Speaker
Description
Space weather forecasting needs fast and reliable prediction of the solar wind in the inner heliosphere. The complex physics of the corona, the turbulent heating and acceleration of the solar wind are thus often bypassed using empirical models driven by observations. In 2023, we introduced a novel technique based on white light coronagraph observations made by LASCO C2 to infer the state of the solar wind at 0.1 AU and propagate it to Earth orbit using an MHD model. This model, HelioCast, has been shown to be very efficient during solar minimum (2018), performing better than full MHD models of the corona for a much lower numerical cost. Yet, as the solar cycle rises, fast varying solar magnetic structures renders white light Carrington maps extracted from the coronagraph increasingly difficult to handle. In this work, we assess quantitatively the performance of this method in the rising solar cycle and compare it with other kind of input, for instance obtained through a PFSS extrapolation of the observed solar magnetic field. We test a hybrid approach combining the data from the coronagraph and magnetograms that show more consistent performances from 2018 to 2024. Finally, we assess the effect of these solar wind solutions on CME propagation.
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