3.3 Enhancing Community Collaborations through NWP Software Containers

Monday, 8 January 2018: 2:45 PM
Room 19AB (ACC) (Austin, Texas)
John Halley Gotway, NCAR, Boulder, CO; and J. K. Wolff, K. R. Fossell, M. Harrold, T. L. Jensen, T. Burek, and J. Exby

A frequent stumbling block when first running a modeling system is properly setting up and compiling all of the necessary code components, including a number of external libraries. In addition to running a forecast model, users often need pre- and post-processing software as well as a means to visualize and verify output from their model runs. To ease the burden of setting up a new system from the ground up, the concept of “containers” has quickly been gaining traction in the modeling community. Containers allow for end-to-end software systems to be bundled and provided to users, including the operating system, libraries, and code. This eliminates a myriad of technical issues frequently encountered when first spinning up on compiling all of the necessary components of NWP systems.

NCAR colleagues have established containers to run a subset of an end-to-end NWP system, including the WRF Pre-Processing System (WPS), Weather Research and Forecasting (WRF) model, and the NCAR Command Language (NCL). To provide additional capabilities, the Developmental Testbed Center (DTC) has developed containers for the Unified Post-Processor (UPP), the Model Evaluation Tools (MET), and the METViewer database and display software systems.

The use of containers lowers the barrier to entry to run NWP systems and evaluate the output; it also allows the DTC to further assist the user community, especially students, with efficiently running NWP components. Containers enable experiments to be easily rerun and shared, facilitating collaboration. DTC staff are presenting a short course on the use of NWP containers in conjunction with the annual AMS meeting. This presentation will outline the motivation behind containers and provide a brief review of the short course material, with particular emphasis on the containerized statistical tools, MET and METViewer.

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