Copyright © 2012 Open Geospatial Consortium.
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9.3.1 Higher-Dimensional Timeseries
One-dimensional, two-dimensional and higher dimensional measurements represent either the results from a sensor that measures along a transect or over an area, or the
synthetic results of a complex process. An example of a higher-dimensional sensor is a rain radar. An example of a complex process is a soil moisture map constructed by
processing a set of individual samples, integrating the results with a topographic model and producing a 2D coverage.
In most cases, higher dimensional timeseries can be modelled by extending the simple timeseries conventions used in Section 9.2 to have a value that consists of a 1D or 2D
array. The following additional considerations apply:
In many cases, data is still being gathered from stations and the station_name variable can still be used to capture the station identifier. Synthetic data can either
discard the station identification completely or include the name of the process as the station name.
In some cases, such as fixed transects or grids, the feature type can be modelled by using an extension to the feature types defined in the CF discrete sampling
geometries. In those cases, a set of distinct feature type names needs to be identified. Table 8 contains an incomplete list of suggested feature types.
A network of monitoring stations, each with a sampling grid, may use different grid mappings see Section 8.6 for the station location and for the grid. For
example, monitoring points may be located via GPS, each with a sensor grid laid out according to a transverse Mercator map grid. Generally, grid values are
eventually mapped down to longitude and latitude variables. However, station locations may need to be encoded as a separate variable.
Table 8 Hydrology Feature Types
Observation Type
CF Feature
Type CSML
Observation Type
Name Variables
Simple monitoring point
timeSeries PointSeries timeSeries
datai,p, xi, yi, zi, ti, p
Horizontal transect from a
fixed monitoring point
timeSeriesTransect datai,p,o, xi,o,
yi,o, zi, ti,p
Horizontal grid GridSeries
7
timeSeriesGrid datai,p,o
1
, o
2
,
7
CSML defines a GridSeries as a series of volumes
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from a fixed point or a
process xi,o
1
,o
2
, yi,o
1
,o
2
, zi, ti,p
Trajectory from a floating sensor
trajectory Trajectory
trajectory datai,p, xi,p,
yi,p, zi,p, ti,p
9.3.2 Record Timeseries