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dc.contributor.authorVolz, Ryan
dc.contributor.authorChau, Jorge L.
dc.contributor.authorErickson, Philip J.
dc.contributor.authorVierinen, Juha P.
dc.contributor.authorUrco, J. Miguel
dc.contributor.authorClahsen, Matthias
dc.date.accessioned2021-12-28T10:33:13Z
dc.date.available2021-12-28T10:33:13Z
dc.date.issued2021-11-17
dc.description.abstractMesoscale 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 and hundreds of kilometers in the horizontal plane. To date, this scale size is challenging observationally, so structures are usually parameterized in global circulation models. The advent of multistatic specular meteor radar networks allows exploration of MLT mesoscale 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), which is a nonparametric 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 1 d of real meteor measurements. Based on the simulation results we find that the GPR implementation is robust, providing wind fields that are statistically unbiased 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 hyperparameters. The latter GPR approach has been applied to a 24 h dataset 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.en_US
dc.identifier.citationVolz, Chau, Erickson, Vierinen, Urco, Clahsen. Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations. Atmospheric Measurement Techniques. 2021;14(11):7199-7219en_US
dc.identifier.cristinIDFRIDAID 1963492
dc.identifier.doi10.5194/amt-14-7199-2021
dc.identifier.issn1867-1381
dc.identifier.issn1867-8548
dc.identifier.urihttps://hdl.handle.net/10037/23518
dc.language.isoengen_US
dc.publisherEuropean Geosciences Union (EGU)en_US
dc.relation.journalAtmospheric Measurement Techniques
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleFour-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observationsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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