Options for custom observations or observation operators

[peanutfun]

As far as I see, functions in this package currently assume that every state dimension is always observed (with some observation error). From my experience with data assimilation, this is rarely the case, and the performance of DA algorithms can strongly differ depending on which, and how many, state dimensions are part of the observation. For a DA comparison framework, I suggest more control over observations (i.e., observation operators) at the user side of things. A first step would be to determine which state dimensions are observed, e.g., via a vector of indices that is supplied as function argument. A further improvement would be the option to add the complete observation operator matrix which calculates the observations via multiplication with the state vector.

openjournals/joss-reviews#4129

[cgrudz]

This issue is resolved, there were options for sparse and nonlinear observations with different types of nonlinearity already built into the system, but this was not sufficiently documented before. It should now be clear how to use these options based on the documentation on the new ObsOperators module:
https://cgrudz.github.io/DataAssimilationBenchmarks.jl/dev/submodules/models/ObsOperators/