Distributed Observations in Meteorological Ensemble Data Assimilation and Forecasting

With the ever increasing amount of meteorological data available, from satellites in particular, it becomes more and more important to use as large fraction of this data as practically possible for operational weather forecasts. Two applications are shown where more data are used in ensemble data assimilation and forecasting by distributing satellite data among different ensemble members. For the Ensemble of Data Assimilations (EDA), a version of the perturbed observations ensemble Kalman filter, the ensemble mean error can in theory be reduced by replacing observation perturbations by distributing different subsets of observations to different members, but in practice this is complicated by observation error correlations and the need to maintain the same level of spread in the ensemble as before. For the ensemble forecasts, it is shown that the initial conditions can be improved by re-centring EDA perturbations on multiple equally good analyses that use different subsets of observations instead of re-centring all EDA perturbations on a single one of those analyses. It is shown how the ensemble mean error decreases for decreased error correlation between analyses. We estimate that in practice only 5–6 analysis are needed to obtain 80% of the root mean square error reduction that would be achieved with infinite number of analyses.