Conservative interpolation of edge and face data on n dimensional structured grids using differential forms
Not Available
Engineering topics
Publications and source records attributed to Pletzer, Alexander.
Not Available
Explore the source record for details and available documents.
To support interactive visualization and analysis of complex, large-scale climate data sets, UV-CDAT integrates a powerful set of scientific computing libraries and applications to foster more efficient knowledge discovery. Connected through a provenance framework, the UV-CDAT components can be loosely coupled for fast integration or tightly coupled for greater functionality and communication with other components. This framework addresses many challenges in the interactive visual analysis of distributed large-scale data for the climate community.
To support interactive visualization and analysis of complex, large-scale climate data sets, UV-CDAT integrates a powerful set of scientific computing libraries and applications to foster more efficient knowledge discovery. Connected through a provenance framework, the UV-CDAT components can be loosely coupled for fast integration or tightly coupled for greater functionality and communication with other components. This framework addresses many challenges in the interactive visual analysis of distributed large-scale data for the climate community.
The project "Analyzing and visualizing next generation climate data" adds block-structured (mosaic) grid support, parallel processing, and 2D/3D curvilinear interpolation to the open-source UV-CDAT climate data analysis tool. Block structured grid support complies to the Gridspec extension submitted to the Climate and Forecast metadata conventions. It contains two parts: aggregation of data spread over multiple mosaic tiles (M-SPEC) and aggregation of temporal data stored in different files (F-SPEC). Together, M-SPEC and F-SPEC allow users to interact with data stored in multiple files as if the data were in a single file. For computational expensive tasks, a flexible, multi-dimensional, multi-type distributed array class allows users to process data in parallel using remote memory access. Both nodal and cell based interpolation is supported; users can choose between different interpolation libraries including ESMF and LibCF depending on the their particular needs.