• Journal of Environmental Science International
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• v.23 no.8
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• pp.1481-1493
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• 2014

In order to understand the relation between the distribution of sea surface temperature and heavy snowfall over western coast of the Korean peninsula, several numerical assessments were carried out. Numerical model used in this study is WRF, and sea surface temperature data were FNL(National Center for Environment Prediction-Final operational global analysis), RTG(Real Time Global analysis), and OSTIA(Operational Sea Surface Temperature and Sea Ice Analysis). There were produced on the basis of remote sensing data, such as a variety of satellite and in situ observation. The analysis focused on the heavy snowfall over Honam districts for 2 days from 29 December 2010. In comparison with RTG and OSTIA SST data, sensible and latent heat fluexes estimated by numerical simulation with FNL data were higher than those with RTG and OSTIA SST data, due to higher sea surface temperature of FNL. General distribution of RTG and OSTIA SST showed similar, however, fine spatial differences appear in near western coast of the peninsula. Estimated snow fall amount with OSTIA SST was occurred far from the western coast because of higher SST over sea far from coast than that near coast. On the other hand, snowfall amount near coast is larger than that over distance sea in simulation with RTG SST. The difference of snowfall amount between numerical assessment with RTG and OSTIA is induced from the fine difference of SST spatial distributions over the Yellow sea. So, the prediction accuracy of snowfall amount is strongly associated with the SST distribution not only over near coast but also over far from the western coast of the Korean peninsula.

• Journal of Environmental Science International
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• v.19 no.2
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• pp.171-184
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• 2010

Accurate simulation of the meteorological field is very important to assess the wind resources. Some researchers showed that sea surface temperature (SST) plays a leading role on the local meterological simulation. New Generation Sea Surface Temperature (NGSST), Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA), and Real-Time Global Sea Surface Temperature (RTG SST) have different spatial distribution near the coast and OSTIA shows the best accuracy compared with buoy data in the southeastern coast of the Korean Peninsula. Those SST products are used to initialize the Weather Research and Forecasting (WRF) Model for November 13-23 2008. The simulation of OSTIA shows better result in comparison with NGSST and RTG SST. NGSST shows a large difference with OSTIA in horizontal and vertical wind fields during the weak synoptic condition, but wind power density shows a large difference during strong synoptic condition. RTG SST shows the similar patterns but smaller the magnitude and the extent.

• Atmosphere
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• v.23 no.2
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• pp.161-169
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• 2013

We examine the effects of the sea surface temperature (SST) distribution on heavy snowfall over the Yellow Sea using high-resolution SST products and WRF (Weather Research and Forecasting) model simulations in 30 December 2010. First, we evaluate the model by comparing the simulated and observed fresh snowfall over the Korean peninsula (Ho-Nam province). The comparison shows that the model reproduces the distributions and magnitudes of the observed snowfall. We then conduct sensitivity model simulations where SST perturbations by ${\pm}1.1^{\circ}C$ relative to baseline SST values (averaged SST for $5{\sim}15^{\circ}C$) are uniformly specified over the region of interest. Results show that ${\pm}1.1^{\circ}C$ SST perturbation simulations result in changes of air temperature by $+0.37/-0.38^{\circ}C$, and by ${\pm}0.31^{\circ}C$ hPa for sea level pressure, respectively, relative to the baseline simulation. Atmospheric responses to SST perturbations are found to be relatively linear. The changes in SST appear to perturb precipitation variability accounting for 10% of snow and graupel, and 18% of snowfall over the Yellow Sea and Ho- Nam province, respectively. We find that anomalies of air temperature, pressure, and hydrometeors due to SST perturbation propagate to the upper part of cloud top up to 500 hPa and show symmetric responses with respect to SST changes.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.518-521
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• 2007

To retrieve Sea Surface Temperature(SST) from NOAA-AVHRR imagery the spilt window atmospheric correction algorithm is generally used. Recently, there have been various new algorithms developed to process these data, namely the variable-coefficient split-window, the R54 transmittance-ratio method, fixed-coefficient nonlinear algorithm, dynamic water vapour (DWV) correction method, Dynamic Water Vapour and Temperature algorithm (DWVT). We used MCSST (Multi-Channel Sea surface temperature) and NLSST(Non linear sea surface temperature) algorithms in this study. The study area is around the Korea sea area (Yellow Sea). We compared and analyzed with various methods by applying each Ocean in-situ data and satellite data. The primary aim of study is to verify and optimize algorithms. Finally, this study proposes an optimized algorithm for SST retrieval.

• Journal of Environmental Impact Assessment
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• v.20 no.2
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• pp.199-205
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• 2011

The Saemangeum embankment construction have changed the flowing on the topography of the coastal marine environment. However, the variety of ecological factors are changing from outside of Saemangeum embankment area. The ecosystem of various marine organisms have led to changes by sea surface temperature. The aim of this study is to monitoring of sea surface temperature(SST) changes were measured by using thermal infrared satellite imagery, MODIS and Landsat. The MODIS data have the high temporal resolution and Landsat satellite data with high spatial resolution was used for time series monitoring. The extracted informations from sea surface temperature changes were compared with the dyke to allow them inside and outside of Saemangeum embankment. The spatial extent of the spread of sea water were analyzed by SST using MODIS and Landsat thermal channel data. The difference of sea surface temperature between inland and offshore waters of Saemangeum embankment have changed by seasonal flow and residence time of sea water in dyke.

• Journal of Environmental Science International
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• v.7 no.5
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• pp.725-733
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• 1998

Three numerical experiments are done using IAP(Institute of Atmospheric Physics) global spectral model(T42L9) to investigate the influence of the surface temperatures on the 7-day simulation. Particularly, the response of the subtropical High in summer to the variation of soil temperature and sea surface temperature(SST) was emphasized through a series of experiments. Experiment 1 uses the June climate data as the earth surface conditions. Experiment 2 is similar to Experiment 1 except for the soil surface temperature. Experiment 3 is the same as Experiment 1 except for the modified SST, which is much warmer than the June climate SST on the sea around the Korean peninsula. The main finding in 7-day simulation is that the response of the subtropical high in summer to the variation of the sell surface temperature was much more than that to the variation of the SST. It is implied that the proper treatment of sell surface temperature is more important than that of the SST for the better 7-day simulation of the subtropical high in summer.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.767-777
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• 1999

In the coastal region air flow changes due to the abrupt change of surface temperature between land and sea. So a numerical simulation for atmospheric flow fields must be considered the correct fields of sea surface temperature(SST). In this study, we used variables such as latent heat flux, sensible heat flux, short and long wave radiation of ocean and atmosphere which exchanged across the sea surface between atmosphere and ocean model. We found that this consideration simulated the more precise SST fields by comparing with those of the observated results. Simulated horizontal SST differences in season were 2.5~4$^{\circ}C$. Therefore we simulated the more precise atmospheric flow fields and the movement and dispersion of the pollutants with the Lagrangian particle dispersion model. In the daytime dispersion pattern of the pollutants emitted from ship sources moved toward inland, in the night time moved toward sea by land/sea breeze criculation. But air pollutants dispersion can be affected by inland topography, especially Yangsan and coastal area because of nocturnal wind speed decrease.

• Korean Journal of Remote Sensing
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• v.38 no.4
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• pp.355-364
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• 2022

In this study a tendency of abnormal sea surface temperature (SST) occurrence in the seas around South Korea is analyzed from daily SST data from satellite and 14 buoys from August 2020 to July 2021. As thresholds 28℃ and 4℃ are used to determine marine heatwaves(MHWs) and abnormal low water temperature (ALWT), respectively, because those values are adopted by the National Institute of Fisheries Science for the breaking news of abnormal temperature. In order to calculate frequency of abnormal SST occurrence spatially by using satellite SST, research area was divided into six areas of coast and three open seas. ALWT dominantly appeared over a wide area (7,745 km²) in Gyeonggi Bay for total 94 days and it was also confirmed from buoy temperature showing an occurrence number of 47 days. MHWs tended to be high in frequency in the coastal areas of Chungcheongdo and Jeollabukdo and the south coastal areas while in case of buoy temperature Jupo was the place of high frequency (32 days). This difference was supposed to be due to the low accuracy of satellite SST at the coasts. MHWs are also dominant in offshore waters around Korean Peninsula. Although detecting abnormal SST by using satellite SST has advantage of understanding occurrence from a spatial point of view, we also need to perform detection using buoys to increase detection accuracy along the coast.

• Korean Journal of Remote Sensing
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• v.6 no.1
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• pp.39-47
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• 1990

The first Marine Observation Satellite(MOS) was launched by National Space Development Agency of Japan on February 19, 1987, and it is equipped with three sensons covering visible, infrared, and microwave region. One of them is Visible and Thermal Infrared Radiometer(VTIR) whose main objective is to detect the Sea Surface Temperature(SST). The objective of this study was to process the MOS data using Cray-2 supercomputer, and to assess the SST in the Yellow Sea. In order to implement this objective, the linear regression model between the ground truth data and the corresponding digital number of VTIR in MOS was used to establish the relationship. After testing the significance of the regression model, the SST map of the whole Yellow Sea was derived based on the model. The digital SST map representing the study area showed certain pattern about the SST of Yellow Sea in March and April. In conclusion, the VTIR data in MOS is also useful in investigating SST which provides the information about the Yellow Sea water current in the spring.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.3
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• pp.263-274
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• 2020

In the Southeast Sea of Korea, a cold water mass is formed intensively in summer every year, causing frequent abnormal sea conditions. In order to analyze the spatial changes of sea surface temperature distribution in this area, ocean fields buoy data observed at Gori and Jeongja and reanalyzed sea surface temperature(SST) data from GHRSST Level 4 were used from June to September 2018. The buoy data were used to analyze the time-series water temperature changes at two stations, and the GHRSST data were used to calculate the daily SST variance and weighted mean center(WMC) across the study area. When the buoy's water temperature was lowered, the variance of SST in the study area trend to increase, but it did not appear consistently for the entire period. This is because GHRSST is a reanalysis data that does not reflect sensitive changes in water temperature along the coast. As such, there is a limit to grasping the local small-scale water temperature change in the coast or detecting the location and extent of the cold water zone only by the statistical variance representing the SST change in the entire sea area. Therefore, as a result of using WMC to quantitatively determine the spatial location of the cold water mass, when the cold water zone occurred, WMC was located in the northwest sea area from the mean center(MC) of the study area. This means that it is possible to quantitatively identify where and to what extent the distribution of cold surface water temperature appears through SST's WMC location information, and we could see the possibility of WMC's use in detecting the scale of cold water zones and the extent of regional spread in the future.