10 citations · 10 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting
Hang Fan, Yu Shi, Zongliang Fu +4
High-quality wind power forecasting is crucial for the operation of modern power grids. However, prevailing data-driven paradigms either train a site-specific model which cannot ge…
Satellite Observations Guided Diffusion Model for Accurate Meteorological States at Arbitrary Resolution
Siwei Tu, Ben Fei, Weidong Yang +7
Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that m…