Details of Award
NERC Reference : NE/Z503964/1
TECTTO: Turbulence Evaluation in Complex Terrain using TEAMx Observations
Grant Award
- Principal Investigator:
- Professor H Dacre, University of Reading, Meteorology
- Co-Investigator:
- Professor IA Renfrew, University of East Anglia, Environmental Sciences
- Grant held at:
- University of Reading, Meteorology
- Science Area:
- None
- Overall Classification:
- Unknown
- ENRIs:
- None
- Science Topics:
- None
- Abstract:
- Mountainous regions are characterised by terrain which is highly variable in space, resulting in significant variations in atmospheric conditions over short distances and times. This variability provides huge challenges for both modelling and observing the processes that govern mountain weather and climate. It is important to understand these processes as over 30% of the land surface can be classified as mountainous and over 25% of the world's population live in or adjacent to these regions. Recent advancements in computer modelling have led to simulations with higher resolutions, improved model physics, and to the development of new unstructured grids. Despite these advancements, there has not been a recent evaluation of model predictions in mountainous terrain. Previous evaluations, conducted over a decade ago (e.g. the Mesoscale Alpine Project,1999), identified significant forecasting issues such as temperature biases, rapid fog dissipation, and precipitation errors. Given the advancements in modelling systems, there is a growing need for updated evaluations of model predictions in mountainous terrains. The TEAMx observation campaign serves as a unique testbed for the evaluation and development of numerical weather prediction models and is specifically tailored for complex terrain at high resolution. By combining field experiments and computer models, the Turbulence Evaluation in Complex Terrain using TEAMx Observations project, TECTTO, will focus on the representation of mountain boundary layer turbulence in the European Alps. Accurate mountain boundary layer turbulence representation is important for predicting surface and elevated stable and well mixed layers, which influence pollution concentrations, fog formation and dissipation and surface temperatures. The overarching aim of the project is to improve weather forecasts, thereby benefiting the vast amount of people who live, work and travel in mountainous regions. Farmers, for instance, will benefit from more accurate weather forecasts, allowing them to make informed decisions regarding crop management and harvesting. Emergency services will benefit from improved predictions of extreme weather events, such as wind-storms and flooding, enabling better preparedness and response measures potentially leading to reductions in the loss of life. These improvements will also feed into advances in climate modelling. This will aid longer term planning for renewable energy systems, managing water resource management and a whole host of climate change mitigation strategies not only for humans but the whole ecosystem.
- NERC Reference:
- NE/Z503964/1
- Grant Stage:
- Awaiting Start Confirmation
- Scheme:
- Research Grants
- Grant Status:
- Accepted
- Programme:
- TEAMxUK
This grant award has a total value of £484,774
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DI - Staff | DA - Estate Costs | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £1,671 | £181,575 | £40,215 | £155,191 | £80,788 | £11,625 | £13,711 |
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