Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval

This study investigates the use of dynamic a priori error informationaccording to atmospheric moistness and the use of quality controls intemperature and water vapor profile retrievals from hyperspectral infrared(IR) sounders. Temperature and water vapor profiles are retrieved fromAtmospheric InfraRed Sounder (AIRS) radiance measurements by applying aphysical iterative method using regression retrieval as the first guess.Based on the dependency of first-guess errors on the degree of atmosphericmoistness, the a priori first-guess errors classified by total precipitablewater (TPW) are applied in the AIRS physical retrieval procedure. Comparedto the retrieval results from a fixed a priori error, boundary layermoisture retrievals appear to be improved via TPW classification of a priorifirst-guess errors. Six quality control (QC) tests, which checknon-converged or bad retrievals, large residuals, high terrain and desertareas, and large temperature and moisture deviations from the first guessregression retrieval, are also applied in the AIRS physical retrievals.Significantly large errors are found for the retrievals rejected by thesesix QCs, and the retrieval errors are substantially reduced via QC overland, which suggest the usefulness and high impact of the QCs, especiallyover land. In conclusion, the use of dynamic a priori error informationaccording to atmospheric moistness, and the use of appropriate QCs dealingwith the geographical information and the deviation from the first-guess aswell as the conventional inverse performance are suggested to improvetemperature and moisture retrievals and their applications.