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

physical understanding of convective phenomena and their prediction: thunderstorms, hail, extreme precipitation, particularly mediterranean severe weather events, and their evolution on climatic scales.
improving kilometric-scale forecasts of thunderstorms and extreme precipitation events, using machine learning, probabilistic forecasts, high frequency updates by observations, impact modelling including flash floods and inundation.
study and evaluation of novel datasets and observation processing techniques at high frequency in time and space. This includes connected near-surface weather stations, polarimetric radar measurements and their data assimilation.
studies of ocean-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.

Some currently active 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.
reprocessing observations of extreme precipitation, in collaboration with hydrologists.
hail observation and prediction: AI hail monitoring using polarimetric radars, high-resolution hail climatology, hail microphysics in atmospheric models, interpretable AI prediction of hail risks.
use of weather radar data: polarimetric information processing, radar simulation operator applied to microphysics studies and thunderstoerm modelling.
new prediction algorithms for tornadoes, high precipitation events, and their impacts high-frequency flash floods warnings.
study of ocean heatwaves and medicane storms.

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)
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)
IRIMONT: studies of mountain-related risks, in cooperation with Univ. Grenoble Alpes, INRAE, CNRS, IRD, BRGM (funded by PEPR IRIMA)
IRICOT: studies of coastal risks, in cooperation with Univ. Bordeaux, INP Bordeaux, CNRS, EPOC, etc. (funded by PEPR IRIMA)
PRAISE: studies on the processing of polarimetric radar data and their data assimilation in order to improve weather prediction, in cooperation with the German DWD (submitted project)

Recent scientific publications

2026

Bechis, H., C. Augros, V. Forcadell, L. Tariot, O. Laurantin: Improved radar-based hail detection and sizing using a convolutional neural network for ordinal regression. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6981440 (submitted to Quart. J. Roy. Meteor. Soc.)
Colas, G., and V. Masson, 2026: Evaluation of snow conditions modelling for various LCZ types. Urban Climate, https://doi.org/10.1016/j.uclim.2025.102767
Lebeaupin Brossier, C., Sevault, F., Malardel, S., Bouin, M.-N., Beuvier, J., Bielli, S., Corale, L., Dalphinet, A., Law Chune, S., Laxenaire, R., Munier, S., Nicolay, F., Aouf, L., Bouttier, F., Caillaud, C., Demortier, A., Faure, G., Giordani, H., Harader-Coustau, E., Maisonnave, E., Mandement, M., Nabat, P., Pianezze, J., Samson, G., Somot, S., Suzat, F., Voldoire, A.: AROBASE: A regional kilometer-scale multi-coupled modelling and forecasting system. (submitted to J. Europ. Met. Soc.)
Makrygianni, N., Berthou, S., Flack, D. L. A., Lebeaupin Brossier, C., Beuvier, J., Castillo, J. M., Renzi, E., O’Neill, C., Peláez-Zapata, D., Dias, F., Lewis, H., and Bruciaferri, D., 2026: Representation of meteotsunamis in km-scale regional simulations coupled at high frequency, Nat. Hazards Earth Syst. Sci., 26, 3185–3211, https://doi.org/10.5194/nhess-26-3185-2026
Mandement M., Delabre L., Lac C., 2026 : Le coup de vent du 18 juin 2022 en Normandie a-t-il été provoqué par un mascaret atmosphérique ? La Météorologie, 132, 32-44, 2026. https://doi.org/10.37053/lameteorologie-2026-0010
Masson, V. … C. Augros,… 2026: The Paris 2024 Olympics Research Demonstration Project. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-25-0008.1
Paz, A., Augros, C., Raspaud, D., Borderies, M., Padullés, R., Cardellach, E., & David, C. (2026). Use of a GNSS-PRO forward operator for the evaluation of global and km-scale NWP models. EGUsphere, 1–31, (submitted to Atmo. Meas. Tech. https://doi.org/10.5194/egusphere-2026-4040

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. Journal of Hydrology 657, 133048. 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
Colas, G., V. Masson, F. Bouttier, and L. Bouilloud, 2025: Traffic impact modelling in SURFEX-TEB V9.0 model for improved road surface temperature prediction. Geosci. Model Dev., 18, 9945–9966 https://doi.org/10.5194/gmd-18-9945-2025
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
Fischer, J., Groenemeijer, P., Holzer, A., Feldmann, M., Schröer, K., Battaglioli, F., Schielicke, L., Púčik, T., Antonescu, B., Gatzen, C., and TIM Partners: Invited perspectives: Thunderstorm intensification from mountains to plains, Nat. Hazards Earth Syst. Sci., 25, 2629–2656, https://doi.org/10.5194/nhess-25-2629-2025
Fleury, A., F. Bouttier, T. Bergot, 2025 : Calibration of parameter perturbations for ensemble prediction using a stochastic inverse problem. Mon. Wea. Rev., 153, 655–672. https://doi.org/10.1175/MWR-D-24-0032.1
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)
Marchal, H., F. Bouttier, O. Nuissier, 2025 : Is considering runs (in)consistency so useless for weather forecasting ? Nat Haz Earth Sys Sci 25(8) 2613-2628, https://doi.org/10.5194/nhess-25-2613-2025
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