Precipitation nowcasting of satellite data using physically-aligned neural networks

Antônio Catão, Melvin Poveda, Leonardo Voltarelli, Paulo Orenstein

TL;DR: We propose a model that leverages physical knowledge to regularize training and improve interpretability for precipitation nowcasting.

Abstract:

Accurate short-term precipitation forecasts predominantly rely on dense weather-
radar networks, limiting operational value in places most exposed to climate ex-
tremes. We present TUPANN, a satellite-only model that decomposes the fore-
cast into physically meaningful components: a variational encoder-decoder infers
motion and intensity fields from recent imagery under optical-flow supervision, a
lead-time-conditioned MaxViT evolves the latent state, and a differentiable advec-
tion operator reconstructs future frames. We evaluate TUPANN on both GOES-16
and IMERG data, in up to four distinct climates at 10–180-min lead times using
the CSI metric. Comparisons against optical-flow, deep learning and hybrid base-
lines show that TUPANN achieves the best or second-best skill in most settings,
with pronounced gains at higher thresholds. The model produces smooth, inter-
pretable motion fields aligned with numerical optical flow and runs in near real
time. These results indicate that physically aligned learning can provide nowcasts
that are skillful, transferable and global.

Submission Number: 40