Probabilistic prediction of Dst storms one-day-ahead using full-disk SoHO images
| Autor: | A. Hu, C. Shneider, A. Tiwari, E. Camporeale |
|---|---|
| Přispěvatelé: | Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Earth and Planetary Astrophysics (astro-ph.EP)
Atmospheric Science Geomagnetic storm Solar images FOS: Physical sciences Space Physics (physics.space-ph) Physics - Space Physics Astrophysics - Solar and Stellar Astrophysics Machine learning Dst Astrophysics - Instrumentation and Methods for Astrophysics Instrumentation and Methods for Astrophysics (astro-ph.IM) Ensemble method Solar and Stellar Astrophysics (astro-ph.SR) Astrophysics - Earth and Planetary Astrophysics |
| Zdroj: | Space Weather, 20(8), e2022SW003064.1-e2022SW003064.17 |
| Popis: | 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. $Dst$ provides essential information about the strength of the ring current around the Earth caused by the protons and electrons from the solar wind, and it is routinely used as a proxy for geomagnetic storms. The model is developed using an ensemble of Convolutional Neural Networks (CNNs) that are trained using SoHO images (MDI, EIT and LASCO). The relationship between the SoHO images and the solar wind has been investigated by many researchers, but these studies have not explicitly considered using SoHO images to predict the $Dst$ index. This work presents a novel methodology to train the individual models and to learn the optimal ensemble weights iteratively, by using a customized class-balanced mean square error (CB-MSE) loss function tied to a least-squares (LS) based ensemble. The proposed model can predict the probability that Dst accepted by journal |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|