NDLI: Combined microwave and hyperspectral infrared retrievals of atmospheric profiles in the presence of clouds using nonlinear stochastic methods

Content Provider	IEEE Xplore Digital Library
Author	Blackwell, W.J. Chen, F.W. Jairam, L.G.
Copyright Year	2007
Description	Author affiliation: Massachusetts Inst. of Technol., Lexington (Blackwell, W.J.; Chen, F.W.; Jairam, L.G.)
Abstract	A nonlinear stochastic method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU), and is presently being adapted for use with the NPOESS Cross-track Infrared Microwave Sounding Suite (CrIMSS) consisting of the hyperspectral Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). The algorithm is implemented in three sequential stages: 1) stochastic cloud clearing (SCC), 2) eigenvector radiance compression and denoising, and 3) neural network (NN) estimation. First, the infrared radiance perturbations due to clouds are estimated and corrected by combined processing of the infrared and microwave data. Second, a Projected Principal Components (PPC) transform is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, a feedforward neural network is used to estimate the desired geophysical parameters from the projected principal components. The performance of the algorithm (henceforth referred to as SCC/NN) was evaluated using global (ascending and descending) EOS-Aqua orbits co-located with ECMWF forecasts (generated every three hours on a 0.5-degree lat/lon grid) and radiosonde observations (RAOBs) for a variety of days throughout 2003 and 2004. Over 500,000 fields of regard (3times3 arrays of footprints) over ocean and land were used in the study. The performance of the SCC/NN algorithm exceeded that of the AIRS Level 2 (Version 4) algorithm throughout most of the troposphere while achieving approximately four times the yield. Furthermore, the SCC/NN performance in the lowest 1 km of the atmosphere greatly exceeds that of the AIRS Level 2 algorithm as the level of cloudiness increases. The SCC/NN algorithm requires significantly less computation than traditional variational retrieval methods while achieving comparable performance, thus the algorithm is particularly suitable for quick-look retrieval generation for post-launch CrIMSS performance validation.
Starting Page	2814
Ending Page	2817
File Size	370874
Page Count	4
File Format	PDF
ISBN	9781424412112
DOI	10.1109/IGARSS.2007.4423428
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-07-23
Publisher Place	Spain
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Microwave theory and techniques Clouds Stochastic processes Neural networks Hyperspectral imaging Information retrieval Ocean temperature Moisture Microwave technology Noise reduction
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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