Corpus ID: 237532741

Machine-Learned HASDM Model with Uncertainty Quantification

@article{Licata2021MachineLearnedHM,
  title={Machine-Learned HASDM Model with Uncertainty Quantification},
  author={Richard Joseph Licata and Piyush M. Mehta and W. Kent Tobiska and Snehalata V. Huzurbazar},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.07651}
}
The first thermospheric neutral mass density model with robust and reliable uncertainty estimates is developed based on the SET HASDM density database. This database, created by Space Environment Technologies (SET), contains 20 years of outputs from the U.S. Space Force’s High Accuracy Satellite Drag Model (HASDM), which represents the state-of-the-art for density and drag modeling. We utilize principal component analysis (PCA) for dimensionality reduction, creating the coefficients upon which… Expand

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References

SHOWING 1-10 OF 51 REFERENCES
A methodology for reduced order modeling and calibration of the upper atmosphere
Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is criticalExpand
A Quasi‐Physical Dynamic Reduced Order Model for Thermospheric Mass Density via Hermitian Space‐Dynamic Mode Decomposition
Thermospheric mass density is a major driver of satellite drag, the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO) pertinent to spaceExpand
High Accuracy Satellite Drag Model (HASDM)
The dominant error source in force models used to predict low perigee satellite trajectories is atmospheric drag. Upper atmospheric density models do not adequately account for dynamic changes inExpand
Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation
TLDR
This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship. Expand
Thermosphere modeling capabilities assessment: geomagnetic storms
The specification and prediction of density fluctuations in the thermosphere, especially during geomagnetic storms, is a key challenge for space weather observations and modeling. It is of greatExpand
Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation
Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. To improve orbit prediction, real-time density estimation is required. In this work, weExpand
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
TLDR
A new theoretical framework is developed casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes, which mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. Expand
New modes and mechanisms of thermospheric mass density variations from GRACE accelerometers
Monitoring and understanding the upper atmosphere processes is important for orbital decay and space physics. Nowadays, Low-Earth-Orbit (LEO) accelerometers provide a unique opportunity to studyExpand
A New Empirical Thermospheric Density Model JB2008 Using New Solar and Geomagnetic Indices
Abstract : A new empirical atmospheric density model, Jacchia-Bowman 2008, is developed as an improved revision to the Jacchia-Bowman 2006 model which is based on Jacchia s diffusion equations.Expand
New density estimates derived using accelerometers on board the CHAMP and GRACE satellites
Atmospheric mass density estimates derived from accelerometers onboard satellites such as CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) are crucial inExpand
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