Innovations and applications of the VERA quality control

Abstract

Quality control (QC) is seen today as an important scientific field to increase the value of observational data. Whereas most QC methods are linked to atmospheric modeling (being part of the data assimilation procedure), in this paper the focus is on the application of a model independent QC method based on data self consistency recently published: VERA-QC. A special challenge is the QC of data in complex terrain which requires special treatment in terms of data selection and data transformation. In this context, some special VERA-QC modules such as the consideration of significant elevation differences of adjacent stations or the consideration of transformed temperature values will be discussed. The system detects gross errors as well as biases and offers objective correction proposals (deviations) for each observation. The essential gross error detection is not only based on the statistical behavior of station specific deviations, but also on the rate of cost function reduction. Beside a two dimensional application, higher dimensionalities may also be chosen, for instance including the time coordinate. Applications and results are discussed for pressure, temperature as well as for precipitation data which needs, however, a very dense observation network. Real time application of VERA-QC allows the production of high quality fields of meteorological parameters, which can be used, e.g. for nowcasting as well as for model unbiased validation of prognostic models.