Improving Model Representation of Turbulent Atmospheric Processes

Support for projects to improve forecasts of extreme weather events and enhance the UK’s management of vulnerability, risk and resilience.

The new Improving Model Representation of Turbulent Atmospheric Processes call is funded by the Natural Environment Research Council (NERC) and the Met Office to improve forecasts of extreme weather events and enhance the UK’s management of vulnerability, risk and resilience. This should be through research into atmospheric turbulent processes and representation in kilometre (km) and sub-kilometre (sub-km) scale weather and climate models.

The accuracy and value of weather and climate models can be improved by increasing their horizontal resolution so that grid boxes are kilometres (or in some cases hundreds of metres) in length, giving better representation of the surface terrain and simultaneously capturing cloud-scale motions and long-range atmospheric flows.

By moving to km and sub-km resolutions, turbulent processes in the atmosphere start to become partially resolved, with energy needing to be exchanged between the resolved grid and the sub-grid physics (a problem known as modelling in the ‘grey zone’). In addition, as turbulent motions are inherently stochastic, the atmosphere carries chaotic uncertainty that needs to be represented in order to provide value to decision makers.

Methods for modelling turbulence in this ‘grey zone’ are still in their infancy, with key problems being the lack of model convergence at different resolutions, and the unrealistic emergence of structures (aliasing) onto the grid scale, leading to systematic biases in projections (eg excessive rainfall at the wrong time). A faithful representation of its effects is vital for ensuring the accuracy of predictions of severe weather, such as intense rainfall, heat extremes and damaging winds.

This programme:

• Aims to combine new observational data and process modelling of the atmosphere with theoretical developments to improve model representations of boundary layer and convective moist turbulence appropriate for km and sub-km scales.
• Will focus exclusively towards improving Met Office weather and climate models.

Funded projects will work closely with the Met Office to ensure developments are aligned with improving the Met Office Unified Model, and proposals will need to demonstrate how the research stands to contribute to making improvements at these scales. The improvements should offer better realism for any given forecast, and the stochastic properties enabling it to provide better probabilistic forecasts. UK summertime convection will be the focus of observations for the programme.

The proposal can either focus on:

• The observational component: observations for boundary layer and convective cloud turbulence; or
• At least one of the three science themes:
  • Theme A: boundary layer evolution.
  • Theme B: moist convective turbulence.
  • Theme C: stochastic processes for ensemble forecasting.

In all themes, the proposed work must include a component that is focused on the translation of scientific understanding into practical formulations that could be implemented in climate and numerical weather prediction (NWP) models (eg findings that are quantitative and testable).

The programme will have three cross-cutting activities, and projects should clearly demonstrate how they contribute to at least one of these:

• Theory and parametrisation development: advancing the underpinning theory, modelling frameworks and numerics required to improve models at km and sub-km scales.
• Use of observations or process modelling: providing evidence to inform or direct parametrisation developments
• Evaluation: to test and refine new developments in models, comparing with observations, and including ensemble performance.

The funders will look to ensure a balanced suite of complementary projects to cover the scope and objectives of the programme. However, they will place priority on projects that stand to contribute towards better model performance.