Results 271 to 280 of about 246,893 (375)

Geospace environment modeling 2008–2009 challenge: Dst index

open access: yes, 2013
L. Rastätter   +17 more
semanticscholar   +1 more source

Applying Hpo Indices to Empirical Thermospheric Density Models During Geomagnetic Storms

open access: yesEarth and Space Science, Volume 13, Issue 2, February 2026.
Abstract Accurate atmospheric drag modeling is essential for precise orbit determination and prediction of Low Earth Orbit satellites. A key component is the thermospheric density, typically estimated using empirical models driven by geomagnetic activity indices such as the 3‐hr Kp or ap.
Kemin Zhu   +5 more
wiley   +1 more source

The Anemomilos prediction methodology for Dst

open access: yes, 2013
W. Tobiska   +8 more
semanticscholar   +1 more source

Ionosphere‐Thermosphere Coupling in the Northern Polar Region During the May 2024 Geomagnetic Superstorm

open access: yesJournal of Geophysical Research: Space Physics, Volume 131, Issue 2, February 2026.
Abstract The May 2024 superstorm, as the most intense geomagnetic storm since 2003, caused a variety of disturbances in the magnetosphere‐ionosphere‐thermosphere system. This study investigates the long‐lasting electron density depletion in the polar region and the underlying ionosphere‐thermosphere coupling, based on a comprehensive set of ...
Lei Cai   +8 more
wiley   +1 more source

Global Morphology of Chorus Waves in the Outer Radiation Belt and the Effect of Geomagnetic Activity and fpe ${f}_{pe}$/fce ${f}_{ce}$

open access: yesJournal of Geophysical Research: Space Physics, Volume 131, Issue 2, February 2026.
Abstract Whistler‐mode chorus waves play a key role in driving radiation belt dynamics by enabling both acceleration of electrons to relativistic energies as well as their loss into the atmosphere via pitch‐angle scattering. The ratio between the electron plasma frequency (fpe ${f}_{pe}$) and the electron gyrofrequency (fce ${f}_{ce}$) significantly ...
K. A. Bunting   +5 more
wiley   +1 more source

Forecasting Local Ionospheric Parameters Using Transformers

open access: yesJournal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract We present a novel method for forecasting key ionospheric parameters using transformer‐based neural networks. The model provides accurate forecasts and uncertainty quantification of the F2‐layer peak plasma frequency (foF2), the F2‐layer peak density height (hmF2), and total electron content for a given geographic location.
D. J. Alford‐Lago   +4 more
wiley   +1 more source

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