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Keyword: nonlinear perturbations ×Clear all

2 claims from 1 paper

1. Earth and Planetary Sciences › Meteorological Phenomena and Simulations

   Are AI weather models learning atmospheric physics? A sensitivity analysis of cyclone Xynthia

   Baño‐Medina, Sengupta, Doyle, Reynolds, Watson‐Parris and Monache · npj Climate and Atmospheric Science · 2025

   The study compared initial-condition sensitivities from an AI weather model with those from a dynamical model's adjoint for cyclone Xynthia, finding very similar structures and similar impacts.

   Unchecked2 claims
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   1. UncheckedAn AI weather model's initial-condition sensitivities for cyclone Xynthia resembled those from a physics-based adjoint, which the paper reads as learned physical links.“This demonstrates the ability of the AI weather model to learn physically meaningful spatio-temporal links between atmospheric processes.”
   2. UncheckedFor cyclone Xynthia, an AI weather model's 36-hour sensitivities had very similar structures to those from a dynamical model's adjoint, with similar evolved impacts.“Here, sensitivities from an AI model at 36-h lead time were compared to those produced by an adjoint of a dynamical model for an extreme weather event, cyclone Xynthia, presenting very similar structures and with the evolved perturbations leading to similar impacts.”