Evaluation of micro rain radar-based precipitation classification algorithms to discriminate between stratiform and convective precipitation
Abstract. In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions (PDFs) in combination with a confidence function and the other one is an artificial neural network (ANN) classification. Both methods use the maximum radar reflectivity per profile, the maximum of the observed mean Doppler velocity per profile and the maximum of the temporal standard deviation (±15 min) of the observed 5 mean Doppler velocity per profile from a micro rain radar (MRR). Training and testing of the algorithms were performed using a two year data set from the Julich Observatory for Cloud Evolution (JOYCE). Both methods agree well giving similar results. However, the results of the artificial neural network are more reasonable since it is also able to distinguish into an inconclusive class, in turn making the stratiform and convective classes more reliable.