Variational assimilation of time sequences of surface observations with serially correlated errors

Assimilation of observations from frequently reporting surface stations wit h a four-dimensional variational assimilation system (4D-Var) is described. A model for the serial observation error correlation is applied to observe d time sequences of surface pressure observations, whereby the relative wei ght of the mean information over the temporal variations is decreased in th e assimilation. Variational quality control is performed jointly for each t ime sequence of observations so as to either keep or reject all observation s belonging to a time sequence. The operational practice at ECMWF has previ ously been to use just one pressure datum from each station within each 6-h assimilation time window. The increase of observational information used i n these assimilation experiments results in a small but systematic increase in the short-range forecast accuracy. The r.m.s. of the analysis increment s is decreased in the experiments, which means there is an improved consist ency between the background and the observations. A study of a rapidly deve loping small-scale synoptic system (the Irish Christmas Storm in 1997) show ed that both the background and the analysis became more accurate when more frequent observations were assimilated. Single-observation experiments sho wed that a surface pressure time-sequence of data from a single surface sta tion can intensify the analysis of a mid-latitude baroclinic system, that w as underestimated in the background, when used in a 6-h 4D-Var. The method to assimilate time sequences presented in this paper has been implemented i nto the ECMWF operational 4D-Var assimilation system.