Data Assimilation
- Flow Ensemble Filter proposes a learned nonlinear analysis update that augments a classical ensemble filter by transporting the forecast ensemble from a classical baseline filter to an analysis ensemble using conditional flow matching. It uses a localized Gaussian source during training, transports forecast ensemble members from a baseline filter at deployment, and conditions its velocity field on ensembles from that baseline filter and the observation. The proposed model therefore learns a nonlinear update while mapping each baseline ensemble independently.
Haoyuan Chen
Research Fellow
My research interests include Gaussian processes, uncerntainty quantification and probabilistic machine learning.
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