Articles | Volume 10, issue 11
https://doi.org/10.5194/amt-10-4235-2017
https://doi.org/10.5194/amt-10-4235-2017
Research article
 | 
08 Nov 2017
Research article |  | 08 Nov 2017

Combined neural network/Phillips–Tikhonov approach to aerosol retrievals over land from the NASA Research Scanning Polarimeter

Antonio Di Noia, Otto P. Hasekamp, Lianghai Wu, Bastiaan van Diedenhoven, Brian Cairns, and John E. Yorks

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
  • RC1: 'Review', Anonymous Referee #2, 31 Jul 2017 Printer-friendly Version
    • AC1: 'Reply', Antonio Di Noia, 18 Sep 2017 Printer-friendly Version
  • RC2: 'review', Alexei Lyapustin, 04 Aug 2017 Printer-friendly Version
    • AC2: 'Reply', Antonio Di Noia, 18 Sep 2017 Printer-friendly Version

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Antonio Di Noia on behalf of the Authors (18 Sep 2017)  Author's response   Manuscript 
ED: Publish as is (20 Sep 2017) by Alexander Kokhanovsky
AR by Antonio Di Noia on behalf of the Authors (22 Sep 2017)
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Short summary
In this paper an algorithm for the retrieval of aerosol properties from NASA Research Scanning Polarimeter (RSP) data is presented. An artificial neural network is used to produce a first estimate of the aerosol properties, which is then improved using an iterative retrieval scheme based on Phillips–Tikhonov regularization. Using the neural network retrievals as a first guess for the Phillips–Tikhonov improved the retrieval convergence, confirming results previously found on ground-based data.