Biblio
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Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models. Nonlinear processes in geophysics 26, (2019).
Reference upper-air observations for climate: From concept to reality. Bulletin of The American Meteorological Society - (BAMS) (2015).doi:10.1175/BAMS-D-14-00072.1
CoCoNet: Towards Coast to Coast Networks of Marine Protected Areas (from the shore to the high and deep sea), coupled with Sea-Based Wind Energy Potential. SCIRES-IT SCIentific RESearch and Information Technology 6, (2017).
CoCoNet: Towards Coast to Coast Networks of Marine Protected Areas (from the shore to the high and deep sea), coupled with Sea-Based Wind Energy Potential. SCIRES-IT : SCIentific RESearch and Information Technology 6, (2016).
Multisensor approach to automated classification of sea ice image data. IEEE Transactions on geoscience and remote sensing 43, 1648-1664 (2005). Abstract
Download: bog05.pdf (2.31 MB)
Automatic Classification of RADARSAT SAR Images of the Northern Sea Route. In proceedings of IGARSS'99, Hamburg Germany, 28June-2July,1999 (1999).
Sea-level variability in the Mediterranean Sea from altimetry and tide gauges. Climate Dynamics (2016).doi:10.1007/s00382-016-3001-2
Wave Climate Change in the North Sea and Baltic Sea. Journal of Marine Science and Engineering (2019).doi:10.3390/jmse7060166
Sea-state contributions to sea-level variability in the European Seas. Ocean Dynamics (2020).doi:10.1007/s10236-020-01404-1
Studies of Ice edge eddies using Synthetic Aperture Radar. (2011). Abstract
Download: Master_thesis-ElinBondevik-2011.pdf (6.06 MB)
Statistical analysis of two dimensional fracture patterns in Granite, from Rogaland region. Second Progress Report ENV-CT97-0456 to EC (1999).
Power law fault length distribution exponent: a synthesis. NGF (Geological Society of Norway), annual meeting, Stavanger 6-8 Jan 1999 (1999).
Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing 13, (2021).
An elastic–viscous–plastic sea ice model formulated on Arakawa B and C grids. Ocean Modelling 27, 174 - 184 (2009).
Sea ice modelling and forecasting. New Frontiers in Operational Oceanography (2018).
Presentation of the dynamical core of neXtSIM, a new sea ice model. Ocean Modelling 91, (2015). Abstract
Download: bouillon_rampal_2015_presentation_of_the_dynamical_core_of_nextsim_a_new_sea_ice_model.pdf (5.11 MB)
On producing sea ice deformation data sets from SAR-derived sea ice motion. The Cryosphere 9, (2015). Abstract
Download: bouillon_rampal_2014_on_producing_sea_ice_deformation_data_sets_from_sar-derived_sea_ice_motion.pdf (3.53 MB)
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
Annual cycles of sea level and sea surface temperature in the western Mediterranean Sea. Journal of Geophysical Research Oceans 108, 4-1-4-20 (2003). Abstract
Download: 2002JC001365.pdf (1.16 MB)
Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters 14, (2019). Abstract
Quantifying the Impact of Wind‐Current Feedback on Mesoscale Variability in Forced Simulation Experiments of the Agulhas Current Using an Eddy‐Tracking Algorithm. Journal of Geophysical Research (JGR): Oceans 125, (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
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).
