• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.770-778
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• 2023

Sea surface wind is an important variable for elucidating the atmospheric-ocean interactions and predicting the dangerous weather conditions caused by oceans. Accurate sea surface wind data are required for making correct predictions; however, there are limited observational datasets for oceans. Therefore, this study aimed to obtain long-period high-resolution sea surface wind data. First, the ERA5 reanalysis wind field, which can be used for a long period at a high resolution, was regridded and synthesized using the asymmetric typhoon wind field calculated via the Generalized Asymmetric Holland Model of the numerical model named ADvanced CIRCulation model. The accuracy of the asymmetric typhoon synthesized wind field was evaluated using data obtained from Korea Meteorological Administration and Japan Meteorological Administration. As a result of the evaluation, it was found that the asymmetric typhoon synthetic wind field reproduce observations relatively well, compared with ERA5 reanalysis wind field and symmetric typhoon synthetic wind field calculated by the Holland model. The sea surface wind data produced in this study are expected to be useful for obtaining storm surge data and conducting frequency analysis of storm surges and sea surface winds in the future.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.1-22
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• 2018

Eight different data sets are examined in order to gain insight into the surface heat flux traits of the East Asian marginal seas. In the case of solar radiation of the East Sea (Japan Sea), Coordinated Ocean-ice Reference Experiments ver. 2 (CORE2) and the Objectively Analyzed Air-Sea Fluxes (OAFlux) are similar to the observed data at meteorological stations. A combination is sought by averaging these as well as the Climate Forecast System Reanalysis (CFSR) and the National Centers for Environmental Prediction (NCEP)-1 data to acquire more accurate surface heat flux for the East Asian marginal seas. According to the Combination Data, the annual averages of net heat flux of the East Sea, Yellow Sea, and East China Sea are -61.84, -22.42, and $-97.54Wm^{-2}$, respectively. The Kuroshio area to the south of Japan and the southern East Sea were found to have the largest upward annual mean net heat flux during winter, at -460- -300 and at $-370--300Wm^{-2}$, respectively. The long-term fluctuation (1984-2004) of the net heat flux shows a trend of increasing transport of heat from the ocean into the atmosphere throughout the study area.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.729-741
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• 2011

Wind speeds have long been estimated from C-band VV-polarized SAR data by using the CMOD algorithms such as CMOD4, CMOD5, and CMOD_IFR2. Some SAR data with HH-polarization without any observations in VV-polarization mode should be converted to VV-polarized value in order to use the previous algorithms based on VV-polarized observation. To satisfy the necessity of polarization ratio (PR) for the conversion, we retrieved the conversion parameter from full-polarized SIR-C SAR image off the east coast of Korea. The polarization ratio for SIR-C SAR data was estimated to 0.47. To assess the accuracy of the polarization ratio coefficient, pseudo VV-polarized normalized radar cross section (NRCS) values were calculated and compared with the original VV-polarized ones. As a result, the estimated psudo values showed a good agreement with the original VV-polarized data with an root mean square error by 0.99 dB. We applied the psudo NRCS to the estimation of wind speeds based on the CMOD wind models. Comparison of the retrieved wind field with the ECMWF and NCEP/NCAR reanalysis wind data showed relatively small rms errors of 1.88 and 1.91 m/s, respectively. SIR-C HH-polarized SAR wind retrievals met the requirement of the scatterometer winds in overall. However, the polarization ratio coefficient revealed dependence on NRCS value, wind speed, and incident angle.

• Journal of the Korean earth science society
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• v.21 no.5
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• pp.503-524
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• 2000

Intercomparisons between four kinds of data have been done to estimate the accuracy of satellite observations and model reanalysis for middle and lower tropospheric thermal state over regional oceans. The data include the Microwave Sounding Units (MSU) Channel 2 (Ch2) brightness temperatures of NOAA satellites and the vertically weighted corresponding temperature of ECMWF GCM (1980-93). The satellite data for midtropospheric temperatures are MSU2 (1980-98) in nadir direction and SC2 (1980-97) in multiple scans, and for lower tropospheric temperature SC2R (1980-97). MSU2 was derived in this study while SC2 and SC2R were described in Spencer and Christy (1992a, 1992b). Temporal correlations between the above data were high (r${\ge}$0.90) in the middle and high latitudes, but low(r${\sim}$0.65) over the low latitude and more convective regions. Their values with SC2R which included the noises due to hydrometeors and surface emission were conspicuously low. The reanalysis shows higher correlation with SC2 than with MSU2 partially because of the hydrometeors screening. SC2R in monthly climatological anomalies was more sensitive to surface thermal condition in northern hemisphere than MSU2 or SC2. The first EOF mode for the monthly mean data of MSU and ECMWF shows annual cycle over most regions except the tropics. The mode in MSU2 over the Pacific suggests the east-west dipole due to the Walker circulation, but this tendency is not clear in other data. In the first and second modes for the Ch2 anomalies over most regions, the MSU and ECMWF data commonly indicate interannual variability due to El Ni${\tilde{n}$o and La Ni${\tilde{n}$a. The substantial disagreement between observations and model reanalysis occurs over the equatorial upwelling region of the western Pacific, suggesting uncertainties in the model parameterization of atmosphere-ocean interaction.

• International Journal of Contents
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• v.18 no.1
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• pp.17-26
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• 2022

With the acceleration of urbanization and industrialization, air pollution has become increasingly serious, and the pollution control situation is not optimistic. Climate change has become a major global challenge faced by mankind. To actively respond to climate change, China has proposed carbon peak and carbon neutral goals. However, atmospheric pollutants and meteorological factors that affect air quality are complex and changeable, and the complex relationship and correlation between them must be further clarified. This paper uses China's 2013-2018 high-resolution air pollution reanalysis open data set, as well as statistical methods of the Pearson Correlation Coefficient (PCC) to calculate and visualize the design and analysis of environmental monitoring big data, which is intuitive and it quickly demonstrated the correlation between pollutants and meteorological factors in the temporal and spatial sequence, and provided convenience for environmental management departments to use air quality routine monitoring data to enable dynamic decision-making, and promote global climate governance. The experimental results show that, apart from ozone, which is negatively correlated, the other pollutants are positively correlated; meteorological factors have a greater impact on pollutants, temperature and pollutants are negatively correlated, air pressure is positively correlated, and the correlation between humidity is insignificant. The wind speed has a significant negative correlation with the six pollutants, which has a greater impact on the diffusion of pollutants.

• Journal of the Korean earth science society
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• v.41 no.2
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• pp.95-110
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• 2020

Comparisons between two different surface temperatures from high-resolution ECMWF ReAnalysis 5 (ERA5) and Automated Synoptic Observing System (ASOS) observations were performed to investigate the reliability of the new reanalysis data over South Korea. As ERA5 has been recently produced and provided to the public, it will be highly used in various research fields. The analysis period in this study is limited to 1999-2018 because regularly recorded hourly data have been provided for 61 ASOS stations since 1999. Topographic characteristics of the 61 ASOS locations are classified as inland, coastal, and mountain based on Digital Elevation Model (DEM) data. The spatial distributions of whole period time-averaged temperatures for ASOS and ERA5 were similar without significant differences in their values. Scatter plots between ASOS and ERA5 for three different periods of yearlong, summer, and winter confirmed the characteristics of seasonal variability, also shown in the time-series of monthly error probability density functions (PDFs). Statistical indices NMB, RMSE, R, and IOA were adopted to quantify the temperature differences, which showed no significant differences in all indices, as R and IOA were all close to 0.99. In particular, the daily mean temperature differences based on 1-hour-averaged temperature had a smaller error than the classical daily mean temperature differences, showing a higher correlation between the two data. To check if the complex topography inside one ERA5 grid cell is related to the temperature differences, the kurtosis and skewness values of 90-m DEM PDFs in a ERA5 grid cell were compared to the one-year period amplitude among those of the power spectrum in the time-series of monthly temperature error PDFs at each station, showing positive correlations. The results account for the topographic effect as one of the largest possible drivers of the difference between ASOS and ERA5.

• Journal of the Korean earth science society
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• v.36 no.1
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• pp.82-89
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• 2015

In this study, the characteristics of sea ice initial conditions generated from a global ocean and sea ice prediction system, the Nucleus for European Modeling of the Ocean (NEMO) - Los Alamos Sea Ice Model (CICE)/NEMOVAR were analyzed for the period June 2013 to May 2014 over the Arctic region. For the purpose, the observed and reanalyzed data were used to compare with the sea ice initial conditions. Results indicated that the variability of the monthly sea ice extent and thickness in model initial conditions were well represented as compared to the observation, while it was found that the extent and thickness of Arctic sea ice in initial data were narrower and thinner than those in reanalysis and observation for the period. The reason for the narrower sea ice extent in model initial conditions seems to be due to the fact that the initial sea ice concentration at the boundary area of sea ice was about 20 percent less than the reanalysis data. Also, the reason for the thinner sea-ice thickness in the Arctic region is due to the underestimation of Arctic sea ice thickness (about 60 cm) of the model initial conditions in the Arctic Ocean area adjacent to Greenland and Arctic archipelago where thick sea ice appears all the year round.

• Journal of Ocean Engineering and Technology
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• v.34 no.1
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• pp.26-36
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• 2020

An extreme value analysis of metocean data which include wave, wind, and current data is a prerequisite for the operation and survival of offshore structures. The purpose of this study was to provide information about the return wave, wind, and current values for the Barents Sea using extreme value analysis. Hindcast datasets of the Global Reanalysis of Ocean Waves 2012 (GROW2012) for a waves, winds and currents were obtained from the Oceanweather Inc. The Gumbel distribution, 2 and 3 parameters Weibull distributions and log-normal distribution were used for the extreme value analysis. The least square method was used to estimate the parameters for the extreme value distribution. The return values, including the significant wave height, spectral peak wave period, wind speed and current speed at surface, were calculated and it will be utilized to design offshore structures to be operated in the Barents Sea.

• Toxicological Research
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• v.34 no.4
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• pp.303-310
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• 2018

The methods of applied genetic toxicology are changing from qualitative hazard identification to quantitative risk assessment. Recently, quantitative analysis with point of departure (PoD) metrics and benchmark dose (BMD) modeling have been applied to in vitro genotoxicity data. Two software packages are commonly used for BMD analysis. In previous studies, we performed quantitative dose-response analysis by using the PROAST software to quantitatively evaluate the mutagenicity of four piperidine nitroxides with various substituent groups on the 4-position of the piperidine ring and six cigarette whole smoke solutions (WSSs) prepared by bubbling machine-generated whole smoke. In the present study, we reanalyzed the obtained genotoxicity data by using the EPA's BMD software (BMDS) to evaluate the inter-platform quantitative agreement of the estimates of genotoxic potency. We calculated the BMDs for 10%, 50%, and 100% (i.e., a two-fold increase), and 200% increases over the concurrent vehicle controls to achieve better discrimination of the dose-responses, along with their BMDLs (the lower 95% confidence interval of the BMD) and BMDUs (the upper 95% confidence interval of the BMD). The BMD values and rankings estimated in this study by using the EPA's BMDS were reasonably similar to those calculated in our previous studies by using PROAST. These results indicated that both software packages were suitable for dose-response analysis using the mouse lymphoma assay and that the BMD modeling results from these software packages produced comparable rank orders of the mutagenic potency.

• Ocean and Polar Research
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• v.37 no.3
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• pp.179-188
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• 2015

This study was carried out to determine the optimal number of ARGO floats in the East Sea in order to maximize their applications. The dominant spatio-temporal scale, size of the domain, and the typical float lifetimes in the East Sea were taken into consideration. The mean spatial de-correlation scale of temperature on isobaric surfaces reaches about 60 km. The minimum necessary number of floats is about 82 on average in order to secure independent ARGO profiles with the de-correlation scale. Considering the float lifetimes, about 27 floats per year should be deployed to maintain the 82 ARGO float array every year. To obtain spatially uniform distribution of ARGO float data, mean residence time and dispersion rate (basin area/residence time) of ARGO floats were evaluated in each basin of the East Sea. A faster (slower) dispersion rate requires more (less) ARGO floats to maintain the spatially uniform number of floats. According to the analysis, it is likely that the optimal ratio of the number of floats for each basin is 1:2:4 corresponding to Ulleung Basin:Yamato Basin:Japan Basin. In order to maintain relatively uniform ARGO observing networks, it is necessary to establish a long-term plan for deployment strategy based on float pathways and the dispersion rate parameters estimated by using currently active ARGO float trajectory data as well as reanalysis data.