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

<p>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 and hundreds of kilo...

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Autores principales:	R. Volz, J. L. Chau, P. J. Erickson, J. P. Vierinen, J. M. Urco, M. Clahsen
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
Acceso en línea:	https://doaj.org/article/c1da7f03c689410fae798ff34258ed4c
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spelling	oai:doaj.org-article:c1da7f03c689410fae798ff34258ed4c2021-11-17T08:35:51ZFour-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations10.5194/amt-14-7199-20211867-13811867-8548https://doaj.org/article/c1da7f03c689410fae798ff34258ed4c2021-11-01T00:00:00Zhttps://amt.copernicus.org/articles/14/7199/2021/amt-14-7199-2021.pdfhttps://doaj.org/toc/1867-1381https://doaj.org/toc/1867-8548<p>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 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.</p>R. VolzJ. L. ChauP. J. EricksonJ. P. VierinenJ. M. UrcoM. ClahsenCopernicus PublicationsarticleEnvironmental engineeringTA170-171Earthwork. FoundationsTA715-787ENAtmospheric Measurement Techniques, Vol 14, Pp 7199-7219 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Environmental engineering TA170-171 Earthwork. Foundations TA715-787
spellingShingle	Environmental engineering TA170-171 Earthwork. Foundations TA715-787 R. Volz J. L. Chau P. J. Erickson J. P. Vierinen J. M. Urco M. Clahsen Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
description	<p>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 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.</p>
format	article
author	R. Volz J. L. Chau P. J. Erickson J. P. Vierinen J. M. Urco M. Clahsen
author_facet	R. Volz J. L. Chau P. J. Erickson J. P. Vierinen J. M. Urco M. Clahsen
author_sort	R. Volz
title	Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
title_short	Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
title_full	Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
title_fullStr	Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
title_full_unstemmed	Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
title_sort	four-dimensional mesospheric and lower thermospheric wind fields using gaussian process regression on multistatic specular meteor radar observations
publisher	Copernicus Publications
publishDate	2021
url	https://doaj.org/article/c1da7f03c689410fae798ff34258ed4c
work_keys_str_mv	AT rvolz fourdimensionalmesosphericandlowerthermosphericwindfieldsusinggaussianprocessregressiononmultistaticspecularmeteorradarobservations AT jlchau fourdimensionalmesosphericandlowerthermosphericwindfieldsusinggaussianprocessregressiononmultistaticspecularmeteorradarobservations AT pjerickson fourdimensionalmesosphericandlowerthermosphericwindfieldsusinggaussianprocessregressiononmultistaticspecularmeteorradarobservations AT jpvierinen fourdimensionalmesosphericandlowerthermosphericwindfieldsusinggaussianprocessregressiononmultistaticspecularmeteorradarobservations AT jmurco fourdimensionalmesosphericandlowerthermosphericwindfieldsusinggaussianprocessregressiononmultistaticspecularmeteorradarobservations AT mclahsen fourdimensionalmesosphericandlowerthermosphericwindfieldsusinggaussianprocessregressiononmultistaticspecularmeteorradarobservations
_version_	1718425697155809280

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

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