There is a strong need for accurate estimation of radiance from satellite regarding establishing a climate records such as global climate circulation, change and Earth’s atmosphere. It is important that exact radiance measurements from satellite to numerical weather prediction models for climate change detection. Furthermore, accurate measurements from satellite rely on calibration of channel data in terms of the radiometric characteristics. Related to improved calibration and inter-calibration of the sensors, the World Meteorological Organization (WMO) and the Coordination Group for Meteorological Satellite (CGMS) initiated the Global Space-based Inter-Calibration System (GSICS) in 2005, which provide coefficients to the user community to adjust satellite observations. To assess influence of the GSICS corrections and impacts of input parameters changes on satellite products, the coefficients of the GSICS corrections were applied to infrared (IR) data from Communication Ocean and Meteorological Satellite (COMS), which have Meteorological Imager (MI) sensor for meteorological missions. The IR data centered at wavelengths of 10.8 (IR1) and 12.0μm (IR2) from the COMS MI were compared with that of the Infrared Atmospheric Sounding Interferometer (IASI) sensor, which is reference sensor of the GSICS corrections. The IR1 and IR2 data that were corrected by GSICS produced Sea Surface Temperature (SST), which has been influenced by input parameters such as IR data and solar zenith angle. As a result of comparison with in situ measurements, the Global Telecommunication System (GTS) buoy data, COMS IR data that were corrected by the GSICS corrections produced high quality products of SST than original COMS IR data.
Snow is a component of the cryosphere which has played an important role in Earth energy balance. Northern
hemisphere snow cover extent (SCE) has steadily decreased since 1980 and in recently the trend of SCE is sharply
decreased. Because Himalaya region's shows most significant changes except for the Arctic, we analyzed this region for
SCE. We used Moderate Resolution Imaging Spectroradiometer (MODIS) snow product from 2001 to 2011 in august.
Analysis was made by considering some conditions (region, elevation, longitude and climate) which can affect the
changes in SCE. The entire SCE in Himalaya for 11 years has steadily increased(+55,098 km2). Trends for SCE in western
region has increased(+77,781km2), But trend for central and eastern have decreased -3,453 km2, -19,230km2, respectively.
According to elevation increases, the ratio of snow in each study area is increased. In 30°N~35°N SCE shows increased
trend, 27°N~28°N shows decreased trend. In tundra climate, trends for SCE are similar to regional analysis. whereas the
result in tropical climate's trend was increased. these performed result shows different side for change of SCE depending
on each condition. The result of this study were similar to the rapid decline of the northern hemisphere SCE area in
recent. The result of this study can be used to help management to water budget in Central-Asia country located to
Air temperature (Ta) plays important role for the circulation of energy and water between the surface and atmosphere. Ta
was accurately measured from ground observation stations. However, the number of ground observation stations is
limited, and Ta is influenced from temporal and spatial change. In this study, Ta was estimated using satellite data from
April 2011 to March 2012 in the Northeast Asia where consist of the various ecosystem. States of surface and
atmosphere were considered through Normalized Difference Water Index (NDWI) and the differences of brightness
temperature values of 11μm (TBB1) and 12μm (TBB2). Dataset was divided into nine cases that had seasonal
characteristics according surface states (NDWI) and atmosphere states (TBB1-TBB2). Ta was acquired from 174 ground
observation stations, and multiple regression equation of each case was consisted of LST, NDVI, TBB1-TBB2. The
weighting region was set to be within 8.33% of total density from boundary area of cases in order to reduce the errors
that can occur due to the small value. The weighting was applied as distance from the nearest four points. The spatial
representativeness of estimated Ta was determined as 9 by 9 window size. R-squared of estimated Ta from satellite was
0.94, RMSE was 2.98 K, Bias was 0.56 K.