Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 13, issue 2
Articles | Volume 13, issue 2
https://doi.org/10.5194/amt-13-1019-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-1019-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 13, issue 2
Research article
 | 
03 Mar 2020
Research article |  | 03 Mar 2020

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

Nicholas Hamilton

Viewed

Total article views: 1,640 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,017 576 47 1,640 48 45
  • HTML: 1,017
  • PDF: 576
  • XML: 47
  • Total: 1,640
  • BibTeX: 48
  • EndNote: 45
Views and downloads (calculated since 11 Sep 2019)
Cumulative views and downloads (calculated since 11 Sep 2019)

Viewed (geographical distribution)

Total article views: 1,640 (including HTML, PDF, and XML) Thereof 1,521 with geography defined and 119 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 24 Apr 2024
Download
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.
Read more