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Journal of Applied Remote Sensing

International MODIS and AIRS processing package: AIRS products and applications
Author(s): Elisabeth Weisz; Allen H.-L. Huang; Jun Li; Eva Borbas; Kevin Baggett; Pradeep K. Thapliyal; Li Guan
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Paper Abstract

The high-spectral-resolution AIRS (Atmospheric InfraRed Sounder) instrument onboard the NASA (National Aeronautics and Space Administration) Earth Observing System (EOS)-Aqua satellite represents the most advanced sounding system in space and provides unprecedented wealth of highly accurate radiance measurements. This paper describes a standalone and fast single field-of-view (FOV) algorithm to retrieve atmospheric sounding profiles (temperature, humidity, ozone) and surface parameters (surface skin temperature, surface emissivity) from AIRS Level 1B (L1B) clear only infrared radiance measurements. The retrieval algorithm is part of the International MODIS (Moderate Resolution Imaging Spectroradiometer)/AIRS Processing Package (IMAPP) software package, which provides international users with the capability of receiving and processing direct broadcast data in real-time. The IMAPP AIRS retrieval algorithm is based on principal component regression to obtain fast and accurate estimates of the atmospheric state at single FOV. This algorithm is designed specifically for real-time direct broadcast applications where sounding products can be processed efficiently at highest possible spatial resolution. Simulated radiance data is trained on a global set of profiles, representative of a wide variety of atmospheric scenes, which makes the algorithm globally applicable. The results presented and discussed in this paper demonstrate that the IMAPP AIRS retrieval product is rigorously evaluated by various product sources such as numerical weather prediction model analysis fields, retrieved parameters from the operational AIRS L2 product and data from other instruments.

Paper Details

Date Published: 1 July 2007
PDF: 23 pages
J. Appl. Remote Sens. 1(1) 013519 doi: 10.1117/1.2766867
Published in: Journal of Applied Remote Sensing Volume 1, Issue 1
Show Author Affiliations
Elisabeth Weisz, Univ. of Wisconsin/Madison (United States)
Allen H.-L. Huang, Univ. of Wisconsin/Madison (United States)
Jun Li, Univ. of Wisconsin/Madison (United States)
Eva Borbas, Univ. of Wisconsin/Madison (United States)
Kevin Baggett, Univ. of Wisconsin/Madison (United States)
Pradeep K. Thapliyal, Indian Space Research Organisation (India)
Li Guan, Nanjing Univ. of Information Science & Technology (China)


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