• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.478-478
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• 2022

섬진강은 하굿둑이 없는 열린 하구로서 하구로부터 약 21km까지 조석의 영향을 받아 강물의 염도가 시간에 따라 변하는 환경이다. 오랫동안 섬진강 하구는 다양한 원인으로부터 바다화로 대표되는 염하구 문제가 지역 현안 사항으로 제기되어 왔다. 상류에서의 용수사용 증가로 인한 하천 유하량 감소 또한 그 원인들 중 하나로 판단됨에 따라 실제 하구까지 내려오는 하천유량과 바다로부터 유입되는 해수를 구분하여 정량화하는 연구가 필요한 사안이다. 본 연구의 목적은 섬진강 수계 하구에서의 다양한 생태환경을 보전하기 위한 적정 염분유지가 요구됨에 따라 섬진강하구 염분계측기(섬진강대교)를 설치하여 염분농도를 관측하고 하천유량, 하천취수 및 해양조위에 따른 염분농도 변화를 모의하여 하천유량과 염분과의 관계를 제시하고자 하였다. 본 연구에서는 EFDC(Environmental Fluid Dynamics Code) 수치모델을 이용하여 상류로는 구례군(송정리) 수위관측소부터 하류로는 여수해만 및 문의리까지의 구역을 설정하고 광양조위, 하동수위 및 고정식 염분 계측기 관측염분농도 자료를 이용하여 수치모델링의 재현성을 검증하였다. 검증 결과, 결정계수(R²)는 0.87(하동수위), 0.93(광양조위), 0.56(섬진강대교 염도)를 나타냈다. 모델 검보정 후 하천유량에 따른 염분변화 실험을 실시하여 염분농도 거동을 분석하였다. 모델 결과, 섬진강하구에서의 염분거동은 소조기때 염분체류 현상으로 인해 대조기 거동과는 큰 차이를 나타냈다. 따라서, 모델링 결과를 이용한 유량-염분 조견표는 각각 대조기와 소조기로 구분하여 산정하였다. 대조기때는 송정유량이 10톤/초의 평균갈수량이 흐를 경우 다압에서의 취수량이 2.52톤/초 ~ 4.63/초로 증가할수록 18.8psu ~ 19.9psu로 증가하였다. 소조기의 경우는 25.5psu ~ 25.7psu로 대조기와 비교하여 크게 증가됨을 나타냈다. 본 연구의 결과는 객관적인 생태환경 보전을 위한 적정염분농도 범위가 도출된다면 이를 유지하기 위한 필요유량과 필요유량을 확보하기 위한 장단기적인 대책수립이 가능할 것으로 기대된다.

• Journal of Korea Water Resources Association
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• v.44 no.3
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• pp.221-230
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• 2011

The climate change has been observed in Korea as well as in the entire world recently. The rainstorm has been gradually increased and then the damage has been grown. It is getting important to predict short-term rainfall. The Korea Meteorological Administration (KMA) generates numerical model outputs which are computed by Global Data Assimilation and Prediction System (GDAPS) and Regional Data Assimilation and Prediction System (RDAPS). The KMA predicts rainfall using RDAPS results. RDAPS model generates 48 hours data which is organized 3 hours data accumulated at 00UTC and 12UTC. RDAPS results which are organized 3 hours time scale are converted into daily rainfall to compare observed daily rainfall. In this study, 9 cases are applied to convert RDAPS results to daily rainfall data. The MAP (mean areal precipitation) in Geum river basin are computed by using KMA which are 2005 are used. Finally, the best case which gives the close value to the observed rainfall data is obtained using the average absolute relative error (AARE) especially for the Geum River basin.

• Journal of Astronomy and Space Sciences
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• v.24 no.4
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• pp.275-284
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• 2007

For the application to the numerical weather prediction (NWP) in active service, it is necessary to ensure that the GPS precipitable water vapor (PWV) data has less than one hour latency and three millimeter accuracy. The comparison and the verification between the daily products from GPS measurement by using the IGS final ephemeris and the conventional meteorological observation has been done in domestic researches. In case of using IGS final ephemeris, GPS measurements can be only post processed in daily basis in three weeks after the observation. Thus this method cannot be applied to any near real-time data processing. In this paper, a GPS data processing method to produce the PWV output with three mm accuracy and one hour latency for the data assimilation in NWP has been planned. For our new data processing strategy, IGS ultra-rapid ephemeris and the sliding window technique are applied. And the results from the new strategy has been verified. The GPS measurements during the first 10 days of January, April, July and October were processed. The results from the observations at Sokcho, where the GPS and radiosonde were collocated, were compared. As the results, a data processing strategy with 0.8 mm of mean bias and 1.7 mm of standard deviation in three minutes forty-three seconds has been established.

• Atmosphere
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• v.29 no.2
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• pp.219-226
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• 2019

Generally, the weather forecast system has been run using prescribed ocean condition. As it is widely known that coupling between atmosphere and ocean process produces consistent initial condition at all-time scales to improve forecast skill, there are many trials on the application of data assimilation of coupled model. In this study, we implemented a weakly coupled data assimilation (short for WCDA) system in global NWP model with low horizontal resolution for coupled forecast with uncoupled initialization, following WCDA system at the Met Office. The experiment is carried out for a typhoon evolution forecast in 2017. Air-sea exchange process provides SST cooling and gives a substantial impact on tendency of central pressure changes in the decaying phase of the typhoon, except the underestimated central pressure. Coupled data assimilation is a challenging new area, requiring further work, but it would offer the potential for improving air-sea feedback process on NWP timescales and finally contributing forecast accuracy.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.763-774
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• 2020

Wind data forecasted from the numerical weather prediction (NWP) model is generally used as the first-guess of the target tracking process to obtain the atmospheric motion vectors(AMVs) because it increases tracking accuracy and reduce computational time. However, there is a contradiction that the NWP model used as the first-guess is used again as the reference in the AMVs verification process. To overcome this problem, model-independent first guesses are required. In this study, we propose the AMVs derivation from Lucas and Kanade optical flow method and then using it as the first guess. To retrieve AMVs, Himawari-8/AHI geostationary satellite level-1B data were used at 00, 06, 12, and 18 UTC from August 19 to September 5, 2015. To evaluate the impact of applying the optical flow method on the AMV derivation, cross-validation has been conducted in three ways as follows. (1) Without the first-guess, (2) NWP (KMA/UM) forecasted wind as the first-guess, and (3) Optical flow method based wind as the first-guess. As the results of verification using ECMWF ERA-Interim reanalysis data, the highest precision (RMSVD: 5.296-5.804 ms^-1) was obtained using optical flow based winds as the first-guess. In addition, the computation speed for AMVs derivation was the slowest without the first-guess test, but the other two had similar performance. Thus, applying the optical flow method in the target tracking process of AMVs algorithm, this study showed that the optical flow method is very effective as a first guess for model-independent AMVs derivation.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.465-480
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• 2020

Numerical simulations of the storm surge and the wave induced by the Typhoon Sanba incident on the south coast of Korea in 2012 are conducted using the JMA-MSM forecast weather field, NCEP-CFSR reanalysis weather field, ECMWF-ERA5 reanalysis weather field, and the pressure and wind fields obtained using the best track information provided by JTWC. The calculated surge heights are compared with the time history observed at harbors along the coasts of Korea. For the waves the calculated significant wave heights are compared with the data measured using the wave buoys and the underwater pressure type wave gauge. As a result the JMA-MSM and the NCEP-CFSR weather fields give the highest reliability. The ECMWF-ERA5 gives in general surge and wave heights weaker than the measured. The ECMWF-ERA5, however, reproduces the best convergence belt formed in front of the typhoon. The weather field obtained using JTWC best track information gives the worst agreement.

• Journal of the Korean Society of Marine Environment & Safety
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• v.26 no.7
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• pp.892-903
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• 2020

The rapid and accurate prediction of storm-surge height during typhoon attacks is essential in responding to coastal disasters. Most methods used for predicting typhoon data are based on numerical modeling, but numerical modeling takes significant computing resources and time. Recently, various studies on the expeditious production of predictive data based on artificial intelligence have been conducted, and in this study, artificial intelligence-based storm-surge height prediction was performed. Several learning data were needed for artificial intelligence training. Because the number of previous typhoons was limited, many synthesized typhoons were created using the tropical cyclone risk model, and the storm-surge height was also generated using the storm surge model. The comparison of the storm-surge height predicted using artificial intelligence with the actual typhoon, showed that the root-mean-square error was 0.09 ~ 0.30 m, the correlation coefficient was 0.65 ~ 0.94, and the absolute relative error of the maximum height was 1.0 ~ 52.5%. Although errors appeared to be somewhat large at certain typhoons and points, future studies are expected to improve accuracy through learning-data optimization.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.5B
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• pp.485-493
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• 2008

The Artificial Neural Network (ANN) model was suggested for predicting probability of precipitation (PoP) using RDAPS NWP model, observation at AWS and upper-air sounding station. The prediction work was implemented for flood season and the data period is the July, August of 2001 and June of 2002. Neural network input variables (predictors) were composed of geopotential height 500/750/1000 hPa, atmospheric thickness 500-1000 hPa, X & Y-component of wind at 500 hPa, X & Y-component of wind at 750 hPa, wind speed at surface, temperature at 500/750 hPa/surface, mean sea level pressure, 3-hr accumulated precipitation, occurrence of observed precipitation, precipitation accumulated in 6 & 12 hrs previous to RDAPS run, precipitation occurrence in 6 & 12 hrs previous to RDAPS run, relative humidity measured 0 & 12 hrs before RDAPS run, precipitable water measured 0 & 12 hrs before RDAPS run, precipitable water difference in 12 hrs previous to RDAPS run. The suggested ANN has a 3-layer perceptron (multi layer perceptron; MLP) and back-propagation learning algorithm. The result shows that there were 6.8% increase in Hit rate (H), especially 99.2% and 148.1% increase in Threat Score (TS) and Probability of Detection (POD). It illustrates that the suggested ANN model can be a useful tool for predicting rainfall event prediction. The Kuipers Skill Score (KSS) was increased 92.8%, which the ANN model improves the rainfall occurrence prediction over RDAPS.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.21 no.1
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• pp.9-17
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• 2003

Typhoon RUSA, which caused serious damage was passed over in Korea peninsula during 30 August to 1 September, 2002. We estimated tropospheric wet delay using GPS data and meteorological data during this period. Integrated Water Vapor(IWV) gives the total amount of water vapor from tropospheric wet delay and Precipitable Water Vapor(PWV) is calculated the IWV scaled by the density of water. We obtained GPS PWV at 13th GPS permanent stations(Seoul, Wonju. Seosan, Sangju, Junju, Cheongju, Taegu, Wuljin, Jinju, Daejeon, Mokpo, Sokcho, Jeju). We retrieve GPS data hourly and use Gipsy-Oasis II software and we compare PWV and precipitation. GPS observed PWV time series demonstrate that PWV is, in general, high before and during the occurrence of the typhoon RUSA, and low after the typhoon RUSA. GPS PWV peak time at each station is related to the progress of a typhoon RUSA. We got very near result as we compare GMS Satellite image with tomograph using GPS PWV and we could present practical use possibility by numerical model for weather forecast.