Articles | Volume 11, issue 5
Atmos. Meas. Tech., 11, 3177–3196, 2018
https://doi.org/10.5194/amt-11-3177-2018
Atmos. Meas. Tech., 11, 3177–3196, 2018
https://doi.org/10.5194/amt-11-3177-2018

Research article 01 Jun 2018

Research article | 01 Jun 2018

Neural network cloud top pressure and height for MODIS

Nina Håkansson et al.

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nina Håkansson on behalf of the Authors (20 Apr 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (23 Apr 2018) by Andrew Sayer
RR by Anonymous Referee #1 (02 May 2018)
ED: Publish subject to technical corrections (03 May 2018) by Andrew Sayer
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Short summary
In this paper a new algorithm for cloud top height retrieval from imager instruments like MODIS is presented. It uses artificial neural networks and reduces the mean absolute error by 32 % compared to two other operational cloud height algorithms. This means that improved cloud height retrieval for nowcasting, as input to models and in cloud climatologies is possible.