Hi Jonathan,

Thanks for the reply. You have proposed some good ideas to ponder. 

I like your idea to use _statistical_ and _subjective_ for the Type A and Type 
B terms. I'll incorporate that suggestion.

I understand the argument against using _stanard_name_ because of the conocial 
units issue. If that is a deal breaker we can find another method.

I will need to think about the _cell_methods_ suggestions some more. But I can 
say I'm not excited about that option. I've been pushing the use of 
_cell_methods_ with my institution and it's not being accepted well. It is 
often quite difficult to encapsulate a description of the process into the 
cell_methods attribute, and most of my colleagues don't want to add that 
attribute. I spend a lot of time ensuring other critical attributes make it 
into the datasets, so I've been pushing less hard lately. I also see this as a 
slippery slope. If cell methods becomes required (in this case it is THE 
metadata to indicate uncertainty) then we should require it for other metadata 
variables. I'd prefer to require a less complicated way to indicate a type of 
data value.

I agree we should not add new attributes if an existing attribute already 
exists. My main concern is to find a method that is simple to write and simple 
to understand for our data users. Most of our data products have different 
types of methods of uncertainty and I need to find a solution that work for all 
of them. Most data users will not care how the uncertainty value was derived, 
only that the institution which created the data file are providing their best 
guess uncertainty estimation. They will then use that uncertainty estimation in 
their work. Often the uncertainty value provided will not be of the form most 
desireable to their research, but if that is all the researcher is provided 
they will find a way to use it. Therefore, I'm trying to not require the 
uncertainty to be defined by the details of the method used to derive the 
uncertainty.

Thanks,

Ken


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