• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.1
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• pp.63-73
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• 2010

A comprehensive mapping project for agroclimatic zoning in South Korea will end by April 2010, which has required 4 years, a billion won (ca. 0.9 million US dollars) and 22 experts from 7 institutions to complete it. The map database from this project may be categorized into primary, secondary and analytical products. The primary products are called "high definition" digital climate maps (HD-DCMs) and available through the state of the art techniques in geospatial climatology. For example, daily minimum temperature surfaces were prepared by combining the climatic normals (1971-2000 and 1981-2008) of synoptic observations with the simulated thermodynamic nature of cold air by using the raster GIS and microwave temperature profiling which can quantify effects of cold air drainage on local temperature. The spatial resolution of the gridded climate data is 30m for temperature and solar irradiance, and 270m for precipitation. The secondary products are climatic indices produced by statistical analysis of the primary products and includes extremes, sums, and probabilities of climatic events relevant to farming activities at a given grid cell. The analytical products were prepared by driving agronomic models with the HD-DCMs and dates of full bloom, the risk of freezing damage, and the fruit quality are among the examples. Because the spatial resolution of local climate information for agronomic practices exceeds the current weather service scale, HD-DCMs and the value-added products are expected to supplement the insufficient spatial resolution of official climatology. In this lecture, state of the art techniques embedded in the products, how to combine the techniques with the existing geospatial information, and agroclimatic zoning for major crops and fruits in South Korea will be provided.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.426-439
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• 2018

Purpose: To continuously monitor soil and climatic properties, a data acquisition system (DAQ) was developed and tested in plum farms (Gyewol-ri and Haechang-ri, Suncheon, Korea). Methods: The DAQ consisted of a Raspberry-Pi processor, a modem, and an ADC board with multiple sensors (soil moisture content (SEN0193), soil temperature (DS18B20), climatic temperature and humidity (DHT22), and rainfall gauge (TR-525M)). In the laboratory, various tests were conducted to calibrate SEN0193 at different soil moistures, soil temperatures, depths, and bulk densities. For performance comparison of the SEN0193 sensor, two commercial moisture sensors (SMS-BTA and WT-1000B) were tested in the field. The collected field data in Raspberry-Pi were transmitted and stored on a web server database through a commercial communications wireless network. Results: In laboratory tests, it was found that the SEN0193 sensor voltage reading increased significantly with an increase in soil bulk density. A linear calibration equation was developed between voltage and soil moisture content depending on the farm soil bulk density. In field tests, the SEN0193 sensor showed linearity (R = 0.76 and 0.73) between output voltage and moisture content; however, the other two sensors showed no linearity, indicating that site-specific calibration is important for accurate sensing. In the long-term monitoring results, it was observed that the measured climate temperature was almost the same as website information. Soil temperature information was higher than the values measured by DS18B20 during spring and summer. However, the local rainfall measured using TR 525M was significantly different from the values on the website. Conclusion: Based on the test results obtained using the developed monitoring system, it is thought that the measurement of various parameters using one device would be helpful in monitoring plum growth. Field data from the local farm monitoring system can be coupled with website information from the weather station and used more efficiently.

• Journal of KIISE:Software and Applications
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• v.31 no.11
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• pp.1534-1542
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• 2004

A novel vision-based scheme of extracting real-time traffic information parameters is presented. The method is based on a region classification followed by a spatio-temporal image analysis. The detection region images for each traffic lane are classified into one of the three categories: the road, the vehicle, and the shadow, using statistical and structural features. Misclassification in a frame is corrected by using temporally correlated features of vehicles in the spatio-temporal image. Since only local images of detection regions are processed, the real-time operation of more than 30 frames per second is realized without using dedicated parallel processors, while ensuring detection performance robust to the variation of weather conditions, shadows, and traffic load.

• Journal of Environmental Science International
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• v.22 no.8
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• pp.1029-1041
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• 2013

In this study, eight episode days of high-concentration $PM_{10}$ occurrences in the Gimhae region between 2006 and 2011 were analyzed. Most of them appeared in winter and the highest concentration was observed around 12 LST. Furthermore, the wind direction, wind velocity, and temperature elements were compared with observed values to verify the WRF numerical simulation results used in this study, and they simulated well in accordance with the trend of the observed values. The wind was generally weak in the high-concentration episode days that were chosen through surface weather chart and the numerical simulation results for wind field, and the air pollutants were congested due to the effects of the resulting local winds, thereby causing a high concentration of air pollutants. Furthermore, the HYSPLIT model was performed with the WRF numerical simulation results as input data. As a result, they originated from China and flowed into Gimhae in all eight days, and the lowest concentration appeared on the days when recirculation occurred.

• Journal of the Korean association of regional geographers
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• v.13 no.4
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• pp.396-408
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• 2007

Natural disasters occurred in Inje and Pyeongchang in 2006 show that unusual changes of weather, which Korean Peninsula has not experienced before, are becoming quite common phenomenon nowadays. In future we have to proceed in the direction of preventing such disasters so as to minimize the damage, by analyzing character and cause of various disasters whenever necessary, performing modeling in simulated real world, and applying the results in disaster prevention policy next year. Applying GIS in this process, the best information for decision-making can be offered. This study has also progressed proceeding from such point of view. The results of this study show that local concentrated heavy rain, caused by the primary topographical factor in the Sulak mountain region, was the main cause of flood disaster occurred in Inje-Gun area in July of 2006. Local concentrated heavy rain is greatly affected by topography. Namely, if there is a mountainous region behind, the area opposite to the direction of rain clouds motion will have high possibility of local concentrated heavy rain.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.1
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• pp.12-25
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• 2011

It is needed to preserve good effects and to prevent bad influences on local climate in urban and environmental planning. This study seeks to develop climate analysis maps to provide realistic information considering local air temperature and wind flows. Quantitative analyses are conducted by CAS for the production, transportation, and stagnation of cold air, wind flow and thermal conditions by incorporating GIS analysis on land cover and elevation and meteorological analysis from MetPhoMod - a mesoscale weather model. The CAS helps The easier analysis and assessment of urban development on local climate. It will contribute to the better life of the people in cities by providing better understanding of the local climate to the urban space planners.

• Journal of Astronomy and Space Sciences
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• v.25 no.4
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• pp.425-434
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• 2008

The mean temperature equation is a key factor in calculating GPS meteorological information. A local mean temperature equation should be used to improve accuracy of GPS PWV (Precipitable Water Vapor). In this paper, four local mean temperature equations, HP, $HP_M,\;HPt_Y,\;and\;HPt_M$ from Ha & Park (2008) were used to analyze the effects of local models in determining GPS PWV. Four different sets of GPS PWVs were compared with radiosonde PWV to validate the accuracies of local models. GPS PWVs of four local models have similar trends compared against radiosonde PWV. The bias and RMS error were the same level: the bias is ${\sim}3mm$ and the RMS is ${\sim}3.6mm$ after the bias was removed. Especially, with $HPt_Y\;and\;HPt_M$ models one can obtain accurate PWVs even without surface temperature measurements. And we investigated dry bias of radiosonde measurements depending on sensor types and observation time at Sokcho weather station. After the radiosonde sensor equipment was changed from RS80-15L to GRS DFM-06, dry bias of radiosonde PWV decreased about 18.2% during daytime (KST 09:00), and 16.1% during nighttime (KST 21:00).

• Atmosphere
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• v.22 no.3
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• pp.357-365
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• 2012

High spatio-temporal resolution hydrologic components can give important information to monitor natural disaster. The objective of this study is to create high spatial-temporal resolution gridded hydrologic components using TOPLATS distributed land surface model and evaluate their accuracy. For this, Andong dam basin is selected as study area and TOPLATS model is constructed to create hourly simulated values in every $1{\times}1km^2$ cell size. The observed inflow at Andong dam and soil moisture at Andong AWS site are collected to directly evaluate the simulated one. RMSEs of monthly simulated flow for calibration (2003~2006) and verification (2007~2009) periods show 36.87 mm and 32.41 mm, respectively. The hourly simulated soil moisture in the cell located Andong observation site for 2009 is well fitted with observed one at -50 cm. From this results, the cell based hydrologic components using TOPLATS distributed land surface model show to reasonably represent the real hydrologic condition in the field. Therefore the model driven hydrologic information can be used to analyze local water balance and monitor natural disaster caused by the severe weather.

• Journal of Apiculture
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• v.34 no.4
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• pp.285-293
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• 2019

The honey bee queen shows extreme polyandry and controlling the mating partners can only be possible either by artificial insemination or having remote isolated mating locations. Here we report on the German North Sea island of Heligoland. Because of its location 60 km from the mainland, the lack of a local population of honey bees, its size of just 1.4 ㎢ and suitable weather conditions during the months of May to July, it is considered an ideal location for controlled inseminations of high-quality virgin queen bees with drones deemed genetically superior to others. Methods how to rear virgin queen bees are described and information is provided on the numbers of queen bees, their supporting workers and drone bees that are taken to the island in the mating season. The bee most commonly involved in the Heligoland mating trials has become Apis mellifera carnica strain "Baltica". In one summer, for example, 80 virgin queens (belonging to beekeepers from nine different locations in northern Germany) each with about 600 worker bees plus two drone populations of around 2,000 drones were taken by ship to Heligoland. On their return to the mainland no later than 3.5 weeks after the mating exercise, the beekeepers could register a mating success rate of 80%. This information can help operation management of the new remote mating centre of Weedo Island, Jeonbuk in Korea, which is currently under construction.

• Journal of Korea Society of Industrial Information Systems
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• v.27 no.6
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• pp.77-93
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• 2022

In this study, we present an algorithm that analyzes the risk by reflecting regional characteristics for factors affected by direct and indirect damage from heavy-snow. Factors affected by heavy-snow damage by 29 regions are selected as influencing variables, and the concept of sensitivity is derived through the relationship with the amount of damage. A snow damage risk prediction model was developed using a machine learning (XGBoost) algorithm by setting weather conditions (snow cover, humidity, temperature) and sensitivity as independent variables, and setting the risk derived according to changes in the independent variables as dependent variables.