Hi @fzhu2e ,
I was going over the doc with Shreya (USC undergrad, who will be trying to apply the code to PAGES2k and maybe CoralHydro2k), and I noticed a couple of things:
many of the pages should start with a brief introduction linking to relevant concepts (e.g. papers) so newcomers like Shreya, or CoralHydro2k collaborators can be clear on terminology.
what is called "Climate" is really model priors (at least so far). Does your concept of Climate also encompass gridded, instrumental data? If yes, the example should show that. If not, then rename that category "ClimatePrior" (in your code and in the doc).
regardless, we need to think more broadly about to pull in instrumental data. Lazy-loading of remotely-hosted netCDF files via xarray could be a nice solution, though I don't know if it will work for all data products. Maybe @khider is aware of a catalog for those, from her MINT work?
the LMR workflow is nice, but ends on some fairly obscure diagnostics that few people outside of DA specialists will understand. At a minimum, it should include some ways to visualize the posterior (e.g. GMST ensemble timeseries, map of Little Ice Age cooling).
It is also important to show how to compare prior and posterior, since that is a key part of working with DA, that too few people presently do. Having the code capabilities to do it, and an example of how it's done, will go a very long way, I think. A good example of this was done in Singh et al 2019, Fig 4 . In general, showing how spatial relations in the prior (e.g. covariance with a field, or across two fields) are affected by assimilating proxy informations would be an excellent idea.
Just a few ideas to operationalize this great package. Keep your audience is mind!
Hi @fzhu2e , I was going over the doc with Shreya (USC undergrad, who will be trying to apply the code to PAGES2k and maybe CoralHydro2k), and I noticed a couple of things:
Just a few ideas to operationalize this great package. Keep your audience is mind!