LAUSR

Data assimilation is usually cycled in time, through a temporal succession of analysis and forecast steps. This implies that forecast errors arise from contributions of observation, model and background errors, which are introduced during successive steps of the cycling. A linearized expansion of forecast errors is here considered, in order to derive expressions and estimates of respective accumulated contributions of these different errors with varying ages. Experiments are conducted in the context of the Météo‐France global numerical weather prediction system ARPEGE.