Thursday, 13 January 2000
The TOVS radiance pathfinder data set provides global coverage of temperature and moisture profiles since October of 1978. The high density of observations in space and time make these data attractive for use in climate studies. However, systematic biases from calibration errors, cloud contamination, and limb effects make it problematic to apply these data for climate research. This study presents results from the second processing stage of the entire TOVS radiance pathfinder data utilizing quality control identified in the first processing stage, cloud detection based on an ISCCP-type methodology, and limb correction based on a multiple linear regression. The resulting data sets provide the climate community multiple formats of radiance data including pixel level and grid point data that both have flags for quality control and cloud detection error. Ultimately these data will reduce the errors seen in efforts that retrieve temperature and water vapor products from TOVS since inversion techniques introduce additional systematic errors that can have negative impacts over long time series. Positive impacts on direct TOVS radiance assimilation into the ECMWF medium range forecast model has spawned interest for similar efforts in Reanalysis projects. A well calibrated TOVS radiance pathfinder data set will facilitate these projects.
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