Weather and Climate Forecasting
Quantum fluid dynamics solution to improve the reliability of weather and climate forecasts.
OWNER
STATUS
Phase 2 – Full Proposal
QUANTUM APPROACH
Linear Systems of Equations and PDEs
SDGs
CONTRIBUTORS
Planqc
ORIRGIN OF CONTRIIBUTORS
IMPACT/ CONTEXT
A key indicator highlighted in the World Meteorological Organization’s (WMO) State of the Climate 2024 report is the global mean near-surface temperature, which reached its highest level on record in 2024 – a year which also marked the first time that each of the previous ten years was individually the warmest year on record. This rise in mean surface temperatures is linked to an increase in the severity of extreme weather events.
The same WMO report noted that extreme weather displaced over 800,000 people and left them homeless, all within one year alone. While developing countries bear the brunt of climate change and its extreme weather impacts, wealthy regions such as the U.S. and the European Union are also affected.
For instance, the estimated cost of extreme weather events in the U.S. reached $180 billion in 2024, with major flooding in eastern Spain resulting in over 200 deaths. Early warning systems that help predict catastrophic events are thus needed to help minimise their impact.
HOW QUANTUM COULD HELP
Early warning systems for extreme weather events require very accurate and reliable weather forecasts. Similarly, developing climate risk and hazard mitigation strategies rely on robust climate predictions over several decades. These problems can in part be addressed by enhanced computational models for weather and climate forecasts.
Leading global weather/climate models typically have a spatial resolution of 10 to 100 kilometres, which is insufficient to explicitly represent the relevant physical processes underlying the time evolution of weather and climate models. These higher spatial resolutions increase the computational requirements and can be very challenging to implement in practice.
Quantum and quantum-inspired tensor networks methods can be used as an extremely powerful tool in theoretical quantum physics for simulating quantum many-body systems. This approach enables the simulation of physical systems that are otherwise intractable, by removing unrealised correlations (entanglement) from the model, with the potential of achieving a speed-up against traditional classical weather models.
