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Why HPE Cray EX Is the Supercomputer of Choice at Leading Weather Centers

[SPONSORED POST] For decades, compute resources used for weather forecasting have tracked with advances in state-of-the-art supercomputing. Which is to say that the weather segment demands systems with the greatest data ingest and storage capacity combined with the most powerful processing capabilities. As the accuracy of daily weather forecasts and warnings of severe weather depend on high-performance computing combined, increasingly, with artificial intelligence, it is perhaps not surprising that weather segment IT spending has not been affected by the COVID-19 pandemic. Hyperion Research predicts that it will in fact grow by an astonishing 33 percent between 2021–20241, significantly outpacing

NCAR Official to Discuss New Supercomputer Friday at Univ. of Wyoming

September 13, 2021 — A top director with the National Center for Atmospheric Research (NCAR) is coming to the University of Wyoming to discuss the capabilities and uses of a new supercomputer that will be installed in the NCAR-Wyoming Supercomputing Center (NWSC) near Cheyenne. Thomas Hauser, director of NCAR’s Computational and Information Systems Laboratory Technology […]

American Meteorological Society to Present Zelinka with Houghton Award

The Council of the American Meteorological Society (AMS) has selected atmospheric scientist Mark Zelinka of Lawrence Livermore National Laboratory (LLNL) to receive the Henry G. Houghton Award. Zelinka was cited by AMS for “innovative advances in understanding the critical involvement of clouds to achieve a better understanding of climate interactions.” According to AMS, the Henry G. Houghton […]

Super-expensive Supercomputers: UK Met Office in £1B+ Deal for Microsoft Weather System; $3B System in China on Way

As widely reported in February, the UK Met Office and Microsoft have come to an agreement to provision a £1.2 billion (over 10 years) supercomputer the Met Office said will be the world’s most powerful weather and climate forecasting system. The agreement, announced on Earth Day, will be in the top 25 of the Top500 […]

NOAA Upgrades Global Weather Model Following 2020 HPC Additions

The U.S. National Oceanic and and Atmospheric Administration (NOAA) said today it is upgrading its Global Forecast System (GFS) weather model to improve hurricane genesis forecasting, modeling for snowfall location, heavy rainfall forecasts and overall model performance. In February 2020, NOAA announced it will triple if its weather and climate supercomputing capacity with the addition […]

Let’s Talk Exascale: Forecasting Water Resources and Severe Weather with Greater Confidence

In this episode of Let’s Talk Exascale, Mark Taylor of Sandia National Laboratories talks about using exascale supercomputers for severe weather and water resource forecasting. A sub-project within the US Department of Energy’s (DOE’s) Exascale Computing Project (ECP) called E3SM-MMF is working to improve the ability to simulate the water cycle and the processes around precipitation. Our guest on the latest episode of ECP’s podcast, Let’s Talk Exascale, is Mark Taylor of Sandia National Laboratories, principal investigator of the E3SM-MMF project.

Deep Learning for Predicting Severe Weather

Researchers from Rice University have introduced a data-driven framework that formulates extreme weather prediction as a pattern recognition problem, employing state-of-the-art deep learning techniques. “In this paper, we show that with deep learning you can do analog forecasting with very complicated weather data — there’s a lot of promise in this approach.”

Computational Evaluation of Cloud HPC with a Global Atmospheric Model

Daniel Arevalo from DeVine Consulting gave this talk at the HPC User Forum. “Prompted by DoD priorities for modernization, cost savings, and redundancy, this project compared the performance of the NAVGEM on an in-house Cray system against the follow cloud offerings: AWS c4.8xlarge, Penguin B30 queue, Azure H16r, and AWS c5n.18xlarge.”

Accelerating High-Resolution Weather Models with Deep-Learning Hardware

Sam Hatfield from the University of Oxford gave this talk at the PASC19 conference. “In this paper, we investigate the use of mixed-precision hardware that supports floating-point operations at double-, single- and half-precision. In particular, we investigate the potential use of the NVIDIA Tensor Core, a mixed-precision matrix-matrix multiplier mainly developed for use in deep learning, to accelerate the calculation of the Legendre transforms in the Integrated Forecasting System (IFS), one of the leading global weather forecast models.”

ClimateNet Looks to Machine Learning for Global Climate Science

Pattern recognition tasks such as classification, localization, object detection and segmentation have remained challenging problems in the weather and climate sciences. Now, a team at the Lawrence Berkeley National Laboratory is developing ClimateNet, a project that will bring the power of deep learning methods to identify important weather and climate patterns via expert-labeled, community-sourced open datasets and architectures.