506 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- P. Dueben2 profiles13 · h 20
- P. Smolarkiewicz2 profiles11 · h 59
- T. Palmer3 profiles10 · h 86
- G. Guerrero2 profiles7 · h 11
- J. Bidlot7 · h 50
- Ø. Breivik6 · h 34
- A. Kosovichev2 profiles5 · h 54
- F. Pappenberger2 profiles5 · h 80
- M. Chantry5 · h 20
- P. Janssen4 · h 47
- E. M. de Gouveia Dal Pino3 · h 22
- L. Magnusson3 · h 32
- University of OxfordGB10 papers
- Norwegian Meteorological InstituteNO8 papers
- University of ReadingGB8 papers
- Imperial College LondonGB7 papers
- Centre National de la Recherche ScientifiqueFR6 papers
- Universidade Federal de Minas GeraisBR6 papers
- New Jersey Institute of TechnologyUS5 papers
- Ames Research CenterUS4 papers
- NSF National Center for Atmospheric ResearchUS4 papers
- University of DebrecenHU4 papers
- Deutscher WetterdienstDE3 papers
- ETH ZurichCH3 papers
4 papers · 2 filters
A comparison of data-driven approaches to build low-dimensional ocean models
Niraj Agarwal, Dmitri Kondrashov, Peter Dueben +2
We present a comprehensive inter-comparison of linear regression (LR), stochastic, and deep-learning approaches for reduced-order statistical emulation of ocean circulation. The re…
What do large-scale patterns teach us about extreme precipitation over the Mediterranean at medium- and extended-range forecasts?
Nikolaos Mastrantonas, Linus Magnusson, Florian Pappenberger +1
Extreme Precipitation Events (EPEs) can have devastating consequences such as floods and landslides, posing a great threat to society and the economy. Predicting such events long i…
Bridging observation, theory and numerical simulation of the ocean using Machine Learning
Maike Sonnewald, Redouane Lguensat, Daniel C. Jones +3
Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques…
Machine learning emulation of gravity wave drag in numerical weather forecasting
Matthew Chantry, Sam Hatfield, Peter Duben +2
We assess the value of machine learning as an accelerator for the parameterisation schemes of operational weather forecasting systems, specifically the parameterisation of non-orog…