Object-oriented processing of CRM precipitation forecasts by stochastic filtering

In order to cope with small-scale unpredictable details of mesoscale structures in cloud resolving models, it is suggested that model outputs are processed following a fuzzy object-oriented approach to extract and track precipitating features (which are associated with a higher predictability than the direct model outputs). The present approach uses the particle filter method to recognize patterns based on predefined texture or spatial variability of the model output. This provides an ensemble of precipitating objects, which are then propagated in time using a stochastic advection-diffusion process. This method is applied to both deterministic and ensemble forecasts provided by the AROME-France convective-scale model. Specific case-studies support the ability of the approach to handle precipitation of different types.