Improving Ensemble Filters with Flow Matching
Data assimilation estimates a dynamical state from partial and noisy observations. Classical ensemble filters are efficient but restrict analysis updates through finite sample covariance and affine Gaussian distribution. We introduce the Flow Ensemble Filter (FlowEF), which uses conditional flow matching to transport the forecast ensemble from a classical baseline filter to an analysis ensemble. FlowEF 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 eac...
| Authors | Haoyuan Chen, Alexandre Thiéry |
|---|---|
| Primary category | stat.ML |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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