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Object-oriented processing of CRM precipitation forecasts by stochastic filtering

Philippe Arbogast 1 Olivier Pannekoucke 1 Laure Raynaud 1 Renaud Lalanne 1 Etienne Mémin 2
2 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
IRMAR - Institut de Recherche Mathématique de Rennes, IRSTEA - Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture, Inria Rennes – Bretagne Atlantique
Abstract : In order to cope with small-scale unpredictable details of mesoscale structures in cloud-resolving models, it is suggested in this paper to process the model outputs following a fuzzy object-oriented approach to extract and track precipitating features (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.
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Submitted on : Friday, October 21, 2016 - 2:10:53 PM
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Philippe Arbogast, Olivier Pannekoucke, Laure Raynaud, Renaud Lalanne, Etienne Mémin. Object-oriented processing of CRM precipitation forecasts by stochastic filtering. Quarterly Journal of the Royal Meteorological Society, Wiley, 2016, ⟨10.1002/qj.2871⟩. ⟨hal-01378366⟩



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