Jim:
The magnitude of the Expected Error is a function of the calculated wind
speed.
In perusing the Expected Error algorithm documentation to compose the last
email, it appears the algorithm does not assume any type of an error
distribution (normal or otherwise). The error estimate is absolute and is
not associated with a confidence level.
The use of this algorithm is based on the results it has achieved on
predecessor weather satellite programs (empirical data has been used to
determine its effectiveness.) I can provide you additional information on
the Expected Error algorithm if you are interested,
The point I am trying to make is that this is a specific error estimation
approach that is unrelated to a sampling distribution. I would think
there are others.
very respectfully,
randy
From: "Jim Biard" <jim.biard at noaa.gov>
Sent: Friday, July 05, 2013 11:22 AM
To: "cf-metadata at cgd.ucar.edu List" <cf-metadata at cgd.ucar.edu>
Cc: "rhorne at excaliburlabs.com Horne" <rhorne at excaliburlabs.com>
Subject: Re: [CF-metadata] Fwd: how to represent a non-standard error
Randy,
Could you help me understand a touch more about this? You say it is an
error that comes from a custom algorithm, but what defines what magnitude
it has? How do you relate it to anything? Does it represent some sort of
confidence interval?
Grace and peace,
Jim
Jim Biard
Research Scholar
Cooperative Institute for Climate and Satellites
Remote Sensing and Applications Division
National Climatic Data Center
151 Patton Ave, Asheville, NC 28801-5001
jim.biard at noaa.gov
828-271-4900
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On Jul 5, 2013, at 9:28 AM, "rhorne at excaliburlabs.com"
<rhorne at excaliburlabs.com> wrote:
Dear Jonathan:
In the case of the GOES-R derived motion winds product, the error estimate
(i.e. more formally referred to as Expected Error) is based on a custom
algorithm.
This expected error algorithm is specific to atmospheric wind vectors
derived from satellte data. The overarching concept of the wind algorithms
generated from satellite data is doing pattern matching of phenomena (like
clouds) across multiple images of the same region separated by some period
of time
The GOES-R incarnation of this Expected Error approach makes use of a set
of error predictors including (1) NWP model data (wind shear, temperature
gradient), (2) wind speed, direction, and consistency quality indicators
output from the winds algorithm proper, and (3) a wavelength dependent
constants (GOES-R generates sets of wind vectors from a visible and several
emissive bands)
I also found an article on the web that discusses it:
https://www.eumetsat.int/cs/idcplg?IdcService=GET_FILE&dDocName=pdf_conf_p42
_s2_le_marshall&allowInterrupt=1&noSaveAs=1&RevisionSelectionMethod=LatestRe
leased
very respectfully,
randy
Dear all
OK, I agree that if it's useful to compare them, then they should be
described
in a standardised way.
Why is this *not* a standard error? I suppose that to be described as a
standard error it should be a number you could regard as the standard
deviation
of the true value around the stated value. If it's not that, are there
other
ways you would use such a number?
Best wishes
Jonathan
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