BEGIN:VCALENDAR
VERSION:2.0
PRODID:Linklings LLC
BEGIN:VTIMEZONE
TZID:Europe/Stockholm
X-LIC-LOCATION:Europe/Stockholm
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:19700308T020000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:19701101T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260522T162633Z
LOCATION:Bldg. 6 - Room 102
DTSTART;TZID=Europe/Stockholm:20260701T113000
DTEND;TZID=Europe/Stockholm:20260701T130000
UID:submissions.pasc-conference.org_PASC26_sess175@linklings.com
SUMMARY:AP2D - ACM Papers: Earth System Modeling
DESCRIPTION:Physics-Aware Multi-Task Learning for Atmospheric Turbulence P
 arameterization: Auxiliary Tasks versus Architectural Conditioning\n\nDyna
 mic subgrid-scale (SGS) turbulence parameterizations in Large Eddy Simulat
 ion (LES) achieve superior physical fidelity but impose 2–4× computational
  overhead compared to static schemes, creating a critical bottleneck for h
 igh-resolution atmospheric modeling on HPC systems. Neural network b...\n\
 n\nSambit Kumar Panda, Todd R. Jones, and Muhammad Shahzad (University of 
 Reading); Bryan N. Lawrence (University of Reading, National Centre for At
 mospheric Science); and Anna-Louise Ellis (Met Office)\n------------------
 ---\nStatistical Equivalence of AI Emulators and Earth System Models: A La
 rge Ensemble Study with Ultra-Low-Resolution E3SM\n\nWe evaluate the stati
 stical fidelity of a very large ensemble of an AI/ML emulator, FourCastNet
 v1, by evaluating it against a similarly large ensemble of an ultra–low–re
 solution configuration of E3SMv3 for forecasts up to 10-day lead time. Fou
 rCastNetv1 is trained on this E3SMv3 configur...\n\n\nSalil Mahajan, Micha
 el Kelleher, and Ming Fan (Oak Ridge National Laboratory)\n---------------
 ------\nAn Algorithm for Feature Extraction from Large Scale Meteorologica
 l Data Stores on Irregular Grids\n\nIn recent years, the volume of meteoro
 logical data has grown rapidly due to higher model resolutions and more fr
 equent data output. To handle this increase efficiently, the European Cent
 re for Medium-Range Weather Forecasts has developed new ways to extract da
 ta without accessing full meteorological...\n\n\nMathilde Leuridan (ECMWF,
  University of Cologne); James Hawkes and Tiago Quintino (ECMWF); and Mart
 in Schultz (Forschungszentrum Jülich, University of Cologne)\n\nSession Ch
 air: Thorsten Kurth (NVIDIA Inc.)
END:VEVENT
END:VCALENDAR