A Neural Network Retrieval Technique for High-Resolution Profiling of Cloudy Atmospheres
Name
Blackwell_NN_JSTARS_final.pdf
Description
Accepted version
Size
6.32 MB
Format
Adobe PDF
Checksum (MD5)
d06af87979907d4559b39d46594f04cf
Author(s)
Blackwell, William J
Milstein, Adam B
Date Issued
April 2014
Journal
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers
Citation
Blackwell, W. J., and A. B. Milstein. "A Neural Network Retrieval Technique for High-Resolution Profiling of Cloudy Atmospheres." Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 4 (2014): 1260-70.
Version
Author's final manuscript
Abstract
The synergistic use of microwave and hyperspectral infrared sounding observations gives rise to a rich array of signal processing challenges. Of particular interest are the following elements which are combined for the first time in the retrieval technique presented here: 1) radiance noise filtering and redundancy removal (compression) using principal components transforms and canonical correlations, 2) data fusion (infrared plus microwave at possibly different spatial and spectral resolutions) and stochastic cloud clearing (SCC), and 3) geophysical product retrieval from spectral radiance measurements using neural networks. In this paper, we describe the algorithm and demonstrate performance using the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU). We show that performance is improved by approximately 25%-50% using the neural network method relative to other common techniques. Furthermore, we quantify the improvement in the vertical resolution of the retrieved products.
MIT Department
Lincoln Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/jstars.2014.2304701
