PRECIP team

Group : GMME

Team Leader : François Bouttier

The PRECIP team investigates issues related to observing, understanding and forecasting deep convective clouds and their impacts in midlatitude regions. This includes measurement data processing, numerical atmospheric simulations, and their practical use.

Deep convection is the fundamental physical mechanism that creates deep, energetic clouds called cumulonimbus. These clouds are called thunderstorms when they produce lightning, and they produce precipitation which can reach the ground as rain, snow, sleet, graupel or hail. Cumulonimbus clouds can produce rotating atmospheric phenomena, including tornadoes.

Field of activity

improving our physical understanding of convective phenomena and their prediction: thunderstorms, hail, extreme precipitation, particularly in mediterranean severe weather events.
probabilistic weather prediction and very short term (nowcasting) prediction at kilometric scales: we aim to improve forecasts of thunderstorms and extreme precipitation using machine learning technology, in order to better manage uncertainties, high-volume real-time observed data streams, impact models (including flash floods and inundation), and decision-making.
studies of new high-resolution atmospheric observing systems, such as smart objects and polarimetric radar meteorology. They can be used to better understand weather events, to improve numerical models of the atmosphere, and they can be assimilated into real-time weather prediction systems.
studies of ocean-wave-atmosphere interactions: the aim is to better understand and improve the representation of these processes in weather forecast and climate models, particularly regarding ocean waves and coastal flooding risks.
These topics are investigated using high resolution datasets and physically based models, including the Méso-NH and AROME models, in order to contribute improvements to the Météo-France operational weather prediction systems.

Research topics

smart objects (Internet of Things) weather data: acquisition, processing, quality control, real time visualization, synergies with other observing systems to study convective events, data assimilation for kilometric resolution weather prediction.
use of weather radar data: use of polarimetric information, simulation of radar data in relation with cloud microphysical processes, numerical simulation and prediction of severe thunderstorms, identification of hailstorms using AI to process radar data.
scientific study and numerical modelling of severe thunderstorms events: hail, tornadoes, high precipitation events, and their impacts. Tools to facilitate the interpretation of probabilistic weather predictions, including automated weather predictions, decision-making tools for expert human forecasters, and weather data used in the prediction of flood, inundation, and hydropower.
3D coupled modelling and process studies at the interface between oceans, waves and the atmosphere, including tools for predicting coastal inundation in real time.

Main projects

In 2025/2026, the PRECIP team participates in the following projects that foster scientific collaboration and funding:

AROBASE : a high-resolution Earth system with 3D coupling between ocean and atmosphere (funded by Météo-France and CNRS)
AMAZOA: ocean/atmosphere coupling during marine heat events (funded by CNRS GDR)
AWARE: impact of ocean waves on ocean/atmosphere fluxes (funded by ANR)
EXDOMO: use of high-volume observation data to study and predict deep atmospheric convection (funded by CNRS INSU/LEFE)
IoT Observations for NWP: use of weather observations from smart objects to improve numerical weather prediction (funded by ECMWF and EUMETNET).
IRICLIM: studies of hydroclimatic risks, notably the prediction of flash floods and inundation, in cooperation with Univ Eiffel, BGRM, INRAE, SCV Vigicrues, CNRS (funded by PEPR IRIMA, France 2030)
PRAISE: studies on the processing of polarimetric radar data and their data assimilation in order to improve weather prediction, in cooperation with the German DFG (submitted ANR project)
PRAMAG : study of the impact of ocean swell on wind (funded by ANR)

Recent scientific publications

2025

Charpentier-Noyer, M., O. Payrastre, P. Nicolle, H. Marchal, F. Bouttier et E. Gaume, 2025 : An agent-based modeling of rescue operations for the evaluation of short-range flash flood ensemble forecasts. Accepted in Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2025.133048
Colas, G., V. Masson, F. Bouttier, L. Bouilloud, L. Pavan, V. Karisto, 2025 : Improved winter conditions in SURFEX-TEB v9.0 with a multi-layer snow model and ice for road winter maintenance. Geophys. Model Dev. https://doi.org/10.5194/egusphere-2024-1039
David, C., Augros, C., Vié, B., Bouttier, F., and Le Bastard, T.: Improved simulation of thunderstorm characteristics and polarimetric signatures with LIMA two-moment microphysics in AROME, Atmos. Meas. Tech., 18, 3715–3745, https://doi.org/10.5194/amt-18-3715-2025
Demortier, A., Mandement, M., Pourret, V., and Caumont, O., 2025 : Assimilation of temperature and relative humidity observations from personal weather stations in AROME-France, Nat. Hazards Earth Syst. Sci., 25, 429–449, https://doi.org/10.5194/nhess-25-429-2025
Fleury, A., F. Bouttier, T. Bergot, 2025 : Calibration of parameter perturbations for ensemble prediction using a stochastic inverse problem. Accepted for publication in Mon. Wea. Rev. in Jan 2025.
Gauvrit, E., Bouin, M.‐N., Delouis, J.‐ M., & Boulanger, F., 2024 : Advances on the links between turbulent and submeso‐ to mesoscales during EUREC4A. Earth and Space Science, 11, e2024EA003865. https://doi.org/10.1029/2024EA003865 (à paraître)
Vernay, M., Lafaysse, M., and Augros, C., 2024 : Radar based high resolution ensemble precipitation analyses over the French Alps. Atmos. Meas. Tech, 18, 1731-1755. https://doi.org/10.5194/amt-18-1731-2025

2024

Bouttier, F. et H. Marchal, 2024: Probabilistic short-range forecasts of high precipitation events : optimal decision thresholds and predictability limits. Nat Hazards Earth Sys Sci, 24, 2793-2816. https://doi.org/10.5194/nhess-24-2793-2024
Bouttier, F., et Mandement, M., 2024 : Vers une anticipation à 1-6h des risques de pluies intenses quasi-stationnaires. LHB, 110(1). https://doi.org/10.1080/27678490.2024.2325698.
Bouin, M-N., Lebeaupin Brossier, C., Malardel, S., Voldoire, A., Sauvage, C., 2024 : The wave-age-dependent stress parameterisation (WASP) for momentum and heat turbulent fluxes at sea in SURFEX v8.1, Geosci. Model Dev., 17, 117–141, https://doi.org/10.5194/gmd-17-117-2024
Brilouet, P.-E., Redelsperger, J.-L., Bouin, M.-N., Couvreux, F. et Villefranque, N., 2024 : A numerical study of ocean surface layer response to atmospheric shallow convection: impact of cloud shading, rain and cold pool. Quarterly Journal Of The Royal Meteorological Society, 150(760), Part A. 1401-1419. https://doi.org/10.1002/qj.4651 ; https://archimer.ifremer.fr/doc/00869/98109/
Combarnous P., M. Martet, O. Caumont, E. Defer, 2024 : Assimilation of satellite lightning data in a storm-scale numerical weather prediction system using a 3D-EnVar data assimilation scheme. Mon. Wea. Rev. https://doi.org/10.1175/MWR-D-23-0100.1
Chambon P. et Mandement M., 2024 : Recherches et développements pour l’exploitation future des observations par assimilation de données pour la prévision numérique du temps. Comité Scientifique Consultatif de Météo-France. 2024. https://meteofrance.hal.science/meteo-04610170
Demortier, A., Mandement, M., Pourret, V., Caumont, O., 2024 : Assimilation of surface pressure observations from personal weather stations in AROME-France, Nat. Hazards Earth Syst. Sci., 24, 907–927, https://doi.org/10.5194/nhess-24-907-2024
Forcadell, V., Augros, C., Caumont, O., Dedieu, K., Ouradou, M., David, C., Figueras i Ventura, J., Laurantin, O., and Al-Sakka, H., 2024 : Severe hail detection with C-band dual-polarisation radars using convolutional neural networks, Atmos. Meas. Tech. 17, 6707-6734, https://doi.org/10.5194/amt-17-6707-2024
Forster, A., Augros, C., Masson, V., 2024. Urban influence on convective precipitation in the Paris region : Hectometric ensemble simulations in a case study. Quart. J. Roy. Met. Soc., https://doi.org/10.1002/qj.4749
Lebeaupin Brossier, C., 2024 : Processus océan-atmosphère, prévision couplée à fine-échelle et précipitations intenses sur le bassin méditerranéen occidental. Habilitation à Diriger Les Recherches, Univ. Toulouse III – Paul Sabatier, 140pp. https://meteofrance.hal.science/tel-04556105