• 한국해양학회지:바다
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• 제22권3호
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• pp.103-117
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• 2017

이 노트는 국립해양조사원이 5년(2012~2016년)간에 걸쳐 지역해(동해, 황 동중국해) 수치예측시스템을 구축하여 자동으로 끊임없이 운영하면서 확보한 기술들 중 다음 3가지를 담고 있다. (1) 끊임없이 3일 해양예측 자료를 생산하기 위한 전략, (2) 매일 특정시각에 외부 해양 기상자료(HYCOM, NOAA/NCEP GFS)를 안정적으로 내려 받는 방법과 (3) 해양예측시스템 운영자들이 휴대전화 단문 메시지 서비스(Short Message Service)를 이용하여 해양예측시스템 수행 시 발생하는 시스템 오류를 신속하게 파악할 수 있는 기능에 관하여 기술하였다. 이들 기본 기술과 운영시스템 구성의 기본 개념은 지역해와 연안 해양 수치예측시스템을 자동으로 운영하는 체계를 구축하는 데 있어서 유용하게 사용될 것이다.

• 한국해안·해양공학회논문집
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• 제30권2호
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• pp.84-94
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• 2018

해양파랑 예측에 있어 단일 수치모델의 불확실성을 보완하기 위하여 앙상블 기법을 적용한 지역 파랑예측시스템을 구축하였다. 기상청 전지구 대기 수치모델의 확률예측시스템에서 생산되는 24개 앙상블 해상풍을 입력자료로 이용, 87시간까지 파랑 예측자료를 생산하였으며, 기상청 계류부이 관측자료와 다양한 통계방법을 적용하여 검증을 수행하였다. 2일예측 이후의 앙상블 예측평균의 평균제곱근오차(RMSE)는 단일모델예측에 비하여 향상된 결과를 보였으며, 특히 3일예측의 경우 단일모델예측 대비 RMSE가 약 15% 정도 향상되었다. 이것은 앙상블 기법이 수치모델의 불확실성을 감소시켜 예측정확도 향상에 크게 기여한 것으로 보인다. ROC(Relative Operating Characteristic) 분석결과, 전체 예측시간에 대하여 ROC 영역이 모두 0.9 이상을 보여 확률예측 성능이 뛰어남을 보였으며, 앙상블 파랑예측 결과가 해상 확률예보에 유용하게 활용될 수 있을 것으로 판단된다.

• 한국해안·해양공학회논문집
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• 제32권6호
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• pp.373-383
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• 2020

본 연구에서는 한반도 중심 해역을 포함하는 북서태평양 영역에서의 폭풍해일 예측을 위해 NEMO(Nucleus for European Modelling of the Ocean) 모형을 이용하여 지역규모의 폭풍해일 예측시스템을 구축하였다. 이 시스템은 조석과 해일 예측으로 구성되어 있으며보다 정확한 폭풍해일을 예측하기 위해 수심자료와 대기-해양 경계에서의 모수화(parameterization) 최적화 과정을 수행하였다. 이를 통해 2018년 8~10월과 태풍 솔릭 사례에 대하여 국립해양조사원 조위 관측자료를 이용한 통계 방법을 적용하여 검증을 수행하고, 이를 POM(Princeton Ocean Model) 기반의 예측모델 결과와 비교하였다. 수행결과 NEMO 기반의 폭풍해일 예측시스템이 POM 기반의 예측결과에 비해 평균오차와 RMSE가 각각 약 29%와 약 20% 감소한 것으로 나타났으며, 태풍 시기에도 NEMO 기반 예측결과에서 전반적으로 오차가 낮게 나타났다.

• Ocean and Polar Research
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• 제43권3호
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• pp.179-192
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• 2021

In this study, an integrated wave model from global to coastal scales was developed to improve the operational wave prediction performance of the Korean Meteorological Administration (KMA). In this system, the wave model was upgraded to the WaveWatch III version 6.07 with the improved parameterization of the source term. Considering the increased resolution of the wind input field and the introduction of the high-performance KMA 5^th Supercomputer, the spatial resolution of global and regional wave models has been doubled compared to the operational model. The physical processes and coefficients of the wave model were optimized for the current KMA global atmospheric forecasting system, the Korean Integrated Model (KIM), which is being operated since April 2020. Based on the sensitivity experiment results, the wind-wave growth parameter (β_max) for the global wave model was determined to be 1.33 with the lowest root mean square errors (RMSE). The value of β_max showed the lowest error when applied to regional/coastal wave models for the period of the typhoon season when strong winds occur. Applying the new system to the case of August 2020, the RMSE for the 48-hour significant wave height prediction was reduced by 13.4 to 17.7% compared to the existing KMA operating model. The new integrated wave prediction system plans to replace the KMA operating model after long-term verification.

• Ocean and Polar Research
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• 제30권2호
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• pp.129-140
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• 2008

As a preliminary effort to establish a data assimilative ocean forecasting system, we reviewed the theory of the Ensemble Kamlan Filter (EnKF) and developed practical techniques to apply the EnKF algorithm in a real ocean circulation modeling system. To verify the performance of the developed EnKF algorithm, a wind-driven double gyre was established in a rectangular ocean using the Regional Ocean Modeling System (ROMS) and the EnKF algorithm was implemented. In the ideal ocean, sea surface temperature and sea surface height were assimilated. The results showed that the multivariate background error covariance is useful in the EnKF system. We also tested the sensitivity of the EnKF algorithm to the localization and inflation of the background error covariance and the number of ensemble members. In the sensitivity tests, the ensemble spread as well as the root-mean square (RMS) error of the ensemble mean was assessed. The EnKF produces the optimal solution as the ensemble spread approaches the RMS error of the ensemble mean because the ensembles are well distributed so that they may include the true state. The localization and inflation of the background error covariance increased the ensemble spread while building up well-distributed ensembles. Without the localization of the background error covariance, the ensemble spread tended to decrease continuously over time. In addition, the ensemble spread is proportional to the number of ensemble members. However, it is difficult to increase the ensemble members because of the computational cost.

• 한국해양공학회지
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• 제24권6호
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• pp.7-15
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• 2010

Analyses of wind wave characteristics near the Korean marginal seas were performed in 2008 and 2009 by comparisons of an operational wind wave forecast model and ocean buoy data. In order to evaluate the model performance, its results were compared with the observed data from an ocean buoy. The model used in this study was very good at predicting the characteristics of wind waves near the Korean Peninsula, with correlation coefficients between the model and observations of over 0.8. The averaged Root Mean Square Error (RMSE) for 48 hrs of forecasting between the modeled and observed waves and storm surges/tide were 0.540 m and 0.609 m in 2008 and 2009, respectively. In the spatial and seasonal analysis of wind waves, long waves were found in July and September at the southern coast of Korea in 2008, while in 2009 long waves were found in the winter season at the eastern coast of Korea. Simulated significant wave heights showed evident variations caused by Typhoons in the summer season. When Typhoons Kalmaegi and Morakot in 2008 and 2009 approached to Korean Peninsula, the accuracy of the model predictions was good compared to the annual mean value.

• 대기
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• 제22권4호
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• pp.455-464
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• 2012

This study investigates a 12 month-lead predictability of PNU Coupled General Circulation Model (CGCM) V1.1 hindcast, for which an oceanic data assimilated initialization is used to generate ocean initial condition. The CGCM, a participant model of APEC Climate Center (APCC) long-lead multi-model ensemble system, has been initialized at each and every month and performed 12-month-lead hindcast for each month during 1980 to 2011. The 12-month-lead hindcast consisted of 2-5 ensembles and this study verified the ensemble averaged hindcast. As for the sea-surface temperature concerns, it remained high level of confidence especially over the tropical Pacific and the mid-latitude central Pacific with slight declining of temporal correlation coefficients (TCC) as lead month increased. The CGCM revealed trustworthy ENSO prediction skills in most of hindcasts, in particular. For atmospheric variables, like air temperature, precipitation, and geopotential height at 500hPa, reliable prediction results have been shown during entire lead time in most of domain, particularly over the equatorial region. Though the TCCs of hindcasted precipitation are lower than other variables, a skillful precipitation forecasts is also shown over highly variable regions such as ITCZ. This study also revealed that there are seasonal and regional dependencies on predictability for each variable and lead.

• 한국해양공학회지
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• 제35권4호
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2023년도 학술발표회
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• pp.31-31
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• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• 한국해양학회지:바다
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• 제20권1호
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• pp.1-15
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• 2015

한반도 주변을 연구해역으로 하는 지역 해양순환예측시스템을 이용하여 관측기반의 분석 자료인 Operational Sea Surface Temperature and Sea Ice Analysis(OSTIA) 해수면 온도 자료의 동화를 통한 초기장 개선효과가 황해, 동중국해 그리고 동해의 해수면온도 예측결과에 미치는 영향을 조사하였다. 이를 위해서, 본 연구에서는 3차원 최적내삽법을 적용한 실험(Exp. DA)과 적용하지 않은 실험(Exp. NoDA)을 수행하여 각각의 실험결과를 관측자료와 비교 분석하였다. 2011년 9월 OSTIA 해수면 온도 자료와의 비교결과, Exp. NoDA는 24, 48, 72 예측시간에서 약 $1.5^{\circ}C$의 비교적 높은 Root Mean Square Error(RMSE)를 보였으나, Exp. DA에서는 모든 예측시간에서 $0.8^{\circ}C$ 이하의 상대적으로 낮은 RMSE가 나타났다. 특히, 초기 24시간 예측결과에서 RMSE는 $0.57^{\circ}C$를 보여 Exp. NoDA에 비해 예측성능이 크게 향상된 결과를 보였다. 해역별로는 황해와 동해에서 자료동화 적용 시, 60% 이상의 높은 RMSE 감소율이 나타났다. 기상청 8개 지점 연안 계류부이의 표층수온 자료를 이용하여 자료동화 효과를 계절적으로 살펴본 결과, 전반적으로 여름철을 제외한 모든 계절에서 자료동화 적용 후 70% 이상의 높은 RMSE 감소율을 보여 한반도 연안 표층수온의 단기 예측성이 향상됨을 확인하였다. 또한, 해수면 온도 자료의 동화로 인한 해양상층부의 수온구조 변화를 살펴보기 위해 동해를 대표해역으로 하여 Argo 수온 프로파일 자료와 실험결과를 비교하였다. 특히 연직 혼합이 강한 겨울철 해양 상층부(<100 m) 경우 Exp. DA의 RMSE가 Exp. NoDA에 비해 약 $1.5^{\circ}C$ 감소한 결과를 보여 해수면 온도의 자료동화 효과가 해양상층부의 수온 예측성 향상에 기여함을 확인하였다. 하지만, 겨울철 혼합층 아래에서는 Argo 관측 대비 수온 오차가 오히려 증가한 해역도 존재하여 해수면 온도 자료동화의 한계성도 나타났다.