Zobrazeno 1 - 10
of 20
pro vyhledávání: '"James R. Maddison"'
Ice sheet models are the main tool to generate forecasts of ice sheet mass loss; a significant contributor to sea-level rise, thus knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e916b2ec922e6b71d599703e2f2f4a0
https://tc.copernicus.org/preprints/tc-2023-27/
https://tc.copernicus.org/preprints/tc-2023-27/
Publikováno v:
Koziol, C P, Todd, J A, Goldberg, D N & Maddison, J R 2021, ' fenics_ice 1.0: A framework for quantifying initialisation uncertainty for time-dependent ice-sheet models ', Geoscientific Model Development . https://doi.org/10.5194/gmd-14-5843-2021
Geoscientific Model Development, Vol 14, Pp 5843-5861 (2021)
Geoscientific Model Development, Vol 14, Pp 5843-5861 (2021)
Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expectedto increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18b73e1a94f8793164769458980fcd01
https://doi.org/10.5194/gmd-14-5843-2021
https://doi.org/10.5194/gmd-14-5843-2021
Publikováno v:
Brolly, M, Maddison, J R, Teckentrup, A L & Vanneste, J 2022, ' Bayesian comparison of stochastic models of dispersion ', Journal of Fluid Mechanics, vol. 944, A2 . https://doi.org/10.1017/jfm.2022.472
Stochastic models of varying complexity have been proposed to describe the dispersion of particles in turbulent flows, from simple Brownian motion to complex temporally and spatially correlated models. A method is needed to compare competing models,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d87feffddac46c5727cd4310b1524cb
https://www.pure.ed.ac.uk/ws/files/281976714/BMC_ArXiv_revision.pdf
https://www.pure.ed.ac.uk/ws/files/281976714/BMC_ArXiv_revision.pdf
Publikováno v:
Poulsen, M B, Jochum, M, Maddison, J R, Marshall, D P & Nuterman, R 2019, ' A Geometric Interpretation of Southern Ocean Eddy Form Stress ', Journal of Physical Oceanography, vol. 49, no. 10, pp. 2553-2570 . https://doi.org/10.1175/JPO-D-18-0220.1
Mads B, P, Markus, J, Maddison, J, Marshall, D P & Nuterman, R 2019, ' A geometric interpretation of Southern Ocean eddy form stress ', Journal of Physical Oceanography, vol. 49, pp. 2553-2570 . https://doi.org/10.1175/JPO-D-18-0220.1
Mads B, P, Markus, J, Maddison, J, Marshall, D P & Nuterman, R 2019, ' A geometric interpretation of Southern Ocean eddy form stress ', Journal of Physical Oceanography, vol. 49, pp. 2553-2570 . https://doi.org/10.1175/JPO-D-18-0220.1
An interpretation of eddy form stress via the geometry described by the Eliassen–Palm flux tensor is explored. Complimentary to previous works on eddy Reynolds stress geometry, this study shows that eddy form stress is fully described by a vertical
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::52cdb29720d923165af14a3c5bcfddee
https://doi.org/10.1175/jpo-d-18-0220.1
https://doi.org/10.1175/jpo-d-18-0220.1
Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::493d9bf15b8da6449927126c336ea524
https://doi.org/10.5194/gmd-2021-90
https://doi.org/10.5194/gmd-2021-90
Publikováno v:
Maddison, J, Goldberg, D & Goddard, B 2019, ' Automated calculation of higher order partial differential equation constrained derivative information ', SIAM Journal on Scientific Computing, vol. 41, no. 5, pp. C417-C445 . https://doi.org/10.1137/18m1209465
Developments in automated code generation have allowed extremely compact representations of numerical models, and also for associated adjoint models to be derived automatically via high level algorithmic differentiation. In this article these princip
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67b3a065a7940cd6f846e8bc5a06c2a5
https://doi.org/10.1137/18m1209465
https://doi.org/10.1137/18m1209465
With the advent of large and dense seismic arrays, there is an opportunity for novel inversion methods that exploit the information captured by stations in close proximity to each other. Estimating surface waves dispersion is an interest for many geo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::df6759b151bb7e756cff3ed1ebd4c575
https://doi.org/10.5194/egusphere-egu2020-22018
https://doi.org/10.5194/egusphere-egu2020-22018
Autor:
Hannah R Hiester, James R. Maddison
Publikováno v:
Maddison, J R & Hiester, H R 2017, ' Optimal constrained interpolation in mesh-adaptive finite element modelling ', SIAM Journal on Scientific Computing, vol. 39, no. 5, pp. A2257-A2286 . https://doi.org/10.1137/15M102054X
Mesh-to-mesh Galerkin $L^2$ projection allows piecewise polynomial unstructured finite element data to be interpolated between two nonmatching unstructured meshes of the same domain. The interpolation is by definition optimal in an $L^2$ sense, and s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c87366fe389dbc7327c1c3df4c7542f
https://doi.org/10.1137/15m102054x
https://doi.org/10.1137/15m102054x
Publikováno v:
Creech, A, Jackson, W & Maddison, J 2018, ' Adapting and optimising Fluidity for high-fidelity coastal modelling ', Computers and Fluids, vol. 168, pp. 46-53 . https://doi.org/10.1016/j.compfluid.2018.03.066
Work undertaken to improve the performance of Fluidity, an open-source finite-element computational fluid dynamics solver from Imperial College London, for both general computational fluid dynamics and tidal modelling problems is outlined. Optimising
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d5dd5790774dcfb36361c00786fc9848
https://hdl.handle.net/20.500.11820/2d67c450-0c2f-4de5-8d95-d24807b5b177
https://hdl.handle.net/20.500.11820/2d67c450-0c2f-4de5-8d95-d24807b5b177
Publikováno v:
Ying, Y K, Maddison, J & Vanneste, J 2019, ' Bayesian inference of ocean diffusivity from Lagrangian trajectory data ', Ocean modelling, vol. 140 . https://doi.org/10.1016/j.ocemod.2019.101401
A Bayesian approach is developed for the inference of an eddy-diffusivity field from Lagrangian trajectory data. The motion of Lagrangian particles is modelled by a stochastic differential equation associated with the advection–diffusion equation.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bef05fc98eee37fd5b94e09e4cdcfb05