Multistatic Specular Meteor Radar Network in Peru: System Description and Initial Results

Chau, Jorge L.; Urco, Juan Miguel; Vierinen, Juha; Harding, Brian J.; Clahsen, Matthias; Pfeffer, Nico; Kuyeng, K.; Milla, Marco; Erickson, P. J.

doi:10.1029/2020ea001293

Cited by 43 publications

(55 citation statements)

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“…By using recent technological developments in atmospheric radars, such as spread-spectrum, MIMO (Multi-input multiple-output), and compressed sensing approaches (Vierinen et al, 2016;Urco et al, 2018Urco et al, , 2019b, SIMONe allows the implementation of MMARIA with several attractive qualities: it is easier, cheaper, and inherently expandable compared to original proposed configurations using traditional pulsed systems. Examples of SIMONe implementations in Germany, Peru and Argentina can be found in several studies (Vierinen et al, 2019;Charuvil Asokan et al, 2020;Vargas et al, 2020;Chau et al, 2021;Conte et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…These studies have the goal of estimating the spatial structure of multi-point projected wind velocities and electric fields (e.g., Nicolls et al, 2014;Hysell et al, 2014;Harding et al, 2015;Stober et al, 2018). For example, a Tikhonov regularization originally developed for a optical network of Fabry-Perot Interferometers (Harding et al, 2015) has been adapted to yield MLT wind fields over Peru (Chau et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Some previous analysis methods have been published on multistatic observations of MLT mesocale dynamics, such as the gradient method and variants of Tikhonov regularization (Chau et al, 2017;Stober et al, 2018;Chau et al, 2021). However, given the novelty of multistatic measurements and the lack of a reliable ground truth observation, different wind field approaches still need to be explored, particularly in the properties of resulting statistical measure bias and variance.…”

Section: Introductionmentioning

confidence: 99%

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

Volz

Chau

Erickson

et al. 2021

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Abstract. Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground- or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens to hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, and so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesocale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four dimensional wind field inversion method that makes use of Gaussian process regression (GPR), a non-parametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally-resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as one day of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased and with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model parameters. The latter GPR approach has been applied to a 24-hour data set and shown to compare well to previously used homogeneous and gradient methods. Small scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20–50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Volz

Chau

Erickson

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comment on amt-2021-40

2021

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Mesoscale dynamics in the mesosphere and lower thermosphere (MLT) region have been difficult to study from either ground-or satellite-based observations. For understanding of atmospheric coupling processes, important spatial scales at these altitudes range between tens to hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, and so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesocale dynamics on these scales using an increased number of detections and a diversity of viewing angles inherent to multistatic networks. In this work, we introduce a four dimensional wind field inversion method that makes use of Gaussian process regression (GPR), a non-parametric and Bayesian approach. The method takes measured projected wind velocities and prior distributions of the wind velocity as a function of space and time, specified by the user or estimated from the data, and produces posterior distributions for the wind velocity. Computation of the predictive posterior distribution is performed on sampled points of interest and is not necessarily regularly sampled. The main benefits of the GPR method include this non-gridded sampling, the built-in statistical uncertainty estimates, and the ability to horizontally-resolve winds on relatively small scales. The performance of the GPR implementation has been evaluated on Monte Carlo simulations with known distributions using the same spatial and temporal sampling as one day of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased and with statistical variances that depend on the geometry and are proportional to the prior velocity variances. A conservative and fast approach can be straightforwardly implemented by employing overestimated prior variances and distances, while a more robust but computationally intensive approach can be implemented by employing training and fitting of model parameters. The latter GPR approach has been applied to a 24-hour data set and shown to compare well to previously used homogeneous and gradient methods. Small scale features have reasonably low statistical uncertainties, implying geophysical wind field horizontal structures as low as 20-50 km. We suggest that this GPR approach forms a suitable method for MLT regional and weather studies.

show abstract