netCDF

"NetCDF is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data."

https://www.unidata.ucar.edu/software/netcdf/


netCDF STANDARDS

CF Conventions and Metadata

"The conventions for CF (Climate and Forecast) metadata are designed to promote the processing and sharing of files created with the NetCDF API. The CF conventions are increasingly gaining acceptance and have been adopted by a number of projects and groups as a primary standard. The conventions define metadata that provide a definitive description of what the data in each variable represents, and the spatial and temporal properties of the data. This enables users of data from different sources to decide which quantities are comparable, and facilitates building applications with powerful extraction, regridding, and display capabilities."

http://cfconventions.org/

ACDD

"The  attributes recommended for describing a NetCDF dataset to discovery systems such as Digital Libraries. THREDDS and other tools can use these attributes to extract metadata from datasets, and exporting to Dublin Core, DIF, ADN, FGDC, ISO 19115 and other metadata formats. This will help systems and users locate and use data efficiently.

The NetCDF User Guide (NUG) provides basic recommendations for creating NetCDF files; the NetCDF Climate and Forecast Metadata Conventions (CF) provides more specific guidance. The ACDD builds upon and is compatible with these conventions; it may refine the definition of some terms in those conventions, but does not preclude the use of any attributes defined by the NUG or CF.
The NUG does not require any global attributes, though it recommends and defines three, title, history, and Conventions. CF specifies more: institution, source, references, comment, and featureType."

http://wiki.esipfed.org/index.php/Attribute_Convention_for_Data_Discovery

NCEI NetCDF Templates

"The NOAA National Centers for Environmental Information (NCEI) have developed netCDF templates based on what are called "feature types" by Unidata and CF. These templates conform to Unidata's netCDF Attribute Convention for Dataset Discovery (ACDD) and netCDF Climate and Forecast (CF) conventions. Adding to these established conventions, NCEI also provides several recommendations for both netCDF variables and attributes. These best practices capture NCEI's experience in providing long-term preservation, scientific quality control, product development, and multiple data re-use beyond its original intent."

https://www.nodc.noaa.gov/data/formats/netcdf/v2.0/

Glider Standard

"The real time exchange format for profiling gliders submitting data to the IOOS Glider Data Assembly Center.  This describes the structure and contents of the netCDF file that will be used as part of the dissemination of real time profiling glider data. In creating this file several objectives were addressed. The file aims to be fully compliant with CF-1.6 including the usage of the so called discrete sampling geometries.  Additionally, the global attributes are compliant with ACDD v1.1.  Finally the data files were designed so as to be easily aggregated using the THREDDS data server.  By using aggregation, thousands of individual netCDF files each containing just one segment of data can be presented to the user as a virtual data set representing an entire deployment."

https://github.com/IOOSProfilingGliders/Real-Time-File-Format




DISCRETE SAMPLING GEOMETRIES AND FEATURE TYPES

Time series, vertical profiles and trajectories are examples of discrete sampling geometries.  Discrete sampling geometry datasets are characterized by a dimensionality that is lower than that of the space-time region that is sampled; discrete sampling geometries are typically “paths” through space-time.

Each type of discrete sampling geometry (point, time series, profile or trajectory) is defined by the relationships among its spatiotemporal coordinates.  We refer to the type of discrete sampling geometry as its featureType.  The term “feature” refers herein to a single instance of the discrete sampling geometry (such as a single time series).  The representation of such features in a CF dataset was supported previous to the introduction of this chapter using a particular convention, which is still supported.

These conventions offer advantages of efficiency and clarity for storing a collection of features in a single file.  When using these new conventions, the features contained within a collection must always be of the same type; and all the collections in a CF file must be of the same feature type.

The featureTypes are:

featureType	Description of a single feature with this discrete sampling geometry
	Form of a data variable containing values defined on a collection of these features Mandatory space-time coordinates for a collection of these features
point	a single data point (having no implied coordinate relationship to other points)
	data(i) x(i) y(i)  t(i)
timeSeries	a series of data points at the same spatial location with monotonically increasing times
	data(i,o) x(i) y(i) t(i,o)
trajectory	a series of data points along a path through space with monotonically increasing times
	data(i,o)    x(i,o) y(i,o) t(i,o)
profile	an ordered set of data points along a vertical line at a fixed horizontal position and fixed time
	data(i,o)    x(i) y(i) z(i,o) t(i)
timeSeriesProfile	a series of profile features at the same horizontal position with monotonically increasing times
	data(i,p,o)      x(i) y(i) z(i,p,o) t(i,p)
trajectoryProfile	a series of profile features located at points ordered along a trajectory
	data(i,p,o)

http://cfconventions.org/Data/cf-conventions/cf-conventions-1.7/build/ch09.html 

http://www.unidata.ucar.edu/software/thredds/current/netcdf-java/reference/FeatureDatasets/CFpointImplement.html 


RELATED SOFTWARE

cf-python - Implements the CF data model for the reading, writing and processing of data and metadata.

https://cfpython.bitbucket.io/

nc-validate -  Validate one or more NetCDF files against a templat.

https://github.com/kerfoot/nc-validate 

pyaxiom -  An ocean data toolkit developed and used by Axiom Data Science 

https://github.com/axiom-data-science/pyaxiom 

pyncml -  A simple python library to apply NcML logic to NetCDF files.

https://github.com/axiom-data-science/pyncml