| Summary: | There is growing consensus that persistent and increasing anthropogenic emissions, since the beginning of the industrial revolution in the 19th century, are increasing atmospheric temperatures, increasing sea levels, melting ice caps and glaciers, increasing the occurrence of severe weather, and causing regional shifts in precipitation patterns. Changes in these parameters or occurrences are responses to changes in climate forcing terms, notably greenhouse gases. The NASA Atmospheric InfraRed Sounder (AIRS), launched in May of 2002, is the first high spectral resolution infrared sounder with nearly complete global coverage on a daily basis. High spectral resolution in the infrared provides sensitivity to nearly all climate forcings, responses and feedbacks. The AIRS radiances are sensitive to changes in carbon dioxide, methane, carbon monoxide, ozone, water vapor, temperature, clouds, aerosols, and surface characteristics. This study uses the raw AIRS data to generate the first ever spectrally resolved infrared radiance (SRIR) dataset (2002- 2006) for monitoring changes in atmospheric temperature and constituents and for assessing the accuracy of climate and weather model analyses and forecasts. The SRIR dataset is a very powerful tool. Spectral signatures derived from the dataset confirmed the largest depletion of ozone over the Arctic in 2005, and also verified that the European Center for Medium Range Weather (ECMWF) model analysis water vapor fields are significantly more accurate than the analyses of the National Centers for Environmental Prediction (NCEP). The NCEP moisture fields are generally 20% more moist than those from ECMWF. This research included computations of radiances from NCEP and ECMWF atmospheric states and compared the calculated radiances with those obtained from the SRIR dataset. Comparisons showed very good agreement between the SRIR data and ECMWF simulated radiances, while the agreement with NCEP values was rather poor. Interannual differences of radiances computed from ECMWF analyses ...
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