"Towards a Data-driven Multi-scale Plasma Modeling Synthesis"
Registration is: closed (for last minute business, write to the organizers: trier }at{ nbi . ku . dk).
Due to space constraints the number of participants is limited to 35, on a first come first served basis.
Limited travel/lodging support is available; will be dispatched on the basis of documented need.
Please contact trier {at} nbi . ku . dk for additional information
The fundamentally most important source of inner heliospheric plasma physics and space weather is the active Sun, its solar active region eruptions. Prediction of the evolution and influence of solar active regions on solar storms in the near-Earth environment is of particular interest to several forecasting institutions, industrial stakeholders, and the public in general.
State-of-the-art solar storm prediction tools are limited to monitoring solar active regions, registering eruptions and mass ejections while attempting, then, at extrapolating subsequent evolution and spatio-temporal propagation: no realistic physics-based and data-driven synthesis tool exists, which is capable of predicting when a solar flare will be triggered, or when a Coronal Mass Ejection will be launched into inter-planetary space. In short, we are not yet able to answer the question: When and how do solar storms launch?
By and large, this fact stem from lack of sufficiently detailed knowledge of the plasma physical conditions and processes which govern solar active regions, and more generally also the proximate solar environment.
- The sub-surface magneto-convective origins of critically stressed magnetized structures — which lead to flaring and CMEs — are not directly observable. We must here rely on predictions -- dominated by modeling effort -- rather than observations, through next-to-fully synthetic computational physics.
- A successful predictive model is forced to include extreme ranges of spatio-temporal scales. The influence and coupling of such scales remain poorly understood, despite ample availability of observational data. Because of this, we must rely heavily on observational resources for predictive data-driven modeling.
