The Utilization of Statistical Properties of Satellite-Derived Atmospheric Motion Vectors to Derive Quality Indicators

Abstract

The extraction of atmospheric motion vectors (AMVs) from cloud and moisture features from successive geostationary satellite images is an established and important data source for numerical weather prediction (NWP). So far the extraction of AMVs has been confined to the main synoptic times only, which grossly underutilizes the potential of these satellite-derived data. The advent of four-dimensional variational assimilation techniques provides the opportunity to utilize data derived at asynoptic times. This will enhance the capabilities of geostationary satellite systems that can provide continuous and near?real time observations. The new assimilation schemes are able to digest data representing various scales and with variable quality, which further enhances the usefulness of the satellite data. In order to fully exploit the AMVs derived with satellite data, it is imperative to accurately assess the quality and representativeness of individual wind vectors and to provide this information to the NWP centers as an integral part of the observations in near real time. The required high production and dissemination frequency cannot be met if manual intervention is required; hence, the emphasis has to be on fully automated schemes. This paper will describe the automatic quality control scheme implemented at EUMETSAT. It is based on the statistical properties of the derived AMVs and it provides a quality indicator (QI), describing the expected quality of every individual vector. The derived QIs are currently disseminated together with the derived vectors. The paper will also provide validation results based on collocated radiosonde statistics and report on first experiences by ECMWF in utilizing the QIs.