An adaptive buddy check for observational quality control

An adaptive buddy-check algorithm is presented that adjusts tolerances for suspect observations, based on the variability of surrounding data. The alg orithm derives from a statistical hypothesis test combined with maximum-lik elihood covariance estimation. Its stability is shown to depend on the init ial-identification of outliers by a simple background check. The adaptive f eature ensures that the final quality-control decisions are not very sensit ive to prescribed statistics of first-guess and observation errors, nor on other approximations introduced into the algorithm. The implementation of the algorithm in a global atmospheric data assimilati on is described. Its performance is contrasted with that of a non-adaptive buddy check, for the surface analysis of an extreme storm that took place o ver Europe on 27 December 1999. The adaptive algorithm allowed the inclusio n of many important observations that differed greatly from the first guess and that would have been excluded on the basis of prescribed statistics. T he analysis of the storm development was much improved as a result of these additional observations.