WSA-ENLIL + Cone ensemble modeling of an Earth-directed ICME : Comparison with in-situ observations by Solar Orbiter and WIND at L1

Sammanfattning: Coronal mass ejections (CMEs) are considered the most energetic phenomenon in the heliosphere. Originated in the solar corona, they are formed by ejected plasma driven by strong magnetic fields. Studying the effects of CMEs on Earth and the interplanetary medium has become priority, since ground- and space-based technology can be affected by strong CMEs. Modeling of CMEs can provide an estimation of the arrival time, and WSA-ENLIL + Cone model is one of the most used models in space weather forecasting around the world. The WSA-ENLIL + Cone model is based on the characterization of the observed coronagraph image of a CME as a projected cone in the plane-of-sky (POS), the use of a synoptic magnetogram and the magnetohydrodynamical approximation to model CME events. In this project, we will study a particular event occurred on the 2021-11-03 and compare it with simulated data using the WSA-ENLIL + Cone model. The main objective is to study the input parameters of the model and assess the forecasting ability of the simulations. To do so, we have used in-situ data obtained from the Solar Orbiter spacecraft (SolO) and WIND spacecraft, that at the time of the event were located over the same line-of-sight, being located at 0.8 AU and the L1 Lagrange point, respectively. A total of 144 runs (divided in 6 ensembles of 24 simulated runs) were provided by the Met Office (UK) for each spacecraft location. Each simulation run has been generated using preset input parameters with small random variations and a different background synoptic magnetogram. Statistical analysis has been carried out, showing a linear relationship between the half-width and the arrival velocity of the CME. No particular relationship has been found between the input parameters and the time of arrival (ToA) of simulated runs, probably due to the small range of variation. Late initialized synoptic maps seems to produce better ToA prediction.