Automated Sunspot Classification and Tracking using SDO Imagery

Automatic detection and classification of solar sunspots using HMII and HMIM images from NASA's Solar Dynamics Observatory provides the best spatial and time resolution imagery available for sunspot analysis. Verification of a sunspot detection and McIntosh based classification algorithm created by Spahr in 2014 is conducted using SDO data and MATLAB computational software to compare automated detection and classification ability and consistency for time periods leading up to and following the peak activity in solar cycle 24. Changes in solar luminosity and the resulting need to adjust threshold values for correct sunspot detection are discussed. Automated classifications are then compared against sunspot reports from the National Weather Service's Space Weather Prediction Center. In addition, a sunspot tracking algorithm is added to the Spahr code to allow for feature tracking across multiple solar images in order to identify the same feature as it transits the solar disk. Tracking is conducted for one month of data and various timescales are tested to find the optimal length of time between consecutive images for accurate tracking. Feature tracking is a foundational step toward automated analysis of sunspot dynamic morphology and connections to solar events such as solar flares.