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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Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Validation and Intercomparisons
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Cited articles

Bormann, N., Hernandez-Carrascal, A., Borde, R., Lutz, H. J., Otkin, J. A., and Wanzong, S.: Atmospheric motion vectors from model simulations. Part I: Methods and characterization as single-level estimates of wind, J. Appl. Meteorol. Clim., 53, 47–64, https://doi.org/10.1175/JAMC-D-12-0336.1, 2014. 
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Coulston, J. W., Blinn, C. E., Thomas, V. A., and Wynne, R. H.: Approximating prediction uncertainty for random forest regression models, Photogramm. Eng. Rem. S., 82, 189–197, 2016. 
Fraley, C. and Raftery, A. E.: MCLUST: Software for model-based clustering, density estimation and discriminant analysis (No. TR-415), Washington University, Seattle Department of Statistics, Seattle, Washington, USA, 2002. 
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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.