Low hanging fruit for the R-Instat climatic menu

[rdstern]

This is prompted by the possibility of more AIMS climatic interns, but trying to "harvest" these fruit might be also of more general interest, independently of this opportunity.
I list 3 "fruit" and then note other (climatic) topics that shouldn't be forgotten. Each relates to a specific package and so "all" we need to do is perhaps incorporate the package in R-Instat more generally.

1. Make more use of chillR. We already use this package in a small way to estimate missing values. It is designed for fruit trees, but (for us) this would additionally provide us with comprehensive facilities for handling temperature data. And it deals mainly with hourly data so we would also have some products from within-day data.
2. Make more use of openair. This is for pollution data. WE use quite a lot of functions from this package already, but not specifically for studying pollution. This would consider how we add, so R-Instat becomes a resource for processing pollution data. The package author already has a book on his package, so we would be able to see how those chapters can be run using dialogues rather than commands.
   3_ Make use of crop models from R-Instat. I was originally thinking of just ApSim, because they have a reasonably mature R package. But there is now also an R package called DSSAT - last updated in September 2021, so the topic should look at that as well. And a paper in 2019 is on aquacropR though I am not sure this has proceeded to become a package. But it may be interesting in that it seems to have implemented aquacrop in R. (The main author is now working at NIAB in UEA).

This third topic, in particular, may also be useful to IDEMS and INNODEMS support work for agricultural research. This includes our McKnight support but perhaps more generally.

In suggesting these topics we shouldn't forget we have bits to complete - that are pretty low hanging also on work started in past years. In particular:

a) Include some time-series models in our options in estimating missing models. Possibly add more on time series generally, particularly simple aspects including decompose.
b) Add homogen function to complete our options for homogenisation
c) Add anomalies into R-Instat to complete our methods for comparing station and satellite/reanalysis data

There are also larger topics, particularly adding resources for hydrology - those need to wait for WMO.