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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 structuresin cloud-resolving models, it is suggested in this paper to process the modeloutputs following a fuzzy object-oriented approach to extract and trackprecipitating features (associated with a higher predictability than the directmodel outputs). The present approach uses the particle filter method torecognize patterns based on predefined texture or spatial variability of themodel output. This provides an ensemble of precipitating objects, which arethen propagated in time using a stochastic advection-diffusion process. Thismethod is applied to both deterministic and ensemble forecasts provided bythe AROME-France convective-scale model. Specific case studies support theability 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, 142 (700), pp.2827-2838. ⟨10.1002/qj.2871⟩. ⟨hal-01378366⟩

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