fire-atlas

UEA impact study to generate a global wildfire atlas with FireCrew

Contents

• plot_hdf5.py - Python script to extract an HDF5 variable and plot a timestamp on a world map using Cartopy
• plot_hdf5.sh - SLURM bash script submit plot_hdf5.py to the ADA job submission queue
• hdf5_2_netcdf.py - Python script to extract HDF5 variable arrays per timestep and save as netCDF4
• merge_netcdfs.py - Python script to concatenate extracted netCDF4 files per variable arrays per timestep
• convert_netcdf_SN_2_NS.py - Python script to flip satellite read row order for latitudes from SN --> NS
• plot_highest_variance_gridcell_timeseries.py - Python script to find maximum variance gridcell per variable and plot extracted timeseries and variable mean map + location
• plot_ipcc_ar6_region_classifications.py - Python script to extract IPCC AR6 region masks and plot classification on world map
• plot_ipcc_ar6_land_aggregated_timeseries_stats.py - Python script to compute gridcell stats for each monthly and yearly sampled variable per IPCC AR6 region
• plot_ipcc_ar6_land_aggregated_timeseries_pruning.py - Python script to aggregrate total variable monthly and yearly sampled timeseries per IPCC AR6 region and then segment the LOESS trend by optimisation of breakpoints
• plot_ipcc_ar6_land_aggregated_timeseries_robust_regression_5yr_means.py - Python script to aggregrate total variable monthly and yearly sampled timeseries per IPCC AR6 region and then perform a regression t-test and plot robust OLS fit + uncertainty band with 5-yr means
• create_ipcc_ar6_region_svg.py - Python script to proudce a SVG world map for IPCC AR6 land regions

The first step is to clone the latest fire-atlas repo and step into the check out directory:

$ git clone https://github.com/patternizer/fire-atlas.git
$ cd fire-atlas

Usage

The code was tested locally in a Python 3.8.16 virtual environment.

$ python plot_hdf5.py (optional)
$ python plot_ipcc_ar6_region_classifications.py (optional)
$ python hdf5_2_netcdf.py
$ python merge_netcdfs.py
$ python convert_netcdf_SN_2_NS.py
$ python plot_ipcc_ar6_land_aggregated_timeseries_stats.py (optional)
$ python plot_ipcc_ar6_land_aggregated_timeseries_pruning.py (optional)
$ python plot_ipcc_ar6_land_aggregated_timeseries_robust_regression_5yr_means.py
$ python plot_ipcc_ar6_land_region_svg.py

License

The code is distributed under terms and conditions of the Open Government License.

Contact information

• Michael Taylor