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

ABSTRACT: Machine learning (ML) has been applied to space weather problems with increasing frequency in recent years, driven by an influx of in-situ measurements and a desire to improve modeling and forecasting capabilities throughout the field. Space weather originates from solar perturbations and is comprised of the resulting complex variations they cause within the numerous systems between the Sun and Earth. These systems are often tightly coupled and not well understood. This creates a need for skillful models with knowledge about the confidence of their predictions. One example of such a dynamical system highly impacted by space weather is the thermosphere, the neutral region of Earth's upper atmosphere. Our inability to forecast it has severe repercussions in the context of satellite drag and

SUBMITTER: Licata RJ 

PROVIDER: S-EPMC9068719 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature