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arXiv AI By Tannaz Goodarzvand Chegini, Elyas Shivanian, Behzad Karimi, Faraz Dadgostari

Sparse-Observation Atmospheric Thermal Forecasting with Physics-Informed Neural Networks for Climate-Aware Digital Twins

Read the original on arXiv AI →

The paper evaluates a physics-informed neural network (PINN) for short‑horizon atmospheric temperature forecasting where observations are sparse. Using ERA5 data at three pressure levels, the PINN outperforms persistence, local‑trend, and two neural‑network baselines, with mean RMSE improvements ranging from 8.1 % at one hour to 23.8 % at three hours. The advantage persists under severe observation sparsity and transfers across regions, though it degrades in complex terrain, highlighting limits of a fixed vertical‑coordinate representation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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