Precipitation nowcasting of satellite data using physically-aligned neural networks

• URL: http://arxiv.org/abs/2511.05471v2
• Date: Wed, 12 Nov 2025 01:46:47 GMT
• Title: Precipitation nowcasting of satellite data using physically-aligned neural networks
• Authors: Antônio Catão, Melvin Poveda, Leonardo Voltarelli, Paulo Orenstein,
• Abstract summary: TUPANN is a satellite-only model trained on GOES-16 RRQPE.<n>It decomposes the forecast into physically meaningful components.<n>TUPANN achieves the best or second-best skill in most settings.
• Score: 1.8468488572500306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate short-term precipitation forecasts predominantly rely on dense weather-radar networks, limiting operational value in places most exposed to climate extremes. We present TUPANN (Transferable and Universal Physics-Aligned Nowcasting Network), a satellite-only model trained on GOES-16 RRQPE. Unlike most deep learning models for nowcasting, TUPANN decomposes the forecast 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 advection operator reconstructs future frames. We evaluate TUPANN on both GOES-16 and IMERG data, in up to four distinct climates (Rio de Janeiro, Manaus, Miami, La Paz) at 10-180min lead times using the CSI and HSS metrics over 4-64 mm/h thresholds. Comparisons against optical-flow, deep learning and hybrid baselines show that TUPANN achieves the best or second-best skill in most settings, with pronounced gains at higher thresholds. Training on multiple cities further improves performance, while cross-city experiments show modest degradation and occasional gains for rare heavy-rain regimes. The model produces smooth, interpretable motion fields aligned with numerical optical flow and runs in near real time due to the low latency of GOES-16. These results indicate that physically aligned learning can provide nowcasts that are skillful, transferable and global.

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