Hybrid forecasting: blending climate predictions with AI models

Louise J. 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, Massimiliano Zappa

Research output: Contribution to journalReview articlepeer-review

20 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1865-1889
Number of pages25
JournalHydrology and Earth System Sciences
Volume27
Issue number9
DOIs
StatePublished - May 15 2023
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Water Science and Technology
  • Earth and Planetary Sciences (miscellaneous)

Fingerprint

Dive into the research topics of 'Hybrid forecasting: blending climate predictions with AI models'. Together they form a unique fingerprint.

Cite this