[netcdf-java] Fwd: RE: interpretation of missing_value by ncWMS

• Subject: [netcdf-java] Fwd: RE: interpretation of missing_value by ncWMS
• From: John Caron <caron@xxxxxxxxxxxxxxxx>
• Date: Wed, 25 Apr 2012 15:48:22 -0600

-------- Original Message --------
Subject:        RE: interpretation of missing_value by ncWMS
Date:   Wed, 25 Apr 2012 11:05:04 +0100
From:   Gaffney, Sean P. <sgaf@xxxxxxxxxx>

Hi John and Jon,

Thank you both for your replies. With your information, I was able to 
re-examine the files in a bit more detail and I discovered that the problem was 
that the values for missing_value in the header and the actual missing data 
values in the file weren't the same, so they were being treated as real data.

This is therefore not a CF issue but a file generation issue and I'll get back 
to the originator and let them know. It does further my resolve to somehow come 
up with a checking mechanism that can automatically assess these sorts of 
issues though.

Cheers

Sean

-----Original Message-----
From: John Caron [mailto:caron@xxxxxxxxxxxxxxxx]
Sent: 24 April 2012 19:40
To: Jon Blower
Cc: Gaffney, Sean P.; Unidata netCDF Java Support
Subject: Re: interpretation of missing_value by ncWMS

Hi all:

missing_value, _FillValue are both mapped to NaNs

details are here:

http://www.unidata.ucar.edu/software/netcdf-java/v4.2/javadoc/ucar/nc2/dataset/EnhanceScaleMissing.html

send me an example file if you think somethings not working.

John

On 4/24/2012 11:00 AM, Jon Blower wrote:

 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:sgaf@xxxxxxxxxx]
 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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