HR: 0800h
AN: A51A-0749    [Abstracts]
TI: Using a Geographic Information System to Improve Satellite Estimates of Rainfall over the Tibetan Plateau
AU: * Yin, Z
EM: zyin@sandiego.edu
AF: University of San Diego, Marine Science and Environmental Studies, 5998 Alcala Park, San Diego, CA 92110 United States
AU: Liu, X
EM: liuxd@loess.llqg.ac.cn
AF: Institute of Earth Environment, Chinese Academy of Sciences, Xi'an, 710075 China
AU: Zhang, X
EM: zhangxq@igsnrr.ac.cn
AF: Insititute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101 China
AU: Chung, C
EM: Chih-Fang.Chung@tetratech.com
AF: Tetra Tech, Inc., R D Division, 3746 Mt. Diablo Blvd., Suite 300, Lafayette, CA 94549 United States
AB: While it is very important to obtain accurate estimates of precipitation over the Tibetan Plateau for the understanding of hydrological and climatological processes, it is also difficult to study spatial variability in precipitation in this region due to sparse distribution of rain gauges and complex terrain characteristics. Satellite rainfall estimates have been proven very useful in filling gaps where gauge data are not available. Common satellite sensor systems include visible light, near and thermal infrared, and passive microwave sensors. However, in previous validation studies, the effects of complex terrain and high elevation have not been fully examined. This study examines the potential of spatial modeling using a geographic information system (GIS) to improve the rainfall estimates based on Special Sensor Microwave/Imager (SSM/I) over the Tibetan Plateau. The SSM/I is a passive microwave sensor system on board of the U.S. Defense Meteorological Satellite Program (DMSP) satellites. The data go back to July 1987. The 1ø x 1ø monthly SSM/I rainfall estimates are based on the algorithm developed at the National Environmental Satellite, Data and Information Service (NESDIS) of NOAA. When using SSM/I estimates to predict station precipitation directly, the coefficients of determination (R$^{2}$ values) range from 0.005 to 0.624, with a mean of 0.334 for all the months of the year during the study period of 1987-1999. When terrain and location variables obtained from the GIS are added to the models, the R$^{2}$ values improve up to 0.739 with a mean of 0.590 for all the months. The models for the winter months generally produced the worst performance due to the effect of snow and ice on ground. These terrain and geographic variables represent the effects of orographic forcing of topography, rain barrier/rain shadow, direction of moisture-bearing winds, and distance to the sources of moisture over the Tibetan Plateau. Results form this study suggest that region-specific algorithms are necessary for the SSM/I precipitation estimates over the Tibetan Plateau and that topographic analysis based on GIS can contribute significantly in improving the performance of SSM/I estimates. A similar approach is also used to examine the data from the Tropical Rainfall Measuring Mission's TMI sensor system. In additional to terrain and geographic variables, other variables representing local and regional atmospheric circulation patterns will also be considered in the future modeling effort. We hope that these empirical relationships obtained through this study will be useful in developing improved and region-specific algorithms to estimate precipitation over the Tibetan Plateau. As a result, an improved spatial data set of precipitation for the Plateau can be constructed by integrating satellite estimates and gauge observations to facilitate future studies of spatial and temporal variation of precipitation over the Tibetan Plateau.
DE: 3354 Precipitation (1854)
DE: 3360 Remote sensing
DE: 3309 Climatology (1620)
SC: Atmospheric Sciences [A]
MN: 2004 AGU Fall Meeting