Land surfaces

Within each grid box there are several different types of ground surface, each covering a proportion of the area surrounding a grid point (Fig2.1.4.1-1 and Fig2.1.4.1-2).  The ground types are described by a set of "tiles" for:  

• bare soil.
• low vegetation (e.g. grass).
• high vegetation (e.g. trees).
• intercepted water at surface and foliage covered by water (e.g. rainfall intercepted by leaves).
• shaded snow or forest snow (e.g. snowy woodland, snow under high vegetation).
• exposed snow (snow on bare ground or low vegetation).
• lakes and coastal waters.
• urban areas.

Up to six different "tiles" (ground types) may be used within a land grid box, each proportional to its coverage.  Each "tile" has its own properties, describing the heat, water and momentum exchanges with the atmosphere.   Each vegetation type for the 'tile' is assigned a height and is also characterised by a number of other (fixed) parameters.   These are used in calculating the surface fluxes, and hence a skin (surface) temperature, for each of the different “tiles.   Particular attention is paid to evaporation, as near-surface temperature and humidity are very closely related to this process.

The total energy fluxes for the whole grid box are derived from the proportional contributions of each "tile".  These depend on the type and relative area of low and high vegetation and the presence of snow and intercepted water.  If no snow or intercepted water, the fractional tile coverage of both high and low vegetation are defined by an offline dataset.  Built up areas are dealt with using the urban tile.  The residual fraction is considered bare ground.  

However, the areal extent of each land surface tile type can vary in a rapid, interactive way during the model run, as rain falls then evaporates, or snow accumulates then melts, etc.  The characteristics of the soil may also change (e.g. infiltration or runoff of rain, temperature structure of soil etc).  The slope and aspect of orography within each grid box (e.g. south-facing, steepness) is not taken into account and HTESSEL may consequently under- or over-estimate solar heating and runoff.

Fig2.1.4.1-1: Schematic of HTESSEL tiles.  Each model grid square is allocated a distribution of surface types to a maximum of six tiles and a weighted average taken. 

Table2.1.4.1-1: List of symbols for parameters shown in Fig2.1.4.1-1.

Considerations

• The average ground type within a grid box is not necessarily representative of an individual location.  Land surface characteristics can be very variable within a grid box.  Users and forecasters should take into account the peculiarities of a location when interpreting model output.
• Representation of an urban or city surface was introduced in Cy49r1 in autumn 2024.  Extensive concrete and buildings may possibly provide a source of heat (the heat island effect) and even moisture (from air-conditioning units).
• The slope and aspect of orography within each grid box (e.g. south-facing, steepness) is not taken into account and HTESSEL may consequently under- or over-estimate solar heating and runoff.
• an urban tile models the fluxes of heat, moisture and momentum at the surface and allows a more realistic representation the heat island effects of towns and cities.
• separation of tiles into high and low vegetation gives a more accurate seasonal variability of vegetation and incorporates the differing albedo effects of underlying snow cover

Table 2.1.4.1-1: Assignment of height characteristic and vegetation coverage for each type of vegetation or surface.  In cases of snow, vegetation assigned as high (H) can have snow cover beneath; vegetation assigned as low (L) are considered as snow covered.  See "Modelling snow" section.  The assigned vegetation coverage gives information to the soil model regarding transpiration and root depth.  See "Modelling soil" section.   Albedo values are associated with the Leaf Area Index.

Fig2.1.4.1-2: An example of the variability of land surface within an approximate grid box illustrating the difficulty in assigning representative HTESSEL "tiles" for the whole grid square area.  The red lines show the extent of a very approximate 9km x 9km schematic Ensemble Control Forecast (ex-HRES) grid square.   The flag locates the grid point.  There is a large variation in ground surface type and the proportional contribution to the heat, moisture and momentum fluxes are difficult to assess.   The tile allocation for this grid box is approximately: high vegetation 7%, low vegetation 70%, estuary (lake) 20%, urban 3%.  An ENS meteogram is interpolated from the four grid points surrounding a given station within the box.  See Section on Selection of grid points for Meteograms for details.

Fig2.1.4.1-3: An example of the variability of land surface within an approximate grid box illustrating the difficulty in assigning a representative HTESSEL "tiles" for the whole grid square area.  The red lines show the extent of a very approximate 9km x 9km schematic Ensemble Control Forecast (ex-HRES) grid square.  The flag locates the grid point.  There is some variation in ground surface type but it is predominantly covered by evergreen needle leaf trees.  The proportional contribution to the heat, moisture and momentum fluxes are rather simpler to assess.  In winter snow the tile would be assigned as forest snow.  Runoff would be rapid over Rocky Mountain sides, much slower over low-lying river valleys.   The tile allocation for this grid box is approximately: high vegetation 75%, low vegetation 5%, lake 5%, bare ground 5%, urban 10%.  An  ENS meteogram is interpolated from the four grid points surrounding a given station within the box.  See Section on Selection of grid points for Meteograms for details.

Additional sources of information

(Note: In older material there may be references to issues that have subsequently been addressed)

• Read more on HTESSEL in atmospheric physics documentation and scroll down to Soil/Surface. 
• Read more on leaf area index.
• Read more on the ECMWF land surface model developments.
• Read more on a revised hydrology for the ECMWF model.

(FUG Associated with Cy49r1)