Meteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithm

Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = +/- 1-2 ms(-1) for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution.

Automated summary

Meteor radars are widely used for studying atmospheric dynamics, especially in the altitude range of 70 to 110 km. In this study, potential biases in vertical wind estimation intrinsic to the radar observation geometry and scattering mechanism are investigated. A mathematical debiasing process using a spatiotemporal Laplace filter is introduced to mitigate these biases. The results show good agreement between the vertical winds obtained from this retrieval algorithm and model data, and the first observational indications of a forward scatter wind bias are presented. A new retrieval algorithm called 3DVAR+DIV is introduced to derive vertical winds and wind variability from multistatic meteor radar networks.