Abstract. Ground-based remote sensing of atmospheric parameters is often limited to single station observations by vertical profiles at a certain geographic location. This is a limiting factor for investigating gravity wave dynamics as the spatial information is often missing, e.g., horizontal wavelength, propagation direction or intrinsic frequency. In this study, we present a new retrieval algorithm for multistatic meteor radar networks to obtain tomographic 3-D wind fields within a pre-defined domain area. The algorithm is part of the Agile Software for Gravity wAve Regional Dynamics (ASGARD) and called 3D-Var, and based on the optimal estimation technique and Bayesian statistics. The performance of the 3D-Var retrieval is demonstrated using two meteor radar networks: the Nordic Meteor Radar Cluster and the Chilean Observation Network De Meteor Radars (CONDOR). The optimal estimation implementation provide statistically sound solutions and diagnostics from the averaging kernels and measurement response. We present initial scientific results such as body forces of breaking gravity waves leading to two counter-rotating vortices and horizontal wavelength spectra indicating a transition between the rotational k−3 and divergent k-5/3 mode at scales of 80–120 km. In addition, we performed a keogram analysis over extended periods to reflect the latitudinal and temporal impact of a minor sudden stratospheric warming in December 2019. Finally, we demonstrate the applicability of the 3D-Var algorithm to perform large-scale retrievals to derive meteorological wind maps covering a latitude region from Svalbard, north of the European Arctic mainland, to central Norway.
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Meteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithm
Abstract. Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. Thesesystems 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 windvariability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometryand scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplacefilter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON modeldata. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the firstobservational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteortrajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, weintroduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability frommultistatic 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 toensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocitiesin the range of w = ± 1–2 m s−1 for most of the analyzed data during 2 years of collection, which is consistent with the values reportedfrom general circulation models (GCMs) for this timescale and spatial resolution.
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- Award ID(s):
- 1828589
- PAR ID:
- 10422087
- Date Published:
- Journal Name:
- Atmospheric Measurement Techniques
- Volume:
- 15
- Issue:
- 19
- ISSN:
- 1867-8548
- Page Range / eLocation ID:
- 5769 to 5792
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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