Thermosphere densities derived from Swarm GPS observations

IJssel, José van den; Doornbos, Eelco; Iorfida, Elisabetta; March, Günther; Siemes, Christian; Montenbruck, Oliver

doi:10.1016/j.asr.2020.01.004

Cited by 56 publications

(56 citation statements)

References 38 publications

(52 reference statements)

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“…In this work, we use the orbit‐average density values derived by Van den IJssel et al., which are more reliable than the local SWARM densities (Van den IJssel et al., 2020). The orbits of the Swarm A and B satellites during the density estimation windows can be found in Table 5.…”

Section: Methodsmentioning

confidence: 99%

“…This is achieved by data assimilation of radar and GPS tracking data in reduced‐order density models by simultaneously estimating the global density and the orbits and ballistic coefficients of multiple objects. To determine the accuracy of the estimated densities they are validated against accurate GPS‐derived thermospheric densities from the Swarm satellites (Van den IJssel et al., 2020). In addition, the densities are compared with NRLMSISE‐00, JB2008, and TLE‐estimated densities.…”

Section: Introductionmentioning

confidence: 99%

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Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Gondelach

Linares

2021

Space Weather

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Gondelach

Linares

2021

Space Weather

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“…Anomalies in the Swarm accelerometer data were noticed early in the mission (Siemes et al., 2016), preventing their use for neutral density determination using established methods (e.g., Bruinsma et al., 2004; Doornbos et al., 2010; Sutton et al., 2007). Instead, GNSS tracking data are used to produce POD solutions of neutral density for the Swarm satellites at a temporal resolution of about 20 min, which is then used to constrain the uncertainties in the accelerometer measurement (van den IJssel et al., 2020).…”

Section: Datasetsmentioning

confidence: 99%

Toward Accurate Physics‐Based Specifications of Neutral Density Using GNSS‐Enabled Small Satellites

Sutton¹,

Thayer

Pilinski

et al. 2021

Space Weather

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Within low-Earth orbit (LEO), a region spanning roughly 100-1,000 km in altitude for the purposes of this study, interactions between man-made satellites and the ambient atmosphere cause large uncertainties in the orbit determination and prediction processes (Berger et al., 2020). During episodic periods of moderate to severe space weather activity, such atmospheric drag uncertainties can amplify by a factor of 2-5 in a matter of minutes to hours (Krauss et al., 2015;Sutton et al., 2005). These uncertainties, when combined with the steadily growing launch rate of small satellites and CubeSats and our advancing ability to track smaller and smaller objects, are poised to overwhelm the U.S. Department of Defense infrastructure currently carrying out the Detect-Track-Catalog mission. Products of this mission are pervasive across the Space Situational Awareness and Space Traffic Management (STM) enterprises and form a critical infrastructure for nearly all space-based activities. Thus, knowledge and prediction of the space environment, particularly the neutral mass density of the thermosphere and lower exosphere, are an essential part of satellite operations within LEO.One of the major obstacles in predicting orbit trajectories hours to days in advance, and in correlating consecutive or irregular object tracking data with a particular orbiting object, comes from the legacy framework used to model the upper atmosphere's state and its interaction with satellites and debris. The current model employed by the Combined Space Operations Center is the High Accuracy Satellite Drag Model (HASDM)

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“…Accurate orbital prediction is needed in order to track the object accurately in both time and space. Tracking of LEO objects is therefore an indispensable task for space agencies (Vallado & Finkleman, 2014;Hejduk & Snow, 2018). The ever-increasing amount of space debris poses problems to active satellites because of increased risk of collisions.…”

Section: Introductionmentioning

confidence: 99%

The Space Weather Atmosphere Models and Indices (SWAMI) project: Overview and first results

Jackson

Bruinsma²,

Negrin

et al. 2020

J. Space Weather Space Clim.

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Space weather driven atmospheric density variations affect low Earth orbit (LEO) satellites during all phases of their operational lifetime. Rocket launches, re-entry events and space debris are also similarly affected. A better understanding of space weather processes and their impact on atmospheric density is thus critical for satellite operations as well as for safety issues. The Horizon 2020 project Space Weather Atmosphere Model and Indices (SWAMI) project, which started in January 2018, aims to enhance this understanding by: Developing improved neutral atmosphere and thermosphere models, and combining these models to produce a new whole atmosphere model. Developing new geomagnetic activity indices with higher time cadence to enable better representation of thermospheric variability in the models, and improving the forecast of these indices. The project stands out by providing an integrated approach to the satellite neutral environment, in which the main space weather drivers are addressed together with model improvement. The outcomes of SWAMI will provide a pathway to improved space weather services as the project will not only address the science issues, but also the transition of models into operational services. The project aims to develop a unique new whole atmosphere model, by extending and blending the Unified Model (UM), which is the Met Office weather and climate model, and the Drag Temperature Model (DTM), which is a semi-empirical model which covers the 120–1500 km altitude range. A user-focused operational tool for satellite applications shall be developed based on this. In addition, improved geomagnetic indices shall be developed and shall be used in the UM and DTM for enhanced nowcast and forecast capability. In this paper, we report on progress with SWAMI to date. The UM has been extended from its original upper boundary of 85 km to run stably and accurately with a 135 km lid. Developments to the UM radiation scheme to enable accurate performance in the mesosphere and lower thermosphere are described. These include addition of non-local thermodynamic equilibrium effects and extension to include the far ultraviolet and extreme ultraviolet. DTM has been re-developed using a more accurate neutral density observation database than has been used in the past. In addition, we describe an algorithm to develop a new version of DTM driven by geomagnetic indices with a 60 minute cadence (denoted Hp60) rather than 3-hourly Kp indices (and corresponding ap indices). The development of the Hp60 index, and the Hp30 and Hp90 indices, which are similar to Hp60 but with 30 minute and 90 minute cadences, respectively, is described, as is the development and testing of neural network and other machine learning methods applied to the forecast of geomagnetic indices.

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Thermosphere densities derived from Swarm GPS observations

Cited by 56 publications

References 38 publications

Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Real‐Time Thermospheric Density Estimation via Radar and GPS Tracking Data Assimilation

Toward Accurate Physics‐Based Specifications of Neutral Density Using GNSS‐Enabled Small Satellites

The Space Weather Atmosphere Models and Indices (SWAMI) project: Overview and first results

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