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UID:pretalx-juliacon-2026-U9ZWXZ@pretalx.com
DTSTART;TZID=CET:20260812T172000
DTEND;TZID=CET:20260812T173000
DESCRIPTION:Hybrid climate modelling combines numerical models with machine
 -learned components. We present the development of multiple machine-learne
 d surface climate processes and their integration into the climate model S
 peedyWeather.jl using PyTorch and Lux.jl. Despite the offline training\, t
 he hybrid model is designed to generalise in space and to different climat
 es. We address speed vs. accuracy tradeoffs using SymbolicRegression.jl an
 d discuss online learning with Enzyme.jl.
DTSTAMP:20260502T103430Z
LOCATION:Room 3
SUMMARY:A learned surface roughness scheme for climate prediction in Speedy
 Weather.jl - Greg Munday\, Maximilian Gelbrecht\, Milan Klöwer\, Niklas V
 iebig
URL:https://pretalx.com/juliacon-2026/talk/U9ZWXZ/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-juliacon-2026-W7FRKU@pretalx.com
DTSTART;TZID=CET:20260813T163000
DTEND;TZID=CET:20260813T164500
DESCRIPTION:Fortran climate models are being adapted to GPUs by automatical
 ly translating loop-by-loop into a kernel. In Julia\, we have more flexibi
 lity to develop the climate model [SpeedyWeather.jl](https://github.com/Sp
 eedyWeather/SpeedyWeather.jl) for the GPU. Many parts are easy to accelera
 te\, leverage multiple dispatch on the GPU and a high level of kernel fusi
 on for modularity and performance\, while being optionally hardware-specif
 ic. The spherical harmonic transforms remain a complex bottleneck but we e
 mploy a multi-algorithm approach with custom linear algebra kernels using 
 Reactant\, Fourier and Legendre transforms.
DTSTAMP:20260502T103430Z
LOCATION:Room 3
SUMMARY:The GPU acceleration of SpeedyWeather.jl\, the friendly and flexibl
 e climate model - Milan Klöwer\, Niklas Viebig\, Maximilian Gelbrecht
URL:https://pretalx.com/juliacon-2026/talk/W7FRKU/
END:VEVENT
BEGIN:VEVENT
UID:pretalx-juliacon-2026-EGUEJP@pretalx.com
DTSTART;TZID=CET:20260814T111500
DTEND;TZID=CET:20260814T113000
DESCRIPTION:Climate models rely on parameterizations that are traditionally
  tuned manually. We present a differentiable calibration framework using E
 nzyme.jl to compute exact reverse mode gradients of energy-balance diagnos
 tics in SpeedyWeather.jl. By batching single- timestep gradients across ch
 aotic dynamics\, we enable systematic\, reproducible optimization of short
 wave radiation parameters\, establishing an extensible workflow for object
 ive calibration in Earth system models.
DTSTAMP:20260502T103430Z
LOCATION:Room 6
SUMMARY:Differentiable Climate Modeling: Calibrating SpeedyWeather with Enz
 yme - Niklas Viebig\, Milan Klöwer\, Maximilian Gelbrecht\, Greg Munday\,
  Brian Groenke
URL:https://pretalx.com/juliacon-2026/talk/EGUEJP/
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