proposal is open for discussion. In fact, this is
why it was posted
Here's my perspective on the matter. I agree that measurement
uncertainties need to be considered, however, this should be do
at the level of what we 'measure' - in the remote sensing case,
it is radiance (or reflectance). A slightly negative reflectance, given
our uncertainties on its retrieval is acceptable. An IOP inversion
algorithm, however, isn't a measurement. The IOPs derived from
an inversion algorithm should
all be geophysically reasonable.
Negative a or bb isn't reasonable. It gets a little fuzzier with adg
and aph, given the manner in which some of the algorithms derive
these parameters - but no way can there be negative bbp.
Now, I'll grant that not all parameters at all wavelengths are the primary
retrieved values from a given IOP inversion routine. With that in mind,
I could see modifying the test to be only on the primary retrieved parameters.
We may eventually arrive at per product, per wavelength standards, but in
reality the products and wavelengths are integral to each other - can't really
have one be good while another is bad - that indicates (to me at least) a
fundamental flaw in the inversion.
Remember, our goal is global
, we can afford to be a bit stringent -
it is a big ocean
If you (that includes anyone out there, hint, hint) want to propose alternative
strategies, by all means do so. My offering was to get the ball (and discussion)
rolling. We're looking for consensus, not edicts
As for basing the uncertainties on those within the validation data set,
I see that as a fool's errand. The current in situ
archive of IOPs
has far too large a measurement uncertainty for practical use beyond
a bulk "am I in the ballpark" analysis.