Add tutorial

[jatkinson1000]

Closes #126

This adds a tutorial for new users to the docs, with associated scripts in the GitHub repo.

It works through

• Installing TCTrack with TE and TSTORMS
• Fetching data
• Preprocessing data
• Running TCTrack (TE and TSTORMS)
• Visualising tracks

Potential issues:

• TSTORMS does not detect any tracks for the input data. Consider using a different period where it does?
• Data pre-processing can take a long time and requires conda env which feels clunky
• We use a mixture of sources for input data when ideally we would use a single source
• We use wget to fetch data, but I would prefer if we used OpenDAP or THREDDS. Perhaps improve later. Open an issue?

On CSD3 I use the following environment:

module purge
module load rhel8/cclake/base
module load intel-oneapi-compilers/2023.2.4/gcc/4lbvg4hv
module load netcdf-cxx4/4.3.1/intel/intel-oneapi-mpi/gxfwrfnl
module load netcdf-fortran/4.6.1/intel/intel-oneapi-mpi/kqukipdf
module load python/3.11.9/gcc/nptrdpl

To run the regridding script I submitted as a batch job (approx 30mins to run) using the following:

#!/bin/bash
#SBATCH -J regrid
#SBATCH -A ICCS-SL2-CPU
#SBATCH -p icelake
#SBATCH --output=regrid_%A_%a.out
#SBATCH --error=regrid_%A_%a.err
#SBATCH --nodes=1
#SBATCH --ntasks=2
#SBATCH --time=01:00:00

. /etc/profile.d/modules.sh

module purge
module load rhel8/cclake/base

cd /home/jwa34/rds/rds-inspire-tc-TqEGHMWTn8A/TCTrack/tutorial

source miniforge3/bin/activate
conda activate regrid

python regrid.py

conda deactivate
conda deactivate

which assumes you have already cloned TCTrack and placed your miniforge installation in the tutorial/ directory.

It would be good if you could work through this when back @sjavis to see how well it works or any sticking points/points for clarification/improvement.

[sjavis]

This seems to mostly work for me locally, with the exception of TSTORMS since I don't have ifort. I have just a few suggested changes.

I think the conda environment is fine, especially since we discussed that this will probably be what we recommend. The pre-processing time also didn't seem too dreadful to me. I think, ultimately, is going to take time for the pre-processing for any reasonable.

I agree that the multiple sources and using wget can probably also be made an issue to look at improving later.

[sjavis]

Instructions for initialising the conda environment for cf-python are given in the pre-processing section. Should those be moved here? Otherwise we will also need to add the pip install instruction in the pre-processing section

[sjavis]

Suggested change

	# - Fortran compiler
	# - Intel Fortran compiler (ifort)

[sjavis]

fetch_data.sh is not executable

[sjavis]

Suggested change

	wget https://dap.ceda.ac.uk/badc/cmip6/data/PRIMAVERA/HighResMIP/MOHC/HadGEM3-GC31-HM/hist-1950/r1i1p1f1/Prim3hrPt/zg7h/gn/files/d20180730/zg7h_Prim3hrPt_HadGEM3-GC31-HM_hist-1950_r1i1p1f1_gn_195009010000-195009302100.nc?download=1 -O data/zg7h_Prim3hrPt_HadGEM3-GC31-HM_hist-1950_r1i1p1f1_gn_195010090000-195009302100.nc
	wget https://dap.ceda.ac.uk/badc/cmip6/data/PRIMAVERA/HighResMIP/MOHC/HadGEM3-GC31-HM/hist-1950/r1i1p1f1/Prim3hrPt/zg7h/gn/files/d20180730/zg7h_Prim3hrPt_HadGEM3-GC31-HM_hist-1950_r1i1p1f1_gn_195009010000-195009302100.nc?download=1 -O data/zg7h_Prim3hrPt_HadGEM3-GC31-HM_hist-1950_r1i1p1f1_gn_195009010000-195009302100.nc

[sjavis]

Suggested change

	plt.figure(figsize=(10, 3))
	ax = plt.axes(projection=ccrs.PlateCarree())
	plt.figure(figsize=(8, 4))
	ax = plt.axes(projection=ccrs.PlateCarree())
	ax.set_extent([-180, 180, -90, 90])

The current plot is hard to see since the latitude range is so small and the title and legend are larger than the axes.

[sjavis]

Suggested change

	pl = ax.plot(
	lons[i], lats[i], "--", transform=ccrs.PlateCarree(), label=f"{label}"
	)
	mask = np.isfinite(lons[i])
	pl = ax.plot(
	lons[i, mask], lats[i, mask], "--", transform=ccrs.PlateCarree(), label=f"{label}"
	)

This avoids the warning appearing.