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FWI simulations with CMIP6-informed perturbations (mirror)

FWI simulations with CMIP6-informed perturbations (mirror)#

General#

Dataset ID

fwi_1981-2010_europe

Spatial coverage

Europe

In workflows

../../_images/icon_s_fire.png FWI response model

Original source

30-Year Canadian Fire Weather Index Simulations over Europe: CMIP6-Informed Temperature and Precipitation Perturbations (DOI 10.5281/zenodo.10458186)

Author

El Garroussi, Siham ORCID iD icon

Year

2024

License

Creative Commons Attribution 4.0 International

Related publication

El Garroussi, S., Di Giuseppe, F., Barnard, C. et al. Europe faces up to tenfold increase in extreme fires in a warming climate. npj Clim Atmos Sci 7, 30 (2024). https://doi.org/10.1038/s41612-024-00575-8

Note

This mirror contains the original data reencoded and with transposed coordinates in a zarr dataset. See below for more information on the applied transformations.

Technical#

URL

https://object-store.os-api.cci1.ecmwf.int/climaax/fwi_1981-2010_europe/

Dataset type

zarr

Warning

This mirror is provided for the convenice of CLIMAAX workflow users only. Please use the original data source for citations and general access. Availability of and URLs associated with this dataset may change without announcement.

Dataset transformation#

The following processing was applied to the original dataset to create this mirror:

# Input original datasets (grib) in ./zenodo
# Intermediate transposed datasets (netcdf) in ./transposed
# Output dataset (zarr) in ./fwi_1981-2010_europe

import os
import glob
import xarray as xr

files = glob.glob("zenodo/*.grib")

# Values from 0 to 500
fwi_encoding = {
    "dtype": "int16",
    "scale_factor": 0.007629627368999298,
    "add_offset": 250,
    "_FillValue": -32768
}

# Create intermediate transposed NetCDF datasets
for file in files:
    fname, _ = os.path.splitext(os.path.basename(file))
    print(fname)
    # Open, drop gridpoints without data and optimize for timeseries access
    data = xr.open_dataset(file, engine="cfgrib", backend_kwargs={"indexpath": ""}, chunks={"time": 1024})
    data = data.dropna("values", how="any")
    data = data.transpose("values", "time")
    # Add perturbation coordinates
    _, dt, dp, *_ = fname.split("_")
    assert dt.startswith("T")
    dt = float(dt[1:])
    assert dp.startswith("P")
    dp = float(dp[1:])
    data = data.assign_coords({"dt": [dt], "dp": [dp]})
    # Write to file
    data.to_netcdf(f"transposed/{fname}.nc", encoding={"fwinx": fwi_encoding})

# Merge all into chunked location-first zarr dataset
x = xr.open_mfdataset("transposed/*.nc", chunks={"values": 32})
x = x.transpose("values", "dp", "dt", "time")
x = x.chunk({"dp": 6, "dt": 6, "values": 32, "time": None})
# Don't chunk the coordinate dimensions
x["latitude"] = x["latitude"].compute()
x["longitude"] = (((x["longitude"].compute() + 180) % 360) - 180)
x["valid_time"] = x["valid_time"].compute()
x.to_zarr("fwi_1981-2010_europe")
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