Lead Supervisor: Miguel Teixeira, Department of Meteorology, University of Reading
Co-supervisors: Bob Plant, Department of Meteorology, University of Reading; Steve Derbyshire, Met Office; Ivana Stiperski, University of Innsbruck, Austria
Computer models used to predict the weather (known as numerical weather prediction or NWP models) and climate (known as climate models) do not simulate the layer of the atmosphere nearest to the ground, known as the atmospheric boundary layer (ABL), accurately by default, because their computational grids are too coarse to represent the turbulence that transports most flow properties across that layer of the atmosphere. The ABL regulates the fluxes of momentum, heat, humidity and pollutants that determine the lower boundary conditions in NWP and climate models, and therefore the resolved profiles of the mean wind, temperature, humidity and pollutant concentrations, and their variability, near the ground. The surface layer (SL), which corresponds to the lowermost 10% of the ABL, is described by classical Monin-Obukhov Similarity Theory (MOST), which is applicable in a comprehensive set of meteorological conditions over flat terrain. The physics of the SL in NWP or climate models must be approximated using so-called parametrizations and, given its success, these parametrizations are almost universally based on MOST. However, this approach, which assumes statistically horizontally homogeneous flow, has been shown to produce large errors over mountainous terrain, and more generally over horizontally heterogenous terrain.
The ultimate aim of this PhD is to develop a new paradigm to represent the SL, to be able to apply an appropriate lower boundary condition in NWP and climate models over mountainous terrain. Some progress has been made in this direction by Stiperski et al. (2019, 2023), who showed that MOST can be adapted so that it becomes more accurate over mountainous terrain, if the turbulence is sorted by different types of anisotropy (i.e., which components of the turbulent velocity fluctuations are dominant). However, connections between anisotropy and flow or terrain characteristics that can be used as input parameters to a parametrization based on this new theory are currently lacking. The main aim of this PhD project is to discover such connections. This will be done under the umbrella of the TEAMx international partnership (http://www.teamx-programme.org/), whose objectives align with those of this PhD project, and which congregates the University of Reading (the host institution), the Met Office and the University of Innsbruck, all of them involved in the supervision of this PhD project. The project will use a combination of NWP data analysis, theory (partly developed by the student, partly provided by the lead supervisor) and processing of extensive field observations.
Met Office forecasts will be used to evaluate the performance of the ABL parametrization in the prediction of near-surface meteorological quantities over mountainous regions compared to those over flat terrain. Then, a large database of field measurements within the SL over mountainous terrain, collected during the last 7 years in 6 mountainous terrain locations by the partner institution University of Innsbruck, available through the TEAMx partnership and an ongoing Royal Society International Exchanges Project, will be used to discover relationships as general as possible between ratios of meteorological quantities characterizing the SL, to build understanding of the processes currently neglected in MOST. Additional relevant measurements of unprecedented quality and detail will be made during the TEAMx observational campaign, to take place between August 2024 and September 2025. Results from a theoretical method known as Rapid Distortion Theory, provided by the lead supervisor, alongside the same kind of scaling analysis that originated MOST and allowed its extension by Stiperski et al. (2019), will be used to obtain insights into SL turbulence over mountainous terrain, and inspire new ideas to parametrize it. This aspect will follow the lead supervisor’s approach of applying RDT to the upper ocean affected by waves (Teixeira, 2018), as the waviness of mountainous terrain has somewhat similar effects on the turbulence, through vorticity distortion by streamline curvature. All of this theoretical knowledge, novel field observations and NWP data will then be brought together to formulate a version of MOST that is suitable for flow over mountainous terrain, and that can be implemented in the ABL parametrizations of NWP models.
This project will potentially lead to improvements in the accuracy of models used to predict the weather and climate over mountainous regions, where many communities reside, benefitting both the academic community and society as a whole.
This project will provide skills in numerical and mathematical modelling and data analysis. It will offer opportunities to attend postgraduate modules and summer schools (e.g., Summer School on Fluid Dynamics of Sustainability and the Environment). The student will interact with a co-supervisor at the Met Office (Exeter) (Dr Steve Derbyshire). This will allow experience of a professional environment involved in operational weather forecast. The student will also interact with an international partner (the University of Innsbruck) via co-supervision by Prof. Ivana Stiperski. All of this will provide additional opportunities for training, via possible attendance of seminars and workshops.
This project is suitable for students with a good (1st class or upper 2nd class) degree in physics, mathematics or a closely related environmental or physical science. Good computational skills are essential. Experience in data processing from large 3D meteorological models and applied mathematics would be an advantage, but are not essential. The student should be enthusiastic, eager to learn, and have a keen interest in physical and mathematical aspects of turbulence and atmospheric dynamics.
Stiperski, I., Calaf, M. and Rotach, M.W. (2019) Scaling, anisotropy and complexity in near-surface atmospheric turbulence. J. Geophys. Res.: Atmos., 124, 1428-1448
Stiperski, I., Calaf, M. (2023) Generalizing Monin-Obukhov Similarity Theory (1954) for complex atmospheric turbulence. Phys. Rev. Lett., 130, 124001
Teixeira, M.A.C. (2018) A model for the wind-driven current in the wavy oceanic surface layer: apparent friction velocity reduction and roughness length enhancement. J. Phys. Oceanogr., 48, 2721-2736