Allow uncertainty variables to be defined along subset of observation variable dimensions, where uncertainty values repeated

[shunt16]

In some cases, uncertainty values will be repeated along a given dimension(s). For example, an uncertainty of X% for every element in an ND array - it is then redundant to store an ND array of X.

[shunt16]

Comment from Zhav Loizeau in #1:

Maybe one needs to distinguish “size in memory” and “conceptual size”. If an error covariance matrix is specified to be of “scalar” type, that is, each measured values are associated with an uncertainty with the same value Image, and the errors are independent, one only needs to store Image in memory. However, knowing that the measured value vector has size Image, and that the error covariance Image matrix has such a structure, they can still perform the required operations. For instance Image will return Image and Image will return Image for Image and Image.
So, I think one needs to specify what is a “complete representation” of an error-covariance matrix by the set of operations one should be able to perform with it. In doing so, one can define the “conceptual size” of the matrix. Probably the “conceptual size” should be stored as an attribute of the matrix anyway (it is fair to assume that user want a result to Image in Image time).

[shunt16]

Zhav perhaps we can talk about this offline? Seems like some good suggestions but I'm not sure I follow all of it!