. 2.1.8. 480㎞ composition image of dual-polarization radar. 38
Fig. 2.1.9. Algorithm of non-precipitation echo identification 39
Fig. 2.1.10. The concept of Non-precipitation echo's identification 39
Fig. 2.1.11. KWK dual-polarization 0.5˚ PPI image at 0700 KST 1 Nov 2017. 40
Fig. 2.1.12. Same as Fig. 2.1.11 except for PSN dual-polarization 0.5˚ PPI image at... 41
Fig. 2.1.13. Same as Fig. 2.1.11 except for GRS dual-polarization 0.09˚ PPI image at... 42
Fig. 2.1.14. Same as Fig. 2.1.11 except for GDK dual-polarization 0.0˚ PPI image at... 43
Fig. 2.1.15. Same as Fig. 2.1.11 except for GSN dual-polarization 0.5˚ PPI image at... 44
Fig. 2.1.16. Flow chart for calculating the calibration bias of reflectivity. 45
Fig. 2.1.17. Temporal variation of the calibration bias of reflectivity on 20 August. 2017. 47
Fig. 2.1.18. Time-series of the calibration bias of reflectivity from May to October 2017 48
Fig. 2.1.19. Flow chart for calculating the calibration bias of differential reflectivity. 49
Fig. 2.1.20. Azimuth-height diagram of (a) reflectivity, (b) correlation coefficient... 50
Fig. 2.1.21. Temporal variation of the calibration bias of reflectivity on 20 August 2017. 50
Fig. 2.1.22. Time-series of the calibration bias of differential reflectivity from May to... 51
Fig. 2.2.1. Result of Hydrometeor Classification on (a) Gwangdeoksan radar(elev. angle... 54
Fig. 2.2.2. Result of Hydrometeor Classification on (a) Gosan radar(elev. angle 1.1˚) and... 54
Fig. 2.2.3. Result of Hydrometeor Classification on Garisan radar(elev. angle 0.5˚) at... 55
Fig. 2.2.4. Example of web-page for providing a hydrometeor classification of... 56
Fig. 2.2.5. Example of hydrometeor classification using KLAPS at (a) 1400 KST, (b) 1500... 57
Fig. 2.2.6. Temperature field in hydrometeor classification algorithm at (a) 1400 KST,... 58
Fig. 2.2.7. Result of hydrometeor classification algorithm using KLAPS at (a) 2100... 59
Fig. 2.2.8. Temperature field in hydrometeor classification algorithm at (a) 2100 KST,... 60
Fig. 2.3.1. Geographical distribution of automatic weather station (AWS) within 180 ㎞... 65
Fig. 2.3.2. Flow chart of reflectivity correction in partial beam blockage area. 66
Fig. 2.3.3. PPIs of beam blockage simulation at elevation angles of (a) 0.0˚ and (b) 0.4˚... 67
Fig. 2.3.4. PPIs of reflectivity (a) before and (b) after selection of rain field at the... 69
Fig. 2.3.5. (a) PPI of reflectivity at the elevation angle of 0.0˚ of JNI radar at 0520 KST... 72
Fig. 2.3.6. Azimuthal variation of (a) parameter á and (b) BBF at the unblocked... 72
Fig. 2.3.7. Z bias caused by blockage calculated by using (a) geometric correction,... 76
Fig. 2.3.8. Spatial distribution of accumulated rainfall estimated from (a) NDNP,... 78
Fig. 2.3.9. Scatter plots of event total (upper panel) and hourly (bottom panel) radar... 79
Fig. 2.3.10. Statistic measures of rainfall estimation from NDNP (black), YDNP (blue),... 80
Fig. 2.4.1. Photograph of Phantom 4 84
Fig. 2.4.2. Authorization Chart for UAV 86
Fig. 2.4.3. Max tilt angle of gimbel(Blue) and max angle of images(Red) 87
Fig. 2.4.4. Flow chart of digital panorama 88
Fig. 2.4.5. Mathematical beam propagation 89
Fig. 2.4.6. (a)The UAV panorama and (b)digital panorama from KSN 91
Fig. 2.4.7. Beam blockage simulation(20 km range) at elevation angles of (a)0.5˚ and (b)0.9˚ at KSN 91
Fig. 2.4.8. Same as Fig. 2.4.6 but from JNI 92
Fig. 2.4.9. Same as Fig. 2.4.7 but from JNI at 0.0˚ 92
Fig. 2.4.10. Same as Fig. 2.4.6 but from GNG 94
Fig. 2.4.11. Same as Fig. 2.4.7 but from GNG at (a)0.4˚, (b)1.8˚ and (c)3.9˚ 94
Fig. 2.4.12. Same as Fig. 2.4.6 but from PSN 95
Fig. 2.4.13. Same as Fig. 2.4.7 but from PSN at (a)0.5˚and (b)1.0˚ 95
Fig. 2.4.14. Same as Fig. 2.4.6 but from KWK 97
Fig. 2.4.15. Same as Fig. 2.4.7 but from KWK at 0.5˚ 97
Fig. 2.4.16. Reflectivity of KWK radar PPI on 0200KST 20 Aug. 2017 at (a)0.5˚and (b)3.8˚ 98
Fig. 3.1.1. Topography of Korean peninsula and the observational area of the operational radar network of KMA(black) and MOLIT (blue). 100
Fig. 3.1.2. Distribution of Automatic Weather Station(AWS) in KMA. 100
Fig. 3.1.3. Schematic diagram of Hybrid Surface Rainfall(HSR). 102
Fig. 3.1.4. Flowchart of Hybrid Surface Rainfall(HSR) of dual-polarization radar. 102
Fig. 3.1.5. The images of (a) Observed-unfiltered reflectivity(DZ), (b) Observed-filtered... 105
Fig. 3.1.6. The images of (a) Observed-unfiltered reflectivity(DZ), (b) Observed... 106
Fig. 3.1.7. The images of (a) ZH-weighted cross-correlation coefficient, (b)...(이미지참조) 108
Fig. 3.1.8. The images of (a) ZH before PIA correction, (b) Path-integrated attenuation,...(이미지참조) 109
Fig. 3.1.9. The results of rain/non-rain classification of BRI(top), KWK(middle) and... 111
Fig. 3.1.10. The images of (a) Observed ZH(DZ), (b) Differential phase shift of BRI at...(이미지참조) 112
Fig. 3.1.11. The differential phase shift before(left) and after(right) unfolding algorithm. 113
Fig. 3.1.12. The range profile of reflectivity and differential phase shift(left top) and... 116
Fig. 3.1.13. Simulated percentage of non-blockage(color shading) of (a) JNI(0.0˚), (b)... 118
Fig. 3.1.14. The relationships for rainfall estimation of dual-polarization. 119
Fig. 3.1.15. Schematic diagram of coordinate transformation from polar to cartesian... 120
Fig. 3.1.16. The results of (a) simulated beam blockage fraction(0.0˚), (b) echo classification... 122
Fig. 3.1.17. The results of (a) simulated beam blockage fraction(-0.5˚), (b) Echo classification... 123
Fig. 3.1.18. The same as Fig. 3.1.16 but for SBS. 124
Fig. 3.1.19. The time-series of AWS rainfall(black), R(PPI0)(green) and R(HSR)(orange). 126
Fig. 3.1.20. Scatter diagrams of the hourly rainfall estimated by R(PPI0)(green dot) and... 127
Fig. 3.1.21. Scatter diagrams of the hourly rainfall estimated by R(ZH)[PPI0](green dot)...(이미지참조) 128
Fig. 3.2.1. Flow chart of storm identification 133
Fig. 3.2.2. Schematic diagram of coordinate transformation 134
Fig. 3.2.3. Results of storm detection without bright band identification in 1900~1950KST 08... 141
Fig. 3.2.4. Same as Fig.3.2.3 but with bright band identification using Giangrande et al.(2008) 141
Fig. 3.2.5. Same as Fig.3.2.3 but with bright band identification using standard deviation 142
Fig. 3.2.6. Same as Fig.3.2.3 but with bright band identification using mean of texture 142
Fig. 3.2.7. Results of storm detection without bright band identification in 1820~1910KST 06... 143
Fig. 3.2.8. Same as Fig.3.2.7 but with bright band identification using Giangrande et al.(2008) 143
Fig. 3.2.9. Same as Fig.3.2.7 but with bright band identification using standard deviation 144
Fig. 3.2.10. Same as Fig.3.2.7 but with bright band identification using mean of texture 144
Fig. 3.2.11. Results of storm detection without bright band identification in... 145
Fig. 3.2.12. Same as Fig.3.2.11 but with bright band identification using Giangrande et al.(2008) 145
Fig. 3.3.1. Location of AWS within 100km KWK radius (left panel), comparison of... 151
Fig. 3.3.2. Distribution map of snowfall measured by KWK radar (a) and the rain gauge (b)... 151
Fig. 3.3.3. Main flowchart of the radar snowfall estimation algorithm including correction... 153
Fig. 3.3.4. Detailed flowchart of the radar snowfall rate algorithm with (a) a real-time... 154
Fig. 3.3.5. Comparison of the field(S field) and the local snowfall rate(Slocal) estimated form...(이미지참조) 155
Fig. 3.3.6. Theoretical derivation of wet bulb correction model and correction of the... 157
Fig. 3.3.7. Skew T-Log P diagram based on the measurements from the sonde at... 159
Fig. 3.3.8. Time series of the surface temperature in the middle of the phase change from... 160
Fig. 3.3.9. Correlation between wet bulb temperature Tw and Gauge/radar snowfall rate using... 160
Fig. 3.3.10. Spatial distribution of Improvement in the accuracy of the radar snowfall... 161
Fig. 3.3.11. Scatter plot of the snowfall rate estimated with αfield (blue) and αTw (red) 162
Fig. 3.4.1. PPI images of the radial velocity of (a) GDK and (b) KSN radar... 165
Fig. 3.4.2. The location of (a) rawinsonde, (b) wind profiler, (c) buoy, and (d) AWS sites. 167
Fig. 3.4.3. Flowchart of Background Wind Retrieval. 168
Fig. 3.4.4. Retrieved wind field from the observations at the height of (a) 0.5... 169
Fig. 3.4.5. Domains for the UM local model 170
Fig. 3.4.6. Flowchart of the data conversion from VDAPS data(grib) to Background... 171
Fig. 3.4.7. Same as Fig. 3.4.4 except from the VDAPS model. 172
Fig. 3.4.8. Radar reflectivity of (a)Case1, (b)Case2, (c)Case3 and (d)Case4. 173
Fig. 3.4.9. Flowchart of the radial velocity unfolding. 174
Fig. 3.4.10. PPI images of the radial velocity of 4 cases. 175
Fig. 3.4.11. PPI images of the folded/unfolded radial velocity using observation and... 176
Fig. 3.4.12. Same as Fig. 3.4.11 except JNI and KSN sites at 0530 and 0930 KST 5... 177
Fig. 3.4.13. Same as Fig. 3.4.11 except KWK and KSN sites at 0930 KST 17 April... 178
Fig. 3.4.14. Same as Fig. 3.4.11 except KWK and KSN sites at 1530 KST 13 May... 179
Fig. 3.4.15. Radar reflectivity(shaded) and retrieved wind vectors using (a) Raw... 180
Fig. 3.5.1. (a) Terrain-following coordinates and (b) IBM coordinates. 188
Fig. 3.5.2. A two-dimensional schematic example of the IBM configuration (Liou et... 189
Fig. 3.5.3. Summary for the main algorithm for processing 3-dimensional wind field... 190
Fig. 3.5.4. Detailed algorithm (step 1, 2) for processing 3-dimensional wind field using... 192
Fig. 3.5.5. Same as Fig. 3.5.4, but for step 3, 4, 5. 193
Fig. 3.5.6. (a) Domain for producing 3-dimensional wind fields from the multiple... 197
Fig. 3.5.7. CAPPI for 3-dimensional radial velocity in 1km resolution at (a) GDK, (b) KWK,... 198
Fig. 3.5.8. Schematic diagram of the grid system composed by bilinear interpolation. 199
Fig. 3.5.9. The results of the spatial interpolation in 1km resolution for multiple... 199
Fig. 3.5.10. The horizontal wind field of the multiple variational Doppler radar is (a)... 200
Fig. 3.6.1. Weighting function of VSRF, KONOS, and KONOS. 205
Fig. 3.6.2. Domains for KMA nowcasting systems. The boxes indicated... 206
Fig. 3.6.3. Spatial distribution of (a) surface weather chart and (b) daily rainfall... 209
Fig. 3.6.4. Observed and forecasting precipitation for heavy rainfall case at 1400 KST 3 July... 210
Fig. 3.6.5. Same as Fig. 3.6.4, except for typhoon case at 0500 KST 5 Oct 2016. 212
Fig. 3.6.6. Same as Fig. 3.6.4, except for snow case at 0200 KST 20 Jan 2017. 213
Fig. 3.6.7. Categorical verification scores of (a) Bias, (b) POD, (c) FAR, and (d) CSI... 214
Fig. 3.6.8. Continuous evaluation scores of (a) RRMSE, (b) MAE, (c) ME, and (d)... 215
Fig. 3.6.9. Flow chart for first guess by using NWP winds in VET. 216
Fig. 3.6.10. Observed and forecasting precipitation for heavy rainfall case at 1020 KST... 218
Fig. 3.6.11. Categorical verification scores of (a) Bias, (b) POD, (c) FAR, and (d) CSI... 219
Fig. 3.6.12. Continuous evaluation scores of (a) RRMSE, (b) MAE, (c) ME, and (d)... 219
Fig. 4.1.1. One-hour rain rate (left) and total accumulated rainfall (right) of 2DVD and... 222
Fig. 4.1.2. Scatterplot of R derived from observed DSDs and R_ret estimated from... 225
Fig. 4.1.3. Comparison of the results of different rainfall relations. 226
Fig. 4.1.4. (a) Time series of accumulated rainfall measured from the rain gauge... 228
Fig. 4.1.5. Time series of the (a) reflectivity, and (b) differential reflectivity by... 229
Fig. 4.1.6. Same as Fig. 4.1.4 except for 11 Sep 2017. 230
Fig. 4.1.7. Same as Fig. 4.1.5 except for 11 Sep 2017. 230
Fig. 4.1.8. Timeseries of ratio of valid correlation coefficient bin to total bin at each... 231
Fig. 4.1.9. PPIs of reflectivity (DZ) and correlation coefficient (RH) at the elevation... 232
Fig. 4.1.10. Ratio of unfiltered reflectivity (top) and Doppler velocity (bottom) to filtered... 233
Fig. 4.1.11. PPIs of reflectivity and Doppler velocity at the elevation angle of 0.0º of MYN radar at 0850 KST 9 May 2017. 233
Fig. 4.2.1. (a) reflectivity and (b)elevation surface of HSR of GDK at 14:21 KST 1 July 2016. 236
Fig. 4.2.2. Frequency distribution of simulated blockage fraction in sever beam blocking area. 236
Fig. 4.2.3. The same as Fig.4.1.1 but after applying elevation angle limit. 236
Fig. 4.2.4. The same as Fig. 4.1.1 and Fig. 4.1.2 but for GNG. 237
Fig. 4.2.5. Reflectivity based on hybrid surface before(top) and after(bottom) applying... 238
Fig. 4.2.6. Rainfall rate based on lowest elevation angle(left), CAPPI-1.5km(middle) and... 239
Fig. 4.2.7. HSR operation for MYN in web-site. 240
Fig. 4.3.1. Quasi-Vertical Profile operation image for Gwanaksan radar in web-site 241
Fig. 4.3.2. Time series of Quasi-Vertical Profile (a) ZH, (b) ZDR, and (c) ρHV on YIT...(이미지참조) 243
Fig. 4.3.3. Time series of QVP wind-barb on YIT (elevation 7.39˚) at 0000-1200 KST... 244
Fig. 4.3.4. (a) Wonju Wind profiler at 0500-1200 KST 25 October 2016. (b) Radio sonde data... 244
Fig. 4.3.5. Time series of Quasi-Vertical Profile (a) ZH, (b) ZDR, and (c) ρHV on BRI...(이미지참조) 245
Fig. 4.3.6. Time series of QVP wind-barb on BRI (elevation 7.89˚) at 0400-1600 KST... 246
Fig. 4.3.7. (a) Paju Wind profiler at 1000-1600 KST 5 April 2017. (b) Radio sonde data in... 247
Fig. 4.4.1. Flowchart of RAR algorithm 249
Fig. 4.4.2. Verification Score of RAR with WRC-RGP raingauge during 10... 252
Fig. 4.4.3. The example of monthly (a): coefficient A (b): exponent b for site mean... 255
Fig. 4.4.3. The scatter plot of Z-R pairs and Z-R relationship for each month during... 256
Fig. 4.4.4. Continue during 2015 257
Fig. 4.4.5. Continue during 2016 259
Fig. 4.4.6. Comparison of RAR using M-P Z-R relationship as default(left), Mean Z-R relationship as... 263
Fig. 4.4.7. Comparison of RAR using M-P Z-R relationship as default(left), Mean Z-R relationship as... 264
Fig. 4.4.8. Error Inverse Weighted Corrected Z-R relationship 266
Fig. 4.4.9. Comparison of RAR applying Error Inverse Weighted Method, before(left),... 268
Fig. 4.4.10. Comparison of RAR applying Error Inverse Weighted Method, before(left),... 269
Fig. 4.4.11. Comparison of RAR applying Error Inverse Weighted Method, before(left),(그림없음) 24
Fig. 5.1.1. Photographs of (a) mobile mode and (b) the fixed mode of... 273
Fig. 5.1.2. Parts replacement history of the ARC-250PM (a) lotary joint, (b)... 275
Fig. 5.1.3. DBMH(left) and DBMV(right) image of February 16, 2017 2215(KST) 277
Fig. 5.1.4. DBMH(left) and DBMV(right) image of February 20, 2017 0933(KST) 277
Fig. 5.2.1. Location of research weather radar installation 279
Fig. 5.2.2. Power and pulse width conceptual diagram. 280
Fig. 5.2.3. WeatherScout menu configuration. 282
Fig. 5.2.4. Images of reflectivity at 1210 KST 2 Aug. 2017 (a) composite of CAPPI... 285
Fig. 5.2.5. Images of reflectivity at 1240 KST 2 Aug. 2017 (a) composite of CAPPI... 286
Fig. 5.2.6. Images of reflectivity at 1310 KST 2 Aug. 2017 (a) composite of CAPPI... 286
Fig. 5.2.7. Results from beam blocking simulation of KMA and K01 radar sites (a)... 287
Fig. 5.2.8. Images of reflectivity at 1300 KST 2 Aug. 2017 (a) composite of CAPPI... 288
Fig. 5.2.9. Images of reflectivity at 1310 KST 2 Aug. 2017 (a) composite of CAPPI... 289
Fig. 5.2.10. Images of reflectivity at 1350 KST 2 Aug. 2017 (a) composite of CAPPI... 289
Fig. 5.2.11. Images of reflectivity at 1400 KST 2 Aug. 2017 (a) composite of CAPPI... 290
Fig. 5.2.12. Results from beam blocking simulation of KMA and K01 radar sites (a)... 290
Fig. 5.2.13. Images of reflectivity at 1310 KST 2 Nov. 2017 (a) composite of CAPPI... 292
Fig. 5.2.14. Images of reflectivity at 1340 KST 2 Nov. 2017 (a) composite of CAPPI... 292
Fig. 5.2.15. Images of reflectivity at 1340 KST 2 Nov. 2017 (a) composite of CAPPI... 293
Fig. 5.2.16. Results from beam blocking simulation of KMA and K02 radar sites (a)... 293
Fig. 5.2.17. Accumulated(15 min) rainfall amount distribution from AWS from... 294
Fig. 5.3.1. Minimum detectable reflectivity of the (a) Gunsan (K02) and (b) PyeongChang... 298
Fig. 5.3.2. PPIs of reflectivity, differential reflectivity, differential phase shift, correlation... 299
Fig. 5.3.3. Same as Fig. 5.3.2 but for Gunsan at 0349KST 11 Sep 2017. 299
Fig. 5.3.4. Same as Fig. 5.3.2 but for PyeongChang at 0500KST 27 Sep 2017. 300
Fig. 5.3.5. Range profile of mean reflectivity, mean differential reflectivity, mean... 301
Fig. 5.3.6. Flow chart for connecting discontinuous differential phase shift. 302
Fig. 5.3.7. PPIs of (a) reflectivity (DZ), (b) differential reflectivity, (c) differential phase... 303
Fig. 5.3.8. Same as Fig. 5.3.7 but after rain field selection. 304
Fig. 5.3.9. Differential phase shift as a function of range (a) before and (b) after... 305
Fig. 5.3.10. PPIs of filtered differential phase shift (a) before and (b) after mitigating... 305
Fig. 5.4.1. The numbers of radar networks and the roles for developing MPAR of FAA,... 308
Fig. 5.4.2. Current radar networks and proposed MPAR network in USA. 308
Fig. 5.4.3. The milestone of the phased array radar development of FAA and... 309
Fig. 5.4.4. The scanning concepts of conventional scanning radar(upper) and the phased... 310
Fig. 5.4.5. The conceptual diagram of the wavefronts of the radio waved emitted by... 311
Fig. 5.4.6. Airspace coverage comparison between current U.S. operational radar... 312
Fig. 5.4.7. AN/SPY-1A phased array radar installed at NSSL, Norman, Oklahoma in... 313
Fig. 5.4.8. Tornado (31 May 2013) measurement using scanning radar(left) and... 314
Fig. 5.4.9. Tornado lead times. MPAR may enable average tornado lead times to be... 314
Fig. 5.4.10. Phased array radar in Osaka university(left) and the networks of the... 315
Fig. 5.4.11. Real-time data flow of Osaka university phased array radar 315
Fig. 5.4.12. Comparison between the phased array radar and Satellite radar(left) and the... 316
Fig. 5.4.13. The measurement of heavy precipitation by the phased array radar(left)... 316