Skip to content
DraconiticResearch
Search Sources API
203,203 studies31 sourcesUpdated 35 min ago
PreprintCC BY-NC-SA 4.0arXiv

Suêtes: An end-to-end differentiable non-hydrostatic limited-area dynamical core

Abstract

We present Suêtes, a fully differentiable, non-hydrostatic, limited-area atmospheric dynamical core implemented in JAX. Suêtes includes both a semi-implicit semi-Lagrangian scheme and a Split-Explicit Runge-Kutta scheme with acoustic substepping, enabling applications from coarse regional simulations to convection-permitting dynamics. Its terrain-following geometry, spatial discretization, time integration, initial conditions, lateral boundary treatment, and included physical parameterizations are all compatible with reverse-mode automatic differentiation. A single reverse-mode pass can therefore compute sensitivities of forecast diagnostics to selected initial and boundary fields, physical parameters, topography, and computational geometry. We provide a series of numerical experiments---including a tracer-source inverse problem, a sensitivity analysis of a three-dimensional squall-line, adversarial perturbation construction an ERA5-driven downslope hurricane force wind event, topography optimization, and terrain-following coordinate optimization---demonstrating the versatility of Suêtes as a framework for gradient-based diagnosis and model development at regional and convection-permitting scale.

Related

  1. Tropical Cyclone-Following Dataset for Limited-Area Machine Learning Applications
    Minh-Khanh Luong, Chanh Kieu
    arXivPreprintCC BY 4.0Full textAtmospheric science & meteorologyarXiv
  2. Meso-NH-ISO v1.0: a water stable isotopologue scheme in the non-hydrostatic mesoscale atmospheric model Meso-NH – application to a 2D West African squall line
    Christelle Barthe, Françoise Vimeux, Camille Risi +1
    Geoscientific Model DevelopmentPeer-reviewedCC BY 4.0Full textAtmospheric science & meteorologyCrossref
  3. Phase-resolved coastal wave modelling using a non-hydrostatic multilayer formulation
    Øystein Lande, Stéphane Popinet, Odin Gramstad +2
    Coastal EngineeringPeer-reviewedCC BY 4.0Full textEnvironmental science & pollutionFluid dynamicsHAL
  4. DiffSWE2d: a differentiable Shallow Water Equations solver for end-to-end flood and tsunami modelling
    Zhonghou Xu
    arXivPreprintCC BY-NC-ND 4.0Full textGeophysics & seismologyFluid dynamicsarXiv
  5. A library for differentiable signal processing and machine learning on the sphere
    Thorsten Kurth, Max Rietmann, Mauro Bisson +6
    arXivPreprintCC BY 4.0Full textAtmospheric science & meteorologyGeophysics & seismologyarXiv
BibTeX
@misc{whittaker2026suêtes,
  title = {{Suêtes: An end-to-end differentiable non-hydrostatic limited-area dynamical core}},
  author = {Tim Whittaker and Seth Taylor and Elsa Cardoso-Bihlo and Alejandro Di Luca and Alex Bihlo},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.36381},
  eprint = {2609.36381},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.36381},
  note = {CC BY-NC-SA 4.0},
}
RIS
TY  - UNPB
TI  - Suêtes: An end-to-end differentiable non-hydrostatic limited-area dynamical core
AU  - Tim Whittaker
AU  - Seth Taylor
AU  - Elsa Cardoso-Bihlo
AU  - Alejandro Di Luca
AU  - Alex Bihlo
PY  - 2026
DA  - 2026/09/28
PB  - arXiv
DO  - 10.48550/arxiv.2609.36381
UR  - https://arxiv.org/abs/2609.36381
AB  - We present Suêtes, a fully differentiable, non-hydrostatic, limited-area atmospheric dynamical core implemented in JAX. Suêtes includes both a semi-implicit semi-Lagrangian scheme and a Split-Explicit Runge-Kutta scheme with acoustic substepping, enabling applications from coarse regional simulations to convection-permitting dynamics. Its terrain-following geometry, spatial discretization, time integration, initial conditions, lateral boundary treatment, and included physical parameterizations are all compatible with reverse-mode automatic differentiation. A single reverse-mode pass can therefore compute sensitivities of forecast diagnostics to selected initial and boundary fields, physical parameters, topography, and computational geometry. We provide a series of numerical experiments---including a tracer-source inverse problem, a sensitivity analysis of a three-dimensional squall-line, adversarial perturbation construction an ERA5-driven downslope hurricane force wind event, topography optimization, and terrain-following coordinate optimization---demonstrating the versatility of Suêtes as a framework for gradient-based diagnosis and model development at regional and convection-permitting scale.
ER  -