Pyrocast is a end-to-end machine learning pipeline for the prediction of extreme and dangerous wildfires. More specifically the code in this repository allows you to find, forecast and understand the causal drivers of pyrocumolonimbus clouds, precursors the most large and unpredictable wildfires. The pipeline includes:
loadersto download and format the datanrl_algorithmto find pyrocumolonimbus clouds and label the datamodelsto forecast the pyrocumolonimbus cloudsicpto understand the causal drivers of the pyrocumolonimbus clouds
The code for this repository is currently incomplete, the authors are contributing to the repository in their spare time so please be patient.
Get in touch with [email protected] to get access to the data on Google Cloud Storage.
The data is in a Zarr format, this allows us to load data that is associated to each hour of each day of each wildfire event using the ID numbers found in the wildfire_events.csv file. The data for the geostationary imagery, the pyrocast flags and masks and fuel and weather data each have their own Zarr directory.
Extracting data from a zarr folder event will yield Nx200x200 cube where N corresponds to the different wavelength channels, climate fields, etc.. These are detailed in the tables below.
PyroCb_flags.zarr (array shape = 1)
| N | Content |
|---|---|
| 0 | PyroCb flag, whether or not scene contains PyroCb |
PyroCb_mask.zarr (array shape = 1 x 200 x 200)
| N | Content |
|---|---|
| 0 | PyroCb mask, classification of pixel types according to NRL PyroCb algorithm |
Array shape = 18 x 200 x 200
Only some dimensions have entries which depend on the satellite source.
Himawari-8
| N | Channel wavelength [μm] |
|---|---|
| 0 | 0.47 |
| 2 | 0.64 |
| 3 | 0.86 |
| 6 | 3.9 |
| 13 | 11.2 |
| 15 | 13.3 |
GOES-16 / GOES-17
| N | Channel wavelength [μm] |
|---|---|
| 0 | 0.47 |
| 1 | 0.64 |
| 2 | 0.86 |
| 6 | 3.9 |
| 13 | 11.2 |
| 15 | 13.3 |
Array shape = 19 x 200 x 200
| N | Content |
|---|---|
| 1 | 10m v component of wind |
| 2 | 10m wind gust since previous post processing |
| 3 | boundary layer height |
| 4 | convective available potential energy |
| 5 | convective inhibition |
| 6 | geopotential |
| 7 | surface latent heat flux |
| 8 | surface sensible heat flux |
| 9 | surface vertical velocity |
| 10 | component of wind at 250hPa |
| 11 | v component of wind at 250hPa |
| 12 | fraction of high vegetation |
| 13 | fraction of low vegetation |
| 14 | type of high vegetation |
| 15 | type of low vegetation |
| 16 | relative humidity at 650hPa |
| 17 | relative humidity at 750hPa |
| 18 | relative humidity at 850hPa |