Vortex Flows in the Solar Atmosphere: Automated Identification, Statistical Analysis and Wave Mode Detection. The automated vortex identification used in the MatLab code (50-60% done) can be spitted on three stages: i) pre-processing, ii) velocity field estimation, iii) vortex identification and lifetime estimation. The pre-processing stage removes rapid intensity fluctuations, while preserving the relative counts as much as possible (mainly to remove the mild seeing effects in observations using a Gaussian filter). The velocity field estimation is performed using fast local correlation tracking (FLCT). After velocity identification we applied the well-known approach by Graftieaux et al. (2001) to identify the vortex centres boundaries and lifetime. This work has been presented last time (2016) but still need some effort to be properly documentated (pdf. file is attached (2016 presentation) and can be used partially for presentation this year.). MatLab code is available. In addition, we are developing a code which will be able to identify wave modes (even higher modes) excited by vortex type motion. Ground-based data will be great to test this method to its full potential. This approach is based on the combination of Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD).

• Tool name: FSAAIT (Flows in the Solar Atmosphere: Automated Identification Tool)
• Developers: Ioannis Giagkiozis, Eamon Scullion, Gary Verth, Viktor Fedun
• Main Contact: Ioannis Giagkiozis; Viktor Fedun, v.fedun@sheffield.ac.uk https://arxiv.org/pdf/1706.05428.pdf
• Basic description: Vortex Flows in the Solar Atmosphere
• Language: Matlab ¥ Resource needed to use: Laptop, Power Station
• Host location: Iceberg (HPC facility), The University of Sheffield
• Current status: 90%
• 6-month plan to availability: available
• Status of documentation: n/a
• Test status: fully tested for vortex flows
• How to reference tool in publication: Vortex Flows in the Solar Atmoshpere: Automated Identification and Statistical Analysis Tool, FSAAIT, (The University of Sheffield), https://arxiv.org/pdf/1706.05428.pdf