| Co-Investigator(Kenkyū-buntansha) |
TAKAHASHI Kazuyoshi Nagaoka University of Technology, Department of Civil and Environmental Engineering, Research Associate, 環境・建設系, 助手 (00332651)
IWASAKI Hiroyuki Gunma University, Faculty of Education, Associate Professor, 教育学部, 助教授 (70261823)
YANG Dawen Graduate School of Engineering, The University of Tokyo, Assistant Professor, 大学院・工学系研究科, 講師 (40334312)
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| Research Abstract |
The data from ground based radar and satellite based microwave radiometer acquired on the Tibetan Plateau in summer 1998, enabled us to investigate the microwave radiometric characteristics of land precipitation, and to develop the precipitation estimation algorithm over land area, taking into account of the effects of land surface non-uniformity by inferring rain fall and soil moisture at the same time. And further more we succeeded in assuming the optical thickness on the rainfall layer and the soilmoisture of the land surface, by applying this algorism to the brightness temperature data at 85 GHz and 10 GHz, observed by the TRMM/TMI. In consequence of comparing this assumed optical thickness with precipitation data in meso scale area, it is proved that 10-day average makes it possible to reduce the effects on the precipitation estimation by the satellite-oriented sampling frequency, and to estimate the land surface precipitation from the assumed optical thickness. When compared with
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the vertical profile of rainfall observed by the ground based radar, the thickness difference in the area of snowfall especially above the melting layer affects strongly on the estimation of optical thickness.
Next, with the radiometric transmission model covered atmospheric-land surface in a lump, we figured out cloud water density, water vapor, as well as rainfall intensity, soil moisture, and land surface temperature, selected by simulation the combination of the frequency zone with high sensitive to atmospheric variable. Together with this information, we developed the calculation method to! figure out rainfall intensity and cloud water density simultaneously. In this method, firstly we calculate microwave emissivity from the land surface and surface temperature, by algorithm for soil moisture without taking the value of water vapor into consideration. Then using this figure as a boundary condition, we calculate the value of water vapor in the air by the algorithm for rainfall intensity and cloud water density. Next, by inputting this value, we develop the algorithm for soil moisture by using iteration method. As a result, it is clarified to distinguish fine, cloudy, from rainy, but it still needs to identify the effects on the surface roughness of the land for the quantitative estimation of precipitation. Less
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