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pro vyhledávání: '"Düben P"'
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:
Lars Berglund
Publikováno v:
De Musica Disserenda, Vol 11, Iss 1-2, Pp 51-66 (2015)
The article describes the practices of acquisition of music behind the üben Collection. Music was copied into manuscripts from prints, but was also obtained in the form of groups of manuscripts from different regions of Europe. Close personal contac
Externí odkaz:
https://doaj.org/article/4d25648a60e44806a4759b0daec2133c
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
The generation of initial conditions via accurate data assimilation is crucial for weather forecasting and climate modeling. We propose DiffDA as a denoising diffusion model capable of assimilating atmospheric variables using predicted states and spa
Externí odkaz:
http://arxiv.org/abs/2401.05932
Autor:
Kochkov, Dmitrii, Yuval, Janni, Langmore, Ian, Norgaard, Peter, Smith, Jamie, Mooers, Griffin, Klöwer, Milan, Lottes, James, Rasp, Stephan, Düben, Peter, Hatfield, Sam, Battaglia, Peter, Sanchez-Gonzalez, Alvaro, Willson, Matthew, Brenner, Michael P., Hoyer, Stephan
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs are physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud for
Externí odkaz:
http://arxiv.org/abs/2311.07222
Autor:
Rasp, Stephan, Hoyer, Stephan, Merose, Alexander, Langmore, Ian, Battaglia, Peter, Russel, Tyler, Sanchez-Gonzalez, Alvaro, Yang, Vivian, Carver, Rob, Agrawal, Shreya, Chantry, Matthew, Bouallegue, Zied Ben, Dueben, Peter, Bromberg, Carla, Sisk, Jared, Barrington, Luke, Bell, Aaron, Sha, Fei
WeatherBench 2 is an update to the global, medium-range (1-14 day) weather forecasting benchmark proposed by Rasp et al. (2020), designed with the aim to accelerate progress in data-driven weather modeling. WeatherBench 2 consists of an open-source e
Externí odkaz:
http://arxiv.org/abs/2308.15560
Autor:
Ben-Bouallegue, Zied, Clare, Mariana C A, Magnusson, Linus, Gascon, Estibaliz, Maier-Gerber, Michael, Janousek, Martin, Rodwell, Mark, Pinault, Florian, Dramsch, Jesper S, Lang, Simon T K, Raoult, Baudouin, Rabier, Florence, Chevallier, Matthieu, Sandu, Irina, Dueben, Peter, Chantry, Matthew, Pappenberger, Florian
Data-driven modeling based on machine learning (ML) is showing enormous potential for weather forecasting. Rapid progress has been made with impressive results for some applications. The uptake of ML methods could be a game-changer for the incrementa
Externí odkaz:
http://arxiv.org/abs/2307.10128
Akademický článek
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Autor:
Ben-Bouallegue, Zied, Weyn, Jonathan A, Clare, Mariana C A, Dramsch, Jesper, Dueben, Peter, Chantry, Matthew
Statistical post-processing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in orde
Externí odkaz:
http://arxiv.org/abs/2303.17195
Deep learning for quality control of surface physiographic fields using satellite Earth observations
Autor:
Kimpson, Tom, Choulga, Margarita, Chantry, Matthew, Balsamo, Gianpaolo, Boussetta, Souhail, Dueben, Peter, Palmer, Tim
A purposely built deep learning algorithm for the Verification of Earth-System ParametERisation (VESPER) is used to assess recent upgrades of the global physiographic datasets underpinning the quality of the Integrated Forecasting System (IFS) of the
Externí odkaz:
http://arxiv.org/abs/2210.16746