Preprints
https://doi.org/10.5194/hess-2022-334
https://doi.org/10.5194/hess-2022-334
20 Sep 2022
 | 20 Sep 2022
Status: a revised version of this preprint is currently under review for the journal HESS.

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

Louise Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa

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 recognised as a promising way of enhancing 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 sub-seasonal to decadal scales, a better appreciation of the strengths of machine learning, plus 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 without explicit bias correction and downscaling, benefit from the strengths of machine learning models, 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. 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 modelled initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.

Louise Slater et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-334', Anonymous Referee #1, 20 Oct 2022
    • AC1: 'Reply to RC1', Louise Slater, 06 Jan 2023
  • RC2: 'Comment on hess-2022-334', Anonymous Referee #2, 09 Nov 2022
    • AC2: 'Reply to RC2', Louise Slater, 06 Jan 2023

Louise Slater et al.

Louise Slater et al.

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Short summary
Hybrid forecasting systems employ data-driven methods to integrate predictions from dynamical physics-based models into a final prediction product. They are recognised as a promising way of enhancing prediction skill of meteorological and hydroclimatic variables and events – including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Here we review recent developments in hybrid forecasting and outline key challenges and opportunities.