• Journal of the Korean earth science society
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• v.28 no.5
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• pp.613-619
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• 2007

Mesoscale low is often observed over the downstream region of the East Sea (or, northwest coast off the Japan Islands) during East-Asia winter monsoon. The low system causes a heavy snowfall at the region. A series of numerical experiments were conducted with the aid of a regional model (MM5 ver. 3.5) to examine the formation mechanism of the mesoscale low. The following results were obtained: 1) A well-developed mesoscale low was simulated by the regional model under real topography, NCEP reanalysis, and OISST; 2) The mesoscale low was simulated under a zonally averaged SST without topography. This implies that the meridional gradient of SST is the main factor in the formation of a mesoscale low; 3) A thermal contrast ($>10^{\circ}C$) of land-sea and topography-induced disturbance served as the second important factor for the formation; 4) Paektu Mountain caused the surface wind to decelerate downstream, which created a more favorable environment for thermodynamic modification than that was found in a flat topography; and 5) The types of cumulus parameterizations did not affect the development of the mesoscale low.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.995-1008
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• 2005

In this study, the principal components of rainfall in Korea are extracted by a method which consists of the independent component analysis combined with the wavelet transform, to examine the spatial correlation between seasonal rainfalls and global sea surface temperatures (SSTs). The 2-8 year band retains a strong wavelet power spectrum and the low frequency characteristics are shown by the wavelet analysis. The independent component analysis is performed by using the Scale Average Wavelet Power(SAWP) that is estimated by wavelet analysis. Interannual-interdecadal variation is the dominant variation, and an increasing trend is observed in the spring and summer seasons. The relationships between principal components of rainfall in the spring/summer seasons and SSTs existed in Indian and Pacific Oceans. Particularly, the SST zones, which represent a statistically significant correlation are located in the Philippine offshore and Australia offshore. Also, the three month leading SSTs in the same region we strongly correlated with the rainfall. Hence, these results propose a promising possibility of seasonal rainfall prediction by SST predictors.

• Korean Journal of Remote Sensing
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• v.3 no.1
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• pp.41-54
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• 1987

Accurate determination of Sea Surface Temperature (SST) is essential for ocean and climate studies. This paper estimated SST in the sea region around the Korea from the Advenced Very High Resolution Radiometer(AVHRR) channel 4 data on board NOAA-9 satellite. The processing procedure used to derive SSTs utilized: 1) Ascending node prediction of satellite orbit 2) Geometric correction 3) Radiometric calibration and radiance to temperature conversion look up table 4) Removing cloudy area. SST product results are displayed as colored video and hardcopy. In this processing, geometric correction is derived from equator crossing time, ascending time and subpoint coordinate information. Also, normalized response function of infrared 10.5-11.5$\mu\textrm{m}$ wavelength is used for temperature conversion. The SST derived from this processing is relatively similar to the measurements made by ship data, but because of water vapor attenuation SST from satellite are in general 2$^{\circ}$- $^{\circ}C$ lower than the ship data.

• International Journal of Aeronautical and Space Sciences
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• v.17 no.1
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• pp.8-19
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• 2016

This paper investigates the effects of inlet turbulence conditions and near-wall treatment methods on the heat transfer prediction of gas turbine vanes within the range of engine relevant turbulence conditions. The two near-wall treatment methods, the wall-function and low-Reynolds number method, were combined with the SST and ${\omega}RSM$ turbulence model. Additionally, the RNG $k-{\varepsilon}$, SSG RSM, and $SST_+{\gamma}-Re_{\theta}$ transition model were adopted for the purpose of comparison. All computations were conducted using a commercial CFD code, CFX, considering a three-dimensional, steady, compressible flow. The conjugate heat transfer method was applied to all simulation cases with internally cooled NASA turbine vanes. The CFD results at mid-span were compared with the measured data under different inlet turbulence conditions. In the SST solutions, on the pressure side, both the wall-function and low-Reynolds number method exhibited a reasonable agreement with the measured data. On the suction side, however, both wall-function and low-Reynolds number method failed to predict the variations of heat transfer coefficient and temperature caused by boundary layer flow transition. In the ${\omega}RSM$ results, the wall-function showed reasonable predictions for both the heat transfer coefficient and temperature variations including flow transition onset on suction side, but, low-Reynolds methods did not properly capture the variation of the heat transfer coefficient. The $SST_+{\gamma}-Re_{\theta}$ transition model showed variation of the heat transfer coefficient on the transition regions, but did not capture the proper transition onset location, and was found to be much more sensitive to the inlet turbulence length scale. Overall, the Reynolds stress model and wall function configuration showed the reasonable predictions in presented cases.

• Journal of Industrial Technology
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• v.31 no.B
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• pp.107-112
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• 2011

Recently, climate change around the world due to global warming has became an important issue and damages by climate change have a bad effect on human life. Changes of Sea Surface Temperature(SST) is associated with natural disaster such as Typhoon and El Nino. So we predicted daily future SST using Statistical Downscaling Method and CGCM 3.1 A1B scenario. 9 points of around Korea peninsular were selected to predict future SST and built up a regression model using Multiple Linear Regression. CGCM 3.1 was simulated with regression model, and that comparing Probability Density Function, Box-Plot, and statistical data to evaluate suitability of regression models, it was validated that regression models were built up properly.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.3
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• pp.307-316
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• 2012

Numerical simulations of 3D aircraft configurations are performed in order to understand the effects of turbulence models on the prediction of aircraft's aerodynamic characteristics. An in-house CFD code that solves 3D RANS equations and two-equation turbulence model equations are used. The code applies Roe's approximated Riemann solver and an AF-ADI scheme. Van Leer's MUSCL extrapolation with van Albada's limiter is also adopted. Various versions of Menter's $k-{\omega}$ SST turbulence models as well as Coakley's $q-{\omega}$ model are incorporated into the CFD code. Menter's $k-{\omega}$ SST models include the standard model, the 2003 model, the model incorporating the vorticity source term, and the model containing controlled decay. Turbulent flows over a wing are simulated in order to validate the turbulence models contained in the CFD code. The results from these simulations are then compared with computational results from the $3^{rd}$ AIAA CFD Drag Prediction Workshop. Numerical simulations of the DLR-F6 wing-body and wing-body-nacelle-pylon configurations are conducted and compared with computational results of the $2^{nd}$ AIAA CFD Drag Prediction Workshop. Aerodynamic characteristics as well as flow features are scrutinized with respect to the turbulence models. The results obtained from each simulation incorporating Menter's $k-{\omega}$ SST turbulence model variations are compared with one another.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2207-2211
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• 2008

한반도 및 동아시아의 여름철은 장마와 태풍으로 인한 집중호우의 발생으로 많은 피해를 입는다. 따라서 여름철에 나타나는 이러한 집중호우가 나타나는 지역, 시기, 기간, 그리고 강수량 등을 예측하는 것은 매우 중요하다. 특히, 효율적인 수자원 관리를 위하여 이러한 예측은 매우 중요한데, 단기적으로 정확하고 신속하게 강수를 예측하는 것도 중요하지만, 장기적으로 계절 강수, 특히 여름철의 장마 또는 우기의 시기와 강수량과 태풍 발생의 시기 등을 미리 예측하여 이에 따른 집중 호우의 발생 지역, 기간, 강수량을 예측하여 사전에 대비하는 것도 매우 중요하다. 특히, 최근에는 6,7월 장마에 의한 집중 호우의 영향보다도 8월에 강수량이 높아지고 있는 경향을 보이므로 강수량의 장기적 경향의 파악이 매우 중요하다. 장기 기후를 예측하는 데는 과거 자료를 이용한 통계 방법도 유용하지만 최근에는 AOGCM (Atmospheric Oceanic General Circulation Model)을 이용한 연구가 활발하게 이루어지고 있다. 하지만 강수와 같이 지역적으로 나타나는 현상은 저해상도의 AOGCM으로는 유용한 정보를 제공하기가 어려움이 따른다. 따라서 본 연구에서는 전구를 삼각형으로 된 20면체로 격자화 시켜 모든 격자의 크기가 거의 동일하고, 해상도 조절이 가능한 Geodesic 격자를 활용한 GME 모델을 사용하였다. GME 모델은 icosahedral-hexagonal grid 격자 체계를 가진 독일 기상청(Deutscher Wetterdient)에서 현업으로 사용 중인 모델이다. 본 연구에서는 수직/수평 해상도를 40km/40layers로 하여 GME 모델을 수행하였으며, 일간격의 장기 기후 자료를 생산하였다. 사용된 초기자료로는 ECMWF (European Centre for Medium Range Weather Forecasts) 자료이며, 경계 자료로는 ERA Climatology의 최근 30년간의 SST (Sea Surface Temperature) 평균 자료를 이용하여 규준 실험(Control Run), 즉, climatology 자료를 생산하였으며, persistent SST 아노말리와 ERA Climatology의 최근 30년간의 SST 자료를 이용하여 내삽 과정을 거친 SST forcing을 주어서 예측 실험(Prediction Run)을 통하여 모의 자료를 생산하였다. 특히, 규준 실험에서는 수치 모델이 가지는 불확실성을 줄이고 예보 정확도를 향상시키기 위하여 각각의 실험은 초기자료를 달리한 앙상블 모의실험을 수행하였다. 장기 모의 3개월을 위하여 모의 기간 1달 전부터 모의를 수행하여, 첫 1달은 모델의 spin-up 시간으로 분석에서 제외 하였다. 생산된 Climatology 자료와 Prediction 자료를 비교하여 아노말리와 Category 분석을 실시하여 한반도 및 동아시아 지역의 강수(Precipitation)를 중심으로 기압장(Pressure), 온도(2m Temperature) 위주로 분석하였다. 이러한 예측된 매 계절의 전망 자료 중에서도 수자원 분야에서 관심이 집중되는 여름철에 초점을 맞추어 실제 관측 자료와 비교하여 GME 모델의 계절 모의 예측성 성능을 분석하여 평가하고 다가올 여름철의 강수량의 장기 변화를 모의하고자 하였다.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1077-1093
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• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.

• Journal of Korean Society for Atmospheric Environment
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• v.27 no.1
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• pp.30-40
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• 2011

Numerical simulations were carried out to investigate the impact of SST spatial distribution on the result of air quality modeling. Eulerian photochemical dispersion model CAMx (Comprehensive Air quality Model with eXtensions, version 4.50) was applied in this study and meteorological fields were prepared by RAMS (Regional Atmospheric Modeling System). Three different meteorological fields, due to different SST spatial distributions were used for air quality modeling to assess the sensitivity of CAMx modeling to the different meteorological input data. The horizontal distributions of surface ozone concentrations were analyzed and compared. In each case, the simulated ozone concentrations were different due to the discrepancies of horizontal SST distributions. The discrepancies of land-sea breeze velocity caused the difference of daytime and nighttime ozone concentrations. The result of statistic analysis also showed differences for each case. Case NG, which used meteorological fields with high resolution SST data was most successfully estimated correlation coefficient, root mean squared error and index of agreement value for ground level ozone concentration. The prediction accuracy was also improved clearly for case NG. In conclusion, the results suggest that SST spatial distribution plays an important role in the results of air quality modeling on high ozone episode at coastal region.