CERFACS CERFACS Scientific Activity Report Jan. 2010 – Dec. 2011

DATA ASSIMILATION
the potential vorticity field. But even if the BECM is a key-component, its evaluation has not yet been the
object of thorough investigations for atmospheric chemistry assimilations.
Meanwhile, in meteorology background (and analysis) errors were successfully assessed with ensemble
data assimilation systems. Outputs of in a cycled assimilation system can produce an estimate of
a flow-dependent BECM. The ensemble approach is not restrained to Kalman Filter methodologies.
Useful information can statistically be extracted from an adequately perturbed ensemble variational data
assimilation system. Up to now, the use of ensembles in combination with a variational assimilation
scheme is relatively unexplored in atmospheric chemistry data assimilation. We have thus performed two
studies to investigate the potential of an ensemble of atmospheric chemistry analyses to provide useful
flow-dependent estimates of the background error variance and correlations, the first one with the fourdimensional
variational assimilation global MOCAGE-Valentina suite [DA13] and the other with the threedimensional
variational assimilation regional MOCAGE-Valentina suite [4].
FIG. 3.3: Mean (top) and standard deviation (bottom) of the difference in terms of total ozone column
(in %) between the OMI (independent) data and the model without assimilation (grey shaded area) or the
analyses as a function of latitude, for January, April, July and October 2008. For the top panel, positive
(negative) values stand for an underestimation (overestimation) of the model or the analyses compared to
OMI data. The analyses differ from the modelling of the BECM : simple formulation (blue line) or from
ensemble-based diagnostics (red line).
The study with the global MOCAGE-Valentina suite showed that the analysis of the MLS ozone profiles
(from the upper troposphere to the lower mesosphere) using all the components of the ensemble-estimated
BECM produces ozone concentrations with a low biases and errors (Fig. 3.3). But estimating the BECM
with ensemble methods has an important cost that could become unaffordable. We thus tried to use simpler
methods to calculate the BECM. Replacing the estimated standard deviation by a standard deviation
proportional to the background value is one of them. This simplification does not modify significantly
the analysis quality. Replacing the estimated length-scales by a constant value is the second simplification
we tried. This dramatically deteriorates the analysis quality for a few time periods. This simplification is
thus irrelevant for analysing the stratospheric ozone.
CERFACS ACTIVITY REPORT 81