Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Brajard Julien"'
Autor:
Guillot Jules, Koenig Guillaume, Minbashian Kadi, Frénod Emmanuel, Flourent Héléne, Brajard Julien
Publikováno v:
ESAIM: Proceedings and Surveys, Vol 70, Pp 137-146 (2021)
The Sea Surface Temperature (SST) plays a significant role in analyzing and assessing the dynamics of weather and also biological systems. It has various applications such as weather forecasting or planning of coastal activities. On the one hand, sta
Externí odkaz:
https://doaj.org/article/5abd7dbe232c45f8a3a77c866df6b33a
Autor:
Bracco, Annalisa, Brajard, Julien, Dijkstra, Henk A., Hassanzadeh, Pedram, Lessig, Christian, Monteleoni, Claire
An exponential growth in computing power, which has brought more sophisticated and higher resolution simulations of the climate system, and an exponential increase in observations since the first weather satellite was put in orbit, are revolutionizin
Externí odkaz:
http://arxiv.org/abs/2408.09627
We make the first steps towards diffusion models for unconditional generation of multivariate and Arctic-wide sea-ice states. While targeting to reduce the computational costs by diffusion in latent space, latent diffusion models also offer the possi
Externí odkaz:
http://arxiv.org/abs/2406.18417
Autor:
Driscoll, Simon, Carrassi, Alberto, Brajard, Julien, Bertino, Laurent, Bocquet, Marc, Olason, Einar
Accurate simulation of sea ice is critical for predictions of future Arctic sea ice loss, looming climate change impacts, and more. A key feature in Arctic sea ice is the formation of melt ponds. Each year melt ponds develop on the surface of the ice
Externí odkaz:
http://arxiv.org/abs/2304.05407
Autor:
Cheng, Sibo, Quilodran-Casas, Cesar, Ouala, Said, Farchi, Alban, Liu, Che, Tandeo, Pierre, Fablet, Ronan, Lucor, Didier, Iooss, Bertrand, Brajard, Julien, Xiao, Dunhui, Janjic, Tijana, Ding, Weiping, Guo, Yike, Carrassi, Alberto, Bocquet, Marc, Arcucci, Rossella
Data Assimilation (DA) and Uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience
Externí odkaz:
http://arxiv.org/abs/2303.10462
Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive biases to learn complex relationships. We here design a hybrid architec
Externí odkaz:
http://arxiv.org/abs/2211.09741
Increasing the resolution of a model can improve the performance of a data assimilation system: first because model field are in better agreement with high resolution observations, then the corrections are better sustained and, with ensemble data ass
Externí odkaz:
http://arxiv.org/abs/2109.08017
Autor:
Driscoll, Simon, Carrassi, Alberto, Brajard, Julien, Bertino, Laurent, Bocquet, Marc, Ólason, Einar Örn
Publikováno v:
In Journal of Computational Science July 2024 79
Autor:
Sonnewald, Maike, Lguensat, Redouane, Jones, Daniel C., Dueben, Peter D., Brajard, Julien, Balaji, Venkatramani
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of
Externí odkaz:
http://arxiv.org/abs/2104.12506
Autor:
Bouget, Vincent, Béréziat, Dominique, Brajard, Julien, Charantonis, Anastase, Filoche, Arthur
Short- or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risk monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill a
Externí odkaz:
http://arxiv.org/abs/2012.05015