• 한국환경과학회지
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• 제18권4호
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• pp.423-434
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• 2009

In recent years, the urban thermal environment has become worse, such as days on which the temperature goes above $30^{\circ}C$, sultry nights and heat stroke increase, due to the changes in terrestrial cover such as concrete and asphalt and increased anthropogenic heat emission accompanied by artificial structure. The land use type is an important determinant to near-surface air temperature. Due to these reasons we need to understand and improve the urban thermal environment. In this study, the fifth-generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model(MMS) was applied to the metropolitan of Daegu area in order to investigate the influence of land cover changes and urban modifications increase of Albedo to the surface energy budget on the simulated near-surface air temperature and wind speed. The single urban category in existing 24-category U.S. Geological survey land cover classification used in MM5 was divided into 6 classes to account for heterogeneity of urban land cover. As a result of the numerical simulation intended for the metropolitan of Daegu assumed the increase of Albedo of roofs, buildings, or roads, the increase of Albedo (Cool scenario)can make decrease radiation effect of surface, so that it caused drops in ambient air temperature from 0.2 to 0.3 on the average during the daylight hours and smaller (or near-zero) decrease during the night. The Sensible heat flux and Wind velocity is decreased. Modeling studies suggest that increased surface albedo in urban area can reduce surface and air temperatures near the ground and affect related meteorological parameters such as winds, surface air temperature and sensible heat flux.

• Asia-Pacific Journal of Atmospheric Sciences
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• 제54권4호
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• pp.527-544
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• 2018

This paper presents a nighttime sea fog detection algorithm incorporating unsupervised learning technique. The algorithm is based on data sets that combine brightness temperatures from the $3.7{\mu}m$ and $10.8{\mu}m$ channels of the meteorological imager (MI) onboard the Communication, Ocean and Meteorological Satellite (COMS), with sea surface temperature from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA). Previous algorithms generally employed threshold values including the brightness temperature difference between the near infrared and infrared. The threshold values were previously determined from climatological analysis or model simulation. Although this method using predetermined thresholds is very simple and effective in detecting low cloud, it has difficulty in distinguishing fog from stratus because they share similar characteristics of particle size and altitude. In order to improve this, the unsupervised learning approach, which allows a more effective interpretation from the insufficient information, has been utilized. The unsupervised learning method employed in this paper is the expectation-maximization (EM) algorithm that is widely used in incomplete data problems. It identifies distinguishing features of the data by organizing and optimizing the data. This allows for the application of optimal threshold values for fog detection by considering the characteristics of a specific domain. The algorithm has been evaluated using the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) vertical profile products, which showed promising results within a local domain with probability of detection (POD) of 0.753 and critical success index (CSI) of 0.477, respectively.

• 대기
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• 제33권4호
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• pp.355-365
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• 2023

To proactively manage climate risk, near-term climate predictions on annual to decadal time scales are of great interest to various communities. This study evaluates the near-term climate prediction skills in East Asia with DePreSys4 retrospective decadal predictions. The model is initialized every November from 1960 to 2020, consisting of 61 initializations with ten ensemble members. The prediction skill is quantitatively evaluated using the deterministic and probabilistic metrics, particularly for annual mean near-surface temperature, land precipitation, and sea level pressure. The near-term climate predictions for May~September and November~March averages over the five years are also assessed. DePreSys4 successfully predicts the annual mean and the five-year mean near-surface temperatures in East Asia, as the long-term trend sourced from external radiative forcing is well reproduced. However, land precipitation predictions are statistically significant only in very limited sporadic regions. The sea level pressure predictions also show statistically significant skills only over the ocean due to the failure of predicting a long-term trend over the land.

• 대기
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• 제26권4호
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• pp.697-709
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• 2016

This study estimates and evaluates the extreme value of 30 m-resolution daily maximum and minimum temperatures over South Korea, using inverse distance weighting (IDW), parameter-elevation regression on independent slopes model (PRISM) and generalized extreme value (GEV) method. The three experiments are designed and performed to find the optimal estimation strategy to obtain extreme value. First experiment (EXP1) applies GEV firstly to automated surface observing system (ASOS) to estimate extreme value and then applies IDW to produce high-resolution extreme values. Second experiment (EXP2) is same as EXP1, but using PRISM to make the high-resolution extreme value instead of IDW. Third experiment (EXP3) firstly applies PRISM to ASOS to produce the high-resolution temperature field, and then applies GEV method to make high resolution extreme value data. By comparing these 3 experiments with extreme values obtained from observation data, we find that EXP3 shows the best performance to estimate extreme values of maximum and minimum temperatures, followed by EXP1 and EXP2. It is revealed that EXP1 and EXP2 have a limitation to estimate the extreme value at each grid point correctly because the extreme values of these experiments with 30 m-resolution are calculated from only 60 extreme values obtained from ASOS. On the other hand, the extreme value of EXP3 is similar to observation compared to others, since EXP3 produces 30m-resolution daily temperature through PRISM, and then applies GEV to that result at each grid point. This result indicates that the quality of statistically produced high-resolution extreme values which are estimated from observation data is different depending on the combination and procedure order of statistical methods.

• Steel and Composite Structures
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• 제34권6호
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• pp.819-835
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• 2020

The effects of atmospheric thermal loads on the response of structural elements that are exposed to open environments have been recognized by research works and design specifications. The main source of atmospheric heat is solar radiation, which dominates the variation of the temperature of air, earth surface and all exposed objects. The temperature distribution along the depth of steel members may differ with the geometry configuration, which means that the different-configuration steel members may suffer different thermally induced strains and stresses. In this research, an experimental steel beam was instrumented with many thermocouples in addition to other sensors. Surface temperatures, air temperature, solar radiation and wind speed measurements were recorded continuously for 21 summer days. Based on a finite element thermal analysis, which was verified using the experimental records, several parametric studies were directed to investigate the effect of the geometrical parameters of AISC standard steel sections on their thermal response. The results showed that the overall size of the beam, its depth and the thickness of its elements are of significant effect on vertical temperature distributions and temperature differences.

• 대한원격탐사학회지
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• 제39권1호
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• pp.99-110
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• 2023

Land surface temperature (LST) is a critical environmental indicator affected by land cover (LC) changes. Currently, the most convenient and fastest way to retrieve LST is to use remote sensing images due to their continuous monitoring of the Earth's surface. The work intended to investigate land cover change and temperature response inAn-Najaf province. Landsat multispectral imageries acquired inAugust 1989, 2004, and 2021 were employed to estimate land cover change and LST responses. The findings exhibited an increase in water bodies, built-up areas, plantations, and croplands by 7.78%, 7.27%, 6.98%, 3.24%, and 7.78%, respectively, while bare soil decreased by 25.27% for the period (1989-2021). This indicates a transition from barren lands to different land cover types. The contribution index (CI) was employed to depict how changes in land cover categories altered mean region surface temperatures. The highest LSTs recorded were in bare lands (42.2℃, 44.25℃, and 46.9℃), followed by built-up zones (41.6℃, 43.96℃, and 44.89℃), cropland (30.9℃, 32.96℃, and 34.76℃), plantations (35.4℃, 36.97℃, and 38.92℃), and water bodies (27.3℃, 29.35℃, and 29.68℃) respectively, in 1989, 2004, and 2021. Consequently, these changes resulted in significant variances in LST between different LC types.

• 대기
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• 제22권2호
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• pp.137-147
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• 2012

The technique of Brightness Temperature Difference (BTD) between 11 and $12{\mu}m$ separates yellow sand dust from clouds according to the difference in absorptive characteristics between the channels. However, this method causes consistent false alarms in many cases, especially over the desert. In order to reduce these false alarms, we should eliminate the background noise originated from surface. We adopted the Background BTD (BBTD), which stands for surface characteristics on clear sky condition without any dust or cloud. We took an average of brightness temperatures of 11 and $12{\mu}m$ channels during the previous 15 days from a target date and then calculated BTD of averaged ones to obtain decontaminated pixels from dust. After defining the BBTD, we subtracted this index from BTD for the Yellow Sand Index (YSI). In the previous study, this method was already verified using the geostationary satellite, MTSAT. In this study, we applied this to the polar orbiting satellite, MODIS, to detect yellow sand dust over Northeast Asia. Products of yellow sand dust from OMI and MTSAT were used to verify MODIS YSI. The coefficient of determination between MODIS YSI and MTSAT YSI was 0.61, and MODIS YSI and OMI AI was also 0.61. As a result of comparing two products, significantly enhanced signals of dust aerosols were detected by removing the false alarms over the desert. Furthermore, the discontinuity between land and ocean on BTD was removed. This was even effective on the case of fall. This study illustrates that the proposed algorithm can provide the reliable distribution of dust aerosols over the desert even at night.

• 한국지구과학회지
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• 제36권2호
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• pp.139-157
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• 2015

본 연구에서는 위성관측 표면온도 및 해당 온도경향의 불확실성을 조사하기 위하여 북반구($30-90^{\circ}N$) 해양 지역에서 2003-2014년 4월 16-24일 기간에 세 종류의 위성관측 자료(MODIS IST, AIRS/AMSU SST, AIRS only SST)를 상호 비교하였다. AIRS/AMSU 표면온도값에 비하여 MODIS는 해빙과 해수의 경계지역에서 계통적으로 최대 1.6 K 높은 반면에, 해빙 지역에서는 2 K 낮았다. 이러한 주요 원인은 표면온도 산출알고리즘의 해표 정보(e.g., 해빙 탐지)를 위하여 MODIS는 적외 채널만을 사용하는 반면에, AIRS/AMSU는 마이크로파 및 적외 채널을 함께 사용하는 데에 있다. 미국 항공우주국(NASA's Goddard Space Flight Center; NASA/GSFC)은 AMSU-A의 노후화를 대비하기 위하여 AIRS/AMSU 알고리즘을 일부 수정하여 AIRS only 알고리즘을 개발하였다. AIRS/AMSU와 AIRS only 표면온도 사이에 평균 제곱근 오차(RMSE)값은 $30-90^{\circ}N$ 해양 지역에서 0.55 K이며, 편차(bias)는 0.13 K이었으며, 해빙/해수 경계 지역에서는 이들 차이가 더 크게 나타났다. 해빙 경계지역에서 AIRS/AMSU와 AIRS only 간의 차이가 다른 지역에 비하여 큰 이유는 AIRS only 알고리즘이 AMSU 마이크로파 자료 대신에 GCM (NOAA Global Forecast System) 온도 산출물을 사용하는 데에 있다. 세 종류의 위성관측 표면온도 자료는 $70-80^{\circ}N$ 위도대에서 유의적인 온도증가($0.23-0.28Kyr^{-1}$)를 보였다. 위성관측 표면온도들 간에 계통적인 불일치는 같은 방향(온도증가 또는 온도감소)으로 해당 온도경향 값들 간의 차이에 영향을 줄 수 있다.

• 한국세라믹학회지
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• 제49권4호
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• pp.301-307
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• 2012

SiAlON glasses are silicates or alumino-silicates, containing Mg, Ca, Y or rare earth (RE) ions as modifiers, in which nitrogen atoms substitute for oxygen atoms in the glass network. These glasses are found as intergranular films and at triple point junctions in silicon nitride ceramics and these grain boundary phases affect their fracture behaviour. This paper provides an overview of the preparation of M-SiAlON glasses and outlines the effects of composition on properties. As nitrogen substitutes for oxygen in SiAlON glasses, increases are observed in glass transition temperatures, viscosities, elastic moduli and microhardness. These property changes are compared with known effects of grain boundary glass chemistry in silicon nitride ceramics. Oxide sintering additives provide conditions for liquid phase sintering, reacting with surface silica on the $Si_3N_4$ particles and some of the nitride to form SiAlON liquid phases which on cooling remain as intergranular glasses. Thermal expansion mismatch between the grain boundary glass and the silicon nitride causes residual stresses in the material which can be determined from bulk SiAlON glass properties. The tensile residual stresses in the glass phase increase with increasing Y:Al ratio and this correlates with increasing fracture toughness as a result of easier debonding at the glass/${\beta}-Si_3N_4$ interface.

• 대한원격탐사학회지
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• 제27권6호
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• pp.663-675
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• 2011

Sea Surface Temperatures (SSTs) using the equations of NOAA (National Oceanic and Atmospheric Administration) / NESDIS (National Environmental Satellite, Data, and Information Service) were validated over the seas around Korea with satellite-tracked drifter data. A total 1,070 of matchups between satellite data and drifter data were acquired for the period of 2009. The mean rms errors of Multi- Channel SSTs (MCSSTs) and Non-Linear SSTs (NLSSTs) were evaluated to, in most of the cases, less than $1^{\circ}C$. However, the errors revealed dependencies on atmospheric and oceanic conditions. For the most part, SSTs were underestimated in winter and spring, whereas overestimated in summer. In addition to the seasonal characteristics, the errors also presented the effect of atmospheric moist that satellite SSTs were estimated considerably low ($-1.8^{\circ}C$) under extremely dry condition ($T_{11{\mu}m}-T_{12{\mu}m}$ < $0.3^{\circ}C$), whereas the tendency was reversed under moist condition. Wind forcings induced that SSTs tended to be higher for daytime data than in-situ measurements but lower for nighttime data, particularly in the range of low wind speeds. These characteristics imply that the validation of satellite SSTs should be continuously conducted for diverse regional applications.