Brian Greene is a 4th year graduate student pursuing a PhD at OUs School of Meteorology, where he earned his Master’s in meteorology in 2018. Brian is originally from the suburbs of Chicago and graduated from the University of Illinois in 2016 with a BS in physics and atmospheric sciences. Since moving to Norman, Brian has been working with Professor Phil Chilson and the Center for Autonomous Sensing and Sampling to develop drones for sampling the atmospheric boundary layer. His research has taken him on field projects and conferences across the globe, namely Spain, Finland, Japan, and now his home office.
Operational numerical weather prediction models typically have effective resolutions too coarse to resolve turbulent processes in the planetary boundary layer (PBL). To overcome this restriction, models rely on turbulence closure schemes in PBL parameterizations to represent exchanges of momentum, heat, and moisture in the lower atmosphere. One common implementation of turbulence closure is through Monin-Obukhov similarity theory (MOST), which non-dimensionalizes vertical gradients of momentum, temperature, and water vapor by scaling with estimates of their respective surface fluxes. The MOST scaling relationships and resulting profiles of momentum, heat, and moisture therefore have the advantage of considerably reducing the computational expense of forecasting these parameters. While MOST has been empirically shown to perform well in unstably stratified atmospheres, it is unable to differentiate between near-neutral and strongly stable regimes due to ill-defined scaling parameters based on weak and intermittent fluxes.
Recent studies using large datasets in the stable boundary layer (SBL; e.g., the SHEBA campaign) have shown success with a turbulence scaling framework based on local vertical gradients of temperature and wind. While formally equivalent to MOST, gradient-based scaling as a function of Richardson number holds several advantages, namely that the scales are well-defined in the SBL and it does not suffer from self-correlation. Therefore, when the stability functions are known, it is possible to estimate vertical profiles of turbulent parameters by only measuring vertical gradients of wind speed and temperature. This task is therefore well-suited to study using in-situ observations from remotely piloted aircraft systems (RPAS).
The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR) field campaigns took place in February 2017 and 2018 on the island of Hailuoto, Finland. This location was chosen specifically for its seasonal sea ice along the coast, allowing for Arctic stable boundary layers to evolve in the evenings. An innovative combination of RPAS, surface eddy covariance towers, sodars, and a Doppler wind lidar was leveraged to improve the conceptual understanding of SBLs over sea ice in the Bothnian Bay.
The present study will begin with an overview of turbulence scaling in the SBL and a description of the 2018 ISOBAR field campaign. Results from gradient-based scaling from eddy covariance observations will be presented, and a framework of how to extend this theory to RPAS observations will be discussed. Finally, representative cases for different stability regimes will be analyzed to gain insight towards how energy is exchanged throughout Arctic SBLs.