Biblio
Filters: Author is Laurent Bertino [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
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).
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 ice concentration. The Cryosphere Discussions (2020).doi:https://doi.org/10.5194/tc-2020-127
Operational Forecasting of Sea Ice in the Arctic Using TOPAZ system. (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. (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
Observing System Evaluation Based on Ocean Data Assimilation and Prediction Systems: On-Going Challenges and a Future Vision for Designing and Supporting Ocean Observational Networks. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00417
Polar Ocean Observations: A critical gap in the observing system and its effect on environmental predictions from hours to a season. Frontiers in Marine Science (2019).doi:10.3389/fmars.2019.00429
Synthesis of ocean observations using data assimilation for operational, real-time and reanalysis systems: a more complete picture of the state of the ocean. Frontiers in Marine Science 6, (2019).
Assimilation of semi-qualitative observations with a stochasticensemble Kalman filter. Quarterly Journal of the Royal Meteorological Society 144, (2018). Abstract
Data assimilation in the geosciences - An overview of methods, issues and perspectives. WIREs Climate Change (2018).doi:doi: 10.1002/wcc.535
