Biblio
Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems. Nonlinear processes in geophysics 19, (2012).
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. Foundations of Data Science (FoDS) 2, (2020). Abstract
Degenerate Kalman filter error covariances and their convergence onto the unstable subspace. SIAM/ASA Journal on Uncertainty Quantification (JUQ) 5, (2017).
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation. Nonlinear processes in geophysics 22, (2015). Abstract
Four-dimensional ensemble variational data assimilation and the unstable subspace. Tellus. Series A, Dynamic meteorology and oceanography 69, (2017).
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) 97, (2016).
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).
Automatic Classification of RADARSAT SAR Images of the Northern Sea Route. In proceedings of IGARSS'99, Hamburg Germany, 28June-2July,1999 (1999).
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)
Contribution of future wide-swath altimetry missions to ocean analysis and forecasting. Ocean Science (2018).doi:10.5194/os-14-1405-2018
Sea-state contributions to sea-level variability in the European Seas. Ocean Dynamics (2020).doi:10.1007/s10236-020-01404-1
Wave Climate Change in the North Sea and Baltic Sea. Journal of Marine Science and Engineering (2019).doi:10.3390/jmse7060166
Sea-level variability in the Mediterranean Sea from altimetry and tide gauges. Climate Dynamics (2016).doi:10.1007/s00382-016-3001-2
Ocean Mesoscale Variability: A Case Study on the Mediterranean Sea From a Re-Analysis Perspective. Frontiers in Earth Science 9, (2021).
Studies of Ice edge eddies using Synthetic Aperture Radar. (2011). Abstract
Download: Master_thesis-ElinBondevik-2011.pdf (6.06 MB)
Power law fault length distribution exponent: a synthesis. NGF (Geological Society of Norway), annual meeting, Stavanger 6-8 Jan 1999 (1999).
Statistical analysis of two dimensional fracture patterns in Granite, from Rogaland region. Second Progress Report ENV-CT97-0456 to EC (1999).
Sea ice rheology experiment (SIREx): 1. Scaling and statistical properties of sea-ice deformation fields. Journal of Geophysical Research (JGR): Oceans 127:e2021JC017667, (2022).
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)
