A Containerized Mesoscale Model and Analysis Toolkit to Accelerate Classroom Learning, Collaborative Research, and Uncertainty Quantification
A Containerized Mesoscale Model and Analysis Toolkit to Accelerate Classroom Learning, Collaborative Research, and Uncertainty Quantification
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容器化中尺度模型和分析工具包，可加速课堂学习、协作研究和不确定性量化
DOI：
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发表时间：
2017
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通讯作者：
Kathryn R. Fossell
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作者：
J. Hacker;J. Exby;D. Gill;I. Jimenez;C. Maltzahn;Timothy W. See;G. Mullendore;Kathryn R. Fossell
摘要数值天气预报（NWP）实验是复杂和耗时的，结果取决于计算环境和众多的输入参数。学习和取得研究成果的延误是不可避免的。学生在课堂上或开始研究生水平的NWP研究时面临不成比例的努力。已发表的NWP研究通常是不可复制的，引入了不确定性并减缓了建立在过去结果基础上的努力。这项工作利用了软件容器技术的快速出现，以产生一个变革性的研究和教育环境。天气研究和预报（WRF）模型锚定了一组链接的基于Linux的容器，其中包括用于初始化和运行模型、分析结果以及向协作者提供输出的软件。集装箱与WRF飓风桑迪模拟演示。演示说明了以下几点：1）如何在编译WRF和它的许多依赖项中消除经常困难的练习，2.
AbstractNumerical weather prediction (NWP) experiments can be complex and time consuming; results depend on computational environments and numerous input parameters. Delays in learning and obtaining research results are inevitable. Students face disproportionate effort in the classroom or beginning graduate-level NWP research. Published NWP research is generally not reproducible, introducing uncertainty and slowing efforts that build on past results. This work exploits the rapid emergence of software container technology to produce a transformative research and education environment. The Weather Research and Forecasting (WRF) Model anchors a set of linked Linux-based containers, which include software to initialize and run the model, to analyze results, and to serve output to collaborators. The containers are demonstrated with a WRF simulation of Hurricane Sandy. The demonstration illustrates the following: 1) how the often-difficult exercise in compiling the WRF and its many dependencies is eliminated, 2...