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Advancing Climate Science with UV-CDAT: An Integrated Analysis and Visualization Framework

The UV-CDAT (Universal Climate Data Analysis Tools) is an open-source application developed at LLNL that unites multiple software subsystems for climate data analysis and visualization. Aimed at enhancing the computational and diagnostic capabilities essential for DOE's climate research, UV-CDAT leverages technologies like CDAT, ParaView, and VisTrails to provide a user-friendly, Python-based environment. It supports efficient data management and exploration through advanced I/O capabilities and metadata preservation, facilitating seamless interaction with disparate climate models and grids.

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Advancing Climate Science with UV-CDAT: An Integrated Analysis and Visualization Framework

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  1. ESMF within UV-CDAT Charles Doutriaux AIMS Team LLNL June 12th 2014

  2. UV-CDAT open source, easy-to-use application that links together disparate software subsystems and packages to form an integrated environment for analysis and visualization. This project seeks to advance climate science by fulfilling computational and diagnostic/visualization capabilities needed for DOE's climate research

  3. UV-CDAT • UV-CDAT builds on the following key technologies: • The Climate Data Analysis Tools (CDAT) framework developed at LLNL for the analysis, visualization, and management of large-scale distributed climate data; • ParaView: an open-source, multi-platform, parallel-capable visualization tool with recently added capabilities to better support specific needs of the climate-science community; • VisTrails, an open-source scientific workflow and provenance management system that supports data exploration and visualization;

  4. UV-CDAT • In short: • Climate (for now) oriented set of tools with python-based end-user interface. • Developed at PCMDI heart of MIPs projects • I/O, Analysis and Viz. • Variables are based upon Numpywith the addition of metadata which enables for “smart software” -> cdms2

  5. CDMS2 UV-CDAT’s primary I/O system. Can read multiple file format Variables read in and based on top of Numpy’s masked arrays. Preserve metadata as much/long as possible

  6. Regrid UV-CDAT developed tools can take advantage of MVs metadata. Because each modeling group has its own (set of) grid, regrid of data often necessary Historically used area-weighted method. Worked only for regular grids. AR5 lot of “non-regular” grids

  7. ESMF CMIP5 users needed easy way to go back and forth between models’ grids. In came ESMF with python bindings BUT learning curve was a bit steep. With the help of Alex Pletzer and TechX ESMF was integrated into cdms2’ regridder(Summer 2012). And actually became the default regridder.

  8. ESMF and UV-CDAT filename = "so_Omon_inmcm4_1pctCO2_r1i1p1_209001-209412_2timesteps.nc” g = cdms2.open(filename) so = g('so’) gLat = cdms2.createGaussianAxis(64) deltaLon = (360/128.) gLon = cdms2.createUniformLongitudeAxis(0, 128, deltaLon) gaussGrid = cdms2.grid.createGenericGrid(gLat[:], gLon[:], gLat.getBounds(), gLon.getBounds()) soN = so.regrid(gaussGrid, rt = 'esmf', rm = 'conserve', coordSys = 'deg', periodicity = 1, fixSrcBounds=True)

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