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Swirling motions in the solar atmosphere have been widely observed in recentyears and suggested to play a key role in channeling energy from thephotosphere into the corona. Here, we present a newly-developed Automated SwirlDetection Algorithm (ASDA) and discuss its applications. ASDA is found to bevery proficient at detecting swirls in a variety of synthetic data with variouslevels of noise, implying our subsequent scientific results are astute.Applying ASDA to photospheric observations with a spatial resolution of 39.2 kmsampled by the Solar Optical Telescope (SOT) on-board Hinode, suggests a totalnumber of $1.62\times10^5$ swirls in the photosphere, with an average radiusand rotating speed of $\sim290$ km and $< 1.0$ km s$^{-1}$, respectively.Comparisons between swirls detected in Bifrost numerical MHD simulations andboth ground-based and space-borne observations, suggest that: 1) the spatialresolution of data plays a vital role in the total number and radii of swirlsdetected; and 2) noise introduced by seeing effects could decrease thedetection rate of swirls, but has no significant influences in determiningtheir inferred properties. All results have shown that there is no significantdifference in the analysed properties between counter-clockwise or clockwiserotating swirls. About 70% of swirls are located in intergranular lanes. Mostof the swirls have lifetimes less than twice of the cadences, meaning futureresearch should aim to use data with much higher cadences than 6 s. In theconclusions, we propose some promising future research applications where ASDAmay provide useful insights.

Automated Swirl Detection Algorithm (ASDA) and Its Application to Simulation and Observational Data