Visual anemometry for physics-informed inference of wind

Abstract

Accurate measurements of atmospheric flows at metre-scale resolution are essential for many sustainability applications, including optimal design of wind and solar farms, navigation and control of air flows in the built environment, monitoring of environmental phenomena such as wildfires and air pollution dispersal, and data assimilation into weather and climate models. Measurement of the relevant multiscale wind flows is inherently challenged by the optical transparency of the wind. This Perspective article 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, although wind itself is transparent, its effect can be seen in the motion of objects embedded in the environment and subjected to wind — swaying trees and flapping flags are commonly encountered examples. We survey emerging efforts to accomplish visual anemometry, the task of quantitatively inferring local wind conditions on the basis of 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 are discussed. Visual anemometry measures winds using observations of associated environmental flow–structure interactions such as swaying trees and flapping flags. This Perspective article outlines opportunities for physics and data science to further develop visual anemometry for renewable energy, urban sustainability and environmental science.