  Accurate measurements of atmospheric flows at meter-scale resolution are
essential for a broad range of sustainability applications, including optimal
design of wind and solar farms, safe and efficient urban air mobility,
monitoring of environmental phenomena such as wildfires and air pollution
dispersal, and data assimilation into weather and climate models. Measurement
of the relevant microscale wind flows is inherently challenged by the optical
transparency of the wind. This review explores new ways in which physics can be
leveraged to "see" environmental flows non-intrusively, that is, without the
need to place measurement instruments directly in the flows of interest.
Specifically, while the wind itself is transparent, its effect can be visually
observed in the motion of objects embedded in the environment and subjected to
wind -- swaying trees and flapping flags are commonly encountered examples. We
describe emerging efforts to accomplish visual anemometry, the task of
quantitatively inferring local wind conditions based on the physics of observed
flow-structure interactions. Approaches based on first-principles physics as
well as data-driven, machine learning methods will be described, and remaining
obstacles to fully generalizable visual anemometry will be discussed.
