Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density

The satellite acceleration data from the CHAMP, GRACE, GOCE, and Swarm missions provide detailed information on the thermosphere density over the last two decades. Recent work on reducing errors in modelling the spacecraft geometry has greatly reduced scale differences between the thermosphere data...

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Autores principales:	March Günther, van den IJssel Jose, Siemes Christian, Visser Pieter N. A. M., Doornbos Eelco N., Pilinski Marcin
Formato:	article
Lenguaje:	EN
Publicado:	EDP Sciences 2021
Materias:	thermosphere mass density satellite aerodynamics gas-surface interactions atmospheric drag accelerometer Meteorology. Climatology QC851-999
Acceso en línea:	https://doaj.org/article/293278c9709b42adac391ba28bfa095e
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spelling	oai:doaj.org-article:293278c9709b42adac391ba28bfa095e2021-11-08T15:22:54ZGas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density2115-725110.1051/swsc/2021035https://doaj.org/article/293278c9709b42adac391ba28bfa095e2021-01-01T00:00:00Zhttps://www.swsc-journal.org/articles/swsc/full_html/2021/01/swsc210025/swsc210025.htmlhttps://doaj.org/toc/2115-7251The satellite acceleration data from the CHAMP, GRACE, GOCE, and Swarm missions provide detailed information on the thermosphere density over the last two decades. Recent work on reducing errors in modelling the spacecraft geometry has greatly reduced scale differences between the thermosphere data sets from these missions. However, residual inconsistencies between the data sets and between data and models are still present. To a large extent, these differences originate in the modelling of the gas-surface interactions (GSI), which is part of the satellite aerodynamic modelling used in the acceleration to density data processing. Physics-based GSI models require in-situ atmospheric composition and temperature data that are not measured by any of the above-mentioned satellites and, as a consequence, rely on thermosphere models for these inputs. To reduce the dependence on existing thermosphere models, we choose a GSI model with a constant energy accommodation coefficient per mission, which we optimize exploiting particular attitude manoeuvres and wind analyses to increase the self-consistency of the multi-mission thermosphere mass density data sets. We compare our results with those based on variable energy accommodation obtained by different studies and semi-empirical models to show the principal differences. The presented comparisons provide novel opportunity to quantify the discrepancies between current GSI models. Among the presented data, density variations with variable accommodation are within ±10%, and peaks can reach up to 15% at the poles. The largest differences occur during low solar activity periods. In addition, we utilize a series of attitude manoeuvres performed in May 2014 by the Swarm A and C satellites, which are flying in close proximity, to evaluate the residual inconsistency of the density observations as a function of the energy accommodation coefficient. Our analysis demonstrates that an energy accommodation coefficient of 0.85 maximizes the consistency of the Swarm density observations during the attitude manoeuvres. Using such coefficient, for Swarm A and Swarm C, the new density would be lower in magnitude with a 4–5% difference. In recent studies, similar energy accommodation coefficients were retrieved for the CHAMP and GOCE missions by investigating thermospheric winds. These new values for the energy accommodation coefficient provide a higher consistency among different missions and models. A comparison of neutral densities between current thermosphere models and observations indicates that semi-empirical models such as NRLMSISE-00 and DTM-2013 significantly overestimate the density, and that an overall higher consistency between the observations from the different missions can be achieved with the presented assumptions. The new densities from this work provide consistencies of 4.13% and 3.65% between the minimum and maximum mean ratios among the selected missions with NRLMSISE-00 and DTM-2013, respectively. A comparison with the WACCM-X general circulation model is also performed. Similar to the other models, WACCM-X seems to provide higher estimates of mass density especially under high and moderate solar activities. This work has the objective to guide density data users over the multiple data sets and highlight the remaining uncertainties associated with different GSI models.March Günthervan den IJssel JoseSiemes ChristianVisser Pieter N. A. M.Doornbos Eelco N.Pilinski MarcinEDP Sciencesarticlethermospheremass densitysatellite aerodynamicsgas-surface interactionsatmospheric dragaccelerometerMeteorology. ClimatologyQC851-999ENJournal of Space Weather and Space Climate, Vol 11, p 54 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	thermosphere mass density satellite aerodynamics gas-surface interactions atmospheric drag accelerometer Meteorology. Climatology QC851-999
spellingShingle	thermosphere mass density satellite aerodynamics gas-surface interactions atmospheric drag accelerometer Meteorology. Climatology QC851-999 March Günther van den IJssel Jose Siemes Christian Visser Pieter N. A. M. Doornbos Eelco N. Pilinski Marcin Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
description	The satellite acceleration data from the CHAMP, GRACE, GOCE, and Swarm missions provide detailed information on the thermosphere density over the last two decades. Recent work on reducing errors in modelling the spacecraft geometry has greatly reduced scale differences between the thermosphere data sets from these missions. However, residual inconsistencies between the data sets and between data and models are still present. To a large extent, these differences originate in the modelling of the gas-surface interactions (GSI), which is part of the satellite aerodynamic modelling used in the acceleration to density data processing. Physics-based GSI models require in-situ atmospheric composition and temperature data that are not measured by any of the above-mentioned satellites and, as a consequence, rely on thermosphere models for these inputs. To reduce the dependence on existing thermosphere models, we choose a GSI model with a constant energy accommodation coefficient per mission, which we optimize exploiting particular attitude manoeuvres and wind analyses to increase the self-consistency of the multi-mission thermosphere mass density data sets. We compare our results with those based on variable energy accommodation obtained by different studies and semi-empirical models to show the principal differences. The presented comparisons provide novel opportunity to quantify the discrepancies between current GSI models. Among the presented data, density variations with variable accommodation are within ±10%, and peaks can reach up to 15% at the poles. The largest differences occur during low solar activity periods. In addition, we utilize a series of attitude manoeuvres performed in May 2014 by the Swarm A and C satellites, which are flying in close proximity, to evaluate the residual inconsistency of the density observations as a function of the energy accommodation coefficient. Our analysis demonstrates that an energy accommodation coefficient of 0.85 maximizes the consistency of the Swarm density observations during the attitude manoeuvres. Using such coefficient, for Swarm A and Swarm C, the new density would be lower in magnitude with a 4–5% difference. In recent studies, similar energy accommodation coefficients were retrieved for the CHAMP and GOCE missions by investigating thermospheric winds. These new values for the energy accommodation coefficient provide a higher consistency among different missions and models. A comparison of neutral densities between current thermosphere models and observations indicates that semi-empirical models such as NRLMSISE-00 and DTM-2013 significantly overestimate the density, and that an overall higher consistency between the observations from the different missions can be achieved with the presented assumptions. The new densities from this work provide consistencies of 4.13% and 3.65% between the minimum and maximum mean ratios among the selected missions with NRLMSISE-00 and DTM-2013, respectively. A comparison with the WACCM-X general circulation model is also performed. Similar to the other models, WACCM-X seems to provide higher estimates of mass density especially under high and moderate solar activities. This work has the objective to guide density data users over the multiple data sets and highlight the remaining uncertainties associated with different GSI models.
format	article
author	March Günther van den IJssel Jose Siemes Christian Visser Pieter N. A. M. Doornbos Eelco N. Pilinski Marcin
author_facet	March Günther van den IJssel Jose Siemes Christian Visser Pieter N. A. M. Doornbos Eelco N. Pilinski Marcin
author_sort	March Günther
title	Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
title_short	Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
title_full	Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
title_fullStr	Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
title_full_unstemmed	Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
title_sort	gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/293278c9709b42adac391ba28bfa095e
work_keys_str_mv	AT marchgunther gassurfaceinteractionsmodellinginfluenceonsatelliteaerodynamicsandthermospheremassdensity AT vandenijsseljose gassurfaceinteractionsmodellinginfluenceonsatelliteaerodynamicsandthermospheremassdensity AT siemeschristian gassurfaceinteractionsmodellinginfluenceonsatelliteaerodynamicsandthermospheremassdensity AT visserpieternam gassurfaceinteractionsmodellinginfluenceonsatelliteaerodynamicsandthermospheremassdensity AT doornboseelcon gassurfaceinteractionsmodellinginfluenceonsatelliteaerodynamicsandthermospheremassdensity AT pilinskimarcin gassurfaceinteractionsmodellinginfluenceonsatelliteaerodynamicsandthermospheremassdensity
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Gas-surface interactions modelling influence on satellite aerodynamics and thermosphere mass density

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