Julien Brajard
Job Position:
Researcher Research Group:
Research interests
My work is in the field of remote sensing, inverse modelling, machine learning and data assimilation. The objective is to propose new methodologies in order to extract knowledge from data and physical systems, more specifically in oceanography.
Those methodologies were apply to estimate and forecast key variables and their associated uncertainty in the ocean such as phytoplankton and surface currents. The methodologies developed were using remote sensing data (satellite sensors).
Peer Review Publications and Books
19 total publications.
2023
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. Forecasting harmful algae blooms: Application to Dinophysis acuminata in northern Norway. Harmful Algae. 2023;126.
2022
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. Super-resolution data assimilation. Ocean Dynamics. 2022;72(8).
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54 years of microboring community history explored by machine learning in a massive coral from Mayotte (Indian Ocean). Frontiers in Marine Science. 2022;9.
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Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods. Ocean Science. 2022;18(5).
2021
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. Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. 2021;379(2194).
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. Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing. 2021;13(2).
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. Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters. 2021;16.
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Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas. Frontiers in Marine Science. 2021;8.
2020
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. Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS). 2020;2(1).
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. Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: A case study with the Lorenz 96 model. Journal of Computational Science. 2020;44.
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. Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing. 2020;12(13).
2019
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. Impact of sparse profile sampling on the reconstruction of subsurface ocean temperature from surface information. I: Proceedings of the 9th International Workshop on Climate informatics: CI 2019. 2019.
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. Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics. 2019;26(3).
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. Learning the hidden dynamics of ocean temperature with neural networks. I: Proceedings of the 9th International Workshop on Climate informatics: CI 2019. 2019.
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. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 [Internett]. 2019. Available from: https://doi.org/10.5065/y82j-f154
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. Connections between data assimilation and machine learning to emulate a numerical model. I: Proceedings of the 9th International Workshop on Climate informatics: CI 2019. 2019.
