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DTSTART;TZID=Europe/Stockholm:20230627T160000
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UID:submissions.pasc-conference.org_PASC23_sess170@linklings.com
SUMMARY:MS4C - 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 numerical simulation accelera
 tion to ensemble prediction. Further, how do we make the best use of AI to
  build or improve Earth digital twins for a wide range of applications fro
 m extreme weather to renewable energy, including at highly localized scale
 s such as cities? The next generation of breakthroughs will require a true
  nexus of HPC and large-scale AI bringing many challenges and opportunitie
 s. This mini-symposium will delve into the challenges and opportunities at
  the nexus of HPC and AI. Presenters will describe scientific and computin
 g challenges and the development of efficient and scalable AI solutions fo
 r weather and climate modeling.\n\nAccurate Medium-Range Global Weather Fo
 recasting with 3D Deep Neural Networks\n\nWeather forecasting is important
  for science and society. Currently, the most accurate forecast system is 
 the numerical weather prediction (NWP) method, which represents atmospheri
 c states as discretized grids and numerically solves partial differential 
 equations (PDEs) that describe the transition ...\n\n\nKaifeng Bi, Lingxi 
 Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian (Huawei)\n---------
 ------------\nGraphCast: Learning Skillful Medium-Range Global Weather For
 ecasting\n\nWe present our recent paper “GraphCast: Learning skillful medi
 um-range global weather forecasting” (https://arxiv.org/abs/2212.12794). G
 raphCast is a machine-learning (ML) based weather simulator, trained from 
 the ERA5 reanalysis archive, which can make forecasts, at 6-hour time inte
 rval...\n\n\nPeter Battaglia (DeepMind, Alphabet)\n---------------------\n
 Accelerating Earth System Emulation with Spherical Fourier Neural Operator
 s\n\nNVIDIA's Earth-2 initiative aims to build digital twins of Earth and 
 has two central goals: (i) Computational: enable high-resolution weather a
 nd climate predictions with principled physics-inspired ML; and (ii) Socie
 tal: nimbly serve high-fidelity, high-resolution predictions via an intuit
 ive inter...\n\n\nAnima Anandkumar (California Institute of Technology, NV
 IDIA Inc.); Thorsten Kurth, Boris Bonev, Christian Hundt, and Karthik Kash
 inath (NVIDIA Inc.); and Mike Pritchard (NVIDIA Inc.; University of Califo
 rnia, Irvine)\n---------------------\nFaster Climate Sciences - from DSLs 
 to AI\n\nAs machine learning applications continue to succeed, the industr
 y is shifting towards optimizing AI workloads. In this paper, we make thre
 e predictions about how this will impact high-performance computing (HPC) 
 technology. We then identify challenges and opportunities for future weath
 er and climat...\n\n\nTorsten Hoefler and Huang Langwen (ETH Zurich)\n\nDo
 main: Climate, Weather and Earth Sciences\n\nSession Chair: Karthik Kashin
 ath (NVIDIA Inc., Lawrence Berkeley National Laboratory)
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