• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.2
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• pp.105-113
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• 2001

This study was conducted to reveal the effects of local climatic conditions on the vegetative growth in a mature stand of Korean white pine based on climatic estimates. For this, the annual increments of stand variables such as DBH, height, basal area and volume were measured and estimated for seven years from 1974 to 1980. The local climatic conditions in the study site were also estimated by both a topoclimatological method and a spatial statistical technique. The local climatic conditions were then correlated with and regressed on the growth factors to reveal the relationships between the climatic estimates and the growth. It is found that relatively high temperatures had positive effects on the diameter growth. The yearly diameter growth increased when each of mean, maximum, and minimum temperature during the growing season was high. Height growth showed positively significant correlation with three climatic variables. The most important variable influencing height growth was the average of maximum temperature for 10 months from January to October. It means that the higher the average of maximum temperature for 10 months from January to October is, the more height growth of Korean white pine increases. Other climatic variables related to height growth were average of minimum temperature for 3 months in the early growing season and mean relative humidity for the growing season. Six climatic variables related to temperature had effects on basal area increment and all of them were positively correlated with basal area increment. Especially, temperatures from January to March were important factors affecting the basal area increment. In volume increment, high correlation was also recognized with most of temperature variables. This tendency was the same as the results in diameter and hight increments. This means that the volume growth increases when temperatures during the growing season are relatively high.

• Journal of Bio-Environment Control
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• v.27 no.1
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• pp.20-26
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• 2018

This study was conducted to develop a device for measuring the air flow by space variation through monitoring program, which acquires data by each point from each environmental sensor located in the greenhouse. The distribution of environmental factors(air temperature, humidity, wind speed, etc.) in the greenhouse is arranged at 12 points according to the spatial variation and a large number of measurement points (36 points in total) on the X, Y and Z axes were selected. Considering data loss and various greenhouse conditions, a bit rate was at 125kbit/s at low speed, so that the number of sensors can be expanded to 90 within greenhouse with dimensions of 100m by 100m. Those system programmed using MATLAB and LabVIEW was conducted to measure distributions of the air flow along the greenhouse in real time. It was also visualized interpolated the spatial distribution in the greenhouse. In order to verify the accuracy of CFD modeling and to improve the accuracy, it will compare the environmental variation such as air temperature, humidity, wind speed and $CO_2$ concentration in the greenhouse.

• Journal of Korea Water Resources Association
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• v.49 no.3
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• pp.227-239
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• 2016

This study is to estimate Surface Energy Balance Algorithm for Land (SEBAL) daily spatial evapotranspiration (ET) comparing with eddy covariance flux tower ET in Seolmacheon mixed forest (SMK) and Cheongmicheon rice paddy (CFK). The SEBAL input data of Albedo, Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) from Terra MODIS products and the meteorological data of wind speed, and solar radiation were prepared for 2 years (2012-2013). For the annual average flux tower ET of 302.8 mm in SMK and 482.0 mm in CFK, the SEBAL ETs were 183.3 mm and 371.5 mm respectively. The determination coefficients ($R^2$) of SEBAL ET versus flux tower ET for total periods were 0.54 in SMK and 0.79 in CFK respectively. The main reason of SEBAL ET underestimation for both sites was from the determination of hot pixel and cold pixel of the day and affected to the overestimation of sensible heat flux.

• Journal of the Korean Society of Propulsion Engineers
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• v.2 no.3
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• pp.99-106
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• 1998

The performance of the turbofan engine, a medium scale civil aircraft which has been developing in Rep. of Korea, was analyzed and the control scheme for optimization the performance was studied. The dynamic and real-time linear simulation was performed in the previous study The result was that the fuel scedule of the step increase overshoot the limit temperature(3105 $^{\cire}R$) of the high pressure turbine and got small surge margine of the high pressure compressor. Therefore a control scheme such as the LQR(Linear Quadratic Regulator) was applied to optimizing the performance in this studies. The linear model was expected for designing controller and the real time linear model was developed to be closed to nonlinear simulation results. The system matrices were derived from sampling operating points in the scheduled range and then the least square method was applied to the interpolation between these sampling points, where each element of matrices was a function of the rotor speed. The control variables were the fuel flow and the low pressure compressor bleed air. The controlled linear model eliminated the inlet temperature overshoot of the high pressure turbine and obtained maximum surge margins within 0.55. The SFC was stabilized in the range of 0.355 to 0.43.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.275-285
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• 2017

National Meteorological Satellite Center(NMSC) has produced Sea Surface Temperature (SST) using Communication, Ocean, and Meteorological Satellite(COMS) data since April 2011. In this study, we have developed a new regional COMS SST algorithm optimized within the North-West Pacific Ocean area based on the Multi-Channel SST(MCSST) method and made a composite SST using polar orbit satellites as well as the COMS data. In order to retrieve the optimized SST at Northwest Pacific, we carried out a colocation process of COMS and in-situ buoy data to make coefficients of the MCSST algorithm through the new cloud masking including contaminant pixels and quality control processes of buoy data. And then, we have estimated the composite SST through the optimal interpolation method developed by National Institute of Meteorological Science(NIMS). We used four satellites SST data including COMS, NOAA-18/19(National Oceanic and Atmospheric Administration-18/19), and GCOM-W1(Global Change Observation Mission-Water 1). As a result, the root mean square error ofthe composite SST for the period of July 2012 to June 2013 was $0.95^{\circ}C$ in comparison with in-situ buoy data.

• Journal of the Korean Association of Geographic Information Studies
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• v.18 no.1
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• pp.90-104
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• 2015

The purpose of this paper is to build a spatio-temporal evapotranspiration(ET) estimation model using Terra MODIS satellite image and by calibrating with the flux tower ET data from watershed. The fundamentals of spatial ET model, Surface Energy Balance Algorithm for Land(SEBAL) was adopted and modified to estimate the daily ET of Yongdam Dam watershed in South Korea. The daily Normalized Distribution Vegetation Index(NDVI), Albedo, and Land Surface Temperature(LST) from MODIS and the ground measured wind speed and solar radiation data were prepared for 2 years(2012-2013). The SEBAL was calibrated with the forest ET measured by Deokyusan flux tower in the study watershed. Among the model parameters, the important parameters were surface albedo, NDVI and surface roughness in order for momentum transport during calculation of sensible heat flux. As a result of the final calibration, the monthly averaged albedo and NDVI were used because the daily values showed big deviation with unrealistic change. The determination coefficient($R^2$) between SEBAL and flux data was 0.45. The spatial ET reflected the geographical characteristics showing the ET of lowland areas was higher than the highland ET.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.34-45
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• 2021

Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.19-26
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• 2023

Humans have adapted and evolved to natural light. However, as humans stay in indoor longer in modern times, the problem of biorhythm disturbance has been induced. To solve this problem, research is being conducted on lighting that reproduces the correlated color temperature(CCT) of natural light that varies from sunrise to sunset. In order to reproduce the CCT of natural light, multiple LED light sources with different CCTs are used to produce lighting, and then a control index DB is constructed by measuring and collecting the light characteristics of the combination of input currents for each light source in hundreds to thousands of steps, and then using it to control the lighting through the light characteristic matching method. The problem with this control method is that the more detailed the steps of the combination of input currents, the more time and economic costs are incurred. In this paper, an LED lighting control method that applies interpolation and combination calculation based on the minimum spectral power distribution information for each light source is proposed to reproduce the CCT of natural light. First, five minimum SPD information for each channel was measured and collected for the LED lighting, which consisted of light source channels with different CCTs and implemented input current control function of a 256-steps for each channel. Interpolation calculation was performed to generate SPD of 256 steps for each channel for the minimum SPD information, and SPD for all control combinations of LED lighting was generated through combination calculation of SPD for each channel. Illuminance and CCT were calculated through the generated SPD, a control index DB was constructed, and the CCT of natural light was reproduced through a matching technique. In the performance evaluation, the CCT for natural light was provided within the range of an average error rate of 0.18% while meeting the recommended indoor illumination standard.

• Korean Journal of Agricultural and Forest Meteorology
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• v.3 no.4
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• pp.185-198
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• 2001

This study was conducted to reveal the effects of local climatic conditions on reproductive growth in a mature stand of Korean white pine based on climatic estimates. For this, the reproductive growth such as production and characteristics of cone and seed were first measured and summarized for seven years from 1974 to 1980. The local climatic conditions in the study site were also estimated by both a topoclimatological method and a spatial statistical technique. The local climatic conditions were then correlated with and regressed on the growth factors to reveal the relationships between the climatic estimates and the reproductive growth. Average number of conelet formation per tree showed highly negative correlation with some climatic variables related to minimum temperature in the year of flower bud differentiation. Especially, the most significant negative correlation were found between average of the minimum temperature for June and July of flower bud differentiation year and the number of conelet formation. There was no significant correlation between the number of cone production and climatic variables. However, total precipitation from December of the flowering year to February of the cone production year showed the most high correlation (r=0.6036) with the number of cone production. It was found that significant climatic variables affecting the amount of cone drop and cone drop percentage were the sum of cloudy days from June of the flowering year to August of the cone production year. Positive correlation was significantly recognized between the average weight of empty seed per cone and total precipitation from December of the flowering year to February of the cone production year. For the percentage of empty seed, five climatic variables among 19 variables were significantly correlated at 10% level. The average weight of a cone showed negative correlation with total precipitation from June of the flowering year to August of the cone production year. It was also found that average weight of a seed had highly negative correlation with total precipitation from December of the flowering year to February of the cone production year. The average weight of cone coat was negatively correlated with two climatic variables derived from clear days, which are sum of clear days from November of the flowering year to March of the cone production year and sum of clear days from December of the flowering year to February of the cone production year. On the other hand, it showed positive correlation with mean temperature of May in the flowering year. The exactly same results were obtained in correlation analysis for the percentage of cone coat.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.