From Soteria

The project has the goal of using at best the information of solar magnetograms to produce a more accurate prediction of the solar wind properties, speed and magnetic field, at 1 AU (orbit of the Earth) and beyond (e.g. interest has been expressed by DTU to extend the model up to include Mars).

The procedure selected is to consider normal solar wind conditions without the effect of coronal mass ejections. The results of our study will help predict the conditions of the solar wind and provide also a more reliable environment in which to study coronal mass ejections. However the study of coronal mass ejections is not part of the project. Our goal is to demonstrate in one realistic but limited physics problem the effectiveness of data assimilation methods. It is a shared opinion of many researchers in the field that data driven physics-based forecasting of CMEs maybe be a premature goal as the detail of data available for the solar conditions is insufficient to constraint reliable predictive simulations. That does not mean that specific events with appropriate heuristic models of CME initiations cannot be done. They can and have been done. But are not yet a operational space weather forecasting tool and will not be so until more information of the solar flow and magnetic field becomes available.

Goal

So our goal is at the same time state of the art and realistic. The prediction of the solar wind speed at the Earth and of the field polarity (but not its intensity) is daily provided by a number of institutions. Our effort considered specifically the NOAA approach (WSA Model at NOAA). We plan to expand on the current state of the art in two ways:

1. We use a full dynamic MHD approach to model the solar wind expansion from the so-called source surface outward. This compares with the effective but not-physically supported approach of the WSA model. Other similar approaches have been already documented and we will keep close collaboration with other teams progressing on a similar path.
2. We will improve the predictive capability by employing data assimilation statistical methods (Kalman filters) to improve the input information of the model, i.e. the solar wind speed and magnetic field on the source surface.

Plan

To accomplish this plan we have designed the following steps:

1. Set-up of a simplified 2D geometry problem (the metric tensor is chosen euclidean) to have a quick run that allows the implementation of the rest of the project. While the geometry is simplifies, the model includes the full physics and realistic values for the magnetic and flow field. We call this simplify geometry problem, model A.
2. We drive model A with a set of spherical harmonics for the magnetic field at the source surface obtained from selected observations from the GONG project.
3. We choose one specific series of data from GONG and compute with model A the output (wind speed and magnetic field) at 1AU. We consider this our ideal run.
4. We apply then the method of Kalman filters to analyse a set of simulations based on a set of input data artificially modified. We know of course the answer but we want the Kalman filters method to find it.
5. We will then remove the geometric limitations and re-analyse the performance of the method
6. We apply the method to real data which means that as required output of the model we will no longer use an ideal simulation but real data from observations taken by ACE at the L1 location.