NOTE: Sections 8.1 through 8.6 refer to dataset organization and techniques previous to the Discrete Sampling Geometries Standard in CF. Skip to Section 8.10 for discussion of Discrete Sampling Geometries data. Support for Discrete Sampling Geometries datasets begins with PyFerret/Ferret v7.6.
Many data sets which are not normally regarded as "gridded" can nonetheless be managed, analyzed, and visualized effectively in a gridded data framework. Track lines, "point data", etc. are common examples of "non-gridded" data. Profiles and time series, although they are individually simple one-dimensional grids, have a non-gridded structure when considered as a collection, which is often essential.
This chapter addresses a number of classes of non-gridded data sets and offers approaches that make it straightforward to work with these data types in Ferret's gridded data framework. The approaches are all conceived to facilitate a fusion of these data types—so that multiple data types may be easily combined in calculations..
"Point data" refers to collections of values at scattered locations and times. An example would be the column burden of oceanic NO3 and the scattered locations and times at which the measurements were made.
- If at each point of the data scattered there is a vertical profile of values then see COLLECTIONS OF VERTICAL PROFILES.
- If at each point of the data scattered there is a time series of values then see COLLECTIONS OF TIME SERIES.
- If at each point of the data scattered there is a 2-dimensional grid in the ZT plane then see COLLECTIONS OF TIME SERIES.
- If at each point of the data scattered there is a time series of values then see COLLECTIONS OF TIME SERIES.