Topical Discussion Meeting - Big-data processing and modelling of solar activities and space weather forecasting

Jiajia Liu (University of Sheffield); Xin Huang (National Astronomical Observatories of China); Marianna Korsos (University of Sheffield); Long Xu (National Astronomical Observatories of China); Robert Erdelyi (University of Sheffield)
Thursday 21/11, 14:00-15:15

Solar activities, especially eruptive events including flares, coronal mass ejections or fast changes in solar wind conditions could induce severe disturbances in the geospace environment. These interruptions could interfere with our communication systems, affect the precision of GPS services, obstruct the smooth operation of satellites and endanger the safety of astronauts. Therefore, the accurate and reliable forecasting of the eruptive solar activities is paramount important to minimize potential risk to our socio-economics.

Up to now, thanks to the fast-growing data acquisition in solar and inter-planetary space observations, large amount of observational solar data are now available. Data mining, machine learning (including artificial intelligence, AI) technologies can help researchers to i) cope with the very rapidly growing amount of observational solar data and ii) discover potential new knowledge from the big data. Furthermore, the enormous amount of solar data enables us to build new and more reliable forecasting models for solar activity prediction. This TDM is dedicated to explore the underlying data processing and modelling technics of predicting solar activities, including, but not limited to, solar data retrieval, automated detection and tracking of solar activities and the construction of solar activity prediction models.