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Atmospheric Science & Global Change

Staff information


Aaron Wang

Regional and Cloud Modeling
Earth Scientist
Pacific Northwest National Laboratory
PO Box 999
Richland, WA 99352


Aaron Wang possesses a diverse interest in turbulence with his background in atmospheric sciences and mechanical engineering. His primary tool of choice is Large Eddy Simulation (LES), though his experience extends to field measurements of boundary-layer turbulence in the Arctic. In the past, Aaron has implemented various modeling approaches of near-surface turbulence into meteorological LES models, with a particular focus on exploring the influence of near-surface turbulence on tornado formation. Currently, he is utilizing LES , direct numerical simulations (DNS), and machine learning to improve the turbulence modeling approach and investigate the impact of turbulence on cloud-aerosol interactions.

Research Interests

  • Turbulence modeling
  • Large-eddy simulation
  • Direct Numerical Simulation
  • Machine learning
  • Boundary layer meteorology
  • Mesoscale meteorology
  • Cloud microphysics

Education and Credentials

  • Ph.D., Meteorology and Atmospheric Science, The Pennsylvania State University
  • M.S., Atmospheric Sciences, National Taiwan University
  • B.S., Mechanical Engineering, National Taiwan University
  • B.S., Atmospheric Sciences, National Taiwan University

Awards and Recognitions

  • John C. Wyngaard Graduate Research Award, The Pennsylvania State University, 2022

PNNL Publications


  • Wang A., M. Ovchinnikov, F. Yang, S. Schmalfuß, and R.A. Shaw. 2024. "Designing a Convection-Cloud Chamber for Collision-Coalescence Using Large-Eddy Simulation with Bin Microphysics." Journal of Advances in Modeling Earth Systems 16, no. 1:Art. No. e2023MS003734. PNNL-SA-183698. doi:10.1029/2023MS003734
  • Wang A., X. Yang, and M. Ovchinnikov. 2024. "An Investigation of LES Wall Modeling for Rayleigh-Bénard Convection via Interpretable and Physics-Aware Feedforward Neural Networks with DNS." Journal of the Atmospheric Sciences 81, no. 2:435-458. PNNL-SA-185473. doi:10.1175/JAS-D-23-0094.1

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