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

Abstract

Assimilation of observations from frequently reporting surface stations with a four-dimensional variational assimilation system (4D-Var) is described. A model for the serial observation errorcorrelation is applied to observed time sequences of surface pressure observations, whereby therelative weight of the mean information over the temporal variations is decreased in the assimilation.Variational quality control is performed jointly for each time sequence of observations soas to either keep or reject all observations belonging to a time sequence. The operationalpractice at ECMWF has previously been to use just one pressure datum from each stationwithin each 6-h assimilation time window. The increase of observational information used inthese assimilation experiments results in a small but systematic increase in the short-range forecast accuracy. The r.m.s. of the analysis increments is decreased in the experiments, whichmeans there is an improved consistency between the background and the observations. A studyof a rapidly developing small-scale synoptic system (the Irish Christmas Storm in 1997) showedthat both the background and the analysis became more accurate when more frequent observationswere assimilated. Single-observation experiments showed that a surface pressure timesequenceof data from a single surface station can intensify the analysis of a mid-latitude baroclinic system, that was underestimated in the background, when used in a 6-h 4D-Var. Themethod to assimilate time sequences presented in this paper has been implemented into theECMWF operational 4D-Var assimilation system.