Zobrazeno 1 - 10
of 515
pro vyhledávání: '"M, Bocquet"'
Publikováno v:
Nonlinear Processes in Geophysics, Vol 31, Pp 409-431 (2024)
We propose denoising diffusion models for data-driven representation learning of dynamical systems. In this type of generative deep learning, a neural network is trained to denoise and reverse a diffusion process, where Gaussian noise is added to sta
Externí odkaz:
https://doaj.org/article/747e81a41e5042b98a211cf7271d0e51
Publikováno v:
Nonlinear Processes in Geophysics, Vol 31, Pp 335-357 (2024)
Because optimal transport (OT) acts as displacement interpolation in physical space rather than as interpolation in value space, it can avoid double-penalty errors generated by mislocations of geophysical fields. As such, it provides a very attractiv
Externí odkaz:
https://doaj.org/article/eb76b1c27df245149e37e8a903cb7750
Autor:
Y. Chen, P. Smith, A. Carrassi, I. Pasmans, L. Bertino, M. Bocquet, T. S. Finn, P. Rampal, V. Dansereau
Publikováno v:
The Cryosphere, Vol 18, Pp 2381-2406 (2024)
In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice
Externí odkaz:
https://doaj.org/article/28326319713a4bbdae8947c3740763cb
Publikováno v:
The Cryosphere, Vol 18, Pp 1791-1815 (2024)
A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we i
Externí odkaz:
https://doaj.org/article/04ee37d97efa4c92a6b184a8eea96925
Publikováno v:
The Cryosphere, Vol 17, Pp 3013-3039 (2023)
Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and
Externí odkaz:
https://doaj.org/article/b4ad1bb6ee21430c8f545af3f8f79294
Publikováno v:
The Cryosphere, Vol 17, Pp 2965-2991 (2023)
We introduce a proof of concept to parametrise the unresolved subgrid scale of sea-ice dynamics with deep learning techniques. Instead of parametrising single processes, a single neural network is trained to correct all model variables at the same ti
Externí odkaz:
https://doaj.org/article/30a6b3d9132c45718bc62300d0899b29
Autor:
J. Dumont Le Brazidec, P. Vanderbecken, A. Farchi, M. Bocquet, J. Lian, G. Broquet, G. Kuhlmann, A. Danjou, T. Lauvaux
Publikováno v:
Geoscientific Model Development, Vol 16, Pp 3997-4016 (2023)
Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed and will exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. Methods for estimating CO
Externí odkaz:
https://doaj.org/article/7dfc4ff3fbbd43b88886220ad07ad0b0
Autor:
P. J. Vanderbecken, J. Dumont Le Brazidec, A. Farchi, M. Bocquet, Y. Roustan, É. Potier, G. Broquet
Publikováno v:
Atmospheric Measurement Techniques, Vol 16, Pp 1745-1766 (2023)
In the next few years, numerous satellites with high-resolution instruments dedicated to the imaging of atmospheric gaseous compounds will be launched, to finely monitor emissions of greenhouse gases and pollutants. Processing the resulting images of
Externí odkaz:
https://doaj.org/article/8d57ec7bdced42b8a4bb12a4654e5fd7
Publikováno v:
Geoscientific Model Development, Vol 16, Pp 1039-1052 (2023)
The accident at the Fukushima Daiichi nuclear power plant (NPP) yielded massive and rapidly varying atmospheric radionuclide releases. The assessment of these releases and of the corresponding uncertainties can be performed using inverse modelling me
Externí odkaz:
https://doaj.org/article/dc5d3dcd4b05465c9b6b8ed74c312bfc
Autor:
C. Grudzien, M. Bocquet
Publikováno v:
Geoscientific Model Development, Vol 15, Pp 7641-7681 (2022)
Ensemble variational methods form the basis of the state of the art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for real-time, short-range forecast systems. We propose a novel estimator in this formalism t
Externí odkaz:
https://doaj.org/article/15d6666e51bd4e61bfb1588a6aae26e2