Zobrazeno 1 - 10
of 5 393
pro vyhledávání: '"A, Alexe"'
Autor:
Lang, Simon, Alexe, Mihai, Clare, Mariana C. A., Roberts, Christopher, Adewoyin, Rilwan, Bouallègue, Zied Ben, Chantry, Matthew, Dramsch, Jesper, Dueben, Peter D., Hahner, Sara, Maciel, Pedro, Prieto-Nemesio, Ana, O'Brien, Cathal, Pinault, Florian, Polster, Jan, Raoult, Baudouin, Tietsche, Steffen, Leutbecher, Martin
Over the last three decades, ensemble forecasts have become an integral part of forecasting the weather. They provide users with more complete information than single forecasts as they permit to estimate the probability of weather events by represent
Externí odkaz:
http://arxiv.org/abs/2412.15832
Autor:
Alexe, Mihai, Boucher, Eulalie, Lean, Peter, Pinnington, Ewan, Laloyaux, Patrick, McNally, Anthony, Lang, Simon, Chantry, Matthew, Burrows, Chris, Chrust, Marcin, Pinault, Florian, Villeneuve, Ethel, Bormann, Niels, Healy, Sean
We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)ana
Externí odkaz:
http://arxiv.org/abs/2412.15687
We propose a novel teacher-student framework to distill knowledge from multiple teachers trained on distinct datasets. Each teacher is first trained from scratch on its own dataset. Then, the teachers are combined into a joint architecture, which fus
Externí odkaz:
http://arxiv.org/abs/2410.22184
Autor:
Rackow, Thomas, Koldunov, Nikolay, Lessig, Christian, Sandu, Irina, Alexe, Mihai, Chantry, Matthew, Clare, Mariana, Dramsch, Jesper, Pappenberger, Florian, Pedruzo-Bagazgoitia, Xabier, Tietsche, Steffen, Jung, Thomas
Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the strong links
Externí odkaz:
http://arxiv.org/abs/2409.18529
Autor:
Nipen, Thomas Nils, Haugen, Håvard Homleid, Ingstad, Magnus Sikora, Nordhagen, Even Marius, Salihi, Aram Farhad Shafiq, Tedesco, Paulina, Seierstad, Ivar Ambjørn, Kristiansen, Jørn, Lang, Simon, Alexe, Mihai, Dramsch, Jesper, Raoult, Baudouin, Mertes, Gert, Chantry, Matthew
A data-driven model (DDM) suitable for regional weather forecasting applications is presented. The model extends the Artificial Intelligence Forecasting System by introducing a stretched-grid architecture that dedicates higher resolution over a regio
Externí odkaz:
http://arxiv.org/abs/2409.02891
Autor:
McNally, Anthony, Lessig, Christian, Lean, Peter, Boucher, Eulalie, Alexe, Mihai, Pinnington, Ewan, Chantry, Matthew, Lang, Simon, Burrows, Chris, Chrust, Marcin, Pinault, Florian, Villeneuve, Ethel, Bormann, Niels, Healy, Sean
Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future w
Externí odkaz:
http://arxiv.org/abs/2407.15586
Autor:
Cai, Shengjuan, Fang, Fangxin, Peuch, Vincent-Henri, Alexe, Mihai, Navon, Ionel Michael, Wang, Yanghua
PM2.5 forecasting is crucial for public health, air quality management, and policy development. Traditional physics-based models are computationally demanding and slow to adapt to real-time conditions. Deep learning models show potential in efficienc
Externí odkaz:
http://arxiv.org/abs/2406.19154
Autor:
Lang, Simon, Alexe, Mihai, Chantry, Matthew, Dramsch, Jesper, Pinault, Florian, Raoult, Baudouin, Clare, Mariana C. A., Lessig, Christian, Maier-Gerber, Michael, Magnusson, Linus, Bouallègue, Zied Ben, Nemesio, Ana Prieto, Dueben, Peter D., Brown, Andrew, Pappenberger, Florian, Rabier, Florence
Machine learning-based weather forecasting models have quickly emerged as a promising methodology for accurate medium-range global weather forecasting. Here, we introduce the Artificial Intelligence Forecasting System (AIFS), a data driven forecast m
Externí odkaz:
http://arxiv.org/abs/2406.01465
Autor:
Seddon, S. D., Haines, C. R. S., Hase, T. P. A., Lees, M. R., Eng, L. M., Alexe, M., Carpenter, M. A.
Pyrrhotite, Fe$_7$S$_8$, provides an example of exceptionally strong magnetoelastic coupling through pinning of ferromagnetic domains by ferroelastic twins. Using direct imaging of both magnetic and ferroelastic domains by magnetic force microscopy (
Externí odkaz:
http://arxiv.org/abs/2403.18747
We consider the problem of setting a confidence interval on a parameter of interest from a high-statistics counting experiment in the presence of systematic uncertainties modeled as unconstrained nuisance parameters. We use the profile-likelihood tes
Externí odkaz:
http://arxiv.org/abs/2401.10542