Temperature and moisture profiles retrieved from Infrared Atmospheric Sounding Interferometer (IASI) data are evaluated using collocated radiosonde data from September 2008 to August 2009 over East Asia. The level-2 products used in this study were provided by the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data, and Information Service. By using radiosonde observations as a reference, the bias and root-mean-square error (RMSE) of the temperature and water vapor profiles are obtained to examine the performance of the IASI retrievals depending on surface types and the degree of atmospheric moisture. Overall, retrievals over the land or under drier atmospheric conditions show degraded performance for both the temperature and the moisture, especially for the boundary layer temperature. It is further shown that the vertical distributions of the RMSEs and the biases of the IASI retrievals resemble the variability pattern of the radiosonde observations from the mean profiles. These retrieval aspects are thought to be related to the surface emissivity effect on the IASI retrieval and the difficulties of accounting for large atmospheric variability in the retrieval process. Although the retrieval performance appears to degrade under cloudy conditions, cloudy- and clear-sky retrievals show similar statistical behaviors varying with surface type and atmospheric moisture. Furthermore, the similar statistical behaviors between first guess and final retrievals suggest that error characteristics inherent to the first guess were not sufficiently resolved by the physical retrieval process, leaving a need to improve the first guess for the better retrieval.

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This study investigates the use of dynamic a priori error information according to atmospheric moistness and the use of quality controls in temperature and water vapor profile retrievals from hyperspectral infrared (IR) sounders. Temperature and water vapor profiles are retrieved from Atmospheric InfraRed Sounder (AIRS) radiance measurements by applying a physical iterative method using regression retrieval as the first guess. Based on the dependency of first-guess errors on the degree of atmospheric moistness, the a priori first-guess errors classified by total precipitable water (TPW) are applied in the AIRS physical retrieval procedure. Compared to the retrieval results from a fixed a priori error, boundary layer moisture retrievals appear to be improved via TPW classification of a priori first-guess errors. Six quality control (QC) tests, which check non-converged or bad retrievals, large residuals, high terrain and desert areas, and large temperature and moisture deviations from the first guess regression retrieval, are also applied in the AIRS physical retrievals. Significantly large errors are found for the retrievals rejected by these six QCs, and the retrieval errors are substantially reduced via QC over land, which suggest the usefulness and high impact of the QCs, especially over land. In conclusion, the use of dynamic a priori error information according to atmospheric moistness, and the use of appropriate QCs dealing with the geographical information and the deviation from the first-guess as well as the conventional inverse performance are suggested to improve temperature and moisture retrievals and their applications. , 2012: Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval.