Biblio
Filters: Author is Julien Brajard [Clear All Filters]
54 years of microboring community history explored by machine learning in a massive coral from Mayotte (Indian Ocean). Frontiers in Marine Science 9, (2022).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters 16, (2021).
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
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 44, (2020).
Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 379, (2021). Abstract
Connections between data assimilation and machine learning to emulate a numerical model. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 (2019).doi:10.5065/y82j-f154
Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).
Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data. Weather and forecasting 38, (2023).
Forecasting harmful algae blooms: Application to Dinophysis acuminata in northern Norway. Harmful Algae 126, (2023).
Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing 13, (2021).
Impact of sparse profile sampling on the reconstruction of subsurface ocean temperature from surface information. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 (2019).doi:10.5065/y82j-f154
Learning the hidden dynamics of ocean temperature with neural networks. Proceedings of the 9th International Workshop on Climate informatics: CI 2019 (2019).doi:10.5065/y82j-f154
Surface circulation properties in the eastern Mediterranean emphasized using machine learning methods. Ocean Science 18, (2022).
Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas. Frontiers in Marine Science 8, (2021).
