Zobrazeno 1 - 10
of 110
pro vyhledávání: '"Hoyer, Stephan"'
Constraining a numerical weather prediction (NWP) model with observations via 4D variational (4D-Var) data assimilation is often difficult to implement in practice due to the need to develop and maintain a software-based tangent linear model and adjo
Externí odkaz:
http://arxiv.org/abs/2408.02767
Autor:
Schiff, Yair, Wan, Zhong Yi, Parker, Jeffrey B., Hoyer, Stephan, Kuleshov, Volodymyr, Sha, Fei, Zepeda-Núñez, Leonardo
Learning dynamics from dissipative chaotic systems is notoriously difficult due to their inherent instability, as formalized by their positive Lyapunov exponents, which exponentially amplify errors in the learned dynamics. However, many of these syst
Externí odkaz:
http://arxiv.org/abs/2402.04467
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:
Lam, Remi, Sanchez-Gonzalez, Alvaro, Willson, Matthew, Wirnsberger, Peter, Fortunato, Meire, Alet, Ferran, Ravuri, Suman, Ewalds, Timo, Eaton-Rosen, Zach, Hu, Weihua, Merose, Alexander, Hoyer, Stephan, Holland, George, Vinyals, Oriol, Stott, Jacklynn, Pritzel, Alexander, Mohamed, Shakir, Battaglia, Peter
Global medium-range weather forecasting is critical to decision-making across many social and economic domains. Traditional numerical weather prediction uses increased compute resources to improve forecast accuracy, but cannot directly use historical
Externí odkaz:
http://arxiv.org/abs/2212.12794
Autor:
Dresdner, Gideon, Kochkov, Dmitrii, Norgaard, Peter, Zepeda-Núñez, Leonardo, Smith, Jamie A., Brenner, Michael P., Hoyer, Stephan
Despite their ubiquity throughout science and engineering, only a handful of partial differential equations (PDEs) have analytical, or closed-form solutions. This motivates a vast amount of classical work on numerical simulation of PDEs and more rece
Externí odkaz:
http://arxiv.org/abs/2207.00556
Autor:
Rasp, Stephan1 (AUTHOR) srasp@google.com, Hoyer, Stephan1 (AUTHOR), Merose, Alexander1 (AUTHOR), Langmore, Ian1 (AUTHOR), Battaglia, Peter2 (AUTHOR), Russell, Tyler1 (AUTHOR), Sanchez‐Gonzalez, Alvaro2 (AUTHOR), Yang, Vivian1 (AUTHOR), Carver, Rob1 (AUTHOR), Agrawal, Shreya1 (AUTHOR), Chantry, Matthew3 (AUTHOR), Ben Bouallegue, Zied3 (AUTHOR), Dueben, Peter3 (AUTHOR), Bromberg, Carla1 (AUTHOR), Sisk, Jared1 (AUTHOR), Barrington, Luke1 (AUTHOR), Bell, Aaron1 (AUTHOR), Sha, Fei1 (AUTHOR)
Publikováno v:
Journal of Advances in Modeling Earth Systems. Jun2024, Vol. 16 Issue 6, p1-25. 25p.
Autor:
Blondel, Mathieu, Berthet, Quentin, Cuturi, Marco, Frostig, Roy, Hoyer, Stephan, Llinares-López, Felipe, Pedregosa, Fabian, Vert, Jean-Philippe
Automatic differentiation (autodiff) has revolutionized machine learning. It allows to express complex computations by composing elementary ones in creative ways and removes the burden of computing their derivatives by hand. More recently, differenti
Externí odkaz:
http://arxiv.org/abs/2105.15183
Autor:
Frerix, Thomas, Kochkov, Dmitrii, Smith, Jamie A., Cremers, Daniel, Brenner, Michael P., Hoyer, Stephan
Variational data assimilation optimizes for an initial state of a dynamical system such that its evolution fits observational data. The physical model can subsequently be evolved into the future to make predictions. This principle is a cornerstone of
Externí odkaz:
http://arxiv.org/abs/2102.11192