• Atmosphere
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• v.32 no.4
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• pp.277-295
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• 2022

The global weather prediction model, Korean Integrated Model (KIM), has been in operation since April 2020 by the Korea Meteorological Administration. This study assessed the performance of heat waves (HWs) in Korea in 2020. Case experiments during 2018-2020 were conducted to support the reliability of assessment, and the factors which affect predictability of the HWs were analyzed. Simulated expansion and retreat of the Tibetan High and North Pacific High during the 2020 HW had a good agreement with the analysis. However, the model showed significant cold biases in the maximum surface temperature. It was found that the temperature bias was highly related to underestimation of downward shortwave radiation at surface, which was linked to cloudiness. KIM tended to overestimate nighttime clouds that delayed the dissipation of cloud in the morning, which affected the shortage of downward solar radiation. The vertical profiles of temperature and moisture showed that cold bias and trapped moisture in the lower atmosphere produce favorable conditions for cloud formation over the Yellow Sea, which affected overestimation of cloud in downwind land. Sensitivity test was performed to reduce model bias, which was done by modulating moisture mixing parameter in the boundary layer scheme. Results indicated that the daytime temperature errors were reduced by increase in surface solar irradiance with enhanced cloud dissipation. This study suggested that not only the synoptic features but also the accuracy of low-level temperature and moisture condition played an important role in predicting the maximum temperature during the HWs in medium-range forecasts.

• Journal of Korean Society for Geospatial Information Science
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• v.23 no.4
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• pp.11-16
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• 2015

Spatial and temporal information on near-surface air temperature is important for understanding global warming and climate change. In this study, the estimation algorithm of near-surface air temperature in Korea was developed by using spatial homogeneous surface information obtained from satellite remote sensing observations. Based on LST(Land Surface Temperature), NDWI(Normalized Difference Water Index) and NDVI(Normalized Difference Vegetation Index) as independent variables, the multiple regression model was proposed for the estimation of near-surface air temperature. The different regression constants and coefficients for warm and cold seasons were calculated for considering regional climate change in Korea. The near-surface air temperature values estimated from the multiple regression algorithm showed reasonable performance for both warm and cold seasons with respect to observed values (approximately $3^{\circ}C$ root mean-square error and nearly zero mean bias). Thus;the proposed algorithm using remotely sensed surface observations and the approach based on the classified warm and cold seasons may be useful for assessment of regional climate temperature in Korea.

• Ocean and Polar Research
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• v.28 no.3
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• pp.245-258
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• 2006

We investigated seasonal variations of the upper ocean temperature and the mixed layer depth (MLD) in an eddy-permitting global ocean general circulation model (OGCM) to assess the OGCM perfermance. The OGCM is based on the GFDL MOM3 which has a horizontal resolution of 0.5 degree and 30 vertical levels. The OGCM was integrated for 68 years using a monthly-mean climatological wind stress forcing. The model sea surface temperature (SST) and sea surface salinity were restored to the Levitus climatology with a time scale of 30 days. Annual-mean model SST shows a cold bias $(<\;-2^{\circ}C)$ in the summer hemisphere and a warm bias $(>\;1^{\circ}C)$ in the winter hemisphere mainly due to the restoring boundary condition of temperature. The model MLD captures well the observed features in most areas, with a slightly deep bias. However, in the Ross Sea and Weddell Sea, the model shows significantly deeper MLD than the climatology-mainly due to weak salinity stratifications in the model. For amplitude of seasonal variation, the model SST is smaller $(1{\sim}3^{\circ}C)$ than the observation largely due to the restoring surface boundary condition while the model MLD has larger seasonal variation $({\sim}50m)$. It is suggested that for more realistic simulation of the upper ocean structure in the present eddy-permitting ocean model, more refinements in the surface boundary condition for the thermohaline forcing and parameterization for vertical mixing are required, together with the incorporation of a sea-ice model.

• Atmosphere
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• v.31 no.5
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• pp.473-488
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• 2021

A new physical/statistical diagnostic downscale model has been developed for use to improve near-surface air temperature forecasts. The model includes a series of physical and statistical correction methods that account for un-resolved topographic and land-use effects as well as statistical bias errors in a low-resolution atmospheric model. Operational temperature forecasts of the Local Data Assimilation and Prediction System (LDAPS) were downscaled at 100 m resolution for three months, which were used to validate the model's physical and statistical correction methods and to compare its performance with the forecasts of the Korea Meteorological Administration Post-processing (KMAP) system. The validation results showed positive impacts of the un-resolved topographic and urban effects (topographic height correction, valley cold air pool effect, mountain internal boundary layer formation effect, urban land-use effect) in complex terrain areas. In addition, the statistical bias correction of the LDAPS model were efficient in reducing forecast errors of the near-surface temperatures. The new high-resolution downscale model showed better agreement against Korean 584 meteorological monitoring stations than the KMAP, supporting the importance of the new physical and statistical correction methods. The new physical/statistical diagnostic downscale model can be a useful tool in improving near-surface temperature forecasts and diagnostics over complex terrain areas.

• Korean Journal of Remote Sensing
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• v.24 no.1
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• pp.65-78
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• 2008

This study compares the relative accuracy and consistency of four split-window land surface temperature (LST) algorithms (Becker and Li, Kerr et ai., Price, Ulivieri et al.) using 24 sets of Terra (Aqua)/Moderate Resolution Imaging Spectroradiometer (MODIS) data, observed ground grass temperature and air temperature over South Korea. The effective spectral emissivities of two thermal infrared bands have been retrieved by vegetation coverage method using the normalized difference vegetation index. The intercomparison results among the four LST algorithms show that the three algorithms (Becker-Li, Price, and Ulivieri et al.) show very similar performances. The LST estimated by the Becker and Li's algorithm is the highest, whereas that by the Kerr et al.'s algorithm is the lowest without regard to the geographic locations and seasons. The performance of four LST algorithms is significantly better during cold season (night) than warm season (day). And the LST derived from Terra/MODIS is closer to the observed LST than that of Aqua/MODIS. In general, the performances of Becker-Li and Ulivieri et al algorithms are systematically better than the others without regard to the day/night, seasons, and satellites. And the root mean square error and bias of Ulivieri et al. algorithm are consistently less than that of Becker-Li for the four seasons.

• Proceedings of the KSRS Conference
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• v.1
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• pp.250-252
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• 2006

The land surface temperature (LST) can be defined as a weighted average temperature of components which constitute a pixel. The coefficients of split-window algorithm for MTSAT-1R were obtained by means of a statistical regression analysis from radiative transfer simulations using MODTRAN 4.0 for a wide range of atmospheric, satellite viewing angle (SVA) and lapse rate conditions. 6 types of atmospheric profile data imbedded in the MODTRAN 4 are used for the radiative transfer simulations. The RMSE is clearly larger on warm and humid profiles than cold and dry profiles, especially when the satellite viewing angle and lapse rate are large. The derivation of LST equations according to the atmospheric profiles clearly decreased the RMSE without regard to the SVA and lapse rate. The bias and RMSE are decreased as the more controls factors included. This preliminary result indicates that the characteristics of atmosphere, SVA and lapse rate should be included in the LST equation.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.385-388
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• 2006

The land surface temperature (LST) can be defined as a weighted average temperature of components which constitute a pixel. The coefficients of split-window algorithm for MTSAT-1R were obtained by means of a statistical regression analysis from radiative transfer simulations using MODTRAN 4.0 for a wide range of atmospheric, satellite viewing angle (SVA) and lapse rate conditions. 6 types of atmospheric profile data imbedded in the MODTRAN 4 are used for the radiative transfer simulations. The RMSE is clearly larger on warm and humid profiles than cold and dry profiles, especially when the satellite viewing angle and lapse rate are large. The derivation of LST equations according to the atmospheric profiles clearly decreased the RMSE without regard to the SVA and lapse rate. The bias and RMSE are decreased as the more controls factors included. This preliminary result indicates that the characteristics of atmosphere, SVA and lapse rate should be included in the LST equation.

• Journal of the Korean earth science society
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• v.40 no.4
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• pp.392-405
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• 2019

Biogeochemical processes play an important role in ocean environments and can affect the entire Earth's climate system. Using an ocean-biogeochemistry model (NEMO-TOPAZ), we investigated the effects of changes in albedo and wind stress caused by phytoplankton in the equatorial Pacific. The simulated ocean temperature showed a slight decrease when the solar reflectance of the regions where phytoplankton were present increased. Phytoplankton also decreased the El $Ni{\tilde{n}}o$-Southern Oscillation (ENSO) amplitude by decreasing the influence of trade winds due to their biological enhancement of upper-ocean turbulent viscosity. Consequently, the cold sea surface temperature bias in the equatorial Pacific and overestimation of the ENSO amplitude were slightly reduced in our model simulations. Further sensitivity tests suggested the necessity of improving the phytoplankton-related equation and optimal coefficients. Our results highlight the effects of altered albedo and wind stress due to phytoplankton on the climate system.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2004.06a
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• pp.32-42
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• 2004

Recently, plasma surface-cleaning or surface-etching techniques have been focused in respect of the decontamination of spent or used nuclear parts and equipment. In this study the removal rate of metallic cobalt surface is experimentally investigated via its surface etching rate with a $CF_4-o_2$mixed gas plasma. Experimental results reveal that a mixed etchant gas with about 80% $CF_4$-20% $O_2$ (molar) gives the highest reaction rate and the rate reaches 0.06 ${\mu}m$/min at $380^{\circ}C$ and ion-assisted etching dramatically enhances the surface reaction rate. With a negative 300 V DC bias voltage applied to the substrate, the surface reaction initiation temperature lowers and the rate increases about 20 times at $350^{\circ}C$ and up to 0.43 ${\mu}m$/min at $380^{\circ}C$, respectively. Surface morphology analysis confirms the etching rate measurements. Auger spectrum analysis clearly shows the adsorption of fluorine atoms on the reacted surface. From the current experimental findings and the results discussed in previous studies, mechanistic understanding of the surface reaction, fluorination and/or fluoro-carbonylation reaction, is provided.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.299-303
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• 2008

In this study, an uncertainty assessment for surface air temperature(T2m) and precipitation(PCP) over East Asia is carried out. The data simulated by the intergovermental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) Atmosphere-Ocean coupled general circulation Model (AOGCM) are used to assess the uncertainty. Examination of the seasonal uncertainty of T2m and PCP variabilities shows that spring-summer cold bias and fall warm bias of T2m are found over both East Asia and the Korea peninsula. In contrast, distinctly summer dry bias and winter-spring wet bias of PCP over the Korea peninsula is found. To investigate the PCP seasonal variability over East Asia, the cyclostationary empirical orthogonal function(CSEOF) analysis is employed. The CSEOF analysis can extract physical modes (spatio-temporal patterns) and their undulation (PC time series) of PCP, showing the evolution of PCP. A comparison between spatio-temporal patterns of observed and modeled PCP anomalies shows that positive PCP anomalies located in northeastern China (north of Korea) of the multi-model ensemble(MME) cannot explain properly the contribution to summer monsoon rainfalls across Korea and Japan. The uncertainty of modeled PCP indicates that there is disagreement between observed and MME anomalies. The spatio-temporal deviation of the PCP is significantly associated with lower- and upper-level circulations. In particular, lower-level moisture transports from the warm pool of the western Pacific and corresponding moisture convergence significantly contribute to summer rainfalls. These lower- and upper-level circulations physically consistent with PCP give a insight of the reason why differences between modeled and observed PCP occur.