• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1153-1164
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• 2008

The ozone data is one of the important environmental data for measurement of the atmospheric condition of the country. In this article, the Autoregressive Error (ARE) model have been considered for analyzing the ozone data at the northern part of the Gyeonggi-Do, Uijeongbu monitoring site in Korea. The result showed that both overall and monthly ARE models are suited for describing the ozone concentration. In the ARE model, seven meteorological variables and four pollution variables are used as the as the explanatory variables for the ozone data set. The seven meteorological variables are daily maximum temperature, wind speed, relative humidity, rainfall, dew point temperature, steam pressure, and amount of cloud. The four air pollution explanatory variables are Sulfur dioxide(SO2), Nitrogen dioxide(NO2), Cobalt(CO), and Promethium 10(PM10). Also, the high level ozone data (over 80ppb) have been analyzed four ARE models, General ARE, HL ARE, PM10 add ARE, Temperature add ARE model. The result shows that the General ARE, HL ARE, and PM10 add ARE models are suited for describing the high level of ozone data.

• Korean Journal of Remote Sensing
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• v.22 no.5
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• pp.403-406
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• 2006

An algorithm for detection of yellow sand aerosols has been developed with infrared bands. This algorithm is a hybrid algorithm that has used two methods combined. The first method used the differential absorption in brightness temperature difference between $11{\mu}m\;and\;12{\mu}m\;(BTD1)$. The radiation at $11{\mu}m$ is absorbed more than at $12{\mu}m$ when yellow sand is loaded in the atmosphere, whereas it will be the other way around when cloud is present. The second method uses the brightness temperature difference between $3.7{\mu}m\;and\;11{\mu}m(BTD2)$. This technique is sensitive to dust loading, which the BTD2 is enhanced by reflection of $3.7{\mu}m$ solar radiation. First the Principle Component Analysis (PCA), a form of eigenvector statistical analysis from the two methods, is performed and the aerosol pixel with the lowest 10% of the eigenvalue is eliminated. Then the aerosol index (AI) from the combination of BTD 1 and 2 is derived. We applied this method to Multi-functional Transport Satellite-l Replacement (MTSAT-1R) data and obtained that the derived AI showed remarkably good agreements with Ozone Mapping Instrument (OMI) AI and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth.

• Journal of Energy Engineering
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• v.25 no.1
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• pp.1-8
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• 2016

When the oil is produced in the low temperature environment, wax can be accumulated in petroleum production system(pipeline, riser) and causes problems such as pipeline stucking, disturbance of the oil production. These problems can be lead to time-consuming and economic losses for flow assurance. For prediction and mitigation of wax deposition, it is necessary to measure the Wax Appearance Temperature(WAT) which is a temperature when the wax crystals start to be formed. WAT standard measurement method of transparent oil has to determine the cloud point of sample to the naked eye and cannot be applied to continuous change of the temperature. In this study, wax behavior of transparent oil samples are recorded depending on temperature using Visualized WAT Measurement System. Also, WATs of transparent oil samples are measured by image processing and compared with the result of the standard method.

• Current Research on Agriculture and Life Sciences
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• v.31 no.1
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• pp.38-44
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• 2013

To estimate the radiative heat loss from greenhouses, a new equation for calculating the sky temperature is presented. The sky temperature in the Daegu region was measured using a pyrgeometer and calculated using different equations according to the cloudiness. The calculated and measured results were then compared to identify the best equation for calculating the sky temperature. The difference value between the air and sky temperature was dissimilar and increased as the cloud amount decreased. On clear days the difference value was 10~20 times greater than that on cloudy and rainy days. When analyzing the correlationship between the calculated and measured sky temperatures on clear days, Bliss's and Clark & Allen's equations were found to be superior to the other equations. However, on cloudy days, the best correlationship between the calculated and measured sky temperatures was exhibited by Fuentes's equation. Therefore, a new equation is proposed for calculating the sky temperature on a cloudy day.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.207-221
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• 2023

This study suggests deep neural network models for estimating air temperature with Level 1B (L1B) datasets of GEO-KOMPSAT-2A (GK-2A). The temperature at 1.5 m above the ground impact not only daily life but also weather warnings such as cold and heat waves. There are many studies to assume the air temperature from the land surface temperature (LST) retrieved from satellites because the air temperature has a strong relationship with the LST. However, an algorithm of the LST, Level 2 output of GK-2A, works only clear sky pixels. To overcome the cloud effects, we apply a deep neural network (DNN) model to assume the air temperature with L1B calibrated for radiometric and geometrics from raw satellite data and compare the model with a linear regression model between LST and air temperature. The root mean square errors (RMSE) of the air temperature for model outputs are used to evaluate the model. The number of 95 in-situ air temperature data was 2,496,634 and the ratio of datasets paired with LST and L1B show 42.1% and 98.4%. The training years are 2020 and 2021 and 2022 is used to validate. The DNN model is designed with an input layer taking 16 channels and four hidden fully connected layers to assume an air temperature. As a result of the model using 16 bands of L1B, the DNN with RMSE 2.22℃ showed great performance than the baseline model with RMSE 3.55℃ on clear sky conditions and the total RMSE including overcast samples was 3.33℃. It is suggested that the DNN is able to overcome cloud effects. However, it showed different characteristics in seasonal and hourly analysis and needed to append solar information as inputs to make a general DNN model because the summer and winter seasons showed a low coefficient of determinations with high standard deviations.

• Journal of the Korean Society of Industry Convergence
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• v.4 no.3
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• pp.319-327
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• 2001

The objective of this study is to clarify the effect of thermal radiation environments on human thermal comfort, depending on different canyon types and surface materials on the human thermal comfort in a housing complex in Pohang city, Korea. For this purpose, the operative temperature and new effective temperature were calculated based on the modified mean radiant temperature of canyon models variated by the existence of direct radiation existence, surface materials, and the width and length of the street spaces in a housing complex. These indices for the canyon have been calculated from the meteorological data of Pohang city, which include air temperature, relative humidity, air velocity, global solar radiation and cloud. And the monthly averages of these climate factors measured at noon have been used. The results are as follows: (1) It is revealed that the short-wave radiosity reached the human body is affected by direct solar radiation and surface materials, and the long-wave radiosity by canyon types. (2) The existence of direct solar radiation, the kinds of surface materials and canyon types affect operative temperature($OT_n$) and new effective temperature($ET^*{_n}$). (3) The analysis of the human heat balance in the canyon indicates that the influence of radiation on human body is marc likely to be affected by the existence of direct solar radiation on human model.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.291-297
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• 2013

Automated weather stations were installed at 9 locations with, three different elevations, (i.e., 50m, 100m, and 300m a.s.l.) with different slope and aspect in a small watershed ($50km^2$ area). Air temperature at 1500 LST and solar radiation accumulated for 1100-1500 LST were collected from January to December 2012. Topography of the study area was defined by a $30{\times}30$ m digital elevation model (DEM) grid. Accumulated solar irradiance was calculated for each location with the spatially averaged slope and aspect of surrounding circles with 5, 10, 15, 20, 25, and 30 grid cell radii, respectively. The 1500 LST air temperature from clear sky conditions with zero cloud amount was regressed to the 1100-1500 LST solar irradiance at 9 locations. We found the highest coefficient of determination ($r^2$ = 0.544) at 25 grid cell radius and the temperature variation in this study was explained by Y = 0.8309X + 0.0438, where Y is 1500 LST temperature (in $^{\circ}C$) and X is 1100-1500 LST accumulated solar irradiance (in $MJ/m^2$).

• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1293-1300
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• 2014

The temperature data influences on various policies of the country. In this article, the autoregressive error (ARE) model has been considered for analyzing the monthly and seasonal temperature data at the northern part of the Chungcheong Namdo, Seosan monitoring site in Korea. In the ARE model, five meteorological variables, four greenhouse gas variables and five pollution variables are used as the explanatory variables for the temperature data set. The five meteorological variables are wind speed, rainfall, radiation, amount of cloud, and relative humidity. The four greenhouse gas variables are carbon dioxide ($CO_2$), methane ($CH_4$), nitrous oxide ($N_2O$), and chlorofluorocarbon ($CFC_{11}$). And the five air pollution explanatory variables are particulate matter ($PM_{10}$), sulfur dioxide ($SO_2$), nitrogen dioxide ($NO_2$), ozone ($O_3$), and carbon monoxide (CO). The result showed that the monthly ARE model explained about 39-63% for describing the temperature. However, the ARE model will be expected better when we add the more explanatory variables in the model.

• Atmosphere
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• v.27 no.4
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• pp.483-498
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• 2017

Sky view factor can quantify the influence of complex obstructions. This study aims to evaluate the best available SVF method that represents an urban thermal condition with land cover in complex city of Korea and also to quantify a correlation between SVF and mean air temperature; the results are as follows. First, three SVF methods comparison result shows that urban thermal study should consider forest canopy induced effects because the forest canopy test (on/off) on SVF reveals significant difference range (0.8, between maximum value and minimum value) in comparison with the range (0.1~0.3) of SVFs (Fisheye, SOLWEIG and 3DPC) difference. The significance is bigger as a forest cover proportion become larger. Second, R-square between SVF methods and urban local mean air temperature seems more reliable at night than a day. And as the value of SVF increased, it showed a positive slope in summer day and a negative slope in winter night. In the SVF calculation method, Fisheye SVF, which is the observed value, is close to the 3DPC SVF, but the grid-based SWG SVF is higher in correlation with the temperature. However, both urban climate monitoring and model/analysis study need more development because of the different between SVF and mean air temperature correlation results in the summer night period, which imply other major factors such as cooling air by the forest canopy, warming air by anthropogenic heat emitted from fuel oil combustion and so forth.

• Atmosphere
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• v.22 no.2
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• pp.209-219
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• 2012

Extreme heavy snowfall episodes have been investigated in case of accumulated snowfall amount larger than 50 cm during the past ten years, in order to understand the association of low-level stability with heavy snowfall in the Yeongdong region. In general, the selected 4 events have similar synoptic setting such as the Siberian High extended to East Sea along with the Low passing by the southern Korean Peninsula, eventually inducing easterly in the Yeongdong region. Specifically moist-adiabatically neutral layer has been observed during the heavy snowfall period, which was easily identified using vertical profiles of equivalent potential temperature observed at Sokcho, whereas convective unstable layer has been formed over the East sea due to relatively warm sea surface temperature (SST) about $8{\sim}10^{\circ}C$ and lower temperature around 1~2 km above the surface, obtained from RDAPS. Difference of equivalent potential temperature between 850 hPa and surface as well as difference between air and sea temperatures altogether gradually increased before the snowfall period. Instability-induced moisture supply to the atmosphere from the East sea, being cooled and saturated by the upper cold surge, would make low-level ice cloud, and eventually move inland by the easterly flow. Heavy snowfall will be enhanced in association with low-level convergence by surface friction and upslope wind against Taebaek mountains. This study emphasizes the importance of low level stability in the Yeongdong region using the radiosonde sounding and RDAPS data, which should quantitatively be examined through numerical model as well as heat and moisture supply from the ocean.