Formation et parcours
- Co-direction équipe projet ARCHES (AI Research for Climate Change and Environmental Sustainability) INRIA-LATMOS
- Professeure Université de Versailles Saint-Quentin-en-Yvelines / LATMOS
- Associate Professor, Observatory of Earth Sciences (UVSQ) / LATMOS
- Habilitation à diriger des recherches (Statistical Modeling Applied to Atmospheric Electromagnetic Wave Propagation and Precipitation Observation)
- Associate Professor, Department of Physics (UVSQ) / CETP
- Ph.D. in Physical Methods in Remote Sensing, University Paris 7, France
- Engineering degree in Telecommunications, University Paris 13, France
Thèmes de recherche
Precipitation Characterization across Various Scales, including Microphysical, Local, Regional, and Global levels and Scale Relations
Multiscale Analysis, Machine Learning, and Deep Learning, specifically applied to Remote Sensing of the Atmosphere.
Space remote sensing, microwave radar and radiometry
Activités d’enseignement
1996 – 2025Head Master TRIED (Machine Learning and Data Processing) at UVSQ – Paris-Saclay University
- Neural Networks – Master TRIED
- Statistical Analysis of Real Datasets – Master TRIED
- Applied IA – Master TRIED
Encadrement doctoral
8thesis directions
(dont 2 en cotutelle internationale)
(dont 2 en cotutelle internationale)
8thesis co-directions
40master’s or engineering internships
8 thesis directions (of which two International cotutelle framework), and 8 thesis co-directions listed hereafter and supervision of 40 master’s or engineering internships, mainly in the field of AI applied to remote sensing observations.
- depuis 2025Matthieu Meignin (PhD student since 2025) — co-advisor Nicolas Viltard
Impact of climate change on global precipitation: AI for merging geostationary satellite observations (MSG and MTG) and micro ondes constellations - depuis 2022Baptiste Guigal (PhD student since 2022) — co-advisor Barthes Laurent and Aymeric Chazottes
Analysis of weather radar images using machine learning methods for the identification, tracking and characterisation of precipitation systems - 2024Vibolroth Sambath (thesis defended in 2024) — co-advisor Nicolas Viltard
Statistical analysis of spatial chronicles based on 20 years of TRMM observations - 2024cotutelleSabrine Derouiche (thesis defended in 2024, International cotutelle framework) — co-supervision Zoubeida Bargaoui
Impact of climate change in regional-scale numerical models - 2022Mr Rivera Martinez (thesis defence in 2022) — Principal-advisor Philippe Ciais
Validation and deployment of greenhouse gas (CH4) sensors for the characterisation of industrial sources - 2021Mr Pierre Lepetit (thesis defence in 2021) — co-advisor Laurent Barthes
Contribution of deep learning algorithms to the characterisation of snow in plains based on geolocated images. - 2018Mr Djallel Dilmi (thesis defence in 2018) — co-advisor Laurent Barthes
Impact of variability and intermittency on the overall characterisation of precipitation - 2016François Mercier (thesis defended in 2016) — Principal-advisor Laurent Barthes
Assimilation of heterogeneous observations for the restoration of rainfall fields - 2015Nawal Akrour (thesis defended in 2015)
Stochastic simulation of fine-scale precipitation: application to urban observation - 2014cotutelleHanen Ghanmi (thesis defended in 2014, International cotutelle framework) — co-supervision Zoubeida Bargaoui
Estimation of IDAF curves for the Tunis region in a multifractal context - 2013Ramses Sivira (thesis defended in 2013) — co-advisor Hélène Brogniez
Water vapour retrieval algorithm for the Megha Tropique space mission - 2011Sébastien Verrier (thesis defended in 2011) — co-advisor Laurent Barthes
Modelling the spatial and temporal variability of precipitation using a multifractal approach - 2008Louis De Montera (thesis defended in 2008) — co-advisor Laurent Barthes
Study of the variability of rain microphysics: Application to the propagation of millimetre waves in satcom - 2007Mr Miguel Angel Pérez Chavarria (thesis defended in 2007) — co-advisor Sylvie Thiria
Retrieval of atmospheric parameters using spatial microwave radiometry. Neural methodology. - 2005Olivier Brisseau (thesis defended in 2005) — co-advisor Laurent Barthes
Development of frequency similarity models for EHF earth-to-space links. - 2000Emmanuel Moreau (thesis defended in 2000) — co-advisor Claude Klapisch
Reconstruction of atmospheric parameters using spatial microwave radiometry. Use of neural methods
Organisations et principales responsabilités
- 2026 –Encadrement des travaux des post-doctorants Victor Enescu et Assaad Zeghina et ingénieur de recherche Amine Laib
- 2025 –Co-PI of the « AI Research for Climate Change and Environmental Sustainability » (ARCHES) INRIA-LATMOS project team
- 2022 –Member of council « Geoscience, Climate, Environment, Planets » Graduate School of Paris-Saclay
- 2021 –Member of the Programmes Committee of the DATAIA Institute at Paris-Saclay University
- 2020 –Member of Atmosphere Group Expert at TOSCA CNES
- 2013 – 2024Member of the LATMOS (CNRS/UVSQ/UPMC) management committee
- 2018 – 2024Head of the research team SPACE/LATMOS
- 2010 – 2022Head of scientific projects (CNES, CMCU du programme PHC Utique)
Projets de recherche récents
- Principal investigator of CNES project title: Quantitative analysis of the impact of precipitation variability on its characterisation within the framework of the GPM mission
- Principal investigator of project under the auspices of the French Office for Biodiversity (OFB, Ministry of Ecological Transition) entitled Deep Learning applied to the characterisation of migratory birdlife.
- AI expert applied to remote sensing in the PNTS Project Use of supervised and unsupervised deep learning methods for the study and reproduction of the atmospheric water cycle from satellite data led by Nicolas Viltard
- AI expert applied to remote sensing in the i-Demo France 2030 Project led by PROMETHE Earth Intelligence in consortium with BOWEN and LATMOS
Publications
39articles in international journals
45international conference proceedings
67papers and posters at international conferences
Author of 39 articles in international journals and 45 international conference proceedings, 67 papers and posters at international conferences.
La liste des publications est disponible en ligne sur HAL.
Four selected recent publications related to the project topic
- Sambath, V., Dubois-Quilici, N., Viltard, N., Martini, A., & Mallet, C. (2024). Unsupervised Domain Adaptation to Mitigate Out-of-Distribution Problem of Spatial Radiometer Images: Application to Quantitative Precipitation Estimation. IEEE Transactions on Geoscience and Remote Sensing, 62, 1-14.
- Guigal, B., Barthes, L., Chazottes, A., Viltard, N., Le Bouar, E., Moreau, E., & Mallet, C. (2024, July). Meta-Analysis of Rain Nowcasting Using Machine Learning and Non-Machine Learning Models. In IGARSS 2024 – 2024 IEEE International Geoscience and Remote Sensing Symposium (pp. 5534-5538). IEEE.
- Gelbart, L., Barthès, L., Mercier-Tigrine, F., Chazottes, A., & Mallet, C. (2024). Enhanced Quantitative Precipitation Estimation (QPE) through the opportunistic use of Ku TV-sat links via a Dual-Channel Procedure. Atmospheric Measurement Techniques Discussions, 2024, 1-34.
- Viltard, N., Sambath, V., Lepetit, P., Martini, A., Barthès, L., & Mallet, C. (2023). Evaluation Of Drain, A Deep-Learning Approach To Rain Retrieval From Gpm Passive Microwave Radiometer. IEEE Transactions on Geoscience and Remote Sensing.