The effectiveness of Data Assimilation for Space Weather forecasting - Dealing with a sparse and limited coverage of the solar system

Δευτέρα 17 Φεβρουαρίου 2020, 14:00-15:00

 

ΣΧΟΛΗ ΘΕΤΙΚΩΝ ΕΠΙΣΤΗΜΩΝ ΕΚΠΑ
ΤΜΗΜΑ ΦΥΣΙΚΗΣ
ΤΟΜΕΑΣ ΑΣΤΡΟΦΥΣΙΚΗΣ ΑΣΤΡΟΝΟΜΙΑΣ ΚΑΙ ΜΗΧΑΝΙΚΗΣ

Σ Ε Μ Ι Ν Α Ρ Ι Ο

Δευτέρα 17 Φεβρουαρίου 2020, 14:00-15:00
Αίθουσα Διαλέξεων Τομέα Αστροφυσικής, Αστρονομίας και Μηχανικής

The effectiveness of Data Assimilation for Space Weather forecasting
Dealing with a sparse and limited coverage of the solar system

Dr Dimitrios Millas
Plasma-astrophysics Section, KU Leuven, Belgium

Abstract:
Data assimilation has been effectively used in meteorological studies and may also become a promising technique to facilitate Space Weather research.
The main drivers for interplanetary space conditions are the solar wind and the solar activity in general. Any long term variation or strong disturbance in these two phenomena leads in turn to measurable events and may affect the conditions near Earth. The economic and technological impact is known to be, in some cases, severe (e.g. on satellites). Over the last decades, the availability of data on the solar wind, coronal mass ejections (CMEs) and other energetic phenomena on the Sun greatly increased, thus improving our understanding of the implications on Earth. However, the ability to forecast and prevent the destructive effects of these phenomena is still quite limited. In short, we stand nowadays in need of reliable forecasting; this may be achieved by an optimal way of handling the available data, improving existing models and by designing the future space missions accordingly.
In this work we apply data assimilation (DA) methods to a series of magnetohydrodynamic (MHD) simulations or empirical models. The project consists of the following, independent parts:
propagation of a CME against a steady background solar wind
the terrestrial magnetosphere
We use typically observed values to model the CMEs and satellite date to recreate the magnetosphere. Then we randomly perturb them to create an ensemble of ''virtual'' data. We then process the data using the representer analysis method. For the simulations we used, in different parts of the project, the MHD codes PLUTO, MPI-AMRVAC, OpenGGCM and EUFHORIA.
We discuss possible improvements via direct observations and present potential applications of this procedure in the planning of future space missions.

This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA, www.aida-space.eu). Support from the AFRL/USAF project (AFRL Award No. FA9550-14-1-0375, 2014-2019) is acknowledged.

---