Slater, Louise J. and Arnal, Louise and Boucher, Marie-Amélie and Chang, Annie Y.-Y. and Moulds, Simon and Murphy, Conor and Nearing, Grey and Shalev, Guy and Shen, Chaopeng and Speight, Linda and Villarini, Gabriele and Wilby, Robert L. and Wood, Andrew and Zappa, Massimiliano (2023) Hybrid forecasting: blending climate predictions with AI models. Hydrology and Earth System Sciences, 27 (9). pp. 1865-1889. ISSN 1607-7938

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Abstract

Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Item Type:	Article
Keywords:	hybrid forecasting; blending climate predictions; AI models;
Academic Unit:	Faculty of Social Sciences > Geography Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	17456
Identification Number:	https://doi.org/10.5194/hess-27-1865-2023
Depositing User:	Conor Murphy
Date Deposited:	22 Aug 2023 13:52
Journal or Publication Title:	Hydrology and Earth System Sciences
Publisher:	Copernicus Publications
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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