• Atmosphere
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• v.12 no.4
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• pp.69-90
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• 2002

The infrared and microwave satellite observations have been used to derive the information of hydrometeors (i.e., cloud and precipitation) and atmospheric temperature. The observations were made by the Nimbus-4 Infrared Interferometer Spectrometer (IRIS) in 1970, and by the Microwave Sounding Unit (MSU) during the period 1980-99, which had channel 1~4 (Chl~4). The IRIS, which has a field of view of ~100 km, has been utilized to examine the cirrus and marine stratus clouds. The cirrus and stratus distributions were obtained, respectively, based on the spectral difference in the infrared window region, and the absorption of water vapor and $CO_2$ in the spectral region $870-980cm^{-1}$. The MSU Ch1 data has been used for low tropospheric temperature and hydrometeors, while the Ch2, Ch3 and Ch4, respectively, for the thermal state of midtroposphere, tropopause, and lower stratosphere. The climatic aspects of El Ni$\tilde{n}$o, Quasi-Biennial Oscillation (QBO) and temperature trends over the globe are discussed with the MSU data. This study suggests that the IRIS and MSU data are useful for monitoring the hydrometeors and atmospheric thermal state in climate system.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1181-1189
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• 2022

Clouds are composed of tiny water droplets, ice crystals, or mixtures suspended in the atmosphere and cover about two-thirds of the Earth's surface. Cloud detection in satellite images is a very difficult task to separate clouds and non-cloud areas because of similar reflectance characteristics to some other ground objects or the ground surface. In contrast to thick clouds, which have distinct characteristics, thin transparent clouds have weak contrast between clouds and background in satellite images and appear mixed with the ground surface. In order to overcome the limitations of transparent clouds in cloud detection, this study conducted cloud detection focusing on transparent clouds using machine learning techniques (Random Forest [RF], Convolutional Neural Networks [CNN]). As reference data, Cloud Mask and Cirrus Mask were used in MOD35 data provided by MOderate Resolution Imaging Spectroradiometer (MODIS), and the pixel ratio of training data was configured to be about 1:1:1 for clouds, transparent clouds, and clear sky for model training considering transparent cloud pixels. As a result of the qualitative comparison of the study, bothRF and CNN successfully detected various types of clouds, including transparent clouds, and in the case of RF+CNN, which mixed the results of the RF model and the CNN model, the cloud detection was well performed, and was confirmed that the limitations of the model were improved. As a quantitative result of the study, the overall accuracy (OA) value of RF was 92%, CNN showed 94.11%, and RF+CNN showed 94.29% accuracy.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.2
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• pp.133-142
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• 2014

Landsat is one of the popular satellites for observing land surface that is used in various areas including monitoring, detecting and classifying changes in land surface. However, shades, which cloud itself and its shadow, interrupted often clear observation and analysis of ground surface. For this reason, the process of removing shades and restoring original ground surfaces are critical for geospatial users. This study is planned to recommend a methodology for more accurate and clear images of Landsat-8 sensor, which provided two additional bands of costal/aerosol and cirrus. In fact, those bands are known as functioned effectively in detecting and restoring shades. Otsu's thresholding technique to detect clouds, we replaced those detective shades by using experimental and reference images. In accurate assessment, the overall accuracy and kappa coefficients were about 85% and 0.7128, respectively. This indicates that the proposed technique is effective for recovering the original land surface.

• Journal of the Korean Geophysical Society
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• v.9 no.1
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• pp.27-36
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• 2006

The radiative convective equilibrium (RCE) temperature was calculated for the climate change study at King Sejong Station in West Antarctica. As a result of RCE model sensitivity test, the increases of surface albedo, solar zenith angle, and cloud optical thickness decrease surface temperature. On the other hand, the increases of carbon dioxide and cirrus cloud amount are caused by surface warming due to the greenhouse effect. According to the model calculation result, annual mean surface temperature shows a upward trend of 0.012oC/year during the period of 1958-2001. During the period of 1989∼2001, the trend of monthly mean surface temperature by model calculation is 0.01oC/month and the observation trend is 0.005oC/month.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.278-281
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• 2007

The purpose of this study is to develop snow and sea ice detection algorithm in Communication, Ocean and Meteorological Satellite (COMS) meteorological data processing system. Since COMS has only five channels, it is not affordable to use microwave or shortwave infrared data which are effective and generally used for snow detection. In order to estimate snow and sea ice coverage, combinations between available channel data(mostly visible and 3.7 ${\mu}m$) are applied to the algorithm based on threshold method. As a result, the COMS snow and sea ice detection algorithm shows reliable performance compared to MODIS products with channel limitation. Specifically, there is partial underestimation over the complicated vegetation area and overestimation over the area of high level clouds such as cirrus. Some corrections are performed by using water vapor and infrared channels to remove cloud contamination and by applying NDVI to detect more snow pixels for the underestimated area.