Articles | Volume 12, issue 11
Atmos. Meas. Tech., 12, 6017–6036, 2019
https://doi.org/10.5194/amt-12-6017-2019
Atmos. Meas. Tech., 12, 6017–6036, 2019
https://doi.org/10.5194/amt-12-6017-2019

Research article 20 Nov 2019

Research article | 20 Nov 2019

Neural network for aerosol retrieval from hyperspectral imagery

Steffen Mauceri et al.

Download

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

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Steffen Mauceri on behalf of the Authors (30 Sep 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (01 Oct 2019) by Andrew Sayer
RR by Anonymous Referee #1 (07 Oct 2019)
ED: Publish subject to minor revisions (review by editor) (11 Oct 2019) by Andrew Sayer
AR by Steffen Mauceri on behalf of the Authors (17 Oct 2019)  Author's response    Manuscript
ED: Publish as is (18 Oct 2019) by Andrew Sayer
Download
Short summary
Aerosols are fine particles that are suspended in Earth’s atmosphere. A better understanding of aerosols is important to lower uncertainties in climate predictions. We propose measuring aerosols from satellites and airplanes equipped with hyperspectral cameras using an artificial neural network, a form of machine learning. We applied our neural network to hyperspectral observations from a recent airplane flight over India and find general agreement with independent aerosol measurements.