enkf.ipynb
URL: https://github.com/tolgacaglar/WIFIRE-enKF/blob/main/enkf.ipynb
This IPython Notebook demonstrates a data assimilation step for a synthetically generated fire incident. It first generates all the necessary files to run an ensemble Kalman Filter, including fetching fire perimeters and weather observations, and generating .lcp to run FARSITE. It then quantifies uncertainties in the observed fire perimeters and weather observations, generates an ensemble of FARSITE outputs, and optimizes the initial prediction using the ensemble Kalman Filter. It finally compares the results, overlaying the predicted, observed, and optimized perimeters.Note that the data assimilation method used in this workflow yields better results over time as more observations are incorporated. The single-step data assimilation is for demonstration purposes only and is not expected to significantly improve the predicted results.
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Additional Information
| Field | Value |
|---|---|
| Data last updated | March 3, 2026 |
| Metadata last updated | March 3, 2026 |
| Created | March 3, 2026 |
| Format | application/x-ipynb+json |
| License | No License Provided |
| Datastore active | False |
| Docsurl | https://github.com/tolgacaglar/WIFIRE-enKF |
| Has views | False |
| Id | 11734d53-de3d-4de4-bcfa-07e4e8636e74 |
| Mimetype | application/x-ipynb+json |
| Package id | 8ecdfb6b-5f62-4f16-a65d-00f7a978336a |
| Position | 4 |
| State | active |
| Status | active |
| Urltype | informational or access link |