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Sea-Ice drift affects various global processes including the air-sea-ice energy system, numerical ocean modelling, and maritime activity in the polar regions. Drift has been investigated via various technologies ranging from satellite based…
Atmospheric aerosols influence the Earth's climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these…
The full Stokes equations are solved by a finite element method for simulation of large ice sheets and glaciers. The simulation is particularly sensitive to the discretization of the grounding line which separates the ice resting on the…
Sea ice, or frozen ocean water, freezes and melts every year in the Arctic. Forecasts of where sea ice will be located weeks to months in advance have become more important as the amount of sea ice declines due to climate change, for…
Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal…
In this study, we integrate the established obstacle problem formulation from ice sheet modeling with cutting-edge deep learning methodologies to enhance ice thickness predictions, specifically targeting the Greenland ice sheet. By…
Two drifting wave buoys were deployed in the central Arctic Ocean, north of the Laptev Sea, where there are historically no wave observations available. An experimental wave buoy was deployed alongside a commercial buoy. The inter-buoy…
The need for fine-grained perception in autonomous driving systems has resulted in recently increased research on online semantic segmentation of single-scan LiDAR. Despite the emerging datasets and technological advancements, it remains…
For the numerical simulation of earth system models, Arakawa grids are largely employed. A quadrilateral mesh is assumed for their classical definition, and different types of grids are identified depending on the location of the…
The MIRO instrument's remote sensing capability is integral in constraining water density, temperature and velocity fields in the coma of 67P/Churyumov-Gersimenko. Our aim is to quantify the contribution to the water density from all facets…
Understanding the non-linear dynamics of satellite halos (a.k.a. "sub-halos") is important for predicting the abundance and distribution of dark matter substructures and satellite galaxies, and for distinguishing among microphysical dark…
Traditional 1D system thermal hydraulic analysis has been widely applied in nuclear industry for licensing purposes due to its numerical efficiency. However, such codes are inherently deficient for modeling of multiscale multidimensional…
Numerical simulations are essential tools to evaluate the solution of the wave equation in complex settings, such as three-dimensional (3D) domains with heterogeneous properties. However, their application is limited by high computational…
We assess the value of calibrating forecast models for significant wave height Hs, wind speed W and mean spectral wave period Tm for forecast horizons between zero and 168 hours from a commercial forecast provider, to improve forecast…
We have explored the evolution of gas distributions from cosmological simulations carried out using the RAMSES adaptive mesh refinement (AMR) code, to explore the effects of resolution on cosmological hydrodynamical simulations. It is vital…
The migration of planets within their nascent protoplanetary disks is a fundamental process that shapes the final architecture of planetary systems. However, studying this phenomenon through direct hydrodynamical simulations is…
We present Flo, a data-driven storm surge model, covering the North Sea, Norwegian Sea and Barents Sea. The model is built using the Anemoi framework for creating machine learning weather forecasting systems, developed by the European…
Climate models are limited by heavy computational costs, often producing outputs at coarse spatial resolutions, while many climate change impact studies require finer scales. Statistical downscaling bridges this gap, and we adapt the…
This paper extends the author's previous two-dimensional work with Ou and LeVeque to high-resolution finite volume modeling of systems of fluids and poroelastic media in three dimensions, using logically rectangular mapped grids. A method…
As an increasing amount of remote sensing data becomes available in the Arctic Ocean, data-driven machine learning (ML) techniques are becoming widely used to predict sea ice velocity (SIV) and sea ice concentration (SIC). However, fully…