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CIME, pronounced “SEAM”, contains the support scripts (configure, build, run, test), data models, essential utility libraries, a “main” and other tools that are needed to build a single-executable coupled Earth System Model. CIME is available in a stand-alone package that can be compiled and tested without active prognostic components but is typically included in the source of a climate model. CIME does not contain: any active components, any intra-component coupling capability (such as atmosphere physics-dynamics coupling).
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  • Software/Code
A modeling architecture that facilitates coupling of multiple hydrological process representations together. WRF-Hydro is both a stand-alone hydrological modeling architecture as well as a coupling architecture for coupling of hydrological models with atmospheric models.
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  • Software/Code
A collection of diagnostic and interpolation routines for use with output from the Weather Research and Forecasting (WRF-ARW) Model. This package provides over 30 diagnostic calculations, several interpolation routines, and utilities to help with plotting via cartopy, basemap, or PyNGL. The functionality is similar to what is provided by the NCL WRF package.
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The Cheyenne system delivers supercomputing capabilities that are central to fulfilling NCAR's mission, including support for climate modeling, weather forecasting, and other critical research in the atmospheric, oceanic, and related sciences. Cheyenne is a 5.34-petaflops SGI ICE XA cluster with 4,032 dual-socket nodes, each with Intel Xeon E5-2697V4 (Broadwell) 2.3-GHz, 18-core processors, for a total of 145,152 processor cores. The system has DDR4-2400 memory, with 3,168 nodes having 64 GB and 864 nodes having 128 GB of memory for a system total of 313 TB. The nodes are connected via a Mellanox EDR InfiniBand interconnect in a 9D enhanced hypercube topology. The Cheyenne environment encompasses the centralized disk storage system, called GLADE, that provides 36 PB of usable capacity and up to 200 GBps aggregate I/O bandwidth.
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This tool creates a time series of downscaled (gridded) crop and pasture land use outcomes, given a scenario for climate and for aggregate cropland and pasture for world regions.
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MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data. The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you take the average GEMPAK script for a weather map, you need to: - read data - calculate a derived field - show on a map/skew-T One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for any meteorological Python application; this means making it easy to pull out the LCL calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides itself on being well-documented and well-tested, so that on-going maintenance is easily manageable. The intended audience is that of GEMPAK: researchers, educators, and any one wanting to script up weather analysis. It doesn't even have to be scripting; all python meteorology tools are hoped to be able to benefit from MetPy. Conversely, it's hoped to be the meteorological equivalent of the audience of scipy/scikit-learn/skimage.
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  • Software/Code
This tool computes heatwave days at each grid cell of the global land region over the Community Earth System Model (CESM) grid and matches population counts at the same spatial scale to compute population exposure to heatwaves, measured in units of person-day.
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  • Software/Code
The CMIP Analysis Platform gives researchers convenient access to climate data from the Coupled Model Intercomparison Project (CMIP), NCAR's GLADE disk storage resource, NCAR's analysis and visualization clusters, and dedicated user support for locating and transferring requested CMIP data. By hosting a "lending library" of published CMIP data on GLADE, the platform allows researchers to use NCAR's data analysis and visualization clusters to perform climate model analyses and intercomparisons on large volumes of CMIP data rather than having to transfer large data sets to their local machines. The CMIP Analysis Platform allows researchers to conduct studies that would otherwise exceed the capacities of their local or campus resources by providing integrated services for locating, moving, storing, and analyzing climate data.
Data Types:
  • Software/Code