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  • Perspective
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Visual anemometry for physics-informed inference of wind

Nature Reviews Physics volume 5, pages 597–611 (2023)Cite this article

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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.

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Fig. 1: Physics of flow–structure interactions.
Fig. 2: Data-driven implementation of visual anemometry based on measurements collected at a research field site and in a laboratory wind tunnel.
Fig. 3: Large-scale wind tunnel measurements of vegetation under controlled wind conditions.
Fig. 4: Compilation of visual anemometry measurements of eight vegetation species.

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Acknowledgements

The authors gratefully acknowledge seminal contributions from J.L. Cardona in development of several of the concepts presented in this Perspective article, as well as discussions with K. Bouman, J. Sun, Y. Yue and P. Perona at Caltech. Additional helpful discussions occurred in the CV4Ecology Summer Workshop, supported by the Caltech Resnick Sustainability Institute. Constructive feedback from the anonymous reviewers led to meaningful improvements to the presentation of the material in this manuscript. Funding was generously provided by the National Science Foundation (Grant CBET-2019712) and the Center for Autonomous Systems and Technologies at Caltech. Additional support from Heliogen is gratefully acknowledged.

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  1. Graduate Aerospace Laboratories, California Institute of Technology, Pasadena, CA, USA

    John O. Dabiri, Matthew K. Fu & Roni H. Goldshmid

  2. Mechanical and Civil Engineering, California Institute of Technology, Pasadena, CA, USA

    John O. Dabiri

  3. Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Michael F. Howland

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Dabiri, J.O., Howland, M.F., Fu, M.K. et al. Visual anemometry for physics-informed inference of wind. Nat Rev Phys 5, 597–611 (2023). https://doi.org/10.1038/s42254-023-00626-8

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