Proceedings - C-SRNWP Project

suggests however to filter the small scale structures in order to filter sampling noise, which is
mostly present in the shortest wavelengths (Berre et al., 2006). Figure 3 shows the result for
maps of standard deviations of two parallel, twin, ensemble of 3 member each. The filtering
function is derived from the cross-correlation function between the two sub-ensembles.
Figure 3: standard deviations of (Arpège) vorticity fields, obtained for two, twin ensembles of
3 member each (each ensemble is re-initialized using the background perturbations from the
twin ensemble). The maps show that both ensembles provide similar dispersions, with usual
flow-dependent information, once small scale sampling noise has been filtered out.
3. Observations and O.S.E. experiments
3.1. Towards the assimilation of microwave surface sensitive channels from AMSU-A,
AMSU-B & SSM/I over land:
Over land:
• Only microwave channels that are the least sensitive to the surface are assimilated
• There exists a significant uncertainty about land emissivity and skin temperature
• Existing emissivity models : facilitate the assimilation of channels that receive a
contribution from the surface
• The models need accurate input parameters which are hardly available at global scale
We search for alternatives to estimate the land emissivity, which has lead to new land surface
emissivity parameterisations at Météo-France:
1) Dynamically varying emissivities derived from only one channel for each instrument
2) Averaged emissivities over 2 weeks prior to the assimilation period
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