• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.373-382
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• 2016

Under the Agricultural technology is influenced from climate that is requisite of seasonal. So this system will cover the problems and develop the agricultural industry as well. So far, the agricultural industry is developing however, it has the points of the weakness because of natural disasters such as wind risk and heavy snow. This paper designs system to change vinyl on the greenhouse. This is a preliminary study for the real-time feedback control of greenhouse. The study developed a wireless IoT sensor system based on authentic technology capacities, to integrate with the protected horticulture Management System. These system was used to evaluate the levels of the snow cover and wind through IoT devices. The existing greenhouse uses the warm water to clear snow or to change methods. This system will recover by changing the vinyl which is covered outside of the greenhouse. The points of the system is changing vinyl to spin pipe. It is contained extra vinyl. The effects of this system are minimized labor protected crops from natural disasters. For this purpose, the study first developed a wireless IoT sensor unit that integrates an MEMS device and wireless communication module. Also, the study developed an operating program that enables real-time response measurement. It will help operational and maintenance greenhouse as a result.

• Journal of the Korean earth science society
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• v.26 no.5
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• pp.395-403
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• 2005

This study is focused on the relationship between snowfall and the Bowen’s Ratio (sensible heat flux/latent heat flux) through calculation of heat exchange between air and sea for snowfall events in Jeju Island from 1993 to 2003. The four weather stations for this study are located at Jeju, Seoguipo, Seongsanpo and Gosan in Jeju Island. In order to improve the reliability of snowfall forecast, the Bowen’s Ratio for snowfall, which includes influences from the atmosphere such as wind, is compared with the temperature difference between air and sea for snowfall. As a results, in the case for fresh snowfall, the minimum temperature differences between air and sea were 10, 12.3, 11.5, and $14.3^{\circ}C$ at Jeju, Seoguipo, Seongsanpo and Gosan, respectively. The probabilities of fresh snowfall were 26, 29, 13, and $23\%$, respectively, when the temperature differences were higher than the previous values. On the other hand, the minimum Bowen ratios were 0.59, 0.60, 0.65 and 0.65 at Jeju, Seoguipo, Seongsanpo and Gosan, respectively. The probabilities of fresh snowfall were 33, 70, 31 and $58\%$ respectively, when the Bowen ratio is higher than those. The reason for this is because the probability of fresh snowfall with the Bowen ratio was higher than the probability with temperature difference between air and sea. This result occurred because heat exchange by wind increased the probability of snowfall, along with the temperature difference between air and sea, and the Bowen ratio. Therefore, snowfall forecast of Jeju Island is significantly influenced by the sea, whereas forecast with Bowen ratio seems to have higher reliability than that with the temperature difference between air and sea. The data analysis for the ten-year period $(1993\~2002)$ showed that when each fresh snowfall was within 0.0 to 0.9cm, the average Bowen’s ratio was 0.63 to 0.67, and when each fresh snowfall was 1.0 to 4.9 cm, the average Bowen’s ratio was over 0.72. Therefore, fresh snowfall shows a proportional relationship with the Bowen’s ratio during snowfall.

• Korean Journal of Environment and Ecology
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• v.10 no.1
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• pp.1-13
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• 1996

This study was conducted to investigate the avifauna and to suggest the management discipline for the protection of bird community in Odaesan National Park. Field survey was carried over 2 main trails by line transect method from mid June to early December in 1996. The 1st section included the area from Woljongas to Sangwonsa 7km distance. The 2nd section survey started from Sangwonsa via Bukdaesa and the summit of Odaesan to Sangwonsa 9.8 km distance. The study results were as follows ; The observed birds belonged to 9 orders 22families 52species, they also had Black Woodpecker(Dryocopus martius)designated as natural monument No. 242, Chinese Sparrow Hawk(Accipiter soloensis) and Kestrel(Falco tinnunculus) as natural munumet No. 323, Scops Owl(Out scops)and Korean Wood Owl(Strix aluco) as No. 324. These birds also were classified into 25 species for residents, 16 species for summer visitors, 8 species for passage migrants, 3 species for winter visitors, respectionely. The 2nd section showed a high species richness and individuals in every season, however, had a difference in species composition with 1st section. Nesting guild of breeding bird community used highly in order of bush, hole, and canopy as a nest resources. It is suggested that high bush-nesting guild had a deep relationship with bush layer located in the ecotone of 1st section and that located in the high elevated zone in 2nd section. Hole-nesting guild such as Black Woodpecker(Dryocopus martius), Gray-headed Woodpecker(Picus canus) and Great Spotted Woodpecker(Dendrocopos major) were surveyed only in 2nd section, so it could be attribute to the small fragmentation and the growing of high diameter at breast height(D.B.H) tree in 2nd section. It is urgent that the management of camping ground and people for the conservation of brook in 1nd section, trail protection for the prevention from trail enlargement in 2nd section for the bird protection. Artificial food in snowy winter will provide the good breeding condition with the residents and migrants. And, the endeavor to lessen the habitat fragmentation will be beneficial to the birds who have a large home range such as Black Woodpecker(Dryocopus martius) and Korean Wood Owl(Strix aluco). For the control of Domestic Dove(Columba livia) populations, it could be recommended that the elimination of their nesting resources by net.

• Korean Journal of Ecology and Environment
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• v.48 no.3
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• pp.176-187
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• 2015

In this study, we hypothesized that the size of wintering crane population would change due to the climate factors. We assumed that wintering population size would differ by climate values in January, which is the coldest period in year. Especially, White-naped cranes were able to choose wintering site between Cheorwon and other alternative place where snow coverage had low influence, differing from Red crowned cranes. For this reason, we predicted the population size of White-naped cranes would fluctuate according to the extent of snow coverage in Cheorwon. Therefore we used snow coverage data based on MODIS and climate data from KMA (Korea Meteorological Administration) that are generally used. We analyzed the crane's population size in Cheorwon in January from 2002 to 2014. The temperature in the Cheorwon increased from 2002 to wintering period in 2007~ 2008 and went down, showing the lowest temperature in 2011~ 2012. With this phenomenon, warmth index showed the similar pattern with temperature. Amount of newly accumulated snow (the amount of snow that fallen from 0:01 am to 11:29 pm in a day) was low after 2002, but rapidly increased in 2010~ 2011 and 2011~ 2012. The area of snow coverage rapidly declined from 2002 to 2005~ 2006 but suddenly expanded in wintering period in 2009~ 2010 and 2010~ 2011. Wintering population size of the White-naped cranes decreased as snow coverage area increased in January and the highest correlation was found between them, compared to the other climatic factors. However, the number of individuals of Red crowned cranes had little relationship with general climate factors including snow cover range. Therefore it seems that population size of the Red crowned crane varied by factors related with habitat selection such as secure roosting site and area of foraging place, not by climatic factors. In multiple regression analysis, wintering population of White-naped cranes showed significant relationship with logarithmic value of snow cover range and its period. Therefore, it suggests that the population size of the White-naped crane was affected by snow cover range n wintering period and this was because it was hard for them to find out rice grains which are their main food items, buried in snow cover. The population size variation in White-naped cranes was caused by some individuals which left Cheorwon for Izumi where snow cover had little influence on them. The wintering population in Izumi and Cheorwon had negative correlation, implying they were mutually related.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.4
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• pp.317-324
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• 2015

GNSS was firstly proposed for application in weather forecasting in the mid-1980s. It has continued to demonstrate the practical uses in GNSS meteorology, and other relevant researches are currently being conducted. Precipitable Water Vapor (PWV), calculated based on the GNSS signal delays due to the troposphere of the Earth, represents the amount of the water vapor in the atmosphere, and it is therefore widely used in the analysis of various weather phenomena such as monitoring of weather conditions and climate change detection. In this study we calculated the PWV through the meteorological information from an Automatic Weather Station (AWS) as well as GNSS data processing of a Continuously Operating Reference Station (CORS) in order to analyze the heavy snowfall of the Ulsan area in early 2014. Song’s model was adopted for the weighted mean temperature model (T_m), which is the most important parameter in the calculation of PWV. The study period is a total of 56 days (February 2013 and 2014). The average PWV of February 2014 was determined to be 11.29 mm, which is 11.34% lower than that of the heavy snowfall period. The average PWV of February 2013 was determined to be 10.34 mm, which is 8.41% lower than that of not the heavy snowfall period. In addition, certain meteorological factors obtained from AWS were compared as well, resulting in a very low correlation of 0.29 with the saturated vapor pressure calculated using the empirical formula of Magnus. The behavioral pattern of PWV has a tendency to change depending on the precipitation type, specifically, snow or rain. It was identified that the PWV showed a sudden increase and a subsequent rapid drop about 6.5 hours before precipitation. It can be concluded that the pattern analysis of GNSS PWV is an effective method to analyze the precursor phenomenon of precipitation.

• Journal of Korea Water Resources Association
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• v.57 no.1
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• pp.35-44
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• 2024

Research in dam inflow prediction has actively explored the utilization of data-driven machine learning and deep learning (ML&DL) tools across diverse domains. Enhancing not just the inherent model performance but also accounting for model characteristics and preprocessing data are crucial elements for precise dam inflow prediction. Particularly, existing rainfall data, derived from snowfall amounts through heating facilities, introduces distortions in the correlation between snow accumulation and rainfall, especially in dam basins influenced by snow accumulation, such as Soyang Dam. This study focuses on the preprocessing of rainfall data essential for the application of ML&DL models in predicting dam inflow in basins affected by snow accumulation. This is vital to address phenomena like reduced outflow during winter due to low snowfall and increased outflow during spring despite minimal or no rain, both of which are physical occurrences. Three machine learning models (SVM, RF, LGBM) and two deep learning models (LSTM, TCN) were built by combining rainfall and inflow series. With optimal hyperparameter tuning, the appropriate model was selected, resulting in a high level of predictive performance with NSE ranging from 0.842 to 0.894. Moreover, to generate rainfall correction data considering snow accumulation, a simulated snow accumulation algorithm was developed. Applying this correction to machine learning and deep learning models yielded NSE values ranging from 0.841 to 0.896, indicating a similarly high level of predictive performance compared to the pre-snow accumulation application. Notably, during the snow accumulation period, adjusting rainfall during the training phase was observed to lead to a more accurate simulation of observed inflow when predicted. This underscores the importance of thoughtful data preprocessing, taking into account physical factors such as snowfall and snowmelt, in constructing data models.

• Korean Journal of Soil Science and Fertilizer
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• v.23 no.2
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• pp.87-93
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• 1990

A statistical model to predict soil temperature from the ambient meteorological factors including mean, maximum and minimum air temperatures, precipitation, wind speed and snow depth combined with Fourier time series expansion was developed with the data measured at the Suwon Meteorolical Service from 1979 to 1988. The stepwise elimination technique was used for statistical analysis. For the yearly oscillation model for soil temperature with 8 terms of Fourier expansion, the mean square error was decreased with soil depth showing 2.30 for the surface temperature, and 1.34-0.42 for 5 to 500-cm soil temperatures. The $r^2$ ranged from 0.913 to 0.988. The number of lag days of air temperature by remainder analysis was 0 day for the soil surface temperature, -1 day for 5 to 30-cm soil temperature, and -2 days for 50-cm soil temperature. The number of lag days for precipitaion, snow depth and wind speed was -1 day for the 0 to 10-cm soil temperatures, and -2 to -3 days for the 30 to 50-cm soil teperatures. For the statistical soil temperature prediction model combined with the yearly oscillation terms and meteorological factors as remainder terms considering the lag days obtained above, the mean square error was 1.64 for the soil surfac temperature, and ranged 1.34-0.42 for 5 to 500cm soil temperatures. The model test with 1978 data independent to model development resulted in good agreement with $r^2$ ranged 0.976 to 0.996. The magnitudes of coeffcicients implied that the soil depth where daily meteorological variables night affect soil temperature was 30 to 50 cm. In the models, solar radiation was not included as a independent variable ; however, in a seperated analysis on relationship between the difference(${\Delta}Tmxs$) of the maximum soil temperature and the maximum air temperature and solar radiation(Rs ; $J\;m^{-2}$) under a corn canopy showed linear relationship as $${\Delta}Tmxs=0.902+1.924{\times}10^{-3}$$ Rs for leaf area index lower than 2 $${\Delta}Tmxs=0.274+8.881{\times}10^{-4}$$ Rs for leaf area index higher than 2.