Use of Artificial Intelligence to understand mountain weather and climate processes

Use of Artificial Intelligence to understand mountain weather and climate processes

Andrew Ross and Leif Denby (School of Earth and Environment)

He Wang (School of Computing)

Simon Vosper, Annelize van Niekerk, Tom Dunstan (Met Office)

Project partners: Met Office (CASE award)

Project description / Motivation

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.

Potential for high-impact outcome

This is a rapidly developing area of atmospheric science, with significant interest in the potential of AI techniques for parametrisation and, as a result, there are excellent opportunities for high impact outcomes including publications in top international journals. The Met Office have a keen interest in exploring the potential for AI and ML techniques and there is also potential for feeding the work through into future model development.


The student will work under the supervision of Dr Andrew Ross and Dr Leif Denby in the School of Earth and Environment, University of Leeds. Dr He Wang in the School of Computing will provide expertise in machine learning. Prof Simon Vosper, Dr Annelize van Niekerk and Dr Tom Dunstan from the Met Office will be co-supervisors. Co-supervision will involve regular meetings between all partners and regular visits to the Met Office in Exeter. The successful PhD student will have access to a broad spectrum of training in numerical modelling and artificial intelligence, through to managing your degree or to preparing for your viva ( This project provides a high level of specialist scientific training in:

  1. Artificial Intelligence and Machine Learning methods applied to the environmental sciences.

  2. Numerical modelling and use of cutting-edge supercomputers;

  3. Analysis of in-situ measurements from aircraft and ground-based monitoring sites and / or satellite remote sensing;

  4. Computer programming languages (e.g. Python) to perform complex analysis techniques;

  5. Effective written and oral communication skills.

The student will be part of the Dynamics and Clouds Group, which is embedded in the Institute for Climate and Atmospheric Science (ICAS) within the School of Earth and Environment. The Dynamics and Clouds Group is a large and active group of people working on a range of problems, with particular interests in the tropics and the role of convection. The group meets regularly and these group meeting provide an excellent opportunity to discuss your work as well as to learn more about what others are working on. We encourage you to be an active member of the Dynamics and Clouds Group as well as other research groups within the School of Earth and Environment.

Industrial partner

The proposal has been agreed as a “Partnership Project” with the UK Met Office and the student will benefit from Met Office supervisors as well as academic supervisors at Leeds. Leeds has a strong record of close collaboration with the Met Office and is one of only 4 universities in the Met Office Academic Partnership. The project aligns with existing collaborations between Leeds and the Met Office on mountain meteorology and parametrisations, as well as the growing joint interests in the application of AI / ML techniques. The successful student will spend time working with the industry supervisors at the Met Office. The project is also eligible for one of the Met Office CASE awards.

Student profile

The student should have a keen interest in the challenges of understanding and modelling the weather / applications of artificial intelligence to the environmental sciences and a strong background in a relevant quantitative science (meteorology, maths, physics, engineering, environmental sciences). Experience of scientific programming / artificial intelligence / data analysis is desirable, but not essential.

Further reading

Gentine P., Pritchard M., Rasp S., Reinaudi G., Yacalis G. (2018) Could Machine Learning Break the Convection Parameterization Deadlock? Geophys. Res. Lett. 45, 5742-5751.

Vosper S.B., Ross A.N., Renfrew I.A., Sheridan P., Elvidge A.D., Grubsic V. (2018) Current Challenges in Orographic Flow Dynamics: Turbulent Exchange Due to Low-Level Gravity-Wave Processes. Atmosphere 9, 361. And other review papers in the same special issue on Atmospheric Processes over Complex Terrain.