Abstract
The netCDF Operator (NCO) software facilitates manipulation and analysis of gridded geoscience data stored in the self-describing netCDF format. NCO is optimized to efficiently analyze large multi-dimensional data sets spanning many files. Researchers and data centers often use NCO to analyze and serve observed and modeled geoscience data including satellite observations and weather, air quality, and climate forecasts. NCO's functionality includes shared memory threading, a message-passing interface, network transparency, and an interpreted language parser. NCO treats data files as a high level data type whose contents may be simultaneously manipulated by a single command. Institutions and data portals often use NCO for middleware to hyperslab and aggregate data set requests, while scientific researchers use NCO to perform three general functions: arithmetic operations, data permutation and compression, and metadata editing. We describe NCO's design philosophy and primary features, illustrate techniques to solve common geoscience and environmental data analysis problems, and suggest ways to design gridded data sets that can ease their subsequent analysis.
Highlights
Gridded geoscience model and sensor data sets present an interesting set of challenges for researchers and the data portals that serve them (Foster et al, 2002)
A software ecosystem has evolved to help researchers exploit this transition with fast data discovery, aggregation, analysis, and dissemination techniques (e.g., Domenico et al, 2002; Cornillon et al, 2003). In this ecosystem are the netCDF Operators (NCO) – software for manipulation and analysis of gridded geoscience data stored in the self-describing netCDF format
This paper describes NCO’s design philosophy and primary features, illustrates techniques to solve common geoscience and environmental data analysis problems, and suggests ways to design gridded data sets that can ease their subsequent analysis
Summary
Gridded geoscience model and sensor data sets present an interesting set of challenges for researchers and the data portals that serve them (Foster et al, 2002). Geoscience researchers use many toolkits besides NCO to analyze large volumes of gridded data These include the Climate Data Analysis Tools (CDAT) (Fiorino and Williams, 2002), the Climate Data Operators (CDO; http://www.mpimet.mpg.de/fileadmin/software/cdo), the Grid Analysis and Display System (GrADS; http:// www.iges.org/grads/grads.html), the Interactive Data Language (IDL; http://www.ittvis.com/idl), MATLAB (http://www.mathworks.com), and the NCAR Command Language (NCL; http:// www.ncl.ucar.edu). Of these toolkits, CDO is closest to NCO in that both use command line operators constructed to perform chainable operations like traditional UNIX filters. Unlike NCO and CDO, the CDAT, GrADS, IDL, MATLAB, and NCL toolkits support comprehensive integrated visualization capabilities, but their design is not optimized for batch-driven operations on large number of files
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