2 citations · 4 across the 5 of their papers we have counts for
5 papers
Deep Learning for predicting rate-induced tipping
Yu Huang, Sebastian Bathiany, Peter Ashwin +1
Nonlinear dynamical systems exposed to changing forcing can exhibit catastrophic transitions between alternative and often markedly different states. The phenomenon of critical slo…
A Generative Machine Learning Approach for Improving Precipitation from Earth System Models
Philipp Hess, Niklas Boers
Quantifying the impacts of anthropogenic global warming requires accurate Earth system model (ESM) simulations. Statistical bias correction and downscaling can be applied to reduce…
Spatial correlation increase in single-sensor satellite data reveals loss of Amazon rainforest resilience
Lana L. Blaschke, Da Nian, Bathiany +4
The Amazon rainforest (ARF) is threatened by deforestation and climate change, which could trigger a regime shift to a savanna-like state. Previous work suggesting declining resili…
Exploring Geometric Deep Learning For Precipitation Nowcasting
Shan Zhao, Sudipan Saha, Zhitong Xiong +2
Precipitation nowcasting (up to a few hours) remains a challenge due to the highly complex local interactions that need to be captured accurately. Convolutional Neural Networks rel…
Uncertainties in critical slowing down indicators of observation-based fingerprints of the Atlantic Overturning Circulation
Maya Ben-Yami, Vanessa Skiba, Sebastian Bathiany +1
Observations are increasingly used to detect critical slowing down (CSD) to measure stability changes in key Earth system components. However, most datasets have non-stationary mis…