Exploring the use of European weather regimes for improving user-relevant hydrological forecasts at the sub-seasonal scale in Switzerland ...

• Main Authors: Chang, Annie Y.-Y., Bogner, Konrad, Grams, Christian M., Monhart, Samuel, Domeisen, Daniela, Zappa, Massimiliano
• Format: Article in Journal/Newspaper
• Language: English
• Published: ETH Zurich 2023
• Subjects: Climate classification/regimes; Hydrology; Operational forecasting; Machine learning; Ensembles; North Atlantic
• Online Access: https://dx.doi.org/10.3929/ethz-b-000622008; http://hdl.handle.net/20.500.11850/622008

Across the globe, there has been an increasing interest in improving the predictability of sub-seasonal hydro-meteorological forecasts as they play a valuable role in medium- to long-term planning in many sectors such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence this study explores the possibilities for improving forecasts through different pre- and post-processing techniques at the interface with a hydrological model (PREVAH). Specifically, this research aims to assess the benefit from European Weather Regime (WR) data into a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of sub-seasonal hydro-meteorological forecasts in Switzerland. The WR data contains information about the large-scale atmospheric circulation in the North-Atlantic European region, and thus allows the hydrological model to exploit ... : Journal of Hydrometeorology, 24 (10) ...