Articles
Estimating the pycnocline depth from the SAR signature of internal waves in Alboran Sea — [PDF]
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022. <doi:10.1109/JSTARS.2022.3214298>Weather forecast for the 35th America’s Cup (2017) winners based on a limited area model — [PDF]
Meteorological Applications, 2020; 27:e1879 <doi:10.1002/met.1879>Use of Sentinel-1 C-Band SAR Images for Convective System Surface Wind Pattern Detection. — [PDF]
Journal of Applied Meteorology and Climatology, American Meteorological Society, 2020, 59 (8), pp.1321-1332. <doi:10.1175/JAMC-D-20-0008.1>Detection of convective systems through surface wind gust estimation based on Sentinel-1 images: A new approach — [PDF]
Atmospheric Science Letters, 2018; 19:e863 <doi:10.1002/asl.863>Stability constraints for oceanic numerical models: implications for the formulation of time and space discretizations. — [HAL] [PDF]
Ocean Modelling, Elsevier, 2015, 92, pp.124-148. <doi:10.1016/j.ocemod.2015.06.006>Identification of equivalent topography in an open channel flow using Lagrangian data assimilation. — [HAL] [PDF]
Computing and Visualization in Science, 2010, 13 (3), pp. 111-119. <doi:10.1007/s00791-009-0130-8>Lagrangian data assimilation for river hydraulics simulations. — [HAL] [PDF]
Computing and Visualization in Science, Springer, 2009, 12 (5), pp. 235-246 <doi:10.1007/s00791-008-0089-x>Automatic differentiation: a tool for variational data assimilation and adjoint sensitivity analysis for flood modeling. — [HAL] [PDF]
Lecture Notes in Computational Science and Engineering, Springer, 2006, 50, pp. 249-262 <doi:10.1007/3-540-28438-9_22>
Book Chapters
- Data
Assimilation (in Numerical Methods, Volume 3)
in Numerical Methods, Environmental Hydraulics Series, Volume 3 (ed. J.-M. Tanguy), John Wiley & Sons, Inc., 2010, pp. 273-293 (chapter 12) <doi:10.1002/9781118557877.ch12>
PhD. Thesis
- Assimilation
de données lagrangiennes pour la simulation numérique en hydraulique
fluviale. — [PDF]
Mathématiques Appliquées. Institut National Polytechnique de Grenoble – INPG, Oct. 2007. French
Reports
Dassflow v1.0: a variational data assimilation software for 2D river flows. — [PDF]
Research Report RR-6150, INRIA. 2007.T2DInverse: Towards calibration and sensitivity analysis into Telemac2D using automatic differentiation. — [PDF]
Research Report RR-5618, INRIA. 2005.
Communications
Use of SAR Imagery and Artificial Intelligence for a Multi-Components Ocean Monitoring — [PDF]
IGARSS 2020 - IEEE International Geoscience and Remote Sensing Symposium, 2020, pp. 3817-3820 <doi:10.1109/IGARSS39084.2020.9323530>Korteweg-deVriès limitations for the interpretation of SAR images in the Strait of Gibraltar: impact of different stratifications on ISW surface signature
EGU General Assembly 2020, Online, 4–8 May 2020 <doi:10.5194/egusphere-egu2020-21497>Artificial Intelligence for Improvement of Convective System Tracking and Its Surface Effect Prediction
EGU General Assembly 2020, Online, 4–8 May 2020 <doi:10.5194/egusphere-egu2020-17825>Non-reasonable but efficient use of schemes in current model to improve realistic explicit convection modelling
EGU General Assembly 2020, Online, 4–8 May 2020 <doi:10.5194/egusphere-egu2020-13855>Combination of Geostationary and Polar Satellite Sensors to Monitor Cumulonimbus and Their Winds at the Ocean Surface — [PDF]
2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020, pp. 5380-5383 <doi:10.1109/IGARSS39084.2020.9323111>High-Resolution Ocean Winds: Hybrid-Cloud Infrastructure for Satellite Imagery Processing — [PDF]
in 2018 IEEE 11th International Conference on Cloud Computing (CLOUD), San Francisco, CA, USA, Jul. 2018, pp. 883–886 <doi:10.1109/CLOUD.2018.00127>Stability constraints for oceanic numerical models: implications for the formulation of space-time discretizations — [HAL]
2014 AGU Ocean Sciences Meeting, Feb 2014, Honolulu, United StatesThe Coupled Multi-scale Downscaling Climate System : a decision-making tool for developing countries. — [PDF]
OCEANS 2010, Seattle, WA, United States, Sep 2010, pp. 1-9 <doi:10.1109/OCEANS.2010.5664389>Assimilation of remote sensed data for river hydraulic simulations. — [PDF] [HAL]
International Congress on Industrial and Applied Mathematics (ICIAM 2007), Zürich, Jul 2007, 7, pp. 1100203-1100204, PAMM Journal <doi:10.1002/pamm.200700680>Variational Data Assimilation for 2d Fluvial Hydraulics Simulations. — [PDF]
CMWR XVI-Computational Methods for Water Ressources. Copenhagen, june 2006., Jun 2006, Copenhagen, Denmark.On variational data assimilation for 1D and 2D fluvial hydraulics. — [PDF]
European Conference on Mathematics for Industry (14th ECMI), 2006, Madrid, Spain. Springer, Progress in Industrial Mathematics at ECMI 2006, pp. 361-365 <doi:10.1007/978-3-540-71992-2_53>Lagrangian data assimilation for river hydraulics simulations. — [PDF] [HAL]
4th European Conference on Computational Fluid Dynamics, ECCOMAS CFD 2006., Wesseling, Oñate and Périaux editors, 2006, Egmond aan Zee, Netherlands. pp. 2421-2429Automatic Differenciation: a tool for variational data assimilation and adjoint sensitivity analysis for flood modeling. — [PDF] [HAL]
In: Bücker M., Corliss G., Hovland P., Naumann U. et Norris B (eds). Automatic Differentiation: Applications, Theory and Tools, proceedings of the 4th International Conference on Automatic Differentiation, Jul 2004, Chicago, United States (AD2004). Lecture Notes in Computational Science and Engineering, Springer, vol 50, pp. 249-262 <doi:10.1007/3-540-28438-9_22>