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UID:submissions.pasc-conference.org_PASC24_sess155@linklings.com
SUMMARY:MS2A - Nexus of AI and HPC for Weather, Climate, and Earth System 
 Modelling
DESCRIPTION:Minisymposium\n\nAccurately and reliably predicting weather an
 d climate change and associated extreme weather events are critical to pla
 n for disastrous impacts well in advance and to adapt to sea level rise, e
 cosystem shifts, and food and water security needs. The ever-growing deman
 ds of high-resolution weather and climate modeling require exascale system
 s. Simultaneously, petabytes of weather and climate data are produced from
  models and observations each year. Artificial Intelligence (AI) offers no
 vel ways to learn predictive models from complex datasets, at scale, that 
 can benefit every step of the workflow in weather and climate modeling: fr
 om data assimilation to process emulation to solver acceleration to ensemb
 le prediction. Further, how do we make the best use of AI to build or impr
 ove Earth digital twins for a wide range of applications from extreme weat
 her to renewable energy, including at highly localized scales such as citi
 es? The next generation of breakthroughs will require a true nexus of HPC 
 and large-scale AI bringing many challenges and opportunities. This minisy
 mposium will delve into the challenges and opportunities at the nexus of H
 PC and AI. Presenters will describe scientific and computing challenges an
 d the development of efficient and scalable AI solutions for weather and c
 limate modeling.\n\nAIFS – ECMWF’s Data-Driven Probabilistic Forecasting S
 ystem\n\nRecent developments in machine learning for weather forecasting h
 ave led to data-driven models that are comparable in skill to leading phys
 ics-based NWP systems. Over the past year, ECMWF has been developing its o
 wn data-driven forecasting system, the AIFS. By leveraging both data and m
 odel parallel...\n\n\nMihai Alexe (ECMWF)\n---------------------\nPanel Di
 scussion on the Future of Machine Learning in Earth System Modelling\n\nIn
  this panel discussion, the speakers of the session will be asked about th
 eir view on how machine learning for Earth system modelling will evolve in
  the coming years, and how machine learning and conventional models will i
 nteract, merge or coexist. The session will start with a couple of questio
 ns...\n\n\nKarthik Kashinath (NVIDIA Inc.)\n---------------------\nThe AI2
  Climate Emulator (ACE): A fast, Skillful Learned Global Atmospheric Model
  for Climate Prediction\n\nThe AI2 Climate Emulator (ACE) marks a signific
 ant leap in climate modeling, employing a deep learning framework to repli
 cate the comprehensive dynamics of the FV3GFS atmospheric model efficientl
 y. ACE incorporates a Spherical Fourier Neural Operator (SFNO) with approx
 imately 200M parameters. Using ...\n\n\nJeremy McGibbon, Spencer K. Clark,
  Gideon Dresdner, James Duncan, Brian Henn, and Oliver Watt-Meyer (Allen I
 nstitute for Artificial Intelligence); Boris Bonev, Noah D. Brenowitz, Kar
 thik Kashinath, and Michael S. Pritchard (NVIDIA Inc.); and Matthew E. Pet
 ers and Christopher S. Bretherton (Allen Institute for Artificial Intellig
 ence)\n---------------------\nTowards Operational Data-Driven Forecasting 
 at a National Weather Service\n\nData-driven probabilistic forecasts have 
 become a tangible possibility within just a couple of years, thanks to bre
 akthroughs mostly driven by the tech industry and building on existing ope
 n datasets from the weather and climate community. National weather servic
 es play a crucial role in providing a...\n\n\nOliver Fuhrer (MeteoSwiss, E
 TH Zurich)\n\nDomain: Climate, Weather, and Earth Sciences, Computational 
 Methods and Applied Mathematics\n\nSession Chair: Karthik Kashinath (NVIDI
 A Inc., Lawrence Berkeley National Laboratory)
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