• Journal of Korea Water Resources Association
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• v.54 no.11
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• pp.999-1009
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• 2021

As the frequency and intensity of extreme weather events due to climate change are increasing in recent years, it is very important to evaluate and analyze climate conditions to manage and respond to the negative effects of climate change in advance. In this study, the trends and characteristics of regional climate change were analyzed by calculating the climate indices for the Chungcheong Province. Annual and monthly UNEP-MP, UNEP-PM and MDM indices were calculated using daily data from 1973-2020 collected from 10 synoptic meteorological stations operated by the Korea Meteorological Administration. The normality of climate data was analyzed through the KS test, and the climate change trend was analyzed by applying the Spearman and Pearson methods. The Chungcheongnam-do region had a relatively humid climate than the Chungcheongbuk-do region, and the annual climate indices showed a dry climate trend in Cheongju and Chungju, while the climate of Seosan and Buyeo was becoming humid. Based on the monthly trend change analysis, a humid climate trend was observed in summer and autumn, while a dry climate trend was observed in spring and winter. Comparison of climate indices during the past (2001-2010) and the recent (2011-2020) years showed a higher decrease in the average climate indices during the last 10 years and a gradually drying climate change trend was recorded.

• Journal of the Korean earth science society
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• v.43 no.3
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• pp.386-404
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• 2022

From April 29 to 30, 2011, under the influence of Asian dust originated from Mongolia, a high concentration of Asian dust was observed nationwide for 4 days in Korea. This study investigated the causes and characteristics of and weather conditions associated with Asian dust at high concentrations at its source in Mongolia. For analysis, Asian dust weather data, Asian dust monitoring tower data, satellite data, backward trajectory data, observation data (PM₁₀ and OPC data), and ECMWF reanalysis data were used. In the synoptic analysis, it was observed that the intervals of isobars were densely distributed in the central region of Mongolia and the pressure gradient force was strong. It could be inferenced that Asian dust occurred due to strong winds. The temperature was relatively high, above 10℃, just before the occurrence of Asian dust, and it decreased sharply at the onset of the dust. The relative humidity had a low value of less than approximately 40%. After the occurrence of Asian dust, it increased sharply to over 50% and then showed a tendency to decrease. In the aerosol index shown by the COMS satellite, a high concentration value of over 25 was detected in Inner Mongolia, and it was consistent with the observations made with naked eyes. In the 72-hour backward trajectory, the northwest airflow streamed into Korea, and on May 2, Heuksando showed the highest PM₁₀ concentration of 1,025 ㎍ m^-3(times the average). Especially, in kinematic vertical analysis, it was observed that low pressure on the ground was strengthened by cyclonic relative vorticity developed in the upper layer. Also, the vertical velocity development is considered to have played a major role in the occurrence of high concentration Asian dust.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.211-220
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• 2021

Land Surface Temperature (LST) is one of the useful parameters to diagnose the growth and development of crop and to detect crop stress. Unmanned Aerial Vehicle (UAV)-based LST (LSTUAV) can be estimated in the regional spatial scale due to miniaturization of thermal infrared camera and development of UAV. Given that meteorological variable, type of instrument, and surface condition can affect the LSTUAV, the evaluation for accuracy of LSTUAV is required. The purpose of this study is to evaluate the accuracy of LSTUAV using LST measured at ground (LSTGround) under various meteorological conditions and growth phases of garlic crop. To evaluate the accuracy of LSTUAV, Relative humidity (RH), absolute humidity (AH), gust, and vegetation index were considered. Root mean square error (RMSE) after minimizing the bias between LSTUAV and LSTGround was 2.565℃ under above 60% of RH, and it was higher than that of 1.82℃ under the below 60% of RH. Therefore, LSTUAV measurement should be conducted under the below 60% of RH. The error depending on the gust and surface conditions was not statistically significant (p-value < 0.05). LSTUAV had reliable accuracy under the wind speed conditions that allow flight and reflected the crop condition. These results help to comprehend the accuracy of LSTUAV and to utilize it in the agriculture field.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.275-292
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• 2022

In order to detect climate changes using satellite imagery, the GCOS (Global Climate Observing System) defines requirements such as spatio-temporal resolution, stability by the time change, and uncertainty. Due to limitation of GK-2A sensor performance, the level-2 products can not satisfy the requirement, especially for spatial resolution. In this paper, we found the optimal pan-sharpening algorithm for GK-2A products. The six pan-sharpening methods included in CS (Component Substitution), MRA (Multi-Resolution Analysis), VO (Variational Optimization), and DL (Deep Learning) were used. In the case of DL, the synthesis property based method was used to generate training dataset. The process of synthesis property is that pan-sharpening model is applied with Pan (Panchromatic) and MS (Multispectral) images with reduced spatial resolution, and fused image is compared with the original MS image. In the synthesis property based method, fused image with desire level for user can be produced only when the geometric characteristics between the PAN with reduced spatial resolution and MS image are similar. However, since the dissimilarity exists, RD (Random Down-sampling) was additionally used as a way to minimize it. Among the pan-sharpening methods, PSGAN was applied with RD (PSGAN_RD). The fused images are qualitatively and quantitatively validated with consistency property and the synthesis property. As validation result, the GSA algorithm performs well in the evaluation index representing spatial characteristics. In the case of spectral characteristics, the PSGAN_RD has the best accuracy with the original MS image. Therefore, in consideration of spatial and spectral characteristics of fused image, we found that PSGAN_RD is suitable for GK-2A products.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2B
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• pp.107-120
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• 2010

The effect of potential future climate change on the inflow of agricultural reservoir and its impact to downstream streamflow by reservoir operation for paddy irrigation water was assessed using the SLURP (semi-distributed land use-based runoff process), a physically based hydrological model. The fundamental input data (elevation, meteorological data, land use, soil, vegetation) was collected to calibrate and validate of the SLURP model for a 366.5 $km^2$ watershed including two agricultural reservoirs (Geumgwang and Gosam) located in Anseongcheon watershed. Then, the CCCma CGCM2 data by SRES (special report on emissions scenarios) A2 and B2 scenarios of the IPCC (intergovernmental panel on climate change) was used to assess the future potential climate change. The future weather data for the year, m ms, m5ms and 2amms was downscaled by Change Factor method through bias-correction using 3m years (1977-2006) weather data of 3 meteorological stations of the watershed. In addition, the future land uses were predicted by modified CA (cellular automata)-Markov technique using the time series land use data fromFactosat images. Also the future vegetation cover information was predicted and considered by the linear regression between monthly NDVI (normalized difference vegetation index) from NOAA AVHRR images and monthly mean temperature using eight years (1998-2006) data.

• Journal of Environmental Impact Assessment
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• v.33 no.1
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• pp.9-17
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• 2024

The purpose of this study is to select and predict optimal heatwave indices for describing and predicting heat-related illnesses. Regression analysis was conducted using Heat-related illness surveillance system data for a number of heat-related illnesses and meteorological data from the Korea Meteorological Administration's Automatic Weather Station (AWS) for the period from 2021 to 2023. Daily average temperature, daily maximum temperature, daily average Wet Bulb Globe Temperature (WBGT), and daily maximum WBGT values were calculated and analyzed. The results indicated that among the four indicators, the daily maximum WBGT showed the highest suitability with an R² value of 0.81 and RMSE of 0.98, with a threshold of 29.94 Celsius. During the entire analysis period, there were a total of 91 days exceeding this threshold, resulting in 339 cases of heat-related illnesses. Predictions of heat-related illness cases from 2021 to 2023 using the regression equation for daily maximum WBGT showed an accuracy with less than 10 cases of error annually, demonstrating a high level of precision. Through continuous research and refinement of data and analysis methods, it is anticipated that this approach could contribute to predicting and mitigating the impact of heatwaves.

• Atmosphere
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• v.17 no.2
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• pp.159-169
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• 2007

A comparison of the three land cover data sets (United States Geological Survey: USGS, International Geosphere Biosphere Programme: IGBP, and University of Maryland: UMd), derived from 1992-1993 Advanced Very High Resolution Radiometer(AVHRR) data sets, was performed over the Asian continent. Preprocesses such as the unification of map projection and land cover definition, were applied for the comparison of the three different land cover data sets. Overall, the agreement among the three land cover data sets was relatively high for the land covers which have a distinct phenology, such as urban, open shrubland, mixed forest, and bare ground (>45%). The ratios of triple agreement (TA), couple agreement (CA) and total disagreement (TD) among the three land cover data sets are 30.99%, 57.89% and 8.91%, respectively. The agreement ratio between USGS and IGBP is much greater (about 80%) than that (about 32%) between USGS and UMd (or IGBP and UMd). The main reasons for the relatively low agreement among the three land cover data sets are differences in 1) the number of land cover categories, 2) the basic input data sets used for the classification, 3) classification (or clustering) methodologies, and 4) level of preprocessing. The number of categories for the USGS, IGBP and UMd are 24, 17 and 14, respectively. USGS and IGBP used only the 12 monthly normalized difference vegetation index (NDVI), whereas UMd used the 12 monthly NDVI and other 29 auxiliary data derived from AVHRR 5 channels. USGS and IGBP used unsupervised clustering method, whereas UMd used the supervised technique, decision tree using the ground truth data derived from the high resolution Landsat data. The insufficient preprocessing in USGS and IGBP compared to the UMd resulted in the spatial discontinuity and misclassification.

• Journal of the Korean earth science society
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• v.30 no.7
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• pp.824-833
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• 2009

We analyzed a total of six months of geomagnetic data obtained at Cheonyang observatory, which is operated by Korean Meteorological Administration, to monitor earthquake precursors. Geomagnetic transfer functions (GTFs) and polarization values, which reflect the time-variations of the resistivity of subsurface, were estimated from 3-component geomagnetic data. The time-variant fluctuations were compared with the earthquake events occurred in the same period. Now that the daily GTFs show fairly irregular variations, we can not identify any correlation with the already occurred earthquakes and Kp index. On the other hand, we detect clear increases of the Ultra-Low-Frequency (ULF) band polarization values before the earthquakes, but the similar features are also observed even though the earthquake did not occur. This result may indicate that these time-variations are not just due to the earthquake precursor. For further understanding about these results, we need to investigate the relationship between the previous earthquake events and the geomagnetic data of other observatories.

• Korean Journal of Remote Sensing
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• v.27 no.6
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• pp.653-662
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• 2011

Land surface temperature (LST) is a one of the key variables of land surface which can be estimated from geostationary meteorological satellite. In this study, we have developed the three sets of LST retrieval algorithm from MTSAT-2 data through the radiative transfer simulations under various atmospheric profiles (TIGR data), satellite zenith angle, spectral emissivity, and surface lapse rate conditions using MODTRAN 4. The three LST algorithms are daytime, nighttime and total LST algorithms. The weighting method based on the solar zenith angle is developed for the consistent retrieval of LST at the early morning and evening time. The spectral emissivity of two thermal infrared channels is estimated by using vegetation coverage method with land cover map and 15-day normalized vegetation index data. In general, the three LST algorithms well estimated the LST without regard to the satellite zenith angle, water vapour amount, and surface lapse rate. However, the daytime LST algorithm shows a large bias especially for the warm LST (> 300 K) at day time conditions. The night LST algorithm shows a relatively large error for the LST (260 ~ 280K) at the night time conditions. The sensitivity analysis showed that the performance of weighting method is clearly improved regardless of the impacting conditions although the improvements of the weighted LST compared to the total LST are quite different according to the atmospheric and surface lapse rate conditions. The validation results of daytime (nighttime) LST with MODIS LST showed that the correlation coefficients, bias and RMSE are about 0.62~0.93 (0.44~0.83), -1.47~1.53 (-1.80~0.17), and 2.25~4.77 (2.15~4.27), respectively. However, the performance of daytime/nighttime LST algorithms is slightly degraded compared to that of the total LST algorithm.

• Journal of Korea Water Resources Association
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• v.32 no.1
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• pp.15-29
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• 1999

A new methodology to analyze and quantify regional meteorological drought based on annual precipitation data has been introduced in this paper In this study, based on posterior probability estimator and Bayesian classifier in Spatial Analysis Neural Network (SANN), point drought probabilities categorized as extreme, severe, mild, and non drought events has been defined, and a Bayesian Drought Severity Index (BPSI) has been introduced to classify the region of interest into four drought severities. In addition, to estimate the regional drought severity for the entire region, regional extreme, severe, mild, and non drought probabilities which are the areal averages of point drought probabilities over the region has been computed and applied. In this study, the proposed methodology has been applied to analyze the regional drought of South Korea during 1967-1996 years. The drought severity for the whole South Korea was defined spatially at each year and each year was classified in a drought severity criterion. The results may be useful for water manager to understand the South Korean drought with respect to the spatial and temporal variation.