Articles | Volume 27, issue 9
https://doi.org/10.5194/hess-27-1865-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-27-1865-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hybrid forecasting: blending climate predictions with AI models
Louise J. Slater
CORRESPONDING AUTHOR
School of Geography and the Environment, University of Oxford, Oxford, UK
Louise Arnal
Centre for Hydrology, University of Saskatchewan, Canmore, Canada
Marie-Amélie Boucher
Department of Civil Engineering, Université de Sherbrooke, Sherbrooke, Canada
Annie Y.-Y. Chang
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland
Simon Moulds
School of Geography and the Environment, University of Oxford, Oxford, UK
Conor Murphy
Irish Climate Analysis and Research Units, Department of Geography, Maynooth University, Kildare, Ireland
Grey Nearing
Google Research, Mountain View, CA, USA
Guy Shalev
Google Research, Tel Aviv, Israel
Chaopeng Shen
Civil and Environmental Engineering, Pennsylvania State University, State College, PA 16801, USA
Linda Speight
School of Geography and the Environment, University of Oxford, Oxford, UK
Gabriele Villarini
IIHR – Hydroscience and Engineering, University of Iowa, IA, USA
Robert L. Wilby
Geography and Environment, Loughborough University, Loughborough, UK
Andrew Wood
National Center for Atmospheric Research, Climate and Global Dynamics, Boulder, CO, USA
Massimiliano Zappa
Swiss Federal Research Institute WSL, Birmensdorf, Switzerland
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Latest update: 20 Nov 2024
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate...