  Recent developments in numerical weather prediction have led to the use of
correlated observation error covariance (OEC) information in data assimilation
and forecasting systems. However, diagnosed OEC matrices are often
ill-conditioned and may cause convergence problems for variational data
assimilation procedures. Reconditioning methods are used to improve the
conditioning of covariance matrices while retaining correlation information. In
this paper we study the impact of using the 'ridge regression' method of
reconditioning to assimilate Infrared Atmospheric Sounding Interferometer
(IASI) observations in the Met Office 1D-Var system. This is the first
systematic investigation of how changing target condition numbers affects
convergence of a 1D-Var routine. This procedure is used for quality control,
and to estimate key variables (skin temperature, cloud top pressure, cloud
fraction) that are not analysed by the main 4D-Var data assimilation system.
Our new results show that the current (uncorrelated) OEC matrix requires more
iterations to reach convergence than any choice of correlated OEC matrix
studied. This suggests that using a correlated OEC matrix in the 1D-Var routine
would have computational benefits for IASI observations. Using reconditioned
correlated OEC matrices also increases the number of observations that pass
quality control. However, the impact on skin temperature, cloud fraction and
cloud top pressure is less clear. As the reconditioning parameter is increased,
differences between retrieved variables for correlated OEC matrices and the
operational diagonal OEC matrix reduce. As correlated choices of OEC matrix
yield faster convergence, using stricter convergence criteria along with these
matrices may increase efficiency and improve quality control.
