|From||Roger Brugge <email@example.com>|
|Date||Wed, 23 Aug 2017 10:21:55 +0000|
Laboratory: National Centre for Meteorological Research, Toulouse, France Title : Object processing of convective-scale model outputs 1 Nov 2017- 30 oct 2020 Supervisors : Dr Laure Raynaud, Dr Philippe Arbogast, Dr Etienne Mémin (HDR) firstname.lastname@example.org, email@example.com, firstname.lastname@example.org Summary The French convective-scale Arome model, operational at Météo-France, is able to accurately represent some severe weather events, such as thunderstorms, heavy precipitation, fog or strong winds. However, the first years of Arome utilization suggest that these forecasts are affected by position, amplitude and timing errors. In order to improve these deterministic forecasts, an ensemble prediction system based on the Arome model has recently been developed, and provides an estimation of the forecast uncertainty. The development of relevant post-processing methods is another way of improving forecasts. Among them, a possible solution is the object-based approach: the main idea behind object-oriented processing consists in extracting the predictable signal from forecasts, under the form of coherent features, while the smaller and less predictable scales are filtered out. In this context, Arbogast et al. (2016) and Destouches (2017) proposed a probabilistic approach to automatically detect and track precipitation objects. The method is based on the use of segmentation methods for the detection part and of a stochastic particle filter for the tracking part. The goal of the PhD is to pursue this work and to extend its application to other meteorological parameters such as cloud cover and wind gusts. Organization of the PhD The first part of the PhD plans to refine the current detection/tracking algorithm for precipitation forecasts. In particular, the goal is to provide a robust algorithm, able to automatically detect and recognize precipitation of different types. The verification of object detection/tracking methods has been mainly subjective so far. Objective verification will be the next important aspect to consider, in order to quantify the added value of the object processing. Finally, this work will be extended to other weather parameters. Basically, all parameters with a high spatial and/or temporal degree of intermittency could benefit from this object processing. Good knowledge of numerical modelization, data assimilation and image processing would be useful. Please contact: email@example.com References M. Destouches, 2017 : Detection and tracking of precipitation objects in convective-scale forecasts, Research internship report. Arbogast, P., O. Pannekoucke, L. Raynaud, R. Lalanne and E. Mémin, 2016 : Object?\oriented processing of CRM precipitation forecasts by stochastic filtering. Quart. J. Roy. Meteor. Soc. Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteor. Soc., 142, 854-866.
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