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

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

doi:https://doi.org/10.5194/amt-14-7199-2021

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

Supplement

https://doi.org/10.5194/amt-14-7199-2021-supplement

Data sets

Meteor observations and wind estimates from the northern Germany SIMONe radar network on November 5, 2018 Ryan Volz, Jorge L. Chau, Philip J. Erickson, Juha P. Vierinen, J. Miguel Urco, and Matthias Clahsen https://zenodo.org/record/5550854

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.