Higher-Dimensional Timeseries Complex Timeseries

Copyright © 2012 Open Geospatial Consortium. 37

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 38 Copyright © 2012 Open Geospatial Consortium. 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