Tracking plasma flows at the solar surface with the Solar Dynamics Observatory
summary
SDO delivers 4k x 4k images at ~1.5 TB per day and Python code implements a vectorized ball tracking optical‑flow method that settles thousands of simulated balls into local minima to follow granules (∼1,000 km, 5–10 min lifetime). Processing a week of data (~1 TB) runs in hours on a CPU, yields calibrated velocity fields, handles empty‑bin relocalization and magnetic magnetogram labeling, and detects flux emergence hours before eruptions — so the claim, tools, numbers and a working, benchmarked pipeline are all explicit.
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chapters (15)
- 0:00Introduction
- 1:29Sun's photosphere and SDO data
- 5:25Local correlation tracking limits
- 6:07Ball tracking for granule motion
- 11:12Velocity fields from granule motion
- 12:41Filtering granule signals
- 14:07Missing bin problem in ball tracking
- 16:16Calibrating tracking accuracy
- 21:17Optimizing ball tracking parameters
- 22:01Visualizing 2D flow patterns
- 24:29Magnetic flux tracking
- 26:36Active regions and sunspots
- 28:00Solar flare precursors case study
- 31:32Optical flow tracking applications
- 33:11Q&A
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