• Title/Summary/Keyword: Infrared System

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Validation of Extreme Rainfall Estimation in an Urban Area derived from Satellite Data : A Case Study on the Heavy Rainfall Event in July, 2011 (위성 자료를 이용한 도시지역 극치강우 모니터링: 2011년 7월 집중호우를 중심으로)

  • Yoon, Sun-Kwon;Park, Kyung-Won;Kim, Jong Pil;Jung, Il-Won
    • Journal of Korea Water Resources Association
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    • v.47 no.4
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    • pp.371-384
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    • 2014
  • This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

IGRINS Design and Performance Report

  • Park, Chan;Jaffe, Daniel T.;Yuk, In-Soo;Chun, Moo-Young;Pak, Soojong;Kim, Kang-Min;Pavel, Michael;Lee, Hanshin;Oh, Heeyoung;Jeong, Ueejeong;Sim, Chae Kyung;Lee, Hye-In;Le, Huynh Anh Nguyen;Strubhar, Joseph;Gully-Santiago, Michael;Oh, Jae Sok;Cha, Sang-Mok;Moon, Bongkon;Park, Kwijong;Brooks, Cynthia;Ko, Kyeongyeon;Han, Jeong-Yeol;Nah, Jakyuong;Hill, Peter C.;Lee, Sungho;Barnes, Stuart;Yu, Young Sam;Kaplan, Kyle;Mace, Gregory;Kim, Hwihyun;Lee, Jae-Joon;Hwang, Narae;Kang, Wonseok;Park, Byeong-Gon
    • The Bulletin of The Korean Astronomical Society
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    • v.39 no.2
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    • pp.90-90
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    • 2014
  • The Immersion Grating Infrared Spectrometer (IGRINS) is the first astronomical spectrograph that uses a silicon immersion grating as its dispersive element. IGRINS fully covers the H and K band atmospheric transmission windows in a single exposure. It is a compact high-resolution cross-dispersion spectrometer whose resolving power R is 40,000. An individual volume phase holographic grating serves as a secondary dispersing element for each of the H and K spectrograph arms. On the 2.7m Harlan J. Smith telescope at the McDonald Observatory, the slit size is $1^{{\prime}{\prime}}{\times}15^{{\prime}{\prime}}$. IGRINS has a plate scale of 0.27" pixel-1 on a $2048{\times}2048$ pixel Teledyne Scientific & Imaging HAWAII-2RG detector with a SIDECAR ASIC cryogenic controller. The instrument includes four subsystems; a calibration unit, an input relay optics module, a slit-viewing camera, and nearly identical H and K spectrograph modules. The use of a silicon immersion grating and a compact white pupil design allows the spectrograph collimated beam size to be 25mm, which permits the entire cryogenic system to be contained in a moderately sized ($0.96m{\times}0.6m{\times}0.38m$) rectangular Dewar. The fabrication and assembly of the optical and mechanical components were completed in 2013. From January to July of this year, we completed the system optical alignment and carried out commissioning observations on three runs to improve the efficiency of the instrument software and hardware. We describe the major design characteristics of the instrument including the system requirements and the technical strategy to meet them. We also present the instrumental performance test results derived from the commissioning runs at the McDonald Observatory.

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Discrimination of African Yams Containing High Functional Compounds Using FT-IR Fingerprinting Combined by Multivariate Analysis and Quantitative Prediction of Functional Compounds by PLS Regression Modeling (FT-IR 스펙트럼 데이터의 다변량 통계분석을 이용한 고기능성 아프리칸 얌 식별 및 기능성 성분 함량 예측 모델링)

  • Song, Seung Yeob;Jie, Eun Yee;Ahn, Myung Suk;Kim, Dong Jin;Kim, In Jung;Kim, Suk Weon
    • Horticultural Science & Technology
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    • v.32 no.1
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    • pp.105-114
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    • 2014
  • We established a high throughput screening system of African yam tuber lines which contain high contents of total carotenoids, flavonoids, and phenolic compounds using ultraviolet-visible (UV-VIS) spectroscopy and Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. The total carotenoids contents from 62 African yam tubers varied from 0.01 to $0.91{\mu}g{\cdot}g^{-1}$ dry weight (wt). The total flavonoids and phenolic compounds also varied from 12.9 to $229{\mu}g{\cdot}g^{-1}$ and from 0.29 to $5.2mg{\cdot}g^{-1}$dry wt. FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and $1,100-950cm^{-1}$, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate the 62 African yam tuber lines into three separate clusters corresponding to their taxonomic relationship. The quantitative prediction modeling of total carotenoids, flavonoids, and phenolic compounds from African yam tuber lines were established using partial least square regression algorithm from FT-IR spectra. The regression coefficients ($R^2$) between predicted values and estimated values of total carotenoids, flavonoids and phenolic compounds were 0.83, 0.86, and 0.72, respectively. These results showed that quantitative predictions of total carotenoids, flavonoids, and phenolic compounds were possible from FT-IR spectra of African yam tuber lines with higher accuracy. Therefore we suggested that quantitative prediction system established in this study could be applied as a rapid selection tool for high yielding African yam lines.

Characteristics of Titanium Dioxide-Impregnated Fibrous Activated Carbon and Its Application for Odorous Pollutant (이산화티타늄 담지 섬유형 활성탄소의 특성 및 악취오염물질 제어를 위한 응용)

  • Jo, Wan-Kuen;Hwang, Eun-Song;Yang, Sung-Bong
    • Clean Technology
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    • v.17 no.1
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    • pp.48-55
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    • 2011
  • The application of fibrous activated carbon (FAC)-titanium dioxide ($TiO_2$) hybrid system has not been reported yet for the control of malodorous dimethyl sulfide (DMS) at residential environmental levels. Accordingly, the current study was designed not only to characterize this hybrid system using x-ray diffraction method, particulate surface measurement and Fourier transform Infrared (FTIR) method, but also to evaluate its adsorptional photocatalytic activity (APA) for the DMS removal. The physical/surface characteristics of FAC-$TiO_2$ which was prepared in this study suggested that the hybrid material might have certain APA for DMS. The Brunauer-Emmett-Teller (BET) specific area, total pore volume, micropore volume and mesopore volume decreased all as the $TiO_2$ amounts coated on FAC increased, whereas the reverse was true for average pore diameter. $TiO_2$ coated onto FAC did not influence the adsorptional activity of FAC for the DMS input concentration of 0.5 ppm. The APA test of the hybrid material presented that the initial removal efficiencies of DMS were 93, 78, 71 and 57% for the flow rates of 0.5, 1.0, l.5 and 2.0 L/min, respectively, and they decreased somewhat 2 h after the experiment started and kept almost constant for the rest experimental period. Under this pseudo-equilibrium condition, the DMS removal efficiencies were 78, 58, 53 and 36% for the four flow rates, respectively. Meanwhile, there were no significant byproducts observed on the surfaces of the hybrid material. Consequently, this study suggests that, under the experimental conditions used in the present study, the hybrid material can be applied for DMS at residential environment levels without being interfered by any byproducts.

Estimation of Wheat Growth using a Microwave Scatterometer (마이크로파 산란계를 이용한 밀 생육 추정)

  • Kim, Yihyun;Hong, Sukyoung;Lee, Kyungdo;Jang, Soyeong
    • Korean Journal of Soil Science and Fertilizer
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    • v.46 no.1
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    • pp.23-31
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    • 2013
  • Microwave remote sensing can help monitor the land surface water cycle and crop growth. This type of remote sensing has great potential over conventional remote sensing using the visible and infrared regions due to its all-weather day-and-night imaging capabilities. In this paper, a ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was developed. This system was used to monitor the wheat over an entire growth cycle. The polarimetric scatterometer components were installed inside an air-conditioned shelter to maintain constant temperature and humidity during the data acquisition period. Backscattering coefficients for the crop growing season were compared with biophysical measurements. Backscattering coefficients for all frequencies and polarizations increased until dat of year 137 and then decreased along with fresh weight, dry weight, plant height, and vegetation water content (VWC). The range of backscatter for X-band was lower than for L- and C-band. We examined the relationship between the backscattering coefficients of each band (frequency/polarization) and the various wheat growth parameters. The correlation between the different vegetation parameters and backscatter decreased with increasing frequency. L-band HH-polarization (L-HH) is best suited for the monitoring of fresh weight (r=0.98), dry weight (r=0.96), VWC (r=0.98), and plant height (r=0.96). The correlation coefficients were highest for L-band observations and lowest for X-band. Also, HH-polarization had the highest correlations among the polarization channels (HH, VV and HV). Based on the correlation analysis between backscattering coefficients in each band and wheat growth parameters, we developed prediction equations using the L-HH based on the observed relationships between L-HH and fresh weight, dry weight, VWC and plant height. The results of these analyses will be useful in determining the optimum microwave frequency and polarizations necessary for estimating vegetation parameters in the wheat.

Comparison of Measured and Calculated Carboxylation Rate, Electron Transfer Rate and Photosynthesis Rate Response to Different Light Intensity and Leaf Temperature in Semi-closed Greenhouse with Carbon Dioxide Fertilization for Tomato Cultivation (반밀폐형 온실 내에서 탄산가스 시비에 따른 광강도와 엽온에 반응한 토마토 잎의 최대 카복실화율, 전자전달율 및 광합성율 실측값과 모델링 방정식에 의한 예측값의 비교)

  • Choi, Eun-Young;Jeong, Young-Ae;An, Seung-Hyun;Jang, Dong-Cheol;Kim, Dae-Hyun;Lee, Dong-Soo;Kwon, Jin-Kyung;Woo, Young-Hoe
    • Journal of Bio-Environment Control
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    • v.30 no.4
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    • pp.401-409
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    • 2021
  • This study aimed to estimate the photosynthetic capacity of tomato plants grown in a semi-closed greenhouse using temperature response models of plant photosynthesis by calculating the ribulose 1,5-bisphosphate carboxylase/oxygenase maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), thermal breakdown (high-temperature inhibition), and leaf respiration to predict the optimal conditions of the CO2-controlled greenhouse, for maximizing the photosynthetic rate. Gas exchange measurements for the A-Ci curve response to CO2 level with different light intensities {PAR (Photosynthetically Active Radiation) 200µmol·m-2·s-1 to 1500µmol·m-2·s-1} and leaf temperatures (20℃ to 35℃) were conducted with a portable infrared gas analyzer system. Arrhenius function, net CO2 assimilation (An), thermal breakdown, and daylight leaf respiration (Rd) were also calculated using the modeling equation. Estimated Jmax, An, Arrhenius function value, and thermal breakdown decreased in response to increased leaf temperature (> 30℃), and the optimum leaf temperature for the estimated Jmax was 30℃. The CO2 saturation point of the fifth leaf from the apical region was reached at 600ppm for 200 and 400µmol·m-2·s-1 of PAR, at 800ppm for 600 and 800µmol·m-2·s-1 of PAR, at 1000ppm for 1000µmol of PAR, and at 1500ppm for 1200 and 1500µmol·m-2·s-1 of PAR levels. The results suggest that the optimal conditions of CO2 concentration can be determined, using the photosynthetic model equation, to improve the photosynthetic rates of fruit vegetables grown in greenhouses.

An Analysis on the Usability of Unmanned Aerial Vehicle(UAV) Image to Identify Water Quality Characteristics in Agricultural Streams (농업지역 소하천의 수질 특성 파악을 위한 UAV 영상 활용 가능성 분석)

  • Kim, Seoung-Hyeon;Moon, Byung-Hyun;Song, Bong-Geun;Park, Kyung-Hun
    • Journal of the Korean Association of Geographic Information Studies
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    • v.22 no.3
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    • pp.10-20
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    • 2019
  • Irregular rainfall caused by climate change, in combination with non-point pollution, can cause water systems worldwide to suffer from frequent eutrophication and algal blooms. This type of water pollution is more common in agricultural prone to water system inflow of non-point pollution. Therefore, in this study, the correlation between Unmanned Aerial Vehicle(UAV) multi-spectral images and total phosphorus, total nitrogen, and chlorophyll-a with indirect association of algal blooms, was analyzed to identify the usability of UAV image to identify water quality characteristics in agricultural streams. The analysis the vegetation index Normalized Differences Index (NDVI), the Normalized Differences Red Edge(NDRE), and the Chlorophyll Index Red Edge(CIRE) for the detection of multi-spectral images and algal blooms collected from the target regions Yang cheon and Hamyang Wicheon. The analysis of the correlation between image values and water quality analysis values for the water sampling points, total phosphorus at a significance level of 0.05 was correlated with the CIRE(0.66), and chlorophyll-a showed correlation with Blue(-0.67), Green(-0.66), NDVI(0.75), NDRE (0.67), CIRE(0.74). Total nitrogen was correlated with the Red(-0.64), Red edge (-0.64) and Near-Infrared Ray(NIR)(-0.72) wavelength at the significance level of 0.05. The results of this study confirmed a significant correlations between multi-spectral images collected through UAV and the factors responsible for water pollution, In the case of the vegetation index used for the detection of algal bloom, the possibility of identification of not only chlorophyll-a but also total phosphorus was confirmed. This data will be used as a meaningful data for counterplan such as selecting non-point pollution apprehensive area in agricultural area.

Analysis of Ice Velocity Variations of Nansen Ice Shelf, East Antarctica, from 2000 to 2017 Using Landsat Multispectral Image Matching (Landsat 다중분광 영상정합을 이용한 동남극 난센 빙붕의 2000-2017년 흐름속도 변화 분석)

  • Han, Hyangsun;Lee, Choon-Ki
    • Korean Journal of Remote Sensing
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    • v.34 no.6_2
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    • pp.1165-1178
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    • 2018
  • Collapse of an Antarctic ice shelf and its flow velocity changes has the potential to reduce the restraining stress to the seaward flow of the Antarctic Ice Sheet, which can cause sea level rising. In this study, variations in ice velocity from 2000 to 2017 for the Nansen Ice Shelf in East Antarctica that experienced a large-scale collapse in April 2016 were analyzed using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) images. To extract ice velocity, image matching based on orientation correlation was applied to the image pairs of blue, green, red, near-infrared, panchromatic, and the first principal component image of the Landsat multispectral data, from which the results were combined. The Landsat multispectral image matching produced reliable ice velocities for at least 14% wider area on the Nansen Ice Shelf than for the case of using single band (i.e., panchromatic) image matching. The ice velocities derived from the Landsat multispectral image matching have the error of $2.1m\;a^{-1}$ compared to the in situ Global Positioning System (GPS) observation data. The region adjacent to the Drygalski Ice Tongue showed the fastest increase in ice velocity between 2000 and 2017. The ice velocity along the central flow line of the Nansen Ice Shelf was stable before 2010 (${\sim}228m\;a^{-1}$). In 2011-2012, when a rift began to develop near the ice front, the ice flow was accelerated (${\sim}255m\;a^{-1}$) but the velocity was only about 11% faster than 2010. Since 2014, the massive rift had been fully developed, and the ice velocity of the upper region of the rift slightly decreased (${\sim}225m\;a^{-1}$) and stabilized. This means that the development of the rift and the resulting collapse of the ice front had little effect on the ice velocity of the Nansen Ice Shelf.

A Study on People Counting in Public Metro Service using Hybrid CNN-LSTM Algorithm (Hybrid CNN-LSTM 알고리즘을 활용한 도시철도 내 피플 카운팅 연구)

  • Choi, Ji-Hye;Kim, Min-Seung;Lee, Chan-Ho;Choi, Jung-Hwan;Lee, Jeong-Hee;Sung, Tae-Eung
    • Journal of Intelligence and Information Systems
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    • v.26 no.2
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    • pp.131-145
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    • 2020
  • In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.

Intercomparison of Shortwave Radiative Transfer Models for a Rayleigh Atmosphere (레일리 대기에서 단파 영역에서의 복사전달모델 결과들의 상호 비교)

  • Yoo, Jung-Moon;Jeong, Myeong-Jae;Lee, Kyu-Tae;Kim, Jhoon;Ho, Chang-Hoi;Ahn, Myoung-Hwan;Hur, Young-Min;Rhee, Ju-Eun;Yoo, Hye-Lim;Chung, Chu-Yong;Shin, In-Chul;Choi, Yong-Sang;Kim, Young Mi
    • Journal of the Korean earth science society
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    • v.28 no.3
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    • pp.298-310
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    • 2007
  • Intercomparison between eight radiative transfer codes used for the studies of COMS (Communications, Ocean, and Meteorological Satellite) in Korea was performed under pure molecular, i.e., Rayleigh atmospheres in four shortwave fluxes: 1) direct solar irradiance at the surface, 2) diffuse irradiance at the surface, 3) diffuse upward flux at the surface, and 4) diffuse upward flux at the top of the atmosphere. The result (hereafter called the H15) from Halthore et al.'s study (2005) which intercompared and averaged 15 codes was used as a benchmark to examine the COMS models. Uncertainty of the seven COMS models except STREAMER was ${\pm}4%$ with respect to the H15, comparable with ${\pm}3%$ of Halthore et al.'s (2005). The uncertainty increased under a large $SZA=75^{\circ}$. The SBDART model generally agreed with the H15 better than the 6S model, but both models in the shortwave infrared region were equally good. The direct solar irradiance fluxes at the surface, computed by the SBDARTs of four different users, were different showing a relative error of 1.4% $(12.1Wm^{-2})$. This reason was partially due to differently installing the wavelength resolution in the flux integration. This study may be useful for selecting the optimum model in the shortwave region.