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[CF-metadata] Question from NODC about interplay of standard name modifiers, cell_methods, etc.

From: Cameron-smith, Philip <cameronsmith1>
Date: Tue, 2 Apr 2013 08:13:44 +0000

Hi Steve,

I think your suggestion has merit. One question is : Would your suggestion make any other changes, eg to std_name modifiers or cell_methods?

If nothing else, it would be good to put something in the CF documentation that explains what is going on, and why (perhaps along the lines of my email?) so that next time we have this discussion it will be shorter.

Best wishes,

    Philip

-----------------------------------------------------------------------
Dr Philip Cameron-Smith, pjc at llnl.gov, Lawrence Livermore National Lab.
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From: CF-metadata [mailto:cf-metadata-bounces at cgd.ucar.edu] On Behalf Of Steve Hankin
Sent: Monday, April 01, 2013 9:13 AM
To: Jonathan Gregory
Cc: cf-metadata at cgd.ucar.edu
Subject: Re: [CF-metadata] Question from NODC about interplay of standard name modifiers, cell_methods, etc.

Hi All,

All interesting questions are questions of balance. This discussion raises interesting questions. What are the issues we are balancing.

  * On the one side is technical precision: how to correctly describe the transformations that have been applied
  * Balancing this is usability: end users need to easily understand and use the data in these files
Our current encoding (standard_name and cell_methods) does well on technical precision and poorly on usability. A user who selects a variable with standard_name "sea water temperature", downloads it, and then realizes only after looking at a plot that it is a variance of sea water temperature, will understandably feel that she has been mislead. Blaming the user for ignorance or the designer of search engines for neglect is not a balanced outlook imho. We can foresee this problem (as demonstrated by this thread). It is our responsibility as designers is to minimize the opportunities for confusion.

How can we strike this balance? That's the (entirely constructive) topic that I'd lobby we should be addressing. I've included an off-the-cuff proposal below in a P.S. I'm sure there are better ideas out there.

    - Steve

P.S. One proposal: in all cases where a significant transformation (to be defined) has been applied to the data after is has been measured, the standard_name gets a generic modifier, say "(transformed)".
            ==> "sea water temperature (transformed)"
This will serve as a signal that forewarns users that the variable is not simply "sea water temperature".
________________________________

On 3/30/2013 6:22 AM, Jonathan Gregory wrote:

Dear all



I think Philip's posting points out that this disagreement is partly caused by

a confusion. I agree with his distinction of two cases.



Perhaps in Ken's use case, the standard deviation describes the spread of a

number of measurements that are regarded as samples from a population. The

difference between the samples is random error, not a dependence on time or

space that is of interest to the user of the dataset. This also sounds like

what Nan means by, "We collect in situ data, and I know that MANY of our

instruments output the mean of several measurements, few do single spot

samples." If the instrument itself does not output the individual

measurements, the variation among them as a function of time or space is

obviously of no geophysical interest.



I agree that this standard deviation is a kind of measurement property. As Ken

says, the standard error is usually calculated as the sample standard

deviation divided by the square root of the number in the sample. However I

appreciate that you might wish to report the standard deviation instead of the

standard error. To do this, I agree that we would need a new standard_name

modifier, which I suggest should be sample_standard_deviation to avoids its

being confused with any other kind of standard deviation.



Perhaps that is the answer I should have given to Ken's first question, instead

of asking whether it was a temporal or a spatial standard deviation. In fact it

is neither.



Going on to the wider question, I agree with Ken that a mean is just as much a

statistical operation as a standard deviation. Only a point measurement (which

is also one of the cell_methods of Appendix E) is the "true" geophysical

quantity. All the other methods are statistical ways of representing variation

of that quantity within the cell. It probably doesn't seem surprising to

regard a mean as the "same" quantity, nor the mode and median perhaps, but

maybe you begin to feel uncomfortable when moving on to the maximum and

minimum, the range (absolute difference between max and min, which is going to

be added as a cell_method in the next version of CF,

https://cf-pcmdi.llnl.gov/trac/ticket/65), and finally the standard deviation

and the variance, the last of which has different units. All these methods

belong to the same family, and it seems to me it would be arbitrary and

therefore unsatisfactory to choose a certain level of surprise or discomfort

in order to decide when it was no longer the "same" geophysical quantity. The

only solution, I think, is for everyone to learn that the standard name is

only a *part* of the description of the attribute, as John says.



John asked whether the difference between cell_methods and standard_name

modifiers could be clearly stated. My understanding is that standard_name

modifiers denote ancillary variables (they were introduced to CF at the same

time), whose purpose is to provide metadata about the individual values of

another data variable (start of section 3.4), while cell methods indicate the

statistical methods whereby the data values represent variation within cells.

This is a difference, but it might not be sufficiently obvious to require them

to be different features in CF.



The sample standard deviation *could* be represented by cell_methods, if we

introduced a notional axis of sample, to index the samples, and then collapsed

it to size one, like we do for a time-mean or a zonal mean. The sample

standard deviation would then be described in cell_methods as "sample:

standard_deviation". Likewise, the number of observations could be regarded as

a cell_method that counted the size of its (sample) axis. If we do not wish

to maintain the difference, we could simplify the standard by abolishing

standard_name modifiers and creating some new cell_methods, some of which

might be of a different kind from before because they wouldn't refer to a

particular axis.



Cheers



Jonathan

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