Optimal rain forecasts for the prediction of floods and inundations F/M
Toulouse, 31057
CDD
01/05/2026- 30/04/2028
3470€
Description
As a public expert in weather and climate, Météo-France is here to help keep you safe every day and assist you in making the best decisions in a changing climate. With dangerous weather events becoming even more intense and frequent due to climate change, our mission to keep you safe is crucial. We mobilize our expertise and scientific and technological excellence to enable you to anticipate and adapt to challenging weather and climate events.
Find us online:https://meteofrance.com/carte-didentite-de-meteo-france
The job will be carried out as staff member in a CNRM research lab team in Toulouse, France. CNRM provides extensive computing, technical and administrative support. The supervisors will be located in neighbouring offices and work meetings will typically be held weekly. Information about CNRM and its teams is available at https://cnrm.sedoo.fr/en/homepage/
Why join us?
Embark on a stimulating adventure that serves everyone, alongside men and women who are committed every day to tackling the challenges posed to our society by weather and climate.
And enjoy the following benefits: flexible working hours, RTT (reduced working time), teleworking, staff restaurant or meal vouchers, 75% contribution to public transport costs, contribution to mutual insurance, sports and cultural associations depending on the site concerned (climbing, gym, pottery, theater, etc.).
Other benefits await you, come and discover them!
Missions
Flooding hazards entail large economical and human costs, that are likely to worsen in the future as climate and vulnerabilities evolve. Real-time hydrometeorological warnings are important tools for reducing losses and casualties, but their generation is often limited by large prediction error, primarily because of uncertainties in the prediction of extreme precipitation at small scales. Ensemble prediction is a key technique for managing uncertainties, but its use raises difficult challenges, both technical (how to estimate probabilities from many numerical forecasts) and human (how to convert very large forecast sets into effective warnings under time constraints).
In the framework of the French IRIMA program https://www.pepr-risques.fr/en, project IRICLIM WP2 aims to design and demonstrate new solutions to this problem, in a form that will be suitable around 2030 for helping state agencies Météo-France and SCV (Service Central Vigicrues) in their related warning duties.
The aim of the proposed work is to optimize the precipitation prediction data that is provided to hydrological models for real time flood prediction. More precisely, we wish to generate innovative time-seamless rain forecast scenarios from ensemble predictions (~50 ensemble prediction members at kilometric scale over mainland France, at ranges 0-24 hours). The algorithms used should (a) be compatible with real-time execution in an operational framework, (b) allow high frequency forecast updates using the latest observations, (c) have optimal predictive quality for the intended purposes, taking into account climatological event severity, false alarm issues, and product acceptability to human forecasters that will be responsible for issuing warnings.
The methodology will rely on post-processing archives of past precipitation forecasts, nowcasts and observations, including extreme historical cases from recent high-resolution ARRA reanalysis data. An innovative scenario generation algorithm will be developed and verified over large datasets, using existing methods as benchmark. Verification will include probabilistic scores, user-oriented metrics, and subjective case studies in collaboration with experienced human forecasters.
24 months contract, from 1st May 2026 to 30 April 2028
For any futher information about the project, please contact M. François BOUTTIER (francois.bouttier@meteo.fr)
Profile
This is a junior scientist position. The candidate should hold a PhD thesis with a relevant specialty.
Required abilities:
The candidate should:
have expert knowledge of mesoscale atmospheric physics, in particular severe convection and precipitation events. Experience with weather radar data will be an advantage
have experience in numerical weather prediction, particularly data processing of high-resolution forecasts
be able to develop and apply a python workflow for data acquisition, processing, numerical experimentation, interactive visualization, and scientific collaboration
be experienced in scientific work as part of a research team, including provision of written and oral presentations, in French and English