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Uncertainty quantification techniques for data-driven space weather modeling: thermospheric density application.

Richard J Licata¹, Piyush M Mehta²

¹Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, WV, 26506, USA. rjlicata@mix.wvu.edu.

Scientific Reports

|May 4, 2022

Summary

This summary is machine-generated.

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Machine learning models now predict thermospheric density with reliable uncertainty estimates. This improves space weather forecasting and satellite collision risk assessment for low Earth orbit operations.

Area of Science:

Space Physics and Aeronomy
Machine Learning Applications
Atmospheric Science

Background:

Space weather, driven by solar activity, significantly impacts Earth's upper atmosphere (thermosphere).
Accurate forecasting of thermospheric neutral mass density is crucial for satellite drag prediction and collision avoidance in low Earth orbit (LEO).
Current models often lack reliable uncertainty estimates, hindering operational decision-making.

Purpose of the Study:

To develop nonlinear machine learning (ML) regression models for predicting thermospheric density.
To provide robust and reliable uncertainty estimates alongside density predictions.
To evaluate the performance of ML models using both local and global datasets.

Main Methods:

Implemented two ML techniques: Monte Carlo (MC) dropout and direct probability distribution prediction.

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Utilized the negative logarithm of predictive density (NLPD) loss function for model training.

Trained and tested models on both local (CHAllenging Minisatellite Payload - CHAMP) and global (High Accuracy Satellite Drag Model - HASDM) density datasets.

Main Results:

Both ML techniques, using NLPD loss, yielded accurate thermospheric density predictions with well-calibrated uncertainty estimates.
The direct probability distribution prediction method demonstrated a significantly lower computational cost.
Global models achieved ~11% error on independent test data, while CHAMP models showed ~13% error, with excellent calibration across prediction intervals.

Conclusions:

Proposed ML models offer a promising approach for accurate thermospheric density forecasting with crucial uncertainty quantification.
These advancements can enhance space situational awareness and improve the safety of space operations in LEO.
The developed models provide a valuable tool for global density predictions and their associated uncertainties.