The similarity-based method: a new object detection method for deterministic and ensemble weather forecasts

Abstract

A new object-oriented method has been developed to detect hazardous phenomena predicted by Numerical Weather Prediction (NWP) models. This method, called similarity-based method, is looking for specific meteorological objects in the forecasts, which are defined by a reference histogram representing the meteorological phenomena to be detected. The similarity-based method enables to cope with small scale unpredictable details of mesoscale structures in meteorological models and to quantify the uncertainties on the location of the predicted phenomena. Applied to ensemble forecasts, the similarity-based method can be viewed as a particular case of neighborhood processing, allowing spatialized probabilities to be computed. An application to rainfall detection using forecasts from the AROME deterministic and ensemble models is presented.