Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-1019-2020
https://doi.org/10.5194/amt-13-1019-2020
Research article
 | 
03 Mar 2020
Research article |  | 03 Mar 2020

Atmospheric condition identification in multivariate data through a metric for total variation

Nicholas Hamilton

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Nicholas Hamilton on behalf of the Authors (24 Dec 2019)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (06 Jan 2020) by Laura Bianco
RR by Ásta Hannesdóttir (19 Jan 2020)
RR by Anonymous Referee #2 (21 Jan 2020)
ED: Publish subject to technical corrections (21 Jan 2020) by Laura Bianco
AR by Nicholas Hamilton on behalf of the Authors (30 Jan 2020)  Author's response    Manuscript
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Short summary
The identification of atmospheric conditions within a multivariable atmospheric data set is an important step in validating emerging and existing models used to simulate wind plant flows and operational strategies. The total variation approach developed here offers a method founded in tested mathematical metrics and can be used to identify and characterize periods corresponding to quiescent conditions or specific events of interest for study or wind energy development.