Extended Abstract (208K)J6.1
Distribution and variability of atmospheric temperature and water vapor derived from HIRS measurement
Lei Shi, NOAA/NESDIS/NCDC, Asheville, NC; and J. J. Bates
A neural network technique is applied to retrieve temperature and water vapor profiles from satellite sounder HIRS measurement. The training dataset is constructed with a diverse sample of atmospheric profiles from global model reanalysis data and simulated HIRS brightness temperatures by a radiative transfer model. Previous work has generated inter-satellite calibrated, cloud-cleared, and limb-corrected HIRS channel brightness temperature data. By applying the neural network retrieval scheme to the HIRS dataset, a twenty-year time series (1980-1999) of temperature and water vapor profiles is constructed at 2.5 degree latitude/longitude grids. With the satellite sounder measurement, the retrieval scheme is able to provide temperature and water vapor profile data at a global scale, covering areas that may not be routinely observed by radiosondes. The long-term variation of temperature and water vapor and the global distribution of these variables at different levels of the atmosphere will be shown.
Joint Session 6, AI in Studies with a Climate Component (Joint between the Fourth Conference on Artificial Intelligence and the 21st International Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology)
Tuesday, 11 January 2005, 1:30 PM-2:45 PM
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