High‐Latitude Neutral Density Structures Investigated by Utilizing Multi‐Instrument Satellite Data and NRLMSISE‐00 Simulations

Horvath, Ildiko; Lovell, Brian C.

doi:10.1002/2017ja024600

Cited by 2 publications

(3 citation statements)

References 49 publications

(99 reference statements)

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“…CHAMP can provide almost global TMD observations, including polar areas, thanks to its 87.3° inclination near-circular orbit (Xu et al, 2013). This lends great support to our ability to understand the variation of the TMD during space weather events (e.g., Horvath & Lovell, 2018). The Dst, Ap, or Kp indices are often used in traditional TMD models to indicate the energy injection process during disturbance times (e.g., Bowman et al, 2008;S.…”

Section: Data Descriptionmentioning

confidence: 95%

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

Liu

Chen

et al. 2023

Space Weather

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Understanding the variation of the Thermospheric Mass Density (TMD) is important for solar‐terrestrial physics and applications for spacecraft safety. The thermosphere, as an open system, is impacted by various space environment conditions and has complicated temporal and spatial features. Consequently, TMD observations contain a wealth of multi‐scale feature information. How to extract such information from observations is a challenge that requires ongoing research. It is vital to improving our understanding of the TMD features. Deep learning (DL) can learn complex representations directly from raw data, which makes it a compelling feature extraction and modeling tool for providing a novel perspective for TMD modeling. The Residual Network is used in this study to build a DL model with deep network architecture. The observations of CHAllenging Minisatellite Payload are utilized in the training phase, while the Gravity Recovery and Climate Experiment, High Accuracy Satellite Drag Model and Naval Research Laboratory Mass Spectrometer and Incoherent Scatter radar Extended model are used to evaluate the performance of the DL model. The results reveal that, compared with the shallow model of the typical Multi‐Layer Perceptron, the DL model can better extract multi‐scale features in the observations while retaining generalization capabilities. Controlled simulation experiments allow us to extract the effects of different physical processes, which improves the interpretability of the DL model. It is demonstrated that the DL model can discriminate the physical processes corresponding to the different space environment indices by simulating Equatorial Mass density Anomaly and geomagnetic storms.

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Section: Data Descriptionmentioning

confidence: 95%

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

Liu

Chen

et al. 2023

Space Weather

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“…The NRLMSISE‐00 model is the standard model of the Earth's atmosphere to aid predictions of satellite orbital decay due to atmospheric drag, and it has widely been used in numerous research studies and applications (e.g., Emmert & Picone, 2010, Horvath & Lovell, 2018; Pardini et al., 2006; Pilinski et al., 2011). This model is based on averaged data from several measurement techniques, including mass spectrometers, incoherent scatter radars, satellite drag data, and solar ultraviolet occultation.…”

Section: Methodsmentioning

confidence: 99%

“…This data set is called the SET‐HASDM density database. Due to the high accuracy of the HASDM data set (Tobiska et al., 2021), the differences to these, along with the comparisons to the existing models, will allow a reasonable validation and uncertainty assessment of the new CASSIOPE densities. The NRLMSISE‐00 model is the standard model of the Earth's atmosphere to aid predictions of satellite orbital decay due to atmospheric drag, and it has widely been used in numerous research studies and applications (e.g., Emmert & Picone, 2010, Horvath & Lovell, 2018; Pardini et al., 2006; Pilinski et al., 2011). This model is based on averaged data from several measurement techniques, including mass spectrometers, incoherent scatter radars, satellite drag data, and solar ultraviolet occultation.…”

Section: Methodsmentioning

confidence: 99%

Thermospheric Mass Density Disturbances Due to Magnetospheric Forcing From 2014–2020 CASSIOPE Precise Orbits

Calabia

Jin

2021

JGR Space Physics

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Long‐term thermospheric mass density disturbances due to magnetospheric forcing are not clear due to a lack of measurement data and imprecise models. In this study, the Global Navigation Satellite System (GNSS) precise orbits of CAScade SmallSat and IOnospheric Polar Explorer (CASSIOPE) are used to infer high‐resolution thermospheric mass densities between the years 2014 and 2020. The CASSIOPE densities are comprehensively validated at altitudes from 325 to 425 km at intervals of 25 km with the High Accuracy Satellite Drag Model (HASDM) density database, and further compared with the Naval Research Laboratory Mass Spectrometer and Incoherent Scatter Radar Exosphere/2000 (NRLMSISE‐00) and the Jacchia‐Bowman/2008 (JB2008) empirical models. The CASSIOPE densities are very similar to the HASDM and JB2008 densities, while the NRLMSISE‐00 largely overestimates (∼150%) during low solar‐flux conditions. For density values above ∼10−12 kg/m3, the correlation of CASSIOPE with HASDM is ∼5% better than the models, and the standard deviation is within 10% of the background density. For density values below ∼10−12 kg/m3, systematic errors have shown to reduce the precision of the CASSIOPE densities. By setting geomagnetic contributions to zero in the models, the density disturbances due to magnetospheric forcing are isolated from the CASSIOPE time‐series, allowing investigation into the correlations and time‐delay responses to the models and to the merging electric field (Em). A new linear dependence of the time delay to the Em was found and then parameterized in this study. Time delays occurred at the 4–7 h range during geomagnetic storms, and at 9–11 h during quiet conditions; neither had significant dependence on altitude. The results represent the validation of the first high‐resolution thermospheric mass density estimates inferred from commercial‐off‐the‐shelf GNSS receivers.

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High‐Latitude Neutral Density Structures Investigated by Utilizing Multi‐Instrument Satellite Data and NRLMSISE‐00 Simulations

Cited by 2 publications

References 49 publications

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

Improving the Extraction Ability of Thermospheric Mass Density Variations From Observational Data by Deep Learning

Thermospheric Mass Density Disturbances Due to Magnetospheric Forcing From 2014–2020 CASSIOPE Precise Orbits

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