Proceedings - C-SRNWP Project

generally very similar from case to case. Runs starting from 12 km data show a significant
time for rates to grow (as would be expected since the model represents convection through
parametrization) and subsequently overshoot before settling down to approximately the
correct value. This highlights the need for a continuous system. However, the assimilation
runs (including the 12 km runs used to start the spin-up runs) also show over-prediction
during the assimilation cycle which decays into the forecast. The large peak around T+2 is
particularly noticeable. This is a subject of continuing work but we have some evidence that
this is related to the VAR assimilation.
In spite of this bias in amount (and it should be born in mind that the radar observation may
have significant error), forecasts show useful spatial skill, as reflected in the FSS discussed
above The use of a relative threshold means that the scores are only sensitive to the spatial
distribution of the rain rather than any overall bias. Figure 7 shows FSS scores for cases from
2004 and 2005 with and without data assimilation. Without assimilation (dashed lines), a
spin-up period at the start of the forecasts is evident which is longer (of order 3 hours) in the 4
km model than in the 1km model. The results with data assimilation are consistently better
than without, showing much less evidence of spin-up, better scores at the end of the forecast,
and improvements at 1 km over 4 km and 12 km. The 1km model with assimilation gives the
best performance for all times after T+1. It is expected that skill will significantly improve
upon the introduction of a new nudging formulation in the assimilation and other model
developments.
3. Parametrization Developments
Our experience with the UM at grid resolutions of around 1 km through a wide variety of case
studies suggests the model is broadly successful with the existing sub-grid parametrizations
(1D non-local boundary layer, 3 category ice-phase microphysics, 2-stream radiation).
However, there are also some aspects of the model forecasts which consistently do not agree
so well with observations and areas where the parametrizations can be improved. There are
active developments to all the parametrizations in the model, but two of the most important
development areas for high resolution (i.e. turbulent mixing and cloud/precipitation
microphysics) are discussed in this section.
Turbulent Mixing
The triggering, number and intensity of convective cells is strongly controlled by the
treatment of turbulence. At present we use the standard 1D boundary layer scheme with ∇ 4
hyper-diffusion along horizontal model surfaces. This has been tuned to give broadly optimal
results, though the value is consistent with the maximum which might be expected from a
“Smagorinsky-like” turbulent mixing parametrization scheme. We are moving towards a 3D
Smagorinsky-Lilly sub-grid parametrization of turbulent mixing both in the boundary layer
and throughout the free troposphere which we anticipate will be required as the resolution of
the model increases to the point where deep convective cells are well resolved. Bearing in
mind the large horizontal to vertical aspect ratio of the grid, it is not obvious what the best
formulation will be. The approach taken in the UM has been to implement the vertical and
horizontal components separately allowing an explicit treatment in the horizontal but implicit
treatment in the vertical (as per the boundary layer scheme) in order to retain numerical
stability for shallow layers and the long timesteps used in the semi-Lagrangian dynamics.
The Smagorinsky-Lilly turbulent mixing scheme in the UM is being tested in a range of
idealised and real case studies. In particular, a dry convective boundary layer case, shallow
cumulus (BOMEX-type) case and a surface flux forced diurnal cycle of deep convection
(GCSS WG Deep Case 4) are being simulated and compared with equivalent simulations with
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