Generation of a Solar Wind Ensemble for Space Weather Forecasting

Abstract:

Knowing the upstream solar wind conditions is essential in forecasting the variations in the geomangetic field and the status of the Earth's ionosphere. Most data-driven simulations or data-assimilation codes, used for space weather forecasting, are based on the solar wind measurements at 1 AU, or more specifically at the first Lagrangian orbit (L1), such as observations from the Advanced Composition Explorer (ACE). However, L1 measurements may not represent the solar wind conditions just outside the magnetosphere. As a result, time-series measurements from L1 by themselves are not adequate to run simulations to derive probabilistic forecasts of the magnetosphere and ionosphere. To obtain confidence levels and uncertainty estimates, a solar wind ensemble data set is desirable. Therefore we used three years of measurements atACE advected using the flat delay method to the Interplanetary Monitoring Platform (IMP8) spacecraft location. Then, we compared both measurements to establish Kernel Density Estimation (KDE) functions for IMP8 measurements based on ACE measurements. In addition, we used a 4-categorization scheme to sort the incoming solar wind into ejecta, coronal-hole-origin, sector-reversal-regions, and streamer-belt-origin categories at both ACE and IMP8. We established the KDE functions for each category and compared with the uncategorized KDE functions. The location of the IMP8 spacecraft allows us to use these KDE functions to generate ensemble of solar wind data close to Earth's magnetopause. The ensemble can then be used to forecast the state of the geomagnetic field and the ionosphere.