Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-1941-2021
https://doi.org/10.5194/amt-14-1941-2021
Research article
 | 
09 Mar 2021
Research article |  | 09 Mar 2021

Using machine learning to model uncertainty for water vapor atmospheric motion vectors

Joaquim V. Teixeira, Hai Nguyen, Derek J. Posselt, Hui Su, and Longtao Wu

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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 Joaquim Teixeira on behalf of the Authors (13 Aug 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (17 Aug 2020) by Ad Stoffelen
RR by Anonymous Referee #2 (28 Aug 2020)
RR by Anonymous Referee #1 (04 Sep 2020)
ED: Reconsider after major revisions (09 Sep 2020) by Ad Stoffelen
AR by Joaquim Teixeira on behalf of the Authors (06 Nov 2020)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (09 Nov 2020) by Ad Stoffelen
RR by Anonymous Referee #2 (16 Nov 2020)
ED: Publish subject to minor revisions (review by editor) (18 Nov 2020) by Ad Stoffelen
AR by Joaquim Teixeira on behalf of the Authors (01 Dec 2020)  Author's response    Manuscript
ED: Publish as is (06 Dec 2020) by Ad Stoffelen
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Short summary
Wind-tracking algorithms produce atmospheric motion vectors (AMVs) by tracking satellite observations. Accurately characterizing the uncertainties in AMVs is essential in assimilating them into data assimilation models. We develop a machine-learning-based approach for error characterization which involves Gaussian mixture model clustering and random forest using a simulation dataset of water vapor, AMVs, and true winds. We show that our method improves on existing AMV error characterizations.