NDLI: Neural network estimation of atmospheric profiles using AIRS/AMSU observations: Improved uncertainty assessments

Content Provider	IEEE Xplore Digital Library
Author	Blackwell, W.J. Pieper, M.L. Zuoyu Tao
Copyright Year	2011
Description	Author affiliation: Lincoln Laboratory, Massachusetts Institute of Technology, 244 Wood Street, Lexington, 02420, USA (Blackwell, W.J.; Pieper, M.L.; Zuoyu Tao)
Abstract	Neural networks are developed for estimating the rms accuracy profiles of individual infrared and microwave atmospheric temperature and humidity profile retrievals, thus potentially significantly improving their assimilation into numerical weather prediction models. Currently most assimilation processes compute retrieval variances or error-covariance matrices as ensemble averages over diverse profiles, or simply flag problematic retrievals. Although retrieval accuracies vary considerable from profile to profile because of clouds, even in cloud-free cases they can differ markedly. The ability to estimate accurately the variances of individual profiles is one of the benefits of hyperspectral infrared and microwave sounding. The variance-estimating neural network was trained to estimate the logarithm of variance, which was then mapped to standard deviation. Examples utilizing AIRS/AMSU/HSB soundings [1] on the NASA Aqua satellite and those from a proposed hyperspectral microwave sounder [2], [3] show that when the predicted rms errors for a single altitude are stratified, they agree with the actual rms errors within perhaps ten percent of the dynamic range of the stratifications thus significantly improving the potential for accurately weighting soundings against model parameters during assimilation. Simple quality indicators using the new variance estimates also favorably compare to AIRS Level 2 Version 5 quality flags.
Starting Page	3269
Ending Page	3272
File Size	131551
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049917
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Training Estimation Ocean temperature Artificial neural networks Clouds
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
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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