Hi Sean,
I must admit I hadn't appreciated the semantic distinction between
missing_value and _FillValue. However, I assumed that ncWMS would treat both
of these essentially the same and recognize missing_value as data outside the
dataset. The Java-NetCDF libs automatically recognise these and convert data
to NaN (I thought).
I've copied to John Caron, who can hopefully comment on whether Java-NetCDF
treats the attributes differently.
Cheers, Jon
-----Original Message-----
From: Gaffney, Sean P. [mailto:address@hidden]
Sent: 24 April 2012 10:20
To: Jon Blower
Subject: interpretation of missing_value by ncWMS
Hi Jon,
I've just found out from John Caron that the attribute missing_value is not
being deprecated in the CF conventions so is an acceptable CF attribute. A lot
of the feedback I've had from the community has been that _FillValue should
only be used to define the actual default value used to generate the file
structure before it was populated, and that if there are any actual absent data
values, these should be indicated using missing_value. I'd puzzled over this
because I thought the missing_value attribute was being lost, but this
obviously is no longer the case.
My understanding of how the ncWMS works at the moment is that it doesn't
recognise missing_value as data outside the dataset - I've had this problem
with the data that Helen sent me, where she had left out _FillValue but
supplied missing_value and the points covering land surface weren't being made
transparent.
Therefore, my question to you is, can the ncWMS be made to treat missing_value
in the same way it treats _FillValue, so that if it encounters either one, it
will regard them as a NaN and make the cell of the model transparent for
visualisation purposes?
Cheers
Sean
--------------------------------------------------------------------------------
Sean Gaffney
Data Scientist
British Oceanographic Data Centre
Joseph Proudman Building
6 Brownlow Street
Liverpool
L3 5DA
UK
+44 (0)151 795 4950
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