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SUMMARY:Global Performance of GPU-Optimized ICON at Kilometer Scale (by An
 dreas Prein)
DTSTART:20250923T093000Z
DTEND:20250923T103000Z
DTSTAMP:20250910T110954Z
UID:presentation-172.series-10
DESCRIPTION:The demand for local-scale weather and climate information is 
 rapidly growing due to the green energy transition\, accelerating climate 
 change\, and the increasing frequency of record-breaking extreme events. A
 dvances in high-performance computing (HPC) architectures and targeted mod
 el development now make it possible to produce global climate information 
 at kilometer scales. Yet\, major challenges remain\, including calibrating
  and developing such models\, adapting them to heterogeneous HPC systems\,
  and managing the massive data volumes they generate. At ETH Zurich\, we a
 re porting the ICON model to GT4Py\, a domain-specific language that enabl
 es improved maintainability\, portability\, and computational efficiency o
 n modern HPC platforms. We present successes and challenges of this effort
 \, along with flagship simulations\, including a global 2.5 km uncoupled I
 CON run covering April 2020–March 2024. This simulation\, based on ICON-
 NWP physics and benefiting from CLM community model tuning\, provides new 
 insights into mean-state biases and the representation of mesoscale phenom
 ena such as precipitation and wind extremes\, convective organization\, tr
 opical cyclones\, and equatorial waves. We further show progress toward co
 upled modelling\, continental-scale kilometer-grid modeling\, and outline 
 our vision for future regional large-eddy simulations\, highlighting key c
 hallenges and priority areas for ICON development.
LOCATION:HS 11.03\, Geography Department\, Heinrichstraße 36
ORGANIZER;CN="Douglas Maraun";ROLE=CHAIR:MAILTO:douglas.maraun@uni-graz.at
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