The atmosphere is strongly affected by exchanges of heat, moisture and momentum with the underlying land or sea surface and these exchanges largely determine the variations of wind, temperature and humidity close to the surface.
Representing surface processes in a numerical forecasting model involves linking the model's atmospheric boundary layer scheme with its surface schemes and determining the structure of the very lowest part of the boundary layer, known as the surface layer. A prerequisite for improving the representation of these processes in models is building a good physical picture of behaviour at the surface. Observational studies play a prominent role in this research, but detailed process models, such as the Large Eddy Model, are also used.
Development of land surface modelling within the Met Office is now centred on the JULES, a community land surface model, with applications in both weather forecasting and climate modelling. The coupling between the atmosphere and the sea surface has been less intensively developed within the context of weather prediction models, but is currently receiving renewed attention.
Better representations of surface processes contribute directly to improvements in the forecasting of near surface variables, such as wind and temperature, while an accurate representation of the surface fluxes is important for the accurate long-term evolution of forecasts. In addition, weather and surface data are necessary inputs to a range of other environmental forecasting models.
To obtain a better understanding of physical processes operating at the surface and in the lowest few metres of the atmosphere.
To improve the forecasting of near-surface weather (notably winds and temperatures) and to provide better driving data to downstream models that depend on weather and surface data
Improving the treatment of marine boundary layers in the operational forecasting system by developing the representation of surface exchanges at the ocean's surface and investigating the possible benefits of enhanced coupling with the Ocean Forecasting.
Using detailed in situ observations of the surface and surface layer to identify deficiencies in current forecasting models.
Using retrievals of land surface temperatures from satellite observations to assess the current representation of the surface in forecasting models and to inform decisions about their possible use in data assimilation.