The Climate and Forecast (CF) metadata conventions are conventions for the description of Earth sciences data, intended to promote the processing and sharing of data files. The metadata defined by the CF conventions are generally included in the same file as the data, thus making the file "self-describing". The conventions provide a definitive description of what the data values found in each netCDF variable represent, and of the spatial and temporal properties of the data, including information about grids, such as grid cell bounds and cell averaging methods. This enables users of files from different sources to decide which variables are comparable, and is a basis for building software applications with powerful data extraction, grid remapping, data analysis, and data visualization capab
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| - The Climate and Forecast (CF) metadata conventions are conventions for the description of Earth sciences data, intended to promote the processing and sharing of data files. The metadata defined by the CF conventions are generally included in the same file as the data, thus making the file "self-describing". The conventions provide a definitive description of what the data values found in each netCDF variable represent, and of the spatial and temporal properties of the data, including information about grids, such as grid cell bounds and cell averaging methods. This enables users of files from different sources to decide which variables are comparable, and is a basis for building software applications with powerful data extraction, grid remapping, data analysis, and data visualization capab
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| - The Climate and Forecast (CF) metadata conventions are conventions for the description of Earth sciences data, intended to promote the processing and sharing of data files. The metadata defined by the CF conventions are generally included in the same file as the data, thus making the file "self-describing". The conventions provide a definitive description of what the data values found in each netCDF variable represent, and of the spatial and temporal properties of the data, including information about grids, such as grid cell bounds and cell averaging methods. This enables users of files from different sources to decide which variables are comparable, and is a basis for building software applications with powerful data extraction, grid remapping, data analysis, and data visualization capabilities.
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