3 code implementations • 2 Feb 2020 • Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey
Data-driven approaches, most prominently deep learning, have become powerful tools for prediction in many domains.
no code implementations • 22 Mar 2021 • David Meyer, Robin J. Hogan, Peter D. Dueben, Shannon L. Mason
The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable.
no code implementations • 26 Apr 2021 • Maike Sonnewald, Redouane Lguensat, Daniel C. Jones, Peter D. Dueben, Julien Brajard, Venkatramani Balaji
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study.
no code implementations • 5 Apr 2022 • Lucy Harris, Andrew T. T. McRae, Matthew Chantry, Peter D. Dueben, Tim N. Palmer
Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables.
no code implementations • 11 Jan 2024 • Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter D. Dueben, Torsten Hoefler
The experiments also show that the initial conditions assimilated from sparse observations (less than 0. 77% of gridded data) and 48-hour forecast can be used for forecast models with a loss of lead time of at most 24 hours compared to initial conditions from state-of-the-art data assimilation in ERA5.