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Fig. 11.9 Global values of
( a ) background cost function
and ( b ) observation cost
function for 4D-Var
assimilation experiments run
with all linearized physical
parametrization schemes
included ( solid line )and
using only very simple
vertical diffusion of Buizza
( 1994 )( dashed line ).
Statistics are shown over
July 2011
a
80000
70000
60000
Jb − nophys
Jb − allphys
50000
12345678910 12 14 16 18 20 22 24 26 28 30
July 2011
b
1.2e+06
1.2e+06
1.1e+06
1.0e+06
Jo − nophys
Jo − allphys
1.0e+06
1234567891012141618202224262830
July 2011
physics parametrization. Furthermore, direct 4D-Var of surface rain data from
ground-based NCEP Stage IV rain radars and gauges over the Eastern USA recently
became operational in ECMWF global forecasting system ( Lopez 2011 ) providing
the clear improvement of short-range precipitation forecasts over the region. In
the longer term, one could consider the assimilation of more radar networks (e.g.
Europe, China, Canada,
:::
) once problems of data availability and quality are
solved.
Experimental studies for assimilation of other observations related to the physical
processes which may be considered for the future operational assimilation and
therefore requiring parametrization schemes being able to provide a realistic coun-
terpart to these observations were also performed at ECMWF. Experiments were
conducted to assimilate spaceborne cloud optical depths (from MODIS, Benedetti
and Janiskova 2008 ), precipitation radar reflectivities (from TRMM precipitation
radar, Benedetti et al. 2005 ) and cloud radar data (from CloudSat, Janiskov´aetal.
2011 ). More recently, the potential benefits of directly assimilating synoptic station
(SYNOP) rain gauge observations in 4D-Var were investigated ( Lopez 2012 )in
both, a high resolution operations-like context and a lower-resolution data-sparse
reanalysis-like framework.
The results from all above mentioned studies are not shown here, since they are
well documented in the literature.
 
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