Work

Lorenz 63 training data for "Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?"

Year: 2026

Type: dataset

Abstract: Training and test trajectories (lorenzS_train.npy, lorenzS_test.npy, lorenzS_test_traj0.npy) for the Lorenz 63 emulators in Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?. σ = 10, ρ = 28, β = 8/3, LSODA dt = 0.01, subsampled ×5. Generation config l63_generate.yaml in https://github.com/Weidong-Li-HydroMet/FB-asymmetry.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.22259223

Authors: Hassanzadeh, Pedram, Li, Weidong, Sun, Y. Qiang, Wang, Jiangdi, Wikner, Alexander, Finkel, Justin, Weare, Jonathan

Institutions: University of Chicago, Nanjing University, New York University

Language: English

Topic: Meteorological Phenomena and Simulations

Subfield: Atmospheric Science

Field: Earth and Planetary Sciences

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7208827850

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