Assimilation of Meteosat Second Generation (MSG) satellite data in a regional numerical weather prediction model using a one-dimensional variational approach
The quality of temperature and humidity retrievals from the infrared SEVIRI sensors on
the geostationary Meteosat Second Generation (MSG) satellites is assessed by means of a
one dimensional variational algorithm. The study is performed with the aim of improving
the spatial and temporal resolution of available observations to feed analysis systems designed
for high resolution regional scale numerical weather prediction (NWP) models. The
non-hydrostatic forecast model COSMO (COnsortium for Small scale MOdelling) in the
ARPA-SIM operational configuration is used to provide background fields. Only clear sky
observations over sea are processed.
An optimised 1D–VAR set-up comprising of the two water vapour and the three window
channels is selected. It maximises the reduction of errors in the model backgrounds while
ensuring ease of operational implementation through accurate bias correction procedures and
correct radiative transfer simulations.
The 1D–VAR retrieval quality is firstly quantified in relative terms employing statistics
to estimate the reduction in the background model errors. Additionally the absolute retrieval
accuracy is assessed comparing the analysis with independent radiosonde and satellite observations.
The inclusion of satellite data brings a substantial reduction in the warm and dry
biases present in the forecast model. Moreover it is shown that the retrieval profiles generated
by the 1D–VAR are well correlated with the radiosonde measurements.
Subsequently the 1D–VAR technique is applied to two three–dimensional case–studies:
a false alarm case–study occurred in Friuli–Venezia–Giulia on the 8th of July 2004 and a
heavy precipitation case occurred in Emilia–Romagna region between 9th and 12th of April 2005. The impact of satellite data for these two events is evaluated in terms of increments
in the integrated water vapour and saturation water vapour over the column, in the 2 meters
temperature and specific humidity and in the surface temperature.
To improve the 1D–VAR technique a method to calculate flow–dependent model error
covariance matrices is also assessed. The approach employs members from an ensemble
forecast system generated by perturbing physical parameterisation schemes inside the model.
The improved set–up applied to the case of 8th of July 2004 shows a substantial neutral
impact
