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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - image-classification
+  - other
+tags:
+  - weather
+  - meteorology
+  - wrf
+  - severe-weather
+  - visualization
+  - numpy
+  - geospatial
+  - tornado
+  - wildfire
+  - hurricane
+  - atmospheric-science
+size_categories:
+  - 10K<n<100K
+pretty_name: WRF 250m Severe Weather Overlays
+---
+
+# WRF 250m Severe Weather Overlays
+
+**18 high-resolution severe weather simulations, ready to plot.**
+
+250-meter WRF model output covering tornadoes, wildfires, hurricanes, blizzards, heat waves, and flooding events across the US. Each event includes surface weather fields at 1-minute temporal resolution on an 800x800 grid — just load with numpy and start making maps.
+
+## What's in the box
+
+Each event folder contains:
+
+- **`coords.npz`** — latitude/longitude arrays (`xlat`, `xlong`, both 800x800 float32)
+- **`t_0000.npz` through `t_NNNN.npz`** — one file per timestep, each containing 16 weather fields
+- **`metadata.json`** — event info, timestamps, field descriptions
+
+## Quick start
+
+```python
+import numpy as np
+
+# Load coordinates
+coords = np.load("carr_fire/coords.npz")
+lat, lon = coords["xlat"], coords["xlong"]
+
+# Load a single timestep
+data = np.load("carr_fire/t_0200.npz")
+
+# Plot wind speed
+import matplotlib.pyplot as plt
+fig, ax = plt.subplots(figsize=(10, 10))
+c = ax.pcolormesh(lon, lat, data["wind_speed_10m"], cmap="YlOrRd", vmin=0, vmax=30)
+ax.set_title("Carr Fire — 10m Wind Speed (m/s)")
+plt.colorbar(c)
+plt.savefig("carr_fire_wind.png", dpi=150)
+```
+
+### Make an animation
+
+```python
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib.animation import FuncAnimation
+import json
+
+with open("carr_fire/metadata.json") as f:
+    meta = json.load(f)
+
+coords = np.load("carr_fire/coords.npz")
+lat, lon = coords["xlat"], coords["xlong"]
+
+fig, ax = plt.subplots(figsize=(10, 10))
+data0 = np.load("carr_fire/t_0000.npz")
+mesh = ax.pcolormesh(lon, lat, data0["refc"], cmap="turbo", vmin=-10, vmax=70)
+title = ax.set_title("")
+
+def update(frame):
+    data = np.load(f"carr_fire/t_{frame:04d}.npz")
+    mesh.set_array(data["refc"].ravel())
+    title.set_text(f"Carr Fire Reflectivity — {meta['times'][frame]}")
+    return mesh, title
+
+anim = FuncAnimation(fig, update, frames=range(0, 421, 5), interval=100)
+anim.save("carr_fire_refc.mp4", dpi=100)
+```
+
+## Fields
+
+Every `.npz` timestep file contains these 16 fields, all `float32` at 800x800:
+
+| Field | Description | Units | Notes |
+|-------|-------------|-------|-------|
+| `t2m` | 2-meter temperature | degC | |
+| `u10` | 10-meter U-wind component | m/s | |
+| `v10` | 10-meter V-wind component | m/s | |
+| `wind_speed_10m` | 10-meter wind speed | m/s | Derived: sqrt(u10^2 + v10^2) |
+| `wind_direction_10m` | 10-meter wind direction | degrees | Meteorological convention (wind FROM) |
+| `surface_pressure` | Surface pressure | hPa | |
+| `pblh` | Planetary boundary layer height | m | |
+| `hfx` | Surface sensible heat flux | W/m2 | |
+| `lh` | Surface latent heat flux | W/m2 | |
+| `rain_rate` | Precipitation rate | mm/hr | Derived from accumulated RAINNC |
+| `refc` | Composite reflectivity | dBZ | Column-max of 3D reflectivity |
+| `wspd10max` | Max 10m wind gust | m/s | Running max since simulation start |
+| `w_up_max` | Max updraft speed | m/s | Running max since simulation start |
+| `w_dn_max` | Max downdraft speed | m/s | Running max since simulation start |
+| `updraft_helicity` | Max updraft helicity 2-5 km | m2/s2 | Running max since simulation start |
+| `hail_max` | Max hail diameter | mm | Running max since simulation start |
+
+**Note on "running max" fields:** `wspd10max`, `w_up_max`, `w_dn_max`, `updraft_helicity`, and `hail_max` are accumulated maxima that increase monotonically over the simulation. They show the worst conditions experienced at each grid point up to that time — useful for swath maps showing the full storm impact.
+
+## Events
+
+| Event | Date | Category | Location | Timesteps | Temporal Res |
+|-------|------|----------|----------|-----------|-------------|
+| Carr Fire | 2018-07-23 | Wildfire | Redding, CA | 421 | 1 min |
+| Hurricane Michael | 2018-10-10 | Hurricane | Panama City, FL | ~23 | 15 min |
+| Camp Fire | 2018-11-08 | Wildfire | Paradise, CA | 418 | 1 min |
+| Nashville EF3 Tornado | 2020-03-03 | Tornado | Nashville, TN | ~28 | 15 min |
+| Death Valley Record Heat | 2020-08-16 | Heat | Death Valley, CA | 357 | 1 min |
+| LA Fires | 2020-09-06 | Wildfire | Los Angeles, CA | 421 | 1 min |
+| SF Bay Area Fires | 2020-09-06 | Wildfire | San Francisco, CA | 421 | 1 min |
+| PNW Windstorm | 2020-09-08 | Wind | Pacific Northwest | 360 | 1 min |
+| Texas Freeze | 2021-02-16 | Winter | Texas | ~22 | 15 min |
+| Seattle Heat Dome | 2021-06-28 | Heat | Seattle, WA | ~10 | 15 min |
+| Mayfield EF4 Tornado | 2021-12-11 | Tornado | Mayfield, KY | ~22 | 15 min |
+| CA Atmospheric River | 2021-12-30 | Flooding | Northern California | 421 | 1 min |
+| Buffalo Blizzard | 2022-12-23 | Winter | Buffalo, NY | ~14 | 15 min |
+| CA Pineapple Express 2023 | 2023-01-04 | Flooding | California | 421 | 1 min |
+| Pineapple Express | 2024-02-04 | Flooding | Southern California | 418 | 1 min |
+| Denver Hailstorm | 2024-05-31 | Hail | Denver, CO | 421 | 1 min |
+| LA Fires Peak 2025 | 2025-01-07 | Wildfire | Los Angeles, CA | 421 | 1 min |
+| Enderlin EF5 Tornado | 2025-06-21 | Tornado | Enderlin, ND | ~360 | 1 min |
+
+12 events have **1-minute** temporal resolution from WRF auxiliary history output. 6 events have **15-minute** resolution from standard WRF output files.
+
+## Grid details
+
+- **Model:** WRF-ARW v4, d03 (innermost nest)
+- **Horizontal resolution:** 250 meters
+- **Grid size:** 800 x 800 points (200 km x 200 km)
+- **Projection:** Lambert Conformal Conic (varies per event)
+- **Coordinates:** Each event has its own `coords.npz` with the exact lat/lon for every grid point
+
+## File sizes
+
+- Individual timestep `.npz`: ~20-25 MB (16 fields, float32, 800x800, numpy compressed)
+- Full event (421 timesteps): ~8-10 GB
+- Total dataset: ~128 GB
+
+## Coordinate system
+
+The WRF model uses a Lambert Conformal Conic projection centered on each event location. The `coords.npz` file contains the exact latitude and longitude of every grid point. Use these for plotting — don't assume a regular lat/lon grid.
+
+```python
+coords = np.load("carr_fire/coords.npz")
+lat = coords["xlat"]   # shape (800, 800), float32
+lon = coords["xlong"]  # shape (800, 800), float32
+
+# These are NOT regularly spaced in lat/lon
+# Use pcolormesh, not imshow, for correct geographic placement
+```
+
+For web map overlays (Leaflet, Mapbox), you'll need to reproject from Lambert Conformal to Web Mercator. The metadata.json for each event includes the projection parameters if you need them.
+
+## Source
+
+Produced by [Fahrenheit Research](https://wxsection.com) using WRF-ARW at 250m resolution. Simulations were run on the d03 innermost nest with 1-minute auxiliary history output enabled for surface fields.
+
+Raw WRF netCDF files were processed into `.npz` format to make them accessible without WRF-specific tools. All fields are on the native d03 mass grid (no interpolation).
+
+## License
+
+CC-BY-4.0 — free to use for any purpose with attribution.
+
+## Citation
+
+```bibtex
+@dataset{fahrenheit_wrf_overlays_2026,
+  author = {Fahrenheit Research},
+  title = {WRF 250m Severe Weather Overlays},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/deepguess/wrf-250m-severe-weather-overlays}
+}
+```