| # FireWx-FM Model Card |
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| ## Summary |
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| FireWx-FM is a compact U-Net model trained for 12-hour active-fire occupancy prediction on a 5 km EPSG:5070 grid over the Lower 48 United States. The final serving checkpoint is intended for gridded wildfire-risk screening and for downstream aggregation to county or sub-county units. |
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| ## Inputs |
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| The model uses a fixed 16-channel tensor containing HRRR weather variables, validity masks, LANDFIRE fuel and canopy layers, Wildfire Risk to Communities housing density, and LandScan population. The final no-exposure serving run zeros the housing and population channels. |
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| ## Output |
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| The output is a probability map with the same spatial grid as the input. Each value represents the model-estimated probability of active-fire occupancy at a 12-hour lead. |
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| ## Training Scope |
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| The released final model family was trained with chronological splitting over 2024 fire-season time maps. Training uses tile sampling to reduce empty-cell dominance, per-channel input normalization for continuous variables, sparse-label-aware loss weighting, and an auxiliary spatial-support objective. |
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| ## Intended Use |
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| - Research evaluation of wildfire-focused gridded prediction. |
| - CONUS-scale active-fire occupancy screening. |
| - County or sub-county summaries produced from the native 5 km grid by transparent aggregation. |
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| ## Not Intended For |
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| - Emergency response decisions without expert review. |
| - Direct replacement of operational fire-weather or incident-management products. |
| - Interpretation as a calibrated forecast without local validation. |
| - Inference from inputs that do not follow the documented channel order and normalization contract. |
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| ## Data Boundary |
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| This repository does not redistribute raw source data. Users must obtain NOAA HRRR, NASA FIRMS, LANDFIRE, Wildfire Risk to Communities, and LandScan resources from their original providers. |
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| ## Known Limitations |
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| - The native model is gridded at 5 km resolution; finer administrative summaries are aggregations, not new fine-scale predictions. |
| - Output quality depends on correct reprojection, normalization, mask handling, and source-variable selection. |
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