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UID:submissions.pasc-conference.org_PASC24_sess158_pos143@linklings.com
SUMMARY:P42 - A Python Dynamical Core for Operational Numerical Weather Pr
 ediction
DESCRIPTION:Poster\n\nChristoph Müller, Daniel Hupp, and Nina Burgdorfer (
 MeteoSwiss); Abishek Gopal and Nicoletta Farabullini (Center for Climate S
 ystems Modeling (C2SM)); Till Ehrengruber (ETH Zurich / CSCS); Samuel Kell
 erhals and Magdalena Luz (Center for Climate Systems Modeling (C2SM)); Wil
 liam Sawyer (ETH Zurich / CSCS); Matthias Röthlin (MeteoSwiss); Enrique G.
  Paredes (ETH Zurich / CSCS); Benjamin Weber (MeteoSwiss); Hannes Vogt and
  Mauro Bianco (ETH Zurich / CSCS); Carlos Osuna (MeteoSwiss); Christina Sc
 hnadt and Anurag Dipankar (Center for Climate Systems Modeling (C2SM)); an
 d Xavier Lapillonne (MeteoSwiss)\n\nNumerical weather prediction is vital 
 for applications like population warnings and energy predictions. However,
  adapting forecasts to diverse hardware poses challenges. MeteoSwiss relie
 s on the ICON model up to a one km resolution, initially ported to GPUs us
 ing OpenACC. While enabling GPU use, OpenACC+Fortran has limitations in po
 rtability and maintenance.\n\nExploring alternatives, we focus on the EXCL
 AIM project, targeting the dynamical core (55% of runtime). Implementing t
 he dynamical core's computational stencils with gt4py Python departs from 
 Fortran traditions. Our work details this shift, emphasizing the productiv
 ity gains with this new Python framework.\n\nWe present optimizations and 
 compare the Python-based dynamical core with the base OpenACC version, hig
 hlighting computational efficiency and development ease. Acknowledging cha
 llenges, especially in operational weather prediction.\n\nSession Chair: I
 va Kavcic (Met Office)
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