• Journal of Mushroom
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• v.19 no.1
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• pp.41-50
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• 2021

The aim of the study was to obtain the extracts of various native wild mushrooms and select the useful resources though biological activity evaluation. The anti-oxidant potential, nitrite scavenging activity, and ��-glucan content of wild mushrooms collected from Eumseong and Bonghwa in Korea were investigated. Based on the results of this study, Ganoderma lingzhi (OK1362) showed the highest DPPH radical scavenging activity (73.2%), ferric reducing anti-oxidant power (0.134), reducing power (0.155), nitrite scavenging activity (53.6%), total polyphenol content (28.9 mg GAE/g), flavonoid content (10.0 mg QE/g), and ��-glucan content (25.2%) when compared to other wild mushrooms sampled in this study. In addition, it was confirmed that Perenniporia fraxinea (OK1360), Amanita sp. (OK1398), and Russula sp. (OK1406) had relatively high anti-oxidant and nitrite scavenging potentials. In conclusion, our results can provide fundamental data for extracting beneficial compounds from wild mushrooms.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.953-966
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• 2022

Since aerosols adversely affect human health, such as deteriorating air quality, quantitative observation of the distribution and characteristics of aerosols is essential. Recently, satellite-based Aerosol Optical Depth (AOD) data is used in various studies as periodic and quantitative information acquisition means on the global scale, but optical sensor-based satellite AOD images are missing in some areas with cloud conditions. In this study, we produced gap-free GeoKompsat 2A (GK-2A) Advanced Meteorological Imager (AMI) AOD hourly images after generating a Random Forest based gap-filling model using grid meteorological and geographic elements as input variables. The accuracy of the model is Mean Bias Error (MBE) of -0.002 and Root Mean Square Error (RMSE) of 0.145, which is higher than the target accuracy of the original data and considering that the target object is an atmospheric variable with Correlation Coefficient (CC) of 0.714, it is a model with sufficient explanatory power. The high temporal resolution of geostationary satellites is suitable for diurnal variation observation and is an important model for other research such as input for atmospheric correction, estimation of ground PM, analysis of small fires or pollutants.

• Journal of Korea Water Resources Association
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• v.56 no.7
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• pp.461-470
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• 2023

The purpose of this research is to derive the optimal temperature, location, and heating control system for a tipping bucket rain gauge heating system used for observing snowfall during winter. We conducted indoor and outdoor experiments by manufacturing a tipping bucket rain gauge that can be variably controlled for heating at the funnel, exterior, and interior, and indoor and outdoor. The indoor experiments involved using a temperature and humidity chamber to compare the performance and derive the appropriate temperature of the precipitation gauge heating system. Subsequently, the outdoor experiments were carried out at the Cloud Physics Observation Center located in Daeguallyeong, heavy snowfall region, to validate the findings. The analysis result was derived that the heating temperature of the funnel should be set at the 10 to 30℃, while the internal heating temperature should be 70℃. Furthermore, the optimal locations for the heating devices, which aim to minimize measurement delay, were identified as the exterior of the rain gauge, the rim of the funnel, and the vertical surface of the funnel. Our result shows that used as the basis for the operating conditions of precipitation gauge heating systems for solid precipitation measurement in winter.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.557-577
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• 2023

This study defines non-precipitation information as areas with weak precipitation or cloud particles that radar cannot detect due to weak returned signals, and suggests methods for its utilization in data assimilation. Previous studies have demonstrated that assimilating radar data from precipitation echoes can produce precipitation in model analysis and improve subsequent precipitation forecast. However, this study also recognizes the non-precipitation information as valuable observation and seeks to assimilate it to suppress spurious precipitation in the model analysis and forecast. To incorporate non-precipitation information into data assimilation, we propose observation operators that convert radar non-precipitation information into hydrometeor mixing ratios and relative humidity for the Weather Research and Forecasting Data Assimilation system (WRFDA). We also suggest a preprocessing method for radar non-precipitation information. A single-observation experiment indicates that assimilating non-precipitation information fosters an environment conducive to inhibiting convection by lowering temperature and humidity. Subsequently, we investigate the impact of assimilating non-precipitation information to a real case on July 23, 2013, by performing a subsequent 9-hour forecast. The experiment that assimilates radar non-precipitation information improves the model's precipitation forecasts by showing an increase in the Fractional Skill Score (FSS) and a decrease in the False Alarm Ratio (FAR) compared to experiments in which do not assimilate non-precipitation information.

• Journal of the Korean earth science society
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• v.44 no.6
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• pp.540-556
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• 2023

This study analyzed rawinsonde soundings observed during the summer and early fall seasons (June, July, August and September) on the Korean peninsula to examine the utility of the Convective Available Potential Energy (CAPE) and Convective Inhibition (CIN) in predicting the occurrence of deep moist convection and precipitation. Rawinsonde soundings are categorized into two groups based on thermodynamic criteria: high CAPE and low CIN represent a high potential for deep moist convection; low CAPE and high CIN indicate conditions unfavorable for deep convection. A statistical hypothesis test is conducted to determine whether the two groups are significantly different in terms of 12-hour cumulative precipitation, 12-hour mean cloud base, and 12-hour mean mid-level cloud cover. The results, in the case of no-precipitation, reveal statistically significant differences between the two groups, except for the 12-hour mean cloud base during the 21:01-09:00 KST time period. This suggests that the group characterized by high CAPE and low CIN is more conducive to the occurrence of deep moist convection and precipitation than the group with low CAPE and high CIN.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.536-542
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• 2023

Satellites represent cutting-edge technology, of ering significant advantages in spatial and temporal observations. National agencies worldwide harness satellite data to respond to marine accidents and analyze ocean fluctuations effectively. However, challenges arise with high-resolution satellite-based sea surface temperature data (Operational Sea Surface Temperature and Sea Ice Analysis, OSTIA), where gaps or empty areas may occur due to satellite instrumentation, geographical errors, and cloud cover. These issues can take several hours to rectify. This study addressed the issue of missing OSTIA data by employing LaMa, the latest deep learning-based algorithm. We evaluated its performance by comparing it to three existing image processing techniques. The results of this evaluation, using the coefficient of determination (R²) and mean absolute error (MAE) values, demonstrated the superior performance of the LaMa algorithm. It consistently achieved R² values of 0.9 or higher and kept MAE values under 0.5 ℃ or less. This outperformed the traditional methods, including bilinear interpolation, bicubic interpolation, and DeepFill v1 techniques. We plan to evaluate the feasibility of integrating the LaMa technique into an operational satellite data provision system.

• Korean Journal of Agricultural and Forest Meteorology
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• v.26 no.1
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• pp.53-62
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• 2024

In this study, we built a model to estimate frost occurrence in South Korea using single-input deep learning and multi-input deep learning. Meteorological factors used as learning data included minimum temperature, wind speed, relative humidity, cloud cover, and precipitation. As a result of statistical analysis for each factor on days when frost occurred and days when frost did not occur, significant differences were found. When evaluating the frost occurrence models based on single-input deep learning and multi-input deep learning model, the model using both GRU and MLP was highest accuracy at 0.8774 on average. As a result, it was found that frost occurrence model adopting multi-input deep learning improved performance more than using MLP, LSTM, GRU respectively.

• Journal of the Korean earth science society
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• v.45 no.3
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• pp.173-191
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• 2024

Typhoons are significant meteorological phenomena that cause interactions among the ocean, atmosphere, and land within Earth's system. In particular, wind speed, a key characteristic of typhoons, is influenced by various factors such as central pressure, trajectory, and sea surface temperature. Therefore, a comprehensive understanding based on actual observational data is essential. In the 2015 revised secondary school textbooks, typhoon wind speed is presented through text and illustrations; hence, exploratory activities that promote a deeper understanding of wind speed are necessary. In this study, we developed a data visualization program with a graphical user interface (GUI) to facilitate the understanding of typhoon wind speeds with simple operations during the teaching-learning process. The program utilizes red-green-blue (RGB) image data of Typhoons Mawar, Guchol, and Bolaven -which occurred in 2023- from the Korean geostationary satellite GEO-KOMPSAT-2A (GK-2A) as the input data. The program is designed to calculate typhoon wind speeds by inputting cloud movement coordinates around the typhoon and visualizes the wind speed distribution by inputting parameters such as central pressure, storm radius, and maximum wind speed. The GUI-based program developed in this study can be applied to typhoons observed by GK-2A without errors and enables scientific exploration based on actual observations beyond the limitations of textbooks. This allows students and teachers to collect, process, analyze, and visualize real observational data without needing a paid program or professional coding knowledge. This approach is expected to foster digital literacy, an essential competency for the future.

• 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.

• Atmosphere
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• v.17 no.4
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• pp.471-481
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• 2007

Temperature and precipitation, particularly August and summer, in the Korean peninsular are analyzed. The analyzed period is 1973-2007 for the Korean peninsular (that is, 60 meteorological station average). In addition, 100 year record (1908-2007) of temperature and precipitation in Seoul are also analyzed. Results indicate that the temperatures (mean, maximun, and minimum) of August and summer of 2007, both in Korean peninsular and Seoul, are higher than normal. The increasing rate of minimum temperature for the August and summer during the period from 1973 to 2007 shows greater than the mean and maximum temperature both in Korean peninsular and Seoul due to the global warming and urbanization. Number of tropical night days, defined by the days with above $25^{\circ}C$ in minimum temperature, shows increasing trend both in August and summer from 1973 to 2007 due to the combination effect of the global warming and urbanization. The amount of precipitation, both in August and summer, for Korean peninsular and Seoul shows increasing trend from 1973 to 2007, and 1908 to 2007, respectively. Amount of precipitation and rainy days, both August and summer, during 2000s show greater than those of the 1970s both in Korean peninsular and Seoul. Extreme rainy days (greater than 120mm/day, greater than 80mm/day, greater than 30mm in any 1-hour period and greater than 10mm in any 10-minute period) show increasing trend from 1973 to 2007 for both in August and in summer.