The Weather Research and Forecasting (WRF) Model as a Tool for Evaluating HPCMP Assets and Capabilities in Grand Scale Numerical Weather Prediction

Abstract

The Weather Research and Forecasting (WRF) model has been used extensively in research and operational theaters on a broad range of computer architectures. There is a need for a standard suite of case studies by which to benchmark a computational platform's potential to efficiently support WRF and, concurrently, a need to push WRF into increasingly larger problem sizes in order to test the capability of WRF and its supporting software and hardware on the truly grand scale domains. The Arctic Region Supercomputing Center is implementing a suite of WRF case studies intended to support benchmarking and testing on all architectures, ranging from small, single CPU systems, to those with hundreds of thousands of cores. The benchmark cases are all based on a single weather event over a 6,075×6,075km domain centered at Fairbanks, Alaska, ranging from horizontal grid resolutions of 81km with 150,000 grid points, to 1km with over one-billion grid points. Each case is available to users in the form of a restart, boundary condition, and parameters file, so that once WRF has been installed on a system the case may be readily-tested. Implementation of the one billion grid point case has been particularly problematic, revealing issues that prevent WRF from operating on large-scale problems unless specialized software and procedures are considered. With the help of WRF and Cray experts, the giga-gridpoint barrier has been conquered, and the pursuit of even bigger computational challenges for WRF continues, with the realization that modelers will always have an insatiable appetite for higher resolution, more complicated physics and bigger domain sizes.