1.7k citations
- University of Colorado BoulderUS109 papers
- National Oceanic and Atmospheric AdministrationUS39 papers
- NOAA National Centers for Environmental InformationUS23 papers
- Goddard Space Flight CenterUS18 papers
- NOAA Space Weather Prediction Center16 papers
- Centre National de la Recherche ScientifiqueFR12 papers
- Centrum Wiskunde & InformaticaNL11 papers
- NSF National Center for Atmospheric ResearchUS11 papers
- NOAA Earth System Research LaboratoryUS9 papers
- Royal Observatory of BelgiumBE9 papers
- Southwest Research InstituteUS9 papers
- Laboratory for Atmospheric and Space PhysicsUS8 papers
13 papers · 1 filter
Advancing Heliophysics and Space Weather Modeling through Open Science
C. Corti, M. M. Kuznetsova, M. A. Reiss +85
We present a community-wide effort to develop a strategy and action plan to advance heliophysics and space weather modeling through open science. While open science has the potenti…
GIC--Related Observations During the May 2024 Geomagnetic Storm in the United States
L. A. Wilkerson, R. S. Weigel, D. Thomas +6
The May 2024 geomagnetic storm was one of the most severe in the past 20~years. Understanding how large geomagnetic disturbances (GMDs) impact geomagnetically induced currents (GIC…
Multi-Hour Ahead Dst Index Prediction Using Multi-Fidelity Boosted Neural Networks
A. Hu, E. Camporeale, B. Swiger
The Disturbance storm time (Dst) index has been widely used as a proxy for the ring current intensity, and therefore as a measure of geomagnetic activity. It is derived by measurem…
Probabilistic prediction of Dst storms one-day-ahead using Full-Disk SoHO Images
A. Hu, C. Shneider, A. Tiwari +1
We present a new model for the probability that the Disturbance storm time (Dst) index exceeds -100 nT, with a lead time between 1 and 3 days. provides essential information…
Parameter Distributions for the Drag-Based Modeling of CME Propagation
Gianluca Napoletano, Raffaello Foldes, Enrico Camporeale +7
In recent years, ensemble modeling has been widely employed in space weather to estimate uncertainties in forecasts. We here focus on the ensemble modeling of CME arrival times and…
Toward a Next Generation Particle Precipitation Model: Mesoscale Prediction Through Machine Learning (a Case Study and Framework for Progress)
Ryan M. McGranaghan, Jack Ziegler, Téo Bloch +7
We advance the modeling capability of electron particle precipitation from the magnetosphere to the ionosphere through a new database and use of machine learning (ML) tools to gain…