On 4/9/2012 1:07 PM, Jonathan Gregory wrote:
Dear Randy
The GOES-R system will be producing a hemispheric lightning detection product.
It will be an array of lightning detection that occur within some number of
seconds across the western hemisphere (i.e. it is not a gridded product).
Is is a timeseries of values, for probability or number of occurrence or
yes/no - something like that? If so, I don't think there's a featureType for
it yet. It's a timeseries which doesn't apply to a station. It would be
among the list of things not covered by chap 9 yet (see sect 9.1). But you
don't need to give it a featureType. Chap 9 is primarily for datasets which
need to use the techniques provided there for saving space.
hmm, my reading is that this would be an appropriate "point" feature
type. Jonathan, not sure why you think not?
A lightning detection has a center location, and needs to be associated with an
area and an interval of time.
(2) Use the cell "bounds" construct to capture the time interval.
Fine. cell_methods should say "time: sum", where time is the name of the time
dimension. From your description, I think "sum" is appropriate because this
quantity is extensive in time. The probability would be larger if the interval
were longer. See App E.
(3) Use the cell "cell_measures" construct to capture area (i.e. ":cell_measure
= area: lightning_detection_area").
That's not quite right, I think. The entry is to indicate what kind of
statistic this is, and I would say that again this is extensive. If you
considered an area smaller than the W hemisphere, the probability would be
smaller, for instance. So I would suggest "area: sum".
if you are trying to describe the location bounds, a "bounds" on the
lat, lon might be one way to do it?
or perhaps you just want to add some notion of error associated with the
location?
John
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