Biblio
Filters: Author is Julien Brajard [Clear All Filters]
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing 13, (2021).
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
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).
Twenty-One Years of Phytoplankton Bloom Phenology in the Barents, Norwegian, and North Seas. Frontiers in Marine Science 8, (2021).
Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters 16, (2021).
