MPI: The Complete Reference
Learning the Bash Shell
Using MPI-2: Advanced Features of the Message Passing Interface
Using MPI-2: Advanced Features of the Message Passing Interface
The Cathedral and the Bazaar
Data Management: NetCDF: an Interface for Scientific Data Access
IEEE Computer Graphics and Applications
A Web Service Model for Climate Data Access on the Grid
International Journal of High Performance Computing Applications
Scaling Properties of Common Statistical Operators for Gridded Datasets
International Journal of High Performance Computing Applications
Server-side parallel data reduction and analysis
GPC'07 Proceedings of the 2nd international conference on Advances in grid and pervasive computing
Parallel accessing massive NetCDF data based on mapreduce
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
MOSAICO, a library for raster based hydrological applications
Computers & Geosciences
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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.