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August 2017
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[Met-jobs] PhD opening: Object processing of convective-scale model outputs

From Roger Brugge <r.brugge@reading.ac.uk>
To "met-jobs@lists.reading.ac.uk" <met-jobs@lists.reading.ac.uk>
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)

laure.raynaud@meteo.fr, philippe.arbogast@meteo.fr, etienne.memin@inria.fr

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: philippe.arbogast@meteo.fr 

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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