Reaching far and wide: accounting for spatially correlated observational errors in an ensemble of 4D-Vars system

Data assimilation (DA) seeks to provide the most likely estimate of the true state of the atmosphere or ocean by combining a background estimate from a numerical model with observations, each weighted by their respective error covariances. For computational reasons, diagonal covariances together with variance inflation have traditionally been favoured for the observational error covariance. However, recent studies have shown that variance inflation can degrade DA performance when the correlation length scales of observational errors are comparable to, or exceed, those of the background errors—a situation frequently encountered when assimilating wind vectors derived from Atmospheric Motion Vectors. These observations are assimilated, alongside many others, in the ensemble DA system at the European Centre for Medium-Range Weather Forecasts (ECMWF). In this system, each ensemble member undergoes an independent 4D-Var minimisation after perturbing its observations and model parameters to represent observational and model uncertainties. This work presents results from efforts to explicitly account for spatial correlations in observational errors within the ensemble DA framework. In particular, it demonstrates the positive impact of introducing spatially correlated perturbations to assimilated observations on ensemble spread, offering a pathway to improved representation of uncertainty in operational forecasting.