Biblio
Filters: Author is Pierre Rampal [Clear All Filters]
Data assimilation using adaptive, non-conservative, moving mesh models. Nonlin. Processes Geophys. 26, 18 (2019).
Data assimilation using adaptive, non-conservative, moving mesh models. Nonlinear processes in geophysics 26, (2019).
SKIM, a Candidate Satellite Mission Exploring Global Ocean Currents and Waves. Frontiers in Marine Science 6, (2019).
Parallel implementation of a Lagrangian-based model on an adaptive mesh in C++: Application to sea-ice. Journal of Computational Physics 350, (2017).
Sea Ice Physics and Modelling. (2017).
Arctic sea-ice diffusion from observed and simulated Lagrangian trajectories. The Cryosphere 10, (2016).
Presentation of the dynamical core of neXtSIM, a new sea ice model. Ocean Modelling 91, (2015). Abstract
On producing sea ice deformation data sets from SAR-derived sea ice motion. The Cryosphere 9, (2015). Abstract
Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends. The Cryosphere 8, (2014).
Validation of sea ice quantities of TOPAZ for the period 1990-2010. NERSC Technical Report (2013).
IPCC climate models do not capture Arctic sea ice drift acceleration: Consequences in terms of projected sea ice thinning and decline. Journal of Geophysical Research: Oceans 116, (2011).
Arctic sea ice velocity field: General circulation and turbulent-like fluctuations. Journal of Geophysical Research: Oceans 114, (2009).
Positive trend in the mean speed and deformation rate of Arctic sea ice, 1979–2007. Journal of Geophysical Research: Oceans 114, (2009).
Space and time scaling laws induced by the multiscale fracturing of the Arctic sea ice cover. IUTAM Bookseries : Symposium on Scaling in Solid Mechanics 10, (Dordrecht, 2009).
Scaling properties of sea ice deformation from buoy dispersion analysis. Journal of Geophysical Research: Oceans 113, (2008).
