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Using causal discovery algorithms to boost subseasonal to seasonal forecast skill of Mediterranean rainfall

Project description

Algorithms for improved rainfall predictions

The Mediterranean region (MED) is a hot-spot of anthropogenic climate change. To assure effective near-term planning, decisionmakers in weather-dependent sectors depend on skilful forecasts of precipitation on sub-seasonal to seasonal (S2S) timescales. However, some fundamental challenges prevent reliable predictions beyond approximately 10 days. The EU-funded CausalBoost project will apply an innovative method to improve S2S forecasts of MED rainfall. The approach relies on a combination of innovative causal discovery algorithms from machine learning with operational forecast models. The project will identify central S2S drivers of MED rainfall, systematically assess them with prediction models and produce process-based bias corrections.


Net EU contribution
€ 212 933,76
Whiteknights Campus Whiteknights House
RG6 6AH Reading
United Kingdom

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South East (England) Berkshire, Buckinghamshire and Oxfordshire Berkshire
Activity type
Higher or Secondary Education Establishments
Other funding
€ 212 933,76