Use of total precipitable water classification of a priori error and quality control in atmospheric temperature and water vapor sounding retrieval |
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Authors: | Eun-Han Kwon Jun Li Jinlong Li B J Sohn Elisabeth Weisz |
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Institution: | School of Earth and Environmental Sciences, Seoul National University, Seoul, 151-747, Korea, Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison
1225 West Dayton Street Madison, WI 53706, USA;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA;School of Earth and Environmental Sciences, Seoul National University, Seoul, 151-!747, Korea;Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison 1225 West Dayton Street Madison, WI 53706, USA |
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Abstract: | 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. |
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Keywords: | atmospheric sounding AIRS total precipitable water a priori error quality control |
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