Maxime Beauchamp

Maxime Beauchamp

Research scientist at the Danish Meteorological Institute and associate researcher at IMT Atlantique/Lab-STICC/Odyssey

Peer-reviewed publications

2025. Fast and scalable Air Quality neural emulator for high-resolution predictions on very large domains. Maxime Beauchamp., Vincent Lécluse, Nicolas Thorr, Anas Oubida, Charles Schillinger, Florent Vasbien, Jérôme Le Paih. Submitted to JGRL.

2025. Enhancing Air Quality simulations with neural downscaling architectures. Beauchamp M., Bessagnet B., Pisoni E., Rey Pommier A., De Meij A. and Thunis P. Atmospheric Sciences Letters., https://doi.org/10.1002/asl.70003

2025. Multiscale neural assimilation scheme for learning Near-Real-Time Level 4 SST products from satellite observations. Beauchamp M.., Karagali I., Gacitúa G., Høyer J.L., Ballarotta M. and Fablet R. Nature Scientific Report, https://doi.org/10.1038/s41598-025-23682-9

2025. Neural variational Data Assimilation with Uncertainty Quantification using SPDE priors. Beauchamp M., Fablet R., Benaı̈chouche S., Tandeo P. and Desassis N. Artificial Intelligence for the Earth Systems. American Meteorological Society. https://doi.org/10.1175/AIES-D-24-0060.1

2024. Data-driven spatio-temporal interpolation for satellite-derived geophysical tracers. Beauchamp M. and Fablet R. chapter 6 in Multitemporal Earth Observation Image Analysis: Remote Sensing Image Sequences, https://doi.org/10.1002/9781394306657.ch6

2023. Ensemble-based 4DVarNet uncertainty quantification for the reconstruction of Sea Surface Height dynamics. Beauchamp M., Febvre Q., and Fablet R. Environmental Data Science2, E18. doi: 10.1017/eds.2023.

2023. Learning Neural Optimal Interpolation Models and Solvers. Beauchamp M., Febvre Q., Georgentum H., and Fablet R. In Computational Science - ICCS 2023, pp. 367-381, doi : 10.1007/978-3-031-36027-5 28

2023. 4DVarNet-SSH: end-to-end learning of variational interpolation schemes for nadir and wide-swath satellite altimetry. Beauchamp M., Febvre Q., Georgenthum H. and Fablet R. GMD Volume 16, Number 8, pp. 2119–2147, doi: 10.5194/gmd-16-2119-

2023. An iterative optimization scheme to accommodate inequality constraints in air quality geostatistical estimation of multivariate PM. Beauchamp M., Bessagnet B. Heliyon. Volume 9, Issue 6, doi: 10.1016/j.heliyon.2023.e

2021. Joint Interpolation and Representation Learning for Irregularly Sampled Satellite-Derived Geophysical Fields. Fablet R., Beauchamp M., Drumetz L. and Rousseau F. Frontiers in Applied Mathematics and Statistics. https://www.frontiersin.org/articles/10.3389/fams.2021.655224

2021. Deep learning techniques applied to super-resolution chemistry transport modeling for operational uses. Bessagnet B., Beauchamp M., Menut L., Fablet R., Pisoni E. and Thunis P. Environ. Res. Commun.Volume 3, Number 8, doi: 10.1088/2515-7620/ac17f

2021. End-to-end learning of variational interpolation schemes for satellite-derived ssh data. Beauchamp M., Amar M.M., Febvre Q., Fablet R. IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 7418-7421, doi: 10.1109/IGARSS47720.2021.9554800.

2020. Intercomparison of Data-Driven and Learning-Based Interpolations of Along-Track Nadir and Wide-Swath SWOT Altimetry Observations. Beauchamp M., Fablet R., Ubelmann C., Ballarotta M. and Chapron B.Remote Sens.2020, 12, 3806. https://doi.org/10.3390/rs

2020. Data-driven and learning-based interpolations of along-track Nadir and wide-swath SWOT altimetry observations. Beauchamp M., Fablet R., Ubelmann C., Ballarotta M. and Chapron B. Proceedings of the 10th International Conference on Climate Informatics.Association for Computing Machinery, New York, NY, USA, 22-29. DOI:https://doi.org/10.1145/3429309.

2020. N-dimensional Fortran interpolation programme (NterGeo.v2020a) for geophysics sciences. Application to a back-trajectory programme (Backplumes.v2020r1) using CHIMERE or WRF outputs. Bessagnet B. and Menut L. and Beauchamp M.Geosci. Model Dev., 14, 91-106, https://doi.org/10.5194/gmd-14-91-2021

2018. A necessary distinction between spatial representativeness of an air quality monitoring station and the delimitation of exceedance area. Beauchamp M., Malherbe L., Letinois L., de Fouquet C.Environmental Monitoring and Assessment, Vol. 190, Pages 441-468, DOI: https://doi.org/10.1007/s10661-018-6788-y

2018 An additive geostatistical model for mixing total and partial PM 10 observations with CHIMERE rCTMBeauchamp M., Bessagnet B., de Fouquet C., Malherbe L., Meleux, F. Atmospheric Environment, Vol. 189, Pages 61-79, DOI: https://doi.org/10.1016/j.atmosenv.2018.06.

2018. A polynomial approximation of the traffic contributions for kriging-based interpolation of urban air quality model Beauchamp M., Malherbe L., de Fouquet C., Letinois L., Tognet, F. Environmental Modelling and Software, Vol. 105, 132-152, DOI: https://doi.org/10.1016/j.envsoft.2018.03.

2018. On numerical computation for the distribution of the convolution of N independent rectified Gaussian variables. Beauchamp M.Journal de la Soci ́et ́e Francaise de Statistique, Vol. 159, No 1. 2018

2017. Dealing with non-stationarity through explanatory variables in kriging-based air quality maps. Beauchamp M., Malherbe L., de Fouquet C.Spatial Statistics, Vol. 22 (2017) 18-46, DOI: https://doi.org/10.1016/j.spasta.2017.08.

2017. Analyzing spatio-temporal data with R: Everything you always wanted to know but were afraid to ask. Allard D. and Beauchamp M., Bel L., Desassis N. and Gabriel E., Geniaux G., Malherbe L., Martinetti D., Opitz T., Parent E., Romary T., Saby N.Journal de la Société Francaise de Statistique, Vol. 158, No 3. 2017

2015. A pragmatic approach to estimate the number of days in exceedances of limit value for PM10. Beauchamp M., Malherbe L., Fouquet C.,Atmospheric Environment, Vol. 111, 79-93, DOI: http://dx.doi.org/10.1016/j.atmosenv.2015.03.

2015. Building spatial composite indicators to analyse environmental health inequalities at a regional scale. Saib M-S., Caudeville J., Beauchamp M., Carre F., Ganry O., Trugeon, A. and Cicolella, A..Environmental Health, 2015, DOI: 10.1186/s12940-015-0054-

2015. High-resolution air quality simulation over Europe with the chemistry transport model CHIMERE. Terrenoire E., Bessagnet B., Rouil L., Tognet F., Pirovano G., Letinois L., Beauchamp M., Colette A., Thunis P., Amann M., and Menut L.Geoscientific Model Development, Vol.8, 21-42, DOI: https://doi.org/10.5194/gmd-8-21-

2014. Can further mitigation of ammonia emissions reduce exceedances of particulate matter air quality standards? Bessagnet B., Beauchamp M., Guerreiro C., de Leeuw F., Tsyrod S., Ruyssenaars R., Sauter F., Velders G., Meleux F., Colette A., Rouil L.,Environmental Science & Policy, Vol. 44, 149-163, DOI: https://doi.org/10.1016/j.envsci.2014.07.