[보고서]해양기상 감시 및 차세대 해양기상 예측시스템 개발

이종호

해양기상 감시 및 차세대 해양기상 예측시스템 개발
Development of Marine Meteorology Monitoring and Next-generation Ocean Forecasting System 원문보기

보고서 정보
주관연구기관	국립기상과학원 National Institute of Meteorological Research
연구책임자	이종호
참여연구자	변재영 , 장필훈 , 박종숙 , 김도연 , 김소연 , 김태균 , 라나리 , 손고은 , 안병웅 , 이효미 , 조형준 , 추성호
보고서유형	최종보고서
발행국가	대한민국
언어	한국어
발행년월	2016-12
과제시작연도	2016
주관부처	기상청 Korea Meteorological Administration(KMA)
등록번호	TRKO201800035575
과제고유번호	1365002293
사업명	기상업무지원기술개발연구
DB 구축일자	2018-07-21
DOI	https://doi.org/10.23000/TRKO201800035575

초록 ▼

Ⅳ. 연구 내용 및 결과
1. 전지구 및 지역 해양 감시와 해양변동성 분석
가. 국립기상과학원 ARGO 플로트 투하 및 운영
- 동해와 북서태평양 일대에 ARGO 플로트 16기를 투하하였고, 실시간 품질관리 과정을 거쳐 전지구 ARGO 자료센터 등에 자료를 분배함
- 2017년 2월 현재 53기의 ARGO 플로트가 실시간 관측 중

나. 서해 해양 관측자료 품질관리 체계 구축 및 서해 해양변동성 분석
- 기상 1호에서 정기관측 중인 수온 염분자료의 품질관리 시스템을 구축함
- 2016년 하계, 서해 중부에서 발생한 표층해수의 저염화 현상 및 이상고온의 발생 요인을 분석하였음
- 2016년 봄-여름에 걸쳐 중국 남동부 집중호우가 지속적으로 발생하여 양자강 방류량이 평년의 1.4배 이상 많았음
- 서해 남부의 표층 저염화로 인하여 밀도 성층이 강화되어 연직 혼합이 제한되어 이상 고온현상으로 이어진 것으로 분석됨

2. 현업 해양기상 예측시스템 개선 연구
가. 현업운영 전지구/지역/국지연안 파랑예측시스템 개선 및 현업화
- 현업 파랑예측시스템의 예측성능 개선을 위해 파랑모델의 버전 변경 및 주요물리과정을 변경함
- 상세 지형효과 적용 및 해상풍의 입력 시간해상도를 개선함
- 다양한 해양기상정보 지원을 위해 풍파와 너울성분을 분리하여 제공함
- 개선된 전지구/지역/국지연안 파랑예측시스템은 2016년 10월부터 정식으로 현업 운영됨

나. 고해상도 전지구 파랑예측시스템 기반 구축
- 고해상도(17 km) 전지구 파랑예측시스템을 구축하고, 현업 예측시스템과의 비교검증을 수행함
- 통계검증(BIAS, RMSE) 결과, 현업 시스템에서 발생하는 유의파고 과대 모의 경향이 완화되고, 연안에서의 초기 예측성능이 향상됨

다. 현업 폭풍해일 예측시스템 검증 및 개선
- 다양한 통계검증 방법을 이용하여 2016년도 현업 폭풍해일 예측시스템의 해역별 예측성능 분석하였음. 결과, 해일고의 예측오차는 전반적으로 낮게 나타났으나, 서해안 일부에서는 관측대비 오차가 상대적으로 높게 나타남.
- 아울러, 2016년 태풍 차바 사례의 경우, 대부분의 검조소에서 관측보다 폭풍해일을 과소 모의하는 경향을 보임
- 현업 지역 폭풍해일예측시스템을 개선하기 위해 NEMO 해양모델을 이용하여 차세대 폭풍해일 예측시스템의 기반을 구축하였음

3. 전지구 해양순환예측시스템 개선 및 활용 연구
가. 전지구 해양자료동화시스템 구축 및 운영
- 전지구 해양자료동화시스템의 경계자료 처리 과정을 개선하였고, 그 결과 표층수온의 분석 및 예측 성능이 향상됨
- 2016년 6월부터 전지구 해양자료동화시스템을 실시간 운영하여, 매일 일평균 해양-해빙 초기 및 예측정보를 생산하고 있음
- 2016년 하계를 연구기간으로 해양 초기장을 검증하였음. 서안경계류와 남극 순환류 등 변동성이 강한해역에서 상대적으로 높은 수온 염분 오차가 나타나나 전반적으로 관측과 잘 일치하는 결과를 보임. 아울러, 해양자료동화 효과는 실험기간 전반에 걸쳐 긍정적으로 나타났으며, 특히 해양의 중규모 현상을 재현하는데 있어서 결정적인 작용을 하였음
- 전지구 해양자료동화시스템의 해빙 예측 정확도를 개선하고 해양-해빙의 상호작용을 진단하기 위해서 해빙의 알베도 민감도 실험과 2016년 여름의 해빙 융해에 관한 분석을 수행함
- 알베도는 해빙의 두께뿐만 아니라 해양의 열 염분 순환에 영향을 주고, 해빙은 또한 시간에 따른 해양과 해빙의 동역학과 열역학 효과의 상호작용에 의존함을 확인함

나. 장기예측시스템의 해양 분석 및 예측장 평가
- 장기예측시스템(GloSea5)의 해양 분석장은 고해상도(1/12도) 모델인 미국 HYCOM의 재분석장에 비해서 한반도 근해의 수온과 염분을 관측에 가깝게 재현함
- 장기예측시스템의 hindcast는 대체로 리드타임이 증가할수록 수온과 염분 오차가 증가하는 것으로 나타남.

다. 서해 해수면 온도와 태풍과의 상호작용
- 2004년 12월, 서해 전 영역에 평년보다 이례적으로 높은 해수면 온도 편차가 발생하여 그 원인을 분석함
- 이 시기 열대저기압들의 활동은 북서태평양 해역에 변칙적인 대기 순환을 야기하며, 서해 전 영역에 동풍 계열의 바람장 편차를 유도함
- 동풍 계열의 바람장 편차에 의한 서해내부로의 에크만 열 수송 증가가 2014년 12월 서해의 이례적인 해수면 온도 상승을 야기한 원인으로 판단됨

(출처 : 요약문 14p)

Abstract ▼

Ⅲ. Methods and results
1. Global and regional ocean real-time monitoring system and analysis of ocean variability
a. Participating in the international ARGO program and operating ARGO system of NIMS
- NIMS deployed 16 ARGO floats in East Sea/North Pacific ocean and distributed real-time profile data to the Global Data Centre in 2017.
- The 53 Argo floats are operating in the East Sea and North Pacific ocean.

b. CTD data quality control system development
- Developed prototype of the quality control system for CTD temperature and salinity data observed by research vessel Gisang1
- In-situ ocean observation data quality was improved and more applicatively

c. 2016 Yellow Sea intensive observation campaign
- Seasonal variation characteristics have been studied with various datasets
- The possible causes suggested for unusual high-temperature and low-salinity in upper layer during the summer 2016

- The Yangtze River discharge during the spring and summer 2016 was more than 1.4 times higher than the normal year average and this was caused by heavy rainfall in the southeastern part of China during that time period

- It is explained that the density stratification was strengthened by the surface stratification in the southern part of the Yellow Sea due to abnormal high temperature and it lead to limited vertical mixing

2. Improvement of the operational marine meteorological prediction system
a. Operating the improved marine meteorological prediction system
- To improve the ocean prediction performance, we upgraded the wave model version and applicated new source-term packages
- New wave prediction system adopted obstruction grids and time-resolution of wind input are improved
- We provides partitioned wind sea and swell output fields
- Improved global/regional/coastal wave prediction system are operated on October 2016

b. Development of a high resolution global wave prediction system
- High resolution (17 km) global wave prediction system is developed and then results are compared to operational forecasting system

- The statistics verification are carried out and we found out high resolution system is more lower wave height than operational system which is shown over estimated wave height and improved forecast performance at coastal region during initial forecast time

c. The verification research for predictability of storm surge forecasting system
- The predictability of storm surge forecasting system is analysed by applying the various statistical indicators
- In the case of typhoon CHABA in 2016, the results show a tendency to underestimate the simulated storm surge compared to the observation in most of points
- Development of a regional storm surge forecasting system based on Nucleus for European Modelling of the Ocean (NEMO)

3. Improvement and assessments of Global Ocean Circulation Forecast Systems
a. Implementation of operational Global Ocean Data Assimilation and Prediction System (GODAPS)
- Pre-processing system for the surface boundary condition is upgraded to improve sea surface temperature analysis
- GODAPS begins operation in June 2016 to produce daily mean ocean and sea-ice analysis and 1-day forecast

- Ocean analysis through a 1-year experiment is generally well consistent with observations, and relatively large errors are found in a large variability regions such as Kuroshio Extension, Gulf Stream, and Antarctic Circumpolar Current

- The ocean data assimilation is effective especially in large variability regions, and it determines to reproduce the meso-scale features of eddies and ocean fronts in strong current regions

- In order to improve the accuracy of the global sea ice prediction model (GODAPS) and to diagnose the sea-ice interaction, we conducted an albedo sensitivity test of Arctic sea ice and an analysis of the sea ice thawing process during the summer 2016

- we confirmed that albedo affects not only the thickness of the sea ice but also the changes of the oceanic thermohaline circulation, and the variation of the sea ice concentration with respect to time is dependent on the interaction between sea ice and ocean dynamic and thermodynamic effects

b. Evaluation of GloSea5 analysis field and hindcast data
- GloSea5 analysis field of the sea temperature and salinity in the Korean seas are more similar to the observations compared to HYCOM reanalysis field
- In general, GloSea5 hindcast simulation error increases as lead time increases

c. Relationship between tropical cyclone activity and sea surface temperature in the Yellow Sea
- Unusual sea surface temperature warming occurred over the entire Yellow Sea in December 2004
- Tropical cyclone activity induces anomalous atmospheric circulation over the Northwest Pacific leading to anomalous easterly winds over the Yellow Sea
- Unusual sea surface temperature warming in the Yellow Sea may be due to tropical cyclones which act as an important source of the Ekman heat transport

(출처 : Summary 18p)

목차 Contents

표지 ... 1
연구 보고서 ... 2
목차 ... 3
표목차 ... 6
그림목차 ... 7
요약문 ... 14
Summary ... 18
제 1 장 서론 ... 23
제 2 장 전지구 및 지역 해양 감시와 해양변동성 분석 ... 25
제 1 절 국립기상과학원 ARGO 플로트 투하 및 운영 ... 25
1. ARGO 관측망 운영 및 확대 ... 25
2. 2016년도 투하 ARGO 자료 특성 및 분배 ... 27
3. 국제 ARGO 공동연구 현황 ... 30
제 2 절 기상1호 CTD 품질관리 체계 구축 ... 33
1. 배경 및 목적 ... 33
2. 자료 ... 33
3. 품질관리 세부 단계 ... 34
4. 결과 ... 42
제 3 절 2016년도 서해 CTD 집중 관측 ... 44
1. 배경 및 목적 ... 44
2. 서해 CTD 집중 관측 개요 ... 45
3. 서해 수온과 염분의 계절변동 특성 ... 46
4. 계절변동 특성의 요인 분석 ... 56
제 3 장 현업 해양기상 예측시스템 개선 연구 ... 62
제 1 절 개선된 파랑예측시스템 현업화 ... 62
1. 서론 ... 62
2. 주요개선 사항 ... 62
3. 검증 ... 70
제 2 절 고해상도 전지구 파랑예측시스템 기반 구축 ... 74
1. 서 론 ... 74
2. 고해상도 전지구 파랑예측시스템 구축 ... 74
3. 결과 검증 ... 75
4. 요 약 ... 78
제 3 절 폭풍해일 현업예측 검증과 예측시스템 개선 ... 79
1. 서론 ... 79
2. 현업 폭풍해일 예측시스템 검증 ... 79
3. NEMO 모델 기반 폭풍해일 예측시스템 기반구축 ... 84
제 4 절 결 론 ... 85
제 4 장 전지구 해양순환예측시스템 개선 및 활용 연구 ... 87
제 1 절 전지구 해양자료동화시스템 구축 및 운영 ... 87
1. 경계 자료 전처리 과정 개선 ... 88
2. 전지구 해양자료동화시스템 검증 ... 95
3. 해빙 albedo 민감도 실험 및 2016년 여름 해빙 변동 특성 ... 100
제 2 절 장기예측시스템 해양 분석장 및 예측성 평가 ... 116
1. 서론 ... 116
2. 자료 ... 117
3. 수온·염분 검증 결과 ... 120
4. 결론 ... 131
제 3 절 서해 해수면 온도와 태풍과의 상호작용 ... 134
1. 서 론 ... 134
2. 연구 방법 및 자료 ... 135
3. 장기예측시스템 기반 서해 냉수대, 해수면 온도 변동성 ... 136
4. 해수면 온도 변동과 태풍과의 상호작용 ... 142
5. 결 론 ... 147
제 5 장 결론 ... 148
참고 문헌 ... 150
부록Ⅰ. 학술연구용역 ... 160
부록2. 학술연구용역 ... 163
끝페이지 ... 164

표/그림 (99)

표 Number of Argo float deployment of NIMS over the East Sea and North Pacific Ocean
표 Deployment time and location of ARGO floats
표 Deployment location of ARGO floats in the East Sea and North Pacific Ocean (2016.7.-8.).
표 Temporal variation of temperature and salinity profiles are show in the figure a, b. (c) Trajectories of ARGO floats deployed in East Sea.
표 Concept of trilinear interpolation method.
표 Flowchart of CTD QC process.
표 Flag lookup table for Spike Test
표 Flag lookup table for Stuck Value Test
표 Flag lookup table for Climatological Range Check
표 Flag lookup table for Density Inversion Check
표 Components of the QC result file
표 Salinity profile of before (black) and after QC (red), (a) for point No.7 in January 2016, and (b) for point No.5 in November 2016.
표 (a) Temperature and (b) salinity profile of before (black) and after QC (red), for point No.8 in July 2016.
표 QC result for point No.9 in July 2016
표 Positions of observation points.
표 Observation date and period in 2016
표 Vertical cross-section for temperature (right) and salinity (left) at 124°25’E in January 2016.
표 Same as Fig 2.3.2, but for March 2016.
표 Same as Fig 2.3.2, but for May 2016.
표 Same as Fig 2.3.2, but for July 2016.
표 Same as Fig 2.3.2, but for September 2016.
표 Same as Fig 2.3.2, but for November 2016.
표 Time-latitude cross-section for vertical temperature differences (CTD minus WOA13).
표 Same as Fig. 2.3.8, but for salinity.
표 Monthly discharge amount at Datong hydrological station.
표 Monthly CMAP accumulated precipitation anomaly.
표 Monthly mean SST anomaly distributions.
표 Daily mean Net Heat Flux (left), wind-vector and wind-speed (right) averaged over sea area around Korean Peninsula.
표 Monthly bias distribution of significant wave height without(a) and with(b) obstruction grids using global satellite data on January 2014. (c) and (d) are bias and RMSE about lead time.
표 Upper panels show Significant wave height of old system(a) and new system(b). Bottom panels show peak period and mean wave direction. (c)old system, (d) new system.
표 Significant wave height(upper), swell wave height(middle) and wind-sea wave height(bottom) on 00 UTC August 29 2016.
표 Flow chart of operational wave forecasting system.
표 Comparison of old system and new system
표 Verification of global significant wave height on January 2015.
표 Verification of regional significant wave height on January 2015.
표 Verification of coastal significant wave height on January 2015.
표 Model configuration for operational and high resolution global wave prediction system
표 Locations of buoys processed by ECMWF which is used for verification.
표 Bias(a) and RMSE(b) of significant wave heights of High resolution GWW3 using global buoy data during July 2017.
표 Bias(a) and RMSE(b) of significant wave heights of high resolution GWW3 using Jason-2 altimeter data during July 2017.
표 Comparison of (a) Bias, (b) RMSE and (c) Correlation between gauge observations and prediction at 24hour of RTSM.
표 Comparison of time-series between model and observation for sea level anomaly, surge and tidal at Masan.
표 Scatter diagrams for model and observed surge height at Mosulpo, Seogwipo, Seongsanpo, Geomun-do, Yeosu, Goheung, Busan, Masan and Ulsan.
표 The comparison of specification for RTSM and NEMO-surge Model
표 (a) Input data of RDAPS 10m Wind (vector) and MSLP (hPa) and (b) bathymetry (m) for NEMO-surge Model.
표 Scripts for flux preprocessing process
표 Inputs for flux preprocessing process
표 Extraction of surface fluxes from GDAPS(see, solid lines), operated every 6-hour(i.e., 00, 06, 12, 18UTC). Dotted arrows show forecasts of KMA-NWP. Upper and Lower panels indicate extraction before and after upgradation of preprocessing system, respectively.
표 Difference of 5-day averaged Sea Surface Temperature (SST) analysis between GODAPS and OSTIA. Left and right panels indicate results for Exp. N512 and Exp. N768, respectively.
표 Same as in Fig. 4.1.3, but for experimental period (2016.6).
표 RMSE difference of SST backgrounds between Exp N512 and Exp.768. Positive values indicate upgradation. Bin size is set to 2 degree.
표 Time variation of sea surface temperature (SST) analysis. Red and black lines indicate SST analysis from operational Global Ocean Data Assimilation and Prediction System and OSTIA, respectively. Left and right panels indicate results after and before upgradation of preprocessing system. GO, PO, AO, IO, and SO stand for Global Ocean, Pacific Ocean, Atlantic Ocean, Indian Ocean, and Southern Ocean, respectively.
표 Surface current RMSE and Mean Error for GODAPS-run and Free-run. Unit is cm s-1
표 Innovation statistics. Left panels show RMSEs for Sea Surface Temperature, sea level anomaly, temperature, and salinity from top to bottom. Right panels indicate RMSE differences between GODAPS-run and Free-run.
표 Innovation statistics. Globally averaged RMSE(solid lines) and Mean Error(dotted lines) for GODAPS and Free-run. Left and right panels are results for temperature and salinity, respectively.
표 SSH analysis fields (color) in 5 July 2015 and movement of surface drifters from 1 to 9 July 2015 (black dots). Left and right panels reveal results for GODAPS-run and Free-run, respectively.
표 Time series of RMSE(solid lines) and Mean Error(dotted lines) for surface velocity (15 m depth): zonal component (upper), meridional component (middle), and speed (lower). Red and blue lines reveal results for Free-run and GODAPS-run, respectively. Unit is cm s-1.
표 Albedo parameters used in the sensitivity study. KMA and KMA_alb represent the default albedo value used in the CICE model and the modified albedo value for the study, respectively. Here ahmax is the constant ice thickness (m) used in albedo calculation
표 Daily sea ice extent (SIE) time series for the Northern Hemisphere (left) and sea ice area (SIA, right) for the year 2015. The blue, black, and pink line represent KMA, KMA_albedo and UK GloSea5, respectively. Sea ice extent (SIE) is defined to be the sum of all area of ocean grid that contains sea ice with concentration above 15%. Sea ice area (SIA) is defined to be the total area of ocean covered by sea ice concentration above 15%.
표 Daily sea ice volume at Northern Hemisphere (left) and Southern Hemisphere (right) calculated by SIA and sea ice thickness.
표 Comparison of August 2015’s SIC, SIT, SSS and SST for three different simulations.
표 Monthly mean differences of sea ice concentration relative to OSISAF during January 2016 (upper) and July 2015 (lower).
표 KMA, KMA_albedo and UK GloSea5’s sea ice thickness (1.5 m) lines during July-August-September (JAS) 2015 (upper) and January-February-March (JFM) 2016 (lower) from left to right panel.
표 KMA (blue line) and KMA_alb (red line)
표 Comparison between KMA (left) and KMA_alb (right)’s correlation of heat contents in each layer depth with respect to the surface layer’s heat content.
표 Comparison between KMA (left) and KMA_alb (right)’s correlation of salt contents in each layer depth with respect to the surface layer’s salt content.
표 Daily mean SIE of Northern and Southern Hemisphere mean sea ice extent.
표 Monthly mean sea ice concentration of summer 2016 and comparison between GODAPS (upper) and NSIDC (lower).
표 First five empirical orthogonal functions and their principal components from principal component analysis upon model derivation from GODAPS SIC for the period of June to October 2016. The component from the first to the fifth contributes around 49, 18, 8, 5, 3% to the total variance of the data set.
표 Total heat flux (upper) summer 2016 and EOF modes of SIC (middle) and SIT (lower).
표 TDD (colour) and sea ice velocity (quiver) (June to September 2016 from left to right, respectively).
표 Differences between Surface and Bottom melting (colour) and ocean currents (quiver) (June to September 2016 from left to right, respectively).
표 Map of research area.
표 Spatial distribution of sea surface temperature from (a) GloSea5, (b) HYCOM, and (c) OISSTv2 for 1993-2010. And differences between observation and model((d) GloSea5 minus OISSTv2, (e) HYCOM minus OISSTv2).
표 Time series of sea surface temperature for (a) Deokjeokdo, (b) Chilbaldo, (c) Geomundo, and (d) Geojedo buoy. (e) Root mean square errors (RMSE) of GloSea5 analysis and HYCOM reanalysis.
표 Vertical sea temperature profiles and RMSE for (a) Yellow Sea (YC), (b) northern Eastern China Sea (NECS), (c) Eastern China Sea (ECS), and (d) East Sea (ES) of CTD, GloSea5 analysis, and HYCOM reanalysis.
표 Same as Fig. 4.2.4, but for salinity (psu).
표 (a) RMSE between GloSea5 hindcast and OISSTv2 as a function of start month and lead time for 1991-2010, and (b) mean of RMSE for all start month.
표 Same as Fig. 4.2.6, but for ARGO at (a,c) surface and (b,d) surface-150 m.
표 Same as Fig. 4.2.6, but for surface CTD data at (a,e) YS, (b,f)NECS, (c,g) ECS, and (d,h) ES.
표 Vertical temperature RMSE profiles between GloSea5 hindcast and CTD at (a) YS, (b) NECS, (c) ECS, and (d) ES as a function of depth and lead time for 1991-2010.
표 Same as Fig. 4.2.7, but for salinity (psu).
표 Same as Fig. 4.2.8, but for salinity (psu).
표 Same as Fig. 4.2.9, but for salinity (psu).
표 KODC data stations in the Yellow Sea
표 Time series of monthly mean sea surface temperature (upper) and temperature at 50 m depth (lower) from KODC (black), GC20 (brown), GS50 (blue), SODA (red), and CGLOS (green). KODC is bi-monthly data.
표 Origin region of the YSBCW (left) and time variation of the YSBCW intensity (right).
표 Latitude-time diagram of temperature at 50 m depth along the 124.5°E line from GloSea5 (upper) and KODC (lower).
표 Time series of origin temperature of the YSBCW (upper), SST (blue bar in lower) and air temperature (red bar in lower) in wintertime.
표 Horizontal distribution of SST and surface current anomaly at the warm (upper) and cold (lower) YSBCW years.
표 Time-series of origin temperature of the YSBCW (purple), downward het heat flux (black), 10 m wind speed (green), difference between sst and air temperature (red), sst (blue bar), and air temperature (red bar) in wintertime.
표 Time-series of origin temperature of the YSBCW (upper left), AO index (upper right), SH index (lower left), and ONI (lower right).
표 Time-series of monthly (left) and weekly (right) mean December SST.
표 Time-series of monthly anomalies of SST (upper left), net heat flux (upper right), and 10 m wind (lower) in each December.
표 Horizontal distribution of monthly anomalies of SST and wind (left), and typhoon best track (right) in December 2004.
표 Horizontal distribution of Ekman volume transport at December 1-3, 2004.
표 Horizontal distribution of SST and Ekman volume transport at the entrance of the YS at November 30 and December 2, 2004, and SST difference between December 2 and November 30.
표 Time-series of daily anomalies of SST (upper left), 10 m wind (upper right), net heat flux (lower left), and Ekman heat transport at the entrance of the YS (lower right) in each December.
표 Vertical distribution of 20 years mean December temperature (left) and monthly mean temperature in December 2004 (right) along the 124.5°E line.

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해양기상 감시 및 차세대 해양기상 예측시스템 개발
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