Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 14, issue 11
Articles | Volume 14, issue 11
https://doi.org/10.5194/amt-14-7199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 14, issue 11
Research article
 | 
17 Nov 2021
Research article |  | 17 Nov 2021

Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations

Ryan Volz, Jorge L. Chau, Philip J. Erickson, Juha P. Vierinen, J. Miguel Urco, and Matthias Clahsen

Viewed

Total article views: 4,103 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,310 1,700 93 4,103 237 91 115
  • HTML: 2,310
  • PDF: 1,700
  • XML: 93
  • Total: 4,103
  • Supplement: 237
  • BibTeX: 91
  • EndNote: 115
Views and downloads (calculated since 26 Feb 2021)
Cumulative views and downloads (calculated since 26 Feb 2021)

Viewed (geographical distribution)

Total article views: 4,103 (including HTML, PDF, and XML) Thereof 4,037 with geography defined and 66 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Nov 2025
Download
Short summary
We introduce a new way of estimating winds in the upper atmosphere (about 80 to 100 km in altitude) from the observed Doppler shift of meteor trails using a statistical method called Gaussian process regression. Wind estimates and, critically, the uncertainty of those estimates can be evaluated smoothly (i.e., not gridded) in space and time. The effective resolution is set by provided parameters, which are limited in practice by the number density of the observed meteors.
Read more
Share