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Accurate measurements of atmospheric flows at meter-scale resolution areessential for a broad range of sustainability applications, including optimaldesign of wind and solar farms, safe and efficient urban air mobility,monitoring of environmental phenomena such as wildfires and air pollutiondispersal, and data assimilation into weather and climate models. Measurementof the relevant microscale wind flows is inherently challenged by the opticaltransparency of the wind. This review explores new ways in which physics can beleveraged to "see" environmental flows non-intrusively, that is, without theneed to place measurement instruments directly in the flows of interest.Specifically, while the wind itself is transparent, its effect can be visuallyobserved in the motion of objects embedded in the environment and subjected towind -- swaying trees and flapping flags are commonly encountered examples. Wedescribe emerging efforts to accomplish visual anemometry, the task ofquantitatively inferring local wind conditions based on the physics of observedflow-structure interactions. Approaches based on first-principles physics aswell as data-driven, machine learning methods will be described, and remainingobstacles to fully generalizable visual anemometry will be discussed.

Visual anemometry: physics-informed inference of wind for renewable energy, urban sustainability, and environmental science