PREDSTORM AND SOLARWIND2GIC: FORECASTING OF SPACE WEATHER EFFECTS AND GICS WITH PYTHON

Rachel Louise Bailey, C Möstl, U. V. Amerstorfer, T. Amerstorfer, A. J. Weiss, J. Hinterreiter, M. A. Reiss, Dennis Albert

Research output: Contribution to conferencePosterResearchpeer-review

Abstract

PREDSTORM is a package developed as part of the project “Enhanced
lead time for geomagnetic storms”. The aim is to build a framework that
will provide a forecast of the solar wind at the L1 point using a
combination of empirical and machine learning methods. Among the
properties that can then be predicted from the L1 data are the
geomagnetic Dst index and the magnitudes of GICs in power grids.
Original languageEnglish
Number of pages1
Publication statusPublished - 2019
EventMachine Learning in Heliophysics - The Royal Tropical Institute - Koninklijk Instituut voor de Tropen (KIT), Amsterdam, Netherlands
Duration: 16 Sep 201920 Sep 2019
https://ml-helio.github.io/

Conference

ConferenceMachine Learning in Heliophysics
Abbreviated titleML-Helio
CountryNetherlands
CityAmsterdam
Period16/09/1920/09/19
Internet address

Keywords

  • Solar Wind
  • Space Weather
  • Forecast
  • GIC
  • Geomagnetically Induced Currents
  • Low Frequency Currents
  • LFC

ASJC Scopus subject areas

  • Earth and Planetary Sciences(all)

Treatment code (Nähere Zuordnung)

  • Application

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  • Cite this

    Bailey, R. L., Möstl, C., Amerstorfer, U. V., Amerstorfer, T., Weiss, A. J., Hinterreiter, J., ... Albert, D. (2019). PREDSTORM AND SOLARWIND2GIC: FORECASTING OF SPACE WEATHER EFFECTS AND GICS WITH PYTHON. Poster session presented at Machine Learning in Heliophysics, Amsterdam, Netherlands.