Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 3
Articles | Volume 14, issue 3
https://doi.org/10.5194/amt-14-1941-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-1941-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 3
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

Viewed

Total article views: 1,776 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,171 545 60 1,776 85 86
  • HTML: 1,171
  • PDF: 545
  • XML: 60
  • Total: 1,776
  • BibTeX: 85
  • EndNote: 86
Views and downloads (calculated since 23 Apr 2020)
Cumulative views and downloads (calculated since 23 Apr 2020)

Viewed (geographical distribution)

Total article views: 1,776 (including HTML, PDF, and XML) Thereof 1,682 with geography defined and 94 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 04 Nov 2024
Download
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.
Read more