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
6 papers · 1 filter
AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS
Maria Luisa Taccari, Kenza Tazi, Oisín M. Morrison +7
Reliable global streamflow forecasting is essential for flood preparedness and water resource management, yet data-driven models often suffer from a performance gap when transition…
Machine Learning Emulation of Urban Land Surface Processes
David Meyer, Sue Grimmond, Peter Dueben +2
Can we improve the modeling of urban land surface processes with machine learning (ML)? A prior comparison of urban land surface models (ULSMs) found that no single model is 'best'…
Machine Learning Emulation of 3D Cloud Radiative Effects
David Meyer, Robin J. Hogan, Peter D. Dueben +1
The treatment of cloud structure in numerical weather and climate models is often greatly simplified to make them computationally affordable. Here we propose to correct the Europea…
Copula-based synthetic data augmentation for machine-learning emulators
David Meyer, Thomas Nagler, Robin J. Hogan
Can we improve machine-learning (ML) emulators with synthetic data? If data are scarce or expensive to source and a physical model is available, statistically generated data may be…
Deep Learning for Post-Processing Ensemble Weather Forecasts
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun +4
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which c…
Predicting Weather Uncertainty with Deep Convnets
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden +4
Modern weather forecast models perform uncertainty quantification using ensemble prediction systems, which collect nonparametric statistics based on multiple perturbed simulations.…