Biblio
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Real time assimilation of HF radar currents into a coastal ocean model. Journal of Marine Systems 161-182 (2001).
Observed sea-level changes along the Norwegian coast. Journal of Marine Science and Engineering 5, (2017).
The plankton community in Norwegian coastal waters - abundance, composition, spatial distribution and diel variation. Continental Shelf Research 31, 14 (2011). Download: Bratbak-2011-14ef840bab756b8233e36bf3288125fc.pdf (2.56 MB)
Data assimilation for marine monitoring and prediction: The MERCATOR operational assimilation systems and the MERSEA developments. Q. J. R. Meteorol. Soc. 131, 3561-3582 (2006).
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
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
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).
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).
Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters 14, (2019). Abstract
Global sea-level contribution from Arctic land ice: 1971-2017. Environmental Research Letters 13:125012, (2018).
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)
Modelling the Arctic wave-affected marginal ice zone: a comparison with ICESat-2 observations. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 380, (2022).
Classification of sea ice types in sentinel-1 SAR data using convolutional neural networks. Remote Sensing 12, (2020).
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)
Sea ice modelling and forecasting. New Frontiers in Operational Oceanography (2018).
An elastic–viscous–plastic sea ice model formulated on Arakawa B and C grids. Ocean Modelling 27, 174 - 184 (2009).
Fusion of Rain Radar Images and Wind Forecasts in a Deep Learning Model Applied to Rain Nowcasting. Remote Sensing 13, (2021).
Sea ice rheology experiment (SIREx): 1. Scaling and statistical properties of sea-ice deformation fields. Journal of Geophysical Research (JGR): Oceans 127:e2021JC017667, (2022).
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).
