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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. 

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• 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

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