By V. Balaji, René Redler, Reinhard Budich
Collected articles during this sequence are devoted to the improvement and use of software program for earth method modelling and goals at bridging the space among IT strategies and weather technological know-how. the actual subject lined during this quantity addresses the difficulty of information input/output and post-processing within the context of Earth procedure modeling, with an emphasis on parallel I/O, garage administration and research subsystems for terribly huge scale facts requirements.
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Additional resources for Earth System Modelling - Volume 4: IO and Postprocessing
1 Background and History 41 In the 1950’s and 1960’s, visualization of model results often consisted of merely a single one-dimensional line plot or two-dimensional contour map. As computer technology progressed further in the 1970’s, visualization libraries such as the Graphical Kernel System (GKS) were developed, offering consistent and complete one- and two-dimensional graphics standards. g. the FORTRAN coded SPHEREPACK and LINPACK). With the recent development of more powerful workstations and personal computers, the availability and diversity of data analysis and visualization options has exploded, generating many powerful suites of tools applicable to the diagnosis of climate model behaviors.
The price of all of this functionality is a very complex file format and an API with a steep learning curve. Surprisingly, neither HDF4 or HDF5 support shared dimensions, although they do have special variables called dimension scales which provide the same functionality as shared dimensions for the common 1-dimensional case. A number of interesting packages exist that extend HDF5, including PyTables, a Python-based program that adds indexing to tables of data, similar to an RDBMS. HDF-EOS is a special version of HDF for NASA’s Earth Observing System satellite data, using both HDF4 (HDF2-EOS) and HDF5 (HDF5-EOS) as the base format.
The software to perform this processing is often model- or framework-specific, frequently using code from the model itself. Calculations and visualizations generated in this mode are usually tailored to the priorities of the developers, such as the need to diagnose long-term drift in the model state variables. ) The next analysis phase focuses on assessing the scientific implications of results from individual model runs, most commonly through comparison with similar runs over the same domain and coordinate system.