Research map / Probabilistic and scientific modeling / Dynamical systems
Variational Data Assimilation with a Learned Inverse Observation Operator
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add learned inverse initialization to observation-space optimization (ablation) | Lorenz96 data assimilation · Lorenz96 | relative L1 forecast error: 1 -> 0.25 | Helped |
| Use hybrid optimization rather than observation-space optimization with averaging initialization (ablation) | Lorenz96 data assimilation · Lorenz96 | relative L1 forecast error: 1 -> 0.08 | Helped |
| Add inverse initialization to hybrid optimization (ablation) | Lorenz96 data assimilation · Lorenz96 | relative L1 forecast error: 0.08 -> 0.07 | Helped |
| Add learned inverse initialization to observation-space optimization (ablation) | Kolmogorov flow data assimilation · Kolmogorov flow | relative L1 forecast error: 1 -> 0.20 | Helped |
| Use hybrid optimization rather than observation-space optimization with interpolation initialization (ablation) | Kolmogorov flow data assimilation · Kolmogorov flow | relative L1 forecast error: 1 -> 0.88 | Helped |
| Use hybrid optimization instead of observation-space optimization with inverse initialization (ablation) | Kolmogorov flow data assimilation · Kolmogorov flow | relative L1 forecast error: 0.20 -> 0.23 | Hurt |
| Add inverse initialization to hybrid optimization (ablation) | Kolmogorov flow data assimilation · Kolmogorov flow | relative L1 forecast error: 0.88 -> 0.23 | Helped |
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