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