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,... 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) how sharing containers provides identical environments for conducting research, 3) that numerically reproducible results are easily obtainable, and 4) how uncertainty in the results can be isolated from uncertainty arising from computing system differences. Numerical experiments designed to simultaneously measure numerical reproducibility and sensitivity to compiler optimization provide guidance for interpreting NWP research. Reproducibility is independent from the operating system and hardware. Results here show numerically identical output on all computing platforms tested. Performance reproducibility is also demonstrated. The result is an infrastructure capable of accelerating classroom learning, graduate research, and collaborative science. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bulletin of the American Meteorological Society American Meteorological Society

A Containerized Mesoscale Model and Analysis Toolkit to Accelerate Classroom Learning, Collaborative Research, and Uncertainty Quantification

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0477
eISSN
1520-0477
D.O.I.
10.1175/BAMS-D-15-00255.1
Publisher site
See Article on Publisher Site

Abstract

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) how sharing containers provides identical environments for conducting research, 3) that numerically reproducible results are easily obtainable, and 4) how uncertainty in the results can be isolated from uncertainty arising from computing system differences. Numerical experiments designed to simultaneously measure numerical reproducibility and sensitivity to compiler optimization provide guidance for interpreting NWP research. Reproducibility is independent from the operating system and hardware. Results here show numerically identical output on all computing platforms tested. Performance reproducibility is also demonstrated. The result is an infrastructure capable of accelerating classroom learning, graduate research, and collaborative science.

Journal

Bulletin of the American Meteorological SocietyAmerican Meteorological Society

Published: Jun 24, 2017

References

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