Earth Scientist
Earth Scientist

Biography

Dr. Wang has a background in atmospheric sciences and mechanical engineering. He joined PNNL in 2022 as a postdoctoral research associate and transitioned to an Earth Scientist in 2023. Dr. Wang currently studies turbulence in the atmosphere using large-eddy simulation, direct numerical simulation, and machine learning. His work helps improve near-surface turbulence models and increase our understanding of turbulence in the atmospheric boundary layer, tornadoes, and cloud-aerosol-turbulence interactions.

Education

  • 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

Affiliations and Professional Service

  • American Geophysical Union Annual Meeting – Session Chair for Boundary Layer Processes and Turbulence
  • American Geophysical Union Annual Meeting – OSPA Liaison for Boundary Layer Processes and Turbulence

Awards and Recognitions

  • EBSD 2024 BESTie Award: Core Values - Impact, Pacific Northwest National Laboratory, 2024
  • John C. Wyngaard Graduate Research Award, The Pennsylvania State University, 2022

Publications

Google Scholar profile

  • Wang, A., M. Ovchinnikov, F. Yang, W. Cantrell, J. Yeom, and R. A. Shaw, 2024: The Dual Nature of Entrainment-Mixing Signatures Revealed through Large-Eddy Simulations of a Convection-Cloud Chamber. Journal of the Atmospheric Sciences, 81, 2017–2039, https://doi.org/10.1175/JAS-D-24-0043.1.
  • Wang, A., S. Krueger, S. Chen, M. Ovchinnikov, W. Cantrell, and R. A. Shaw, 2024: Glaciation of mixed-phase clouds: insights from bulk model and bin-microphysics large-eddy simulation informed by laboratory experiment. Atmos. Chem. Phys., 24, 10245–10260, https://doi.org/10.5194/acp-24-10245-2024.
  • Wang, A., X. I. A. 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, 435–458, https://doi.org/10.1175/JAS-D-23-0094.1.
  • 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, e2023MS003734, https://doi.org/10.1029/2023MS003734.