Analysis of self-describing gridded geoscience data with netCDF Operators (NCO)

摘要

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.