“A research team from Rice University utilized three supercomputers (TACC’s Stampede 2, Wrangler, and Pittsburg Supercomputing Center’s Bridges system) to see if data on heat waves and cold spells could be predicted by analysis of atmospheric circulation and prior surface temperature. The results of these tests indicated that this deep learning approach is more accurate at predicting extreme weather.”
Intel Powers Fujitsu Supercomputer at Japan’s Meteorological Research Institute
March 3, 2020 by
Today Fujitsu announced the deployment of a new supercomputer system for the Meteorological Research Institute of the Japan Meteorological Agency. The new system consists of approximately 900 nodes including the latest x86 servers Fujitsu Server PRIMERGY CX2550 M5. With high-speed Intel Omni-Path interconnect, the system has a peak performance of 2.81 petaflops. “With the new supercomputer system, the Meteorological Research Institute plans to further advance research and development in areas such as the prediction and analysis of earthquakes, tsunamis, and volcanoes.”
Video: Data Driven Ocean & Atmosphere Sciences at MoES in India
January 19, 2020 by
Dr. Suryachandra Rao from MoES gave this talk at the DDN User Group. “The Ministry of Earth Sciences (MoES) is mandated to provide services for weather, climate, ocean and coastal state, hydrology, seismology, and natural hazards; to explore and harness marine living and non-living resources in a sustainable way and to explore the three poles (Arctic, Antarctic and Himalayas). MoES recently inaugurated a new supercomputer at the Indian Institute of Tropical Meteorology (IITM) in Pune, dedicated to improving weather and climate forecasts across the country.”
Job of the Week: NRL Research Scientist at SAIC
December 14, 2019 by
SAIC in Monterey, California is seeking an NRL Research Scientist in our Job of the Week. “SAIC is a premier technology integrator, solving our nation’s most complex modernization and systems engineering challenges across the defense, space, federal civilian, and intelligence markets. Our robust portfolio of offerings includes high-end solutions in systems engineering and integration; enterprise IT, including cloud services; cyber; software; advanced analytics and simulation; and training.”
Météo-France to Boost Weather Forecasting Capabilities with Atos Supercomputer
November 17, 2019 by
Today Atos announced a new four-year contract with French national meteorological service, Météo-France, to supply two supercomputers based on its latest BullSequana XH2000 technology. “We’re really excited to be working again with Météo-France to boost its computing capacity with our BullSequana XH2000 supercomputers” said Pierre Barnabé, SVP, Head of Big Data & Security at Atos. “Equipped with the latest generation processors and accelerators, our BullSequana architechture offers Météo-France a combined peak performance of 20 petaflops to deliver even more precise results. It is also highly-energy efficient and minimizes global energy consumption.”
Progress and Challenges for the Use of Deep Learning to Improve Weather Forecasts
November 1, 2019 by
Peter Dueben from ECMWF gave this talk at the UK HPC Conference. “I will present recent studies that use deep learning to learn the equations of motion of the atmosphere, to emulate model components of weather forecast models and to enhance usability of weather forecasts. I will then talk about the main challenges for the application of deep learning in cutting-edge weather forecasts and suggest approaches to improve usability in the future.”
NEC SX-Aurora Vector Supercomputer to Power Weather Forecasting at DWD in Germany
July 1, 2019 by
In this video from ISC 2019, Dr. Rudolf Fischer from NEC describes how the company’s recent win at DWD demonstrates the power and efficiency of the SX-Aurora supercomputer. “The new HPC system will enable the development of seamless prediction of severe weather events, including thunderstorms or heavy rain. The system combines forecasting based on observations with very demanding numerical weather prediction models in order for a more precise prediction of the development and the tracks of such small-scale weather events up to twelve hours into the future.”
MeteoSwiss to Improve Weather Forecasting with Cray CS-Storm Supercomputer
June 26, 2019 by
At ISC 2019, Cray announced that CSCS in Switzerland is adding a third Cray CS-Storm supercomputer to support the development of cutting-edge weather service products at MeteoSwiss. “MeteoSwiss found success with its existing Cray supercomputers and selected this new CS-Storm to provide the additional computational power required to process increasing volumes of weather observations and produce higher fidelity forecasts. The CS-Storm system was also selected for its ability to run numerical weather forecasts within a reduced energy footprint (as compared to competing solutions), and for the reliability the platform provides MeteoSwiss when running critical workloads.”
NEC receives 50 Million Euro order from Germany’s DWD
June 18, 2019 by
Today NEC announced that it received an order for an NEC SX-Aurora TSUBASA supercomputer with a value of 50 Million Euro from the Deutscher Wetterdienst (DWD), the German weather forecasting service. “The new HPC system will enable the development of seamless prediction of severe weather events, including thunderstorms or heavy rain. The system combines forecasting based on observations with very demanding numerical weather prediction models in order for a more precise prediction of the development and the tracks of such small-scale weather events up to twelve hours into the future.”
Cray Powers Weather Forecasting at ZAMG in Austria
May 9, 2019 by
Today Cray announced that the Central Institution for Meteorology and Geodynamics in Austria (ZAMG) is using a Cray supercomputer to support a multi-year weather nowcasting project with the University of Vienna to benefit society and industry. “Using deep learning methods, ZAMG is leveraging its Cray CS-Storm supercomputer to optimize the orientation of wind-powered generators for maximum efficiency and to train neural networks with current and historical weather data.”
