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.
Review article
| 
15 May 2023
Review article |
| 15 May 2023
| 15 May 2023
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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- Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning S. Du et al. 10.1016/j.scitotenv.2023.166422
- Enhancing the capabilities of the Chao Phraya forecasting system through the integration of pre-processed numerical weather forecasts T. Charoensuk et al. 10.1016/j.ejrh.2024.101737
- Combining Synthetic and Observed Data to Enhance Machine Learning Model Performance for Streamflow Prediction S. López-Chacón et al. 10.3390/w15112020
- Optimal Postprocessing Strategies With LSTM for Global Streamflow Prediction in Ungauged Basins S. Tang et al. 10.1029/2022WR034352
- Identification of influential weather parameters and seasonal drought prediction in Bangladesh using machine learning algorithm M. Al Mamun et al. 10.1038/s41598-023-51111-2
- A review of hybrid deep learning applications for streamflow forecasting K. Ng et al. 10.1016/j.jhydrol.2023.130141
- Forecasting bathing water quality in the UK: A critical review K. Krupska et al. 10.1002/wat2.1718
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12 citations as recorded by crossref.
- Investigating permafrost carbon dynamics in Alaska with artificial intelligence B. Gay et al. 10.1088/1748-9326/ad0607
- Skillful Decadal Flood Prediction S. Moulds et al. 10.1029/2022GL100650
- Elevation-dependent warming of streams in mountainous regions: implications for temperature modeling and headwater climate refugia D. Isaak & C. Luce 10.1080/07011784.2023.2176788
- Impacts of hot-dry conditions on hydropower production in Switzerland N. Otero et al. 10.1088/1748-9326/acd8d7
- A comparison of seasonal rainfall forecasts over Central America using dynamic and hybrid approaches from Copernicus Climate Change Service seasonal forecasting system and the North American Multimodel Ensemble K. Kowal et al. 10.1002/joc.7969
- Hybrid Deep Learning and S2S Model for Improved Sub-Seasonal Surface and Root-Zone Soil Moisture Forecasting L. Xu et al. 10.3390/rs15133410
- Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop A. Dasgupta et al. 10.1111/jfr3.12880
- Monthly Ocean Primary Productivity Forecasting by Joint Use of Seasonal Climate Prediction and Temporal Memory L. Xu et al. 10.3390/rs15051417
- A Statistical Forecasting Model for Extremes of the Fire Behaviour Index in Australia R. Taylor et al. 10.3390/atmos15040470
- Energy Forecasting Model for Ground Movement Operation in Green Airport A. Ajayi et al. 10.3390/en16135008
- Resilience of UK crop yields to compound climate change L. Slater et al. 10.5194/esd-13-1377-2022
- Soil Dynamics and Crop Yield Modeling Using the MONICA Crop Simulation Model and Time Series Forecasting Methods I. Mirpulatov et al. 10.3390/agronomy13082185
Discussed (final revised paper)
Latest update: 18 Apr 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...
