• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.337-349
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• 2013

Communication Ocean Meteorological Satellite (COMS) for the hybrid mission of meteorological observation, ocean monitoring, and telecommunication service was launched onto Geostationary Earth Orbit on June 27, 2010 and it is currently under normal operation service on $128.2^{\circ}$ East of the geostationary orbit since April 2011. In order to perform the three missions, the COMS has 3 separate payloads, the meteorological imager (MI), the Geostationary Ocean Color Imager (GOCI), and the Ka-band antenna. The MI and GOCI perform the Earth observation mission of meteorological observation and ocean monitoring, respectively. For this Earth observation mission the COMS requires daily mission commands from the satellite control ground station and daily mission is affected by the satellite control activities. For this reason daily mission planning is required. The Earth observation mission operation of COMS is described in aspects of mission operation characteristics and mission planning for the normal operation services of meteorological observation and ocean monitoring. And the first one-year normal operation results after the In-Orbit-Test (IOT) are investigated through statistical approach to provide the achieved COMS normal operation status for the Earth observation mission.

• Proceedings of the KSRS Conference
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• v.1
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• pp.216-219
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• 2006

This paper analyzes on LHGS (LRIT/HRIT Generation Subsystem) processing timeline for COMS LHGS design. The LHGS shall transmit LRIT/HRIT (Low Rate Information Transmission/ High Rate Information Transmission) data to the users within 15 minutes after the end of the image acquisition. So, this paper performs experiment using MTSAT-1R LRIT/HRIT (11 days) and calculates minimum LHGS processing time. Only HRIT FD (Full Disk) image is considered in this paper because data size of HRIT FD image is the largest. As a result of experiment, COMS LHGS should be able to receive MI Level 1B product within 157 seconds at least.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.115-121
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• 2013

The first geostationary satellite in Korea, COMS (Communication, Ocean, and Meteorological Satellite), has been operating properly since its successful completion of the IOT (In Orbit Test). COMS MI (Meteorological Imager) acquires Earth observation images from visible and infrared channels. This paper describes a method to compute the degradation of the COMS visible detectors and the result of the degradation during the two years of the operation. The visible channel detectors' performance was determined based on the comparison between the instrument-based measurements and ROLO model-based values. The degradation rate of the visible channel detectors of COMS MI showed a normal condition.

• Journal of Astronomy and Space Sciences
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• v.23 no.4
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• pp.415-424
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• 2006

In this paper, requirements of Meteorological Administration about Meteorological Image. (MI) of Communications, Ocean and Meteorological Satellite (COMS) is analyzed for the design of COMS ground station and according to the analysis results, the distribution image size of each observation area suitable for satellite Field Of View (FOV) stated at the requirements of meteorological administration is determined and the precise satellite FOV and the size of distribution image is calculated on the basis of the image size of the determined observation area. The results in this paper were applied to the detailed design for COMS ground station and also are expected to be used for the future observation scheduling and the scheduling of distribution of user data.

• Journal of Satellite, Information and Communications
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• v.8 no.1
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• pp.89-100
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• 2013

Communication Ocean Meteorological Satellite (COMS) for the hybrid mission of meteorological observation, ocean monitoring, and telecommunication service was launched onto Geostationary Earth Orbit on June 27, 2010 and it is currently under normal operation service after the In-Orbit Test (IOT) phase. The COMS is located on $128.2^{\circ}$ East of the geostationary orbit. In order to perform the three missions, the COMS has 3 separate payloads, the meteorological imager (MI), the Geostationary Ocean Color Imager (GOCI), and the Ka-band antenna. Each payload is dedicated to one of the three missions, respectively. The MI and GOCI perform the Earth observation mission of meteorological observation and ocean monitoring, respectively. During the IOT phase the functionalities and the performances of the COMS satellite and ground station have been checked through the Earth observation mission operation for the observation of the meteorological phenomenon over several areas of the Earth and the monitoring of marine environments around the Korean peninsula. The operation characteristics of meteorological mission and ocean mission are described and the mission planning for the COMS is discussed. The mission operation results during the COMS IOT are analyzed through statistical approach for the study of both the mission operation capability of COMS verified during the IOT and the satellite image reception capacity achieved during the IOT.

• Proceedings of the KSRS Conference
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• v.1
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• pp.67-70
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• 2006

KARI is developing Image Data Acquisition and Control System (IDACS) for pre-processing meteorological and ocean data acquired on geostationary orbit. This paper describes the functions and architecture of IDACS and gives its operation policy including backup operation to overcome limitation of single-configured antenna system. The COMS IDACS provides the capability to receive the raw sensor data and disseminate processed MI data to users via a satellite. From the processed image data, users can produce a set of meteorological and ocean products for a wide range of applications. Most of IDACS subsystems are being developed by Korean technologies and experience acquired from previous projects. In case of COMS geometric correction software module, as it is closely dependent on the characteristics of imagers and spacecraft bus system, it is being co-developed with overseas prime contractor who develops spacecraft bus system.

• Aerospace Engineering and Technology
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• v.9 no.2
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• pp.44-50
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• 2010

COMS accommodates multiple payloads; Meteorological Image(MI), Ocean Color Imager(GOCI) and Ka-band communication payloads. In order to improve the quality of MI visible channel, the moon image has been taken into account as backup reference in addition to Albedo monitoring. However, obtaining the moon image by adding special mission schedule is not recommended after IOT, because we may miss chances to obtain meteorological images during the time slots for special imaging. As an alternative solution, an approach extracting moon image from MI FD(Full Disk) image has been proposed when the moon is positioned near to the earth. However, prediction of acquisition time of moon image is somewhat difficult as the moon moves while the MI is scanning type sensor. And the moon can not be seen when it is behind the earth or outside of FD field of view. This paper discusses how effectively the moon can be detected by the MI FD imaging. For that purpose, this paper describes an approach taken to predict the time when the moon image is achievable and then introduces the results obtained from computer simulation.

• Korean Journal of Remote Sensing
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• v.27 no.4
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• pp.495-506
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• 2011

COMS In-Orbit Tests(IOT), performed from July, 2010 to Jan, 2011, were successfully completed and the scientific data from MI and GOCI has been distributed officially from April, 2011. This paper focuses on the geometric calibration system tests conducted during the IOT. The geometric calibration process, which is one of the primary objectives of the IOT is the final step of COMS data pre-processing. The basic principles of the geometric calibration (or image navigation and registration, INR) algorithm for COMS are described and the functional and performance tests of COMS INR system were summarized according to the COMS IOT phases. Final performance testes were carried out using data sets acquired from the real-time COMS data pre-processing system. Geometric calibration accuracy of the COMS data showed excellent quality and met requirement specifications.

• Korean Journal of Remote Sensing
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• v.29 no.1
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• pp.151-160
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• 2013

Recently, although many efforts have been made to estimate insolation over Korean Peninsula based on satellite imagery, most of them have utilized overseas satellite imagery. This paper aims to estimate insolation over the Korean Peninsula based on the Korean stationary orbit satellite imagery. It utilizes level 1 data and level 2 cloud image of COMS MI, the first meteorological satellite of Korea, and OMI image of NASA as input data. And Kawamura physical model which has been known to be suitable for East Asian area is applied. Daily global horizontal insolation was estimated by using satellite images of every fifteen minutes for the period from May 2011 to April 2012, and the estimates were compared to the ground based measurements. The estimated and observed daily insolations are highly correlated as the $R^2$ value is 0.86. The error rates of monthly average insolation was under ${\pm}15%$ in most stations, and the annual average error rate of horizontal global insolation ranged from -5% to 5% except for Seoul. The experimental results show that the COMS MI based approach has good potential for estimating insolation over the Korean Peninsula.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.286-289
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• 2007

This study is to develop a cloud detection algorit1un for COMS and it is currently tested by using MODIS level 2B and MTSAT-1R satellite radiance data. Unlike many existing cloud detection schemes which use a threshold method and traditional statistical methods, in this study a feed-forward neural network method with back-propagation algorit1un is used. MODIS level 2B products are matched with feature information of five-band MTSAT 1R image data to form the training dataset. The neural network is trained over the global region for the period of January to December in 2006 with 5 km spatial resolution. The main results show that this model is capable to detect complex cloud phenomena. And when it is applied to seasonal images, it shows reliable results to reflect seasonal characteristics except for snow cover of winter. The cloud detection by the neural network method shows 90% accuracy compared to the MODIS products.