Biblio
Filtre: Forfatter er Laurent Bertino [Slett Alle Filtre]
The Arctic marine forecasting center in the first Copernicus period. Proceedings of the 9th EuroGoos International Conference "Advances in Operational Oceanography": Expanding Europe's Observing and Forecasting Capacity (2021).doi:10.13155/83160
Combined influence of oceanic and atmospheric circulations on Greenland sea ice concentration. The Cryosphere 15, (2021).
Combining data assimilation and machine learning to infer unresolved scale parametrization. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences (2021).doi:10.1098/rsta.2020.0086 Abstract
Freshwater in the Arctic Ocean 2010–2019. Ocean Science (2021).doi:10.5194/os-17-1081-2021
The relationship between the eddy-driven jet stream and northern European sea level variability. Tellus. Series A, Dynamic meteorology and oceanography 73, (2021).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). 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).
Copernicus Marine Service Ocean State Report, issue 4. Journal of operational oceanography. Publisher: The Institute of Marine Engineering, Science & Technology 13, (2020).
Evaluating satellite retrieved fractional snow-covered area at a high-Arctic site using terrestrial photography. Remote Sensing of Environment 239, (2020). Abstract
Impact of assimilating altimeter data on eddy characteristics in the South China Sea. Ocean Modelling 155, (2020).
The impact of atmospheric and oceanic circulations on the Greenland Sea iceconcentration. The Cryosphere Discussions (2020).doi:https://doi.org/10.5194/tc-2020-127
Operational Forecasting of Sea Ice in the Arctic Using TOPAZ system. Sea Ice in the Arctic, Past, Present and Future (2020).doi:10.1007/978-3-030-21301-5_9
Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment. Tellus. Series A, Dynamic meteorology and oceanography 72, (2019). 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).
From Observation to Information and Users: The Copernicus Marine Service Perspective. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00234
