Tropical Cyclone Forecasting via Latent Rectified Flow using Satellite Imagery and Atmospheric Fields

2026-08-08

AI Summary

This work addresses the challenges in tropical cyclone forecasting, where existing methods struggle to efficiently generate satellite imagery and multivariate atmospheric fields jointly, often suffering from high computational costs, multi-step sampling procedures, and physically inconsistent track predictions. To overcome these limitations, the authors propose a single-pass generative model that integrates a five-channel variational autoencoder for input compression, a conditional Rectified Flow UNet to model spatiotemporal dynamics, a factorized temporal attention module, and a DRaFT reward-based fine-tuning mechanism leveraging differentiable trajectory error. The approach achieves, for the first time, one-step joint generation of infrared satellite images and four atmospheric variables, attaining a PSNR of 16.35 dB and SSIM of 0.759 at +9-hour lead time, accelerating sampling by approximately 30×, reducing track error to 62.4 km (a 15% improvement over the baseline), with further 8–11% error reduction through reward fine-tuning.

Abstract

Tropical cyclones are growing more destructive in a changing climate, and efficient forecasting of their structure and track has become a necessity. Deep generative models promise an alternative to computationally expensive numerical weather prediction (NWP), yet current systems produce either satellite imagery or atmospheric fields, never both; they need many sampling steps, putting them out of reach of modest hardware; and their storm tracks come from regression heads with no physical link to the generated atmosphere. This work presents a single-pass model that jointly forecasts GRIDSAT-B1 infrared imagery and four ERA5 atmospheric fields (U-wind, V-wind, air temperature, and surface pressure) out to nine hours. A five-channel variational autoencoder compresses each 5 x 256 x 256 frame to a 4 x 64 x 64 latent, and a conditional rectified-flow UNet with a factorized temporal-attention module predicts the next three frames from three past frames, their best-track coordinates, and timestamps. The model is then reward-fine-tuned (DRaFT) against a differentiable track error derived from the predicted winds through a steering-flow calculation. On held-out 2022 storms the model reaches 16.35 dB PSNR and 0.759 SSIM, ahead of a reproduced cascaded-diffusion baseline at every lead time (+0.84 dB at +9 h) while sampling ~30x faster (56 ms vs. 1673 ms). Track error at +9 h is 62.4 km, 15% below the baseline, and a reward fine-tuning study demonstrates a further 8-11% track-error reduction across sampler budgets.

Problem

• Tropical Cyclone Forecasting
• Satellite Imagery
• Atmospheric Fields
• Storm Track Prediction
• Deep Generative Models

Innovation

• Rectified Flow
• Joint Forecasting
• Reward Fine-tuning
• Temporal Attention
• Latent Space Compression