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Related Concept Videos

Boundary Layer Characteristics01:18

Boundary Layer Characteristics

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When a fluid encounters a solid surface, a boundary layer forms due to the interaction between the fluid's motion and the stationary surface. This phenomenon is characterized by a thin region adjacent to the surface where viscous forces dominate, influencing the fluid's velocity profile. The development of the boundary layer begins at the leading edge of the surface and evolves as the fluid moves downstream.As the fluid flows over the surface, friction between the fluid and the wall slows down...
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The study of external flow is essential for creating structures and objects that interact efficiently and safely with moving fluids, such as air or water. When a body is immersed in a flowing fluid, it experiences two primary forces: drag, which opposes motion along the flow direction, and lift, which acts perpendicular to the flow. The shape, size, and orientation of the object influence these forces.Streamlined and Blunt Bodies in External FlowObjects in fluid flow are classified as...
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Updated: Jan 8, 2026

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Trends in vertical wind velocity variability reveal cloud microphysical feedback.

Donifan Barahona1, Katherine H Breen2,3, Derek Ngo4

  • 1Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA. donifan.o.barahona@nasa.gov.

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Machine learning predicts enhanced atmospheric turbulence, indicated by increased vertical wind velocity standard deviation (σW). This turbulence influences cloud formation and has slightly mitigated greenhouse warming since 1900.

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Area of Science:

  • Atmospheric Science
  • Climate Science
  • Cloud Physics

Background:

  • Vertical air motion is crucial for aerosol activation into cloud droplets and ice crystals.
  • Atmospheric models struggle to accurately represent sub-kilometer wind motion, impacting cloud simulations.
  • Machine learning offers a potential solution for modeling these microscale atmospheric processes.

Purpose of the Study:

  • To predict the spatial standard deviation in vertical wind velocity (σW) using a generative machine learning technique.
  • To identify trends in atmospheric turbulence and their drivers.
  • To quantify the radiative impact of associated cloud microphysical changes.

Main Methods:

  • Utilized a generative technique combining storm-resolving simulations, observational data, and climate reanalysis.
  • Applied machine learning to predict the spatial standard deviation in vertical wind velocity (σW).
  • Analyzed trends in σW and linked them to shifts in water vapor, temperature, and convection.

Main Results:

  • Significant trends in σW were observed, with increases up to 1%yr⁻¹ in oceanic regions, indicating enhanced atmospheric turbulence.
  • Attributed these trends to global shifts in water vapor, temperature, and convection.
  • Identified a feedback loop between warming, turbulence, and cloud hydrometeor activation.

Conclusions:

  • Enhanced atmospheric turbulence, driven by global climate shifts, influences cloud formation.
  • This effect has led to a radiative impact of approximately -0.10 ± 0.04 Wm⁻² since 1900.
  • The microphysical changes in clouds have slightly mitigated greenhouse warming.