activity
20242026
most citedCausal machine learning for sustainable agroecosystems

5 citations · 8 across the 5 of their papers we have counts for

collaborators

5 papers

stat.AP2026

Exploring climate change effects on concurrent floods and concurrent droughts via statistical deep learning

C. J. R. Murphy-Barltrop, J. Richards, B. Poschlod +2

Concurrent floods and concurrent droughts in nearby catchments pose challenges to risk assessment and water management. Climate change is affecting extremely high and low discharge…

physics.ao-ph2026

Omega-blocks with spatially compounding extremes over Europe are highly sensitive to remote atmospheric drivers

Magdalena Mittermeier, Christian M. Grams, Urs Beyerle +5

Omega-blocks can trigger spatially compounding heat-precipitation extremes with severe societal impacts, as seen in September 2023 when a heatwave over France coincided with devast…

physics.ao-ph20252 cited

Numerical models outperform AI weather forecasts of record-breaking extremes

Zhongwei Zhang, Erich Fischer, Jakob Zscheischler +1

Artificial intelligence (AI)-based models are revolutionizing weather forecasting and have surpassed leading numerical weather prediction systems on various benchmark tasks. Howeve…

nlin.CD20251 cited

Regularization of ML models for Earth systems by using longer model timesteps

Raghul Parthipan, Mohit Anand, Hannah M Christensen +3

Regularization is a technique to improve generalization of machine learning (ML) models. A common form of regularization in the ML literature is to train on data where similar inpu…

cs.LG20245 cited

Causal machine learning for sustainable agroecosystems

Vasileios Sitokonstantinou, Emiliano Díaz Salas Porras, Jordi Cerdà Bautista +8

In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its…