HPE has delivered a two-systems-in-one supercomputer to support weather modeling and forecasting for the U.S. Air force and Army. The HPC system, powered by two HPE Cray EX supercomputers, is now operational at Oak Ridge National Laboratory, where it is managed by ORNL.
HPE said that at 7.2 petaflops peak performance, the combined systems are 6.5 times faster than Air Force Weather’s existing system, allowing larger computations at higher resolution, better accuracy in global weather simulations, and reducing from 17 kilometers between model grid points to 10 kilometers. The systems, called “Fawbush” and Miller,” are named after meteorologists Major Ernest Fawbush and Captain Robert Miller, who predicted the first tornado forecast at the Tinker Air Force Base in Oklahoma in 1948.
“Air Force Weather uses the weather intelligence across atmospheric and solar data, when delivering ongoing alerts, analyses and forecasts to U.S. defense missions worldwide to help military aircraft mitigate weather conditions and achieve readiness,” HPE said in its announcement.
HPE said it one of the first operational systems to be powered by the HPE Cray EX supercomputer architecture, formerly known as “Cray Shasta,” that will also power the upcoming three U.S. exascale systems, including Frontier, expected to be installed this year at Oak Ridge.
The Air Force systems, conducted via a partnership with the Oak Ridge and Air Force Weather, feature 2nd Gen AMD EPYC CPUs.
HPE said the system will enable the Air Force, in collaboration with ORNL’s Computational Earth Sciences Division, to focus on the following areas:
- Forecast stream flow, flooding, or inundation to predict how much of a given land will be submerged in water and the level of its depth. Researchers plan to achieve this by creating a global hydrology model that involves simulating hundreds of watershed and drainage basins to eventually increase accuracy in predicting future events.
- Remote sensing of a cloud-covered area to address how to navigate impacted missions through forecasting the formation, growth and precipitation of atmospheric clouds. Researchers plan to achieve this by using comprehensive cloud physics that are not made possible with existing statistical regression models.
