10 papers
Koopman early warning signals for bifurcation and rate-induced tipping
Juan Nathaniel, Carla Roesch, Derek DeSantis +5
Abrupt transitions in complex systems are often preceded by early warning signals. However, most indicators rely on the notion of critical slowing down and do not generally extend…
Learning more physically realistic dynamics in machine-learning based weather forecasting with latent-space constraints
Hang Fan, Yi Xiao, Yongquan Qu +5
Data-driven machine learning (ML) models are reshaping weather forecasting and have shown the potential to accelerate and surpass traditional physics-based approaches, leading to a…
In-context learning to predict critical transitions in dynamical systems
Yunus Sevinchan, Juan Nathaniel, Kai Ueltzhöffer +8
Critical transitions - abrupt, often irreversible changes in system dynamics - arise across human and natural systems, often with catastrophic consequences. Real-world observations…
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
Yi Xiao, Qilong Jia, Hang Fan +4
Many downstream decisions in complex terrain require fast wind estimates at a small number of user-specified locations and heights for a given forecast valid time, rather than anot…
Earth-o1: A Grid-free Observation-native Atmospheric World Model
Junchao Gong, Kaiyi Xu, Wangxu Wei +22
Despite the unprecedented volume of multimodal data provided by modern Earth observation systems, our ability to model atmospheric dynamics remains constrained. Traditional modelin…
Accurate and Efficient Hybrid-Ensemble Atmospheric Data Assimilation in Latent Space with Uncertainty Quantification
Hang Fan, Juan Nathaniel, Yi Xiao +5
Data assimilation (DA) combines model forecasts and observations to estimate the optimal state of the atmosphere with its uncertainty, providing initial conditions for weather pred…