Use of Artificial Intelligence to understand mountain weather and climate processes

This project will explore the potential of new data science approaches such as machine learning (ML) to develop understanding of complex flows over orography (hills and mountains) and improve their representation in models.
The flow in mountainous regions is complex and poorly represented in global and weather and climate models. These flows have important effects on both the local weather and also by influencing the larger scale flow. For example, drainage currents affect the local conditions in mountain valleys, but also influence the turbulent exchange of heat, moisture and pollutants between the boundary layer and the free atmosphere across the larger mountain-range scales. The turbulence associated with breaking mountain waves is a dangerous hazard to aviation, but the process of wave breaking also imparts a drag force on the atmospheric flow, which affects the circulation on global scales.

Current approaches (parametrizations) to representing these processes in models are based on simple theoretical concepts which are derived from highly idealised problems. Further advances are hampered by a lack of a theoretical framework to account for the complexity of the processes. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) provide an exciting opportunity to make further progress. This is an area which is now being embraced by the atmospheric science community. A number of recent papers have begun to investigate the potential of Machine Learning approaches to the problem of parametrising convection, but currently there has been little work on parametrising the effects of orographic processes.

The student will use cutting edge high-resolution model simulations (e.g. of flows over UK, the Rockies and Himalayas) combined with observations obtained in field experiments and new machine learning techniques to develop new physical understanding of orographic flow phenomena and to derive novel methods to represent their effects in weather and climate models. A range of phenomena will be considered, starting with those whose effects are known to be important but the complexity is such that they are not currently represented in weather and climate models (e.g. mountain lee-waves). Other phenomena which could be considered include drainage flows, valley cold pools, fog, turbulent rotors and orographic enhancement of precipitation. The results of the project will then be used to assess the potential for new forecasting techniques and new parametrizations.

Key questions might include:
- How can AI techniques be used to synthesise and classify large atmospheric data sets (either model or observational)?
- Which Machine Learning approaches are most suitable for representing orographic processes?
- How well do ML techniques compare to traditional downscaling techniques or high resolution models for providing detailed forecasts in mountain areas (e.g. local temperature and fog forecasts or orographic rainfall)?
- How well do ML techniques compare to physically-based parametrisations or high resolution models in predicting the bulk effects of orography on the large scale e.g. the orographic drag exerted on the atmosphere).
- Which inputs (both type and quantity of data) provide the best and most efficient predictors in each case?
- How well do networks trained in one area of the world predict in other geographic areas?

The fusion of data science with simulation is a key theme of the 2020-2030 Met Office Science Strategy and the student will benefit from the growing use of machine learning techniques at the Met Office, plus their world-leading expertise in weather and climate simulation. The student will be based in the School of Earth and Environment at the University of Leeds, which is also growing activity in this field, with strong links to the School of Computing and the Leeds Institute for Data Analytics.