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

Gunter Stober; Alan Liu; Alexander Kozlovsky; Zishun Qiao; Ales Kuchar; Christoph Jacobi; Chris Meek; Diego Janches; Guiping Liu; Masaki Tsutsumi; Njål Gulbrandsen; Satonori Nozawa; Mark Lester; Evgenia Belova; Johan Kero; Nicholas Mitchell

doi:10.5194/amt-15-5769-2022

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

Gunter Stober, Alan Liu, Alexander Kozlovsky, Zishun Qiao, Ales Kuchar, Christoph Jacobi, Chris Meek, Diego Janches, Guiping Liu, Masaki Tsutsumi, Njål Gulbrandsen, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, Nicholas Mitchell

Department of Electronic & Electrical Engineering

Research output: Contribution to journal › Article › peer-review

12 Citations (SciVal)

Abstract

Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110km 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 wCombining double low line±1-2ms-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.

Original language	English
Pages (from-to)	5769-5792
Number of pages	24
Journal	Atmospheric Measurement Techniques
Volume	15
Issue number	19
DOIs	https://doi.org/10.5194/amt-15-5769-2022
Publication status	Published - 13 Oct 2022

Bibliographical note

Funding Information:
Gunter Stober is a member of the Oeschger Center for Climate Change Research (OCCR). The work by Alan Liu is supported by (while serving at) the National Science Foundation (NSF), USA. Chris Meek is grateful for the logistical support of the Institute of Space and Atmospheric Studies at the University of Saskatchewan. Diego Janches was supported by the NASA Heliophysics ISFM program. The Esrange meteor radar operation, maintenance, and data collection were provided by the Esrange Space Center of the Swedish Space Corporation. The 3DVar retrievals were developed as part of the ARISE design study ( http://arise-project.eu/ , last access: 8 October 2020) funded by the European Union's Seventh Framework Programme for Research and Technological Development. We thank Hauke Schmidt (Max Planck Institute for Meteorology) for providing the UA-ICON output. Njål Gulbrandsen acknowledges the support of the Leibniz Institute of Atmospheric Physics (IAP), Kühlungsborn, Germany, for their contributions to the upgrade of the TRO meteor radar. Calculations were performed on UBELIX ( http://www.id.unibe.ch/hpc , last access: 12 October 2022), the HPC cluster at the University of Bern.

Funding Information:
This research has been supported by the National Science Foundation (NSF, grant no. 1828589), the Deutsche Forschungsgemeinschaft (grant no. JA 836/43-1), the NASA Heliophysics ISFM program, NASA NESC assessment TI-17-01204, the NASA Meteoroid Environment Office (grant no. 80NSSC18M0046), the STFC (grant no. ST/S000429/1), and the Japan Society for the Promotion of Science (JSPS, Grants-in-Aid for Scientific Research, grant no. 17H02968).

ASJC Scopus subject areas

Atmospheric Science

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10.5194/amt-15-5769-2022Licence: CC BY

Cite this

Stober, G., Liu, A., Kozlovsky, A., Qiao, Z., Kuchar, A., Jacobi, C., Meek, C., Janches, D., Liu, G., Tsutsumi, M., Gulbrandsen, N., Nozawa, S., Lester, M., Belova, E., Kero, J., & Mitchell, N. (2022). Meteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithm. Atmospheric Measurement Techniques, 15(19), 5769-5792. https://doi.org/10.5194/amt-15-5769-2022

Stober, G, Liu, A, Kozlovsky, A, Qiao, Z, Kuchar, A, Jacobi, C, Meek, C, Janches, D, Liu, G, Tsutsumi, M, Gulbrandsen, N, Nozawa, S, Lester, M, Belova, E, Kero, J & Mitchell, N 2022, 'Meteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithm', Atmospheric Measurement Techniques, vol. 15, no. 19, pp. 5769-5792. https://doi.org/10.5194/amt-15-5769-2022

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title = "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 110km 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 wCombining double low line±1-2ms-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.",

author = "Gunter Stober and Alan Liu and Alexander Kozlovsky and Zishun Qiao and Ales Kuchar and Christoph Jacobi and Chris Meek and Diego Janches and Guiping Liu and Masaki Tsutsumi and Nj{\aa}l Gulbrandsen and Satonori Nozawa and Mark Lester and Evgenia Belova and Johan Kero and Nicholas Mitchell",

note = "Funding Information: Gunter Stober is a member of the Oeschger Center for Climate Change Research (OCCR). The work by Alan Liu is supported by (while serving at) the National Science Foundation (NSF), USA. Chris Meek is grateful for the logistical support of the Institute of Space and Atmospheric Studies at the University of Saskatchewan. Diego Janches was supported by the NASA Heliophysics ISFM program. The Esrange meteor radar operation, maintenance, and data collection were provided by the Esrange Space Center of the Swedish Space Corporation. The 3DVar retrievals were developed as part of the ARISE design study ( http://arise-project.eu/ , last access: 8 October 2020) funded by the European Union's Seventh Framework Programme for Research and Technological Development. We thank Hauke Schmidt (Max Planck Institute for Meteorology) for providing the UA-ICON output. Nj{\aa}l Gulbrandsen acknowledges the support of the Leibniz Institute of Atmospheric Physics (IAP), K{\"u}hlungsborn, Germany, for their contributions to the upgrade of the TRO meteor radar. Calculations were performed on UBELIX ( http://www.id.unibe.ch/hpc , last access: 12 October 2022), the HPC cluster at the University of Bern. Funding Information: This research has been supported by the National Science Foundation (NSF, grant no. 1828589), the Deutsche Forschungsgemeinschaft (grant no. JA 836/43-1), the NASA Heliophysics ISFM program, NASA NESC assessment TI-17-01204, the NASA Meteoroid Environment Office (grant no. 80NSSC18M0046), the STFC (grant no. ST/S000429/1), and the Japan Society for the Promotion of Science (JSPS, Grants-in-Aid for Scientific Research, grant no. 17H02968). ",

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doi = "10.5194/amt-15-5769-2022",

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T1 - Meteor radar vertical wind observation biases and mathematical debiasing strategies including the 3DVAR+DIV algorithm

AU - Stober, Gunter

AU - Liu, Alan

AU - Kozlovsky, Alexander

AU - Qiao, Zishun

AU - Kuchar, Ales

AU - Jacobi, Christoph

AU - Meek, Chris

AU - Janches, Diego

AU - Liu, Guiping

AU - Tsutsumi, Masaki

AU - Gulbrandsen, Njål

AU - Nozawa, Satonori

AU - Lester, Mark

AU - Belova, Evgenia

AU - Kero, Johan

AU - Mitchell, Nicholas

N1 - Funding Information: Gunter Stober is a member of the Oeschger Center for Climate Change Research (OCCR). The work by Alan Liu is supported by (while serving at) the National Science Foundation (NSF), USA. Chris Meek is grateful for the logistical support of the Institute of Space and Atmospheric Studies at the University of Saskatchewan. Diego Janches was supported by the NASA Heliophysics ISFM program. The Esrange meteor radar operation, maintenance, and data collection were provided by the Esrange Space Center of the Swedish Space Corporation. The 3DVar retrievals were developed as part of the ARISE design study ( http://arise-project.eu/ , last access: 8 October 2020) funded by the European Union's Seventh Framework Programme for Research and Technological Development. We thank Hauke Schmidt (Max Planck Institute for Meteorology) for providing the UA-ICON output. Njål Gulbrandsen acknowledges the support of the Leibniz Institute of Atmospheric Physics (IAP), Kühlungsborn, Germany, for their contributions to the upgrade of the TRO meteor radar. Calculations were performed on UBELIX ( http://www.id.unibe.ch/hpc , last access: 12 October 2022), the HPC cluster at the University of Bern. Funding Information: This research has been supported by the National Science Foundation (NSF, grant no. 1828589), the Deutsche Forschungsgemeinschaft (grant no. JA 836/43-1), the NASA Heliophysics ISFM program, NASA NESC assessment TI-17-01204, the NASA Meteoroid Environment Office (grant no. 80NSSC18M0046), the STFC (grant no. ST/S000429/1), and the Japan Society for the Promotion of Science (JSPS, Grants-in-Aid for Scientific Research, grant no. 17H02968).

PY - 2022/10/13

Y1 - 2022/10/13

N2 - Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110km 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 wCombining double low line±1-2ms-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.

AB - Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110km 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 wCombining double low line±1-2ms-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.

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

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