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#117: add example to 5.7 for multi-time dimension data
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Reporter: graybeal | Owner: cf-conventions@…
Type: enhancement | Status: new
Priority: medium | Milestone:
Component: cf-conventions | Version:
Resolution: | Keywords:
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Comment (by jonathan):
Dear John
Thanks you for bringing this subject back. It has often been asked about
and I think it is sufficiently important to include it in the standard
document as well as the FAQ. Section 5.7 is about scalar coord variables,
so I don't think that's the right place for the new example about
multiple-valued time coordinates. I would suggest that we add a new
subsection 4.4.2. Here is some proposed text (drawing on your draft FAQ
and the email discussion).
Best wishes
Jonathan
= 4.4.2. Time coordinates for forecast data =
In forecast applications, two kinds of time are distinguished: the time to
which the forecast applies ("validity", "valid" or "forecast" time), and
the time of the analysis from which the forecast was made ("analysis",
"run", "data" or "reference" time). These kinds of time coordinate are
recorded in separate variables, identified by `standard_name`. For the
validity time, the `standard_name` of `time` is used, as is usual for the
time coordinate of observed or simulated data. The `standard_name` for the
analysis time is `forecast_reference_time`.
A single-valued analysis time or validity time can be stored in either a
size-one coordinate variable or a scalar coordinate variable. If either
the analysis time or the validity time is multiple-valued, but the other
one is single-valued, it is recommended that there should be a coordinate
variable (with dimension greater than one) for the multiple-valued one,
and a size-one or a scalar coordinate variable for the other one. There
could thus be two time dimensions for the data variable. Example 5.12
shows a case of multiple forecasts from a single analysis, where the
analysis time is a scalar coordinate variable, and there is a dimension
(of size greater than one) for validity time.
If both analysis time and validity time are multiple-valued, it is
recommended to introduce a discrete axis (Section 4.5), and store both
analysis time and validity time as one-dimensional auxiliary coordinate
variables of this axis. This method is preferred because it is flexible,
and can be used for the cases of multiple validity and analysis times
where all combinations exist, multiple forecast periods from various
analyses where all combinations exist, and multiple validity and analysis
times where not all combinations exist. Example 4.7 illustrates the last
of these cases. It is not recommended to have two multiple-valued time
dimensions for the data variable.
Example 4.7. Multiple validity and analysis times
{{{
dimensions:
record=9;
variables:
double reftime(record);
time1:standard_name = "forecast_reference_time" ;
time1:units = "hours since 2003-01-01 00:00" ;
double valtime(record);
time2:standard_name = "time" ;
time2:units = "hours since 2003-01-01 00:00" ;
double period(record);
time2:standard_name = "forecast_period" ;
float temp(record,level,lat,lon);
temp:long_name = "Air temperature on model levels" ;
temp:standard_name = "air_temperature" ;
temp:units = "K" ;
temp:coordinates = "valtime reftime"
data:
reftime = 12., 12., 12., 0., 0., 0., 0., 6., 6. ;
valtime = 12., 24., 48., 6., 12., 18., 24., 12., 24. ;
period = 0., 12., 36., 6., 12., 18., 24., 6., 18. ;
}}}
In this example, forecasts of air temperature have been made from analyses
at 2003-01-01 12:00, 2003-01-01 00:00 and 2003-01-01 06:00. From the
analysis of 2003-01-01 12:00, forecasts have been made for validity times
of 2003-01-01 12:00 (i.e. the analysis time), 2003-01-02 00:00 (12 h
later) and 2003-01-03 00:00 (36 h later). The example also includes an
auxiliary coordinate variable of `forecast_period`, which is the
difference between validity time and analysis time for each forecast. This
information is redundant, but may be convenient.
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Ticket URL: <http://cf-pcmdi.llnl.gov/trac/ticket/117#comment:2>
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