@charleskawczynski suggest looking at https://github.com/ronisbr/PrettyTables.jl to make output for human reading part of #1086 even more beautiful.

I think we should move towards a hydrological approach instead of the "cost route" algorithm we are currently using.

Chhota Shigri is a good example of things going wrong.

Currently, data/overview/solutions.csv holds the sector names as published in the 2017 Project Drawdown book. The Drawdown Review in 2020 updated the sectors, now called Areas, with new names and organization. A number of the solutions are now present in multiple Areas. For example, Waste to Energy was former

Follow this:

[ dataset_name ]( data_link) A short description

make sure it's a public dataset!

Steps:

1. 🌟 the repository
2. Fork it
3. Make changes to the README by adding the dataset name and link as mentioned above on yours forked repo.
4. Make PR for that
5. That's all
   Congratulation 🎉

See pangeo-data/climpred#439. We added discrimination and reliability from xskillscore, which returns the data necessary to make these plots. Would be good to have a small section in the docs + maybe a convenience function in graphics.py to actually construct these plots with the output.

See "reliability diagram" and "discrimination diagram" under https://www.cawcr.