• Korean Journal of Remote Sensing
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• v.34 no.6_1
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• pp.925-939
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• 2018

Absorbed shortwave radiation at the surface is an important component of energy analysis among the atmosphere, land, and ocean. In this study, the absorbed shortwave radiation was calculated using a radiation model and surface broadband albedo data for application to Geostationary Earth Orbit Korea Multi-Purpose SATellite (GEO-KOMPSAT-2A; GK-2A). And the results (GWNU algorithm) were compared with CERES data and calculation results using pyranometer and MODIS (Moderate Resolution Imaging Spectroradiometer) data to be selected as the reference absorbed shortwave radiation. This GWNU algorithm was also compared with the physical and statistical algorithms of GOSE-R ABI and two algorithms (Li et al., 1993; Kim and Jeong, 2016) using regression equation. As a result, the absorbed shortwave radiation calculated by GWNU algorithm was more accurate than the values calculated by the other algorithms. However, if the problem about computing time and accuracy of albedo data arise when absorbed shortwave radiation is calculated by GWNU algorithm, then the empirical algorithms explained above should be used with GWNU algorithm.

• Journal of the Korean earth science society
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• v.43 no.5
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• pp.591-603
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• 2022

The color and optical properties of seawater are determined by the interaction between dissolved organic and inorganic substances and plankton contained in it. The Ieodo - Ocean Research Institute (I-ORS), located in the East China Sea, is affected by the low salinity of the Yangtze River in the west and the Tsushima Warm Current in the south. Thus, it is a suitable site for analyzing the fluctuations in circulation and optical properties around the Korean Peninsula. In this study, seawater surrounding the I-ORS was classified according to its optical characteristics using the satellite remote reflectance observed with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua and National Aeronautics and Space Administration (NASA) bio-Optical Marine Algorithm Dataset (NOMAD) from January 2016 to December 2020. Additionally, the variation characteristics of optical water types (OWTs) from different seasons were presented. A total of 59,532 satellite match-up data (d ≤ 10 km) collected from seawater surrounding the I-ORS were classified into 23 types using the spectral angle mapper. The OWTs appearing in relatively clear waters surrounding the I-ORS were observed to be greater than 50% of the total. The maximum OWTs frequency in summer and winter was opposite according to season. In particular, the OWTs corresponding to optically clear seawater were primarily present in the summer. However, the same OWTs were lower than overall 1% rate in winter. Considering the OWTs fluctuations in the East China Sea, the I-ORS is inferred to be located in the transition zone of seawater. This study contributes in understanding the optical characteristics of seawater and improving the accuracy of satellite ocean color variables.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.1-10
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• 2016

Currently available surface seawater partial pressure carbon dioxide ($pCO_2$) data sets in the East Sea are not enough to quantify statistically the carbon dioxide flux through the air-sea interface. To complement the scarcity of the $pCO_2$ measurements, we construct a neural network (NN) model based on satellite data to map $pCO_2$ for the areas, which were not observed. The NN model is constructed for the Ulleung Basin, where $pCO_2$ data are best available, to map and estimate the variability of $pCO_2$ based on in situ $pCO_2$ for the years from 2003 to 2012, and the sea surface temperature (SST) and chlorophyll data from the MODIS (Moderate-resolution Imaging Spectroradiometer) sensor of the Aqua satellite along with geographic information. The NN model was trained to achieve higher than 95% of a correlation between in situ and predicted $pCO_2$ values. The RMSE (root mean square error) of the NN model output was $19.2{\mu}atm$ and much less than the variability of in situ $pCO_2$. The variability of $pCO_2$ with respect to SST and chlorophyll shows a strong negative correlation with SST than chlorophyll. As SST decreases the variability of $pCO_2$ increases. When SST is lower than $15^{\circ}C$, $pCO_2$ variability is clearly affected by both SST and chlorophyll. In contrast when SST is higher than $15^{\circ}C$, the variability of $pCO_2$ is less sensitive to changes in SST and chlorophyll. The mean rate of the annual $pCO_2$ increase estimated by the NN model output in the Ulleung Basin is $0.8{\mu}atm\;yr^{-1}$ from 2003 to 2014. As NN model can successfully map $pCO_2$ data for the whole study area with a higher resolution and less RMSE compared to the previous studies, the NN model can be a potentially useful tool for the understanding of the carbon cycle in the East Sea, where accessibility is limited by the international affairs.

• Journal of the Korean earth science society
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• v.38 no.4
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• pp.283-292
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• 2017

In this study, the empirical models were established to estimate the concentrations of surface-level $PM_{2.5}$ over Seoul, Korea from 1 January 2012 to 31 December 2013. We used six different multiple linear regression models with aerosol optical thickness (AOT), ${\AA}ngstr{\ddot{o}}m$ exponents (AE) data from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Terra and Aqua satellites, meteorological data, and planetary boundary layer depth (PBLD) data. The results showed that $M_6$ was the best empirical model and AOT, AE, relative humidity (RH), wind speed, wind direction, PBLD, and air temperature data were used as input data. Statistical analysis showed that the result between the observed $PM_{2.5}$ and the estimated $PM_{2.5}$ concentrations using $M_6$ model were correlations (R=0.62) and root square mean error ($RMSE=10.70{\mu}gm^{-3}$). In addition, our study show that the relation strongly depends on the seasons due to seasonal observation characteristics of AOT, with a relatively better correlation in spring (R=0.66) and autumntime (R=0.75) than summer and wintertime (R was about 0.38 and 0.56). These results were due to cloud contamination of summertime and the influence of snow/ice surface of wintertime, compared with those of other seasons. Therefore, the empirical multiple linear regression model used in this study showed that the AOT data retrieved from the satellite was important a dominant variable and we will need to use additional weather variables to improve the results of $PM_{2.5}$. Also, the result calculated for $PM_{2.5}$ using empirical multi linear regression model will be useful as a method to enable monitoring of atmospheric environment from satellite and ground meteorological data.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.48-59
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• 2023

Ground-level ozone affects human health and plant growth. Ozone is produced by chemical reactions between oxides of nitrogen (NOx) and volatile organic compounds (VOCs) from anthropogenic and biogenic sources. In this study, two different land cover and emission factor datasets were input to the MEGAN v2.1 emission model to examine how these parameters contribute to the biogenic emissions and ozone production. Four input sensitivity scenarios (A, B, C and D) were generated from land cover and vegetation emission factors combination. The effects of BVOCs emissions by scenario were also investigated. From air quality modeling result using CAMx, maximum 1 hour ozone concentrations were estimated 62 ppb, 60 ppb, 68 ppb, 65 ppb, 55 ppb for scenarios A, B, C, D and E, respectively. For maximum 8 hour ozone concentration, 57 ppb, 56 ppb, 63 ppb, 60 ppb, and 53 ppb were estimated by scenario. The minimum difference by land cover was up to 25 ppb and by emission factor that was up to 35 ppb. From the modeling performance evaluation using ground ozone measurement over the six regions (East Seoul, West Seoul, Incheon, Namyangju, Wonju, and Daegu), the model performed well in terms of the correlation coefficient (0.6 to 0.82). For the 4 urban regions (East Seoul, West Seoul, Incheon, and Namyangju), ozone simulations were not quite sensitive to the change of BVOC emissions. For rural regions (Wonju and Daegu) , however, BVOC emission affected ozone concentration much more than previously mentioned regions, especially in case of scenario C. This implies the importance of biogenic emissions on ozone production over the sub-urban to rural regions.