earthflux

Compute

Compute on demand

When the ready-made products don’t fit, compute your own. Choose input data, an index and its settings, a region and a destination. You get a script that runs on your own computer or server, with your own credentials.

PreviewGenerated scripts use the climate_indices command and package names. Previews draw on a demonstration sample.

  1. Input data

  2. Index

    Drought
    Fire
    Flood
    Time scales, in months
    Distribution
    Calibration period

  3. Region

    Area

  4. Results

    Save to
    Format

Run it

Run with

The command line is the fastest path today: it splits the grid across your CPU cores using shared memory.

run.sh
# 1. Get the inputs from NOAA's public bucket (no AWS account needed)
mkdir -p data out
aws s3 cp --no-sign-request s3://noaa-nclimgrid-monthly-pds/nclimgrid_prcp.nc data/
aws s3 cp --no-sign-request s3://noaa-nclimgrid-monthly-pds/nclimgrid_tavg.nc data/

# 2. Cut out your region
python - <<'EOF'
import xarray as xr
for name in ["prcp", "tavg"]:
    ds = xr.open_dataset(f"data/nclimgrid_{name}.nc")
    ds.sel(lat=slice(31.0, 37.0), lon=slice(-104.0, -94.0)).to_netcdf(f"data/region_{name}.nc")
EOF

# 3. Install climate_indices 3.0 from PyPI (a separate library that Earthflux imports)
pip install "climate-indices>=3.0.0"

# 4. Compute, splitting the grid across all CPU cores
process_climate_indices --index spei \
  --periodicity monthly \
  --scales 3 12 24 \
  --netcdf_precip data/region_prcp.nc --var_name_precip prcp \
  --netcdf_temp data/region_tavg.nc --var_name_temp tavg \
  --calibration_start_year 1991 --calibration_end_year 2020 \
  --output_file_base out/earthflux \
  --multiprocessing all

Preview

A quick look at what the result will show, drawn from the demonstration sample rather than computed from your settings.

SPEI-3, gamma

Loading

Loading index data

  • Extremely dry≤ −2.0
  • Severely dry−2.0 to −1.5
  • Moderately dry−1.5 to −1.0
  • Near normal−1.0 to 1.0
  • Moderately wet1.0 to 1.5
  • Very wet1.5 to 2.0
  • Extremely wet≥ 2.0
Demonstration sample: climate_indices example output on a reduced nClimGrid grid (about 0.67°), January 1991 to February 2017. Placeholder data, not for analysis.