title: Physics-Assisted and Topology-Informed Deep Learning for Weather
    Prediction
summary: Although deep learning models have demonstrated remarkable potential in
  weather prediction, most of them overlook either the physics of the
  underlying weather evolution or the topology of the Earth's surface.
  In light of these disadvantages, we develop PASSAT, a novel Physics-ASSisted
  And Topology-informed deep learning model for weather prediction. PASSAT
  attributes the weather evolution to two key factors: (i) the advection process
  that can be characterized by the advection equation and the Navier-Stokes
  equation; (ii) the Earth-atmosphere interaction that is difficult to both model
  and calculate. PASSAT also takes the topology of the Earth's surface into
  consideration, other than simply treating it as a plane. With these
  considerations, PASSAT numerically solves the advection equation and the
  Navier-Stokes equation on the spherical manifold, utilizes a spherical graph
  neural network to capture the Earth-atmosphere interaction, and generates the
  initial velocity fields that are critical to solving the advection equation
  from the same spherical graph neural network. In the 5.625^circ-resolution
  ERA5 data set, PASSAT outperforms both the state-of-the-art deep learning-based
  weather prediction models and the operational numerical weather prediction
  model IFS T42. Code and checkpoint are available at
  https://github.com/Yumenomae/PASSAT_5p625.
paper_authors._id: 681e0786377fcb995a1a3cde
paper_authors.name: Jiaqi Zheng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-12T06:50:54.481Z
paper_authors._id: 681e0624479a016a7b4fb88a
paper_authors.avatarUrl: /avatars/98d2c88cad56e4453dead6e50a64957b.svg
paper_authors.fullname: Jiaqi Zheng
paper_authors.type: user
paper_authors.user: Yumenomae
paper_authors._id: 681e0786377fcb995a1a3cdf
paper_authors.name: Qing Ling
paper_authors._id: 681e0786377fcb995a1a3ce0
paper_authors.name: Yerong Feng
paper_title: Physics-Assisted and Topology-Informed Deep Learning for Weather
    Prediction
paper_summary: Although deep learning models have demonstrated remarkable potential in
  weather prediction, most of them overlook either the physics of the
  underlying weather evolution or the topology of the Earth's surface.
  In light of these disadvantages, we develop PASSAT, a novel Physics-ASSisted
  And Topology-informed deep learning model for weather prediction. PASSAT
  attributes the weather evolution to two key factors: (i) the advection process
  that can be characterized by the advection equation and the Navier-Stokes
  equation; (ii) the Earth-atmosphere interaction that is difficult to both model
  and calculate. PASSAT also takes the topology of the Earth's surface into
  consideration, other than simply treating it as a plane. With these
  considerations, PASSAT numerically solves the advection equation and the
  Navier-Stokes equation on the spherical manifold, utilizes a spherical graph
  neural network to capture the Earth-atmosphere interaction, and generates the
  initial velocity fields that are critical to solving the advection equation
  from the same spherical graph neural network. In the 5.625^circ-resolution
  ERA5 data set, PASSAT outperforms both the state-of-the-art deep learning-based
  weather prediction models and the operational numerical weather prediction
  model IFS T42. Code and checkpoint are available at
  https://github.com/Yumenomae/PASSAT_5p625.
paper_ai_summary: PASSAT, a physics-assisted and topology-informed deep learning model, enhances weather prediction by integrating the advection equation, Navier-Stokes equation, and a spherical graph neural network to model Earth-atmosphere interactions on a spherical manifold.
paper_ai_keywords: Physics-assisted Topology-informed PASSAT Advection equation Navier-Stokes equation Spherical graph neural network Spherical manifold