Monitoring the thermospheric state is a highly relevant problem due to its impact on ionospheric dynamics and satellite drag. A novel approach based on routine ionosonde observations, originally developed for mid-latitudes (Perrone and Mikhailov, 2018), has proven effective in retrieving a self-consistent set of key aeronomic parameters and is now widely used in our analyses.
The method,...
Abstract
Machine learning (ML) methods are increasingly utilized in space weather research; however, their performance is often limited by sparse observations during extreme events and a lack of physical constraints. Physics-based models, on the other hand, rely on empirical parameterizations and simplifying assumptions that can limit their
performance. Bridging these two approaches offers a...
Spacecraft do not sample Earth’s magnetosphere uniformly. Measurements cluster along orbital tracks and mission-targeted regions, making it hard to build global maps of plasma environments directly from data. In this talk, I will describe a fully data-driven approach for constructing a magnetospheric atlas from a decade of NASA Magnetospheric Multiscale (MMS) observations. We first compress...
Operational space weather forecasting requires magnetospheric models that balance physical accuracy with computational feasibility. Magnetohydrodynamic (MHD) simulations are affordable in terms of computational cost but miss critical kinetic effects, while hybrid/kinetic models provide accuracy at prohibitive costs.
We intend to pursue a novel approach based on the existing muphy2 framework....
Accurate specification and forecasting of Earth’s radiation belt electron environment remain critical for understanding radiation belt dynamics and mitigating space weather hazards to spacecraft. The Versatile Electron Radiation Belt (VERB) code has become a widely used physics-based model for simulating the evolution of relativistic electrons through radial diffusion and wave–particle...
