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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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. 
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. 
Cassola, F. and Burlando, M.: Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output, Appl. Energ., 99, 154–166, 2012. 
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.
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