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These processes are mostly unresolved due to not well resolved because of their small scales compared to model resolution.  Because of To deal this, they are handled by physical parameterisation in a statistical way that describes the mean effect of sub-grid processes.

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Basic prognostic equations are efficiently processed in spectral space and this is reflected in a need only a relatively small proportion of computer time required .  However, for a forecast.  But many processes are computed in grid point space (e.g. rainfall) which and this requires a larger, but still relatively smallmodest, proportion of computer time.   This and processes Processes in grid-point space, in spectral space, together with and in the associated spectral transforms , are approximately similar.  The broadly similar in computer time but the necessary transpositions between spectral and grid point spaces is a significant overhead in computer time.  Semi-lagrangian computations also take up an important proportion of processing time.

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Fig2.1.2: A schematic pie-chart showing approximate proportions of computer processing time during execution of an atmospheric model forecast based on T799 (regular 25km resolution grid) and 91 levels (current resolution for ENS is T_co640 1279 (18km 9km resolution) and 137 levels).   Parameterised physical processes consume about 30% of computer processing time.  Computations in grid point space and spectral space together take about 20% of computer processing time while rather more time (~27%) is taken in transposing data from one space to the other.

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