PrecipDiff: Leveraging image diffusion models to enhance satellite-based precipitation observations

2025-01-13

AI Summary

Satellite-based precipitation products suffer from low accuracy, substantial biases, coarse spatial resolution (e.g., 10 km), and dependence on ground-based observations—limiting their utility in data-sparse regions. Method: This paper proposes the first image diffusion model tailored for multi-source precipitation data harmonization. It employs a residual-learning-driven conditional denoising U-Net to jointly perform end-to-end super-resolution and bias correction directly from raw precipitation fields, without requiring meteorological priors or auxiliary radar/ground truth measurements. Contribution/Results: By innovatively adapting diffusion modeling to precipitation fusion and downscaling, our approach achieves a tenfold resolution enhancement—from 10 km to 1 km—in the Seattle region. Quantitative evaluation shows significant reductions in RMSE and bias, alongside markedly improved terrain responsiveness and preservation of fine-scale precipitation structures. This establishes a new paradigm for high-accuracy, real-time precipitation monitoring in ungauged areas.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

A recent report from the World Meteorological Organization (WMO) highlights that water-related disasters have caused the highest human losses among natural disasters over the past 50 years, with over 91% of deaths occurring in low-income countries. This disparity is largely due to the lack of adequate ground monitoring stations, such as weather surveillance radars (WSR), which are expensive to install. For example, while the US and Europe combined possess over 600 WSRs, Africa, despite having almost one and half times their landmass, has fewer than 40. To address this issue, satellite-based observations offer a global, near-real-time monitoring solution. However, they face several challenges like accuracy, bias, and low spatial resolution. This study leverages the power of diffusion models and residual learning to address these limitations in a unified framework. We introduce the first diffusion model for correcting the inconsistency between different precipitation products. Our method demonstrates the effectiveness in downscaling satellite precipitation estimates from 10 km to 1 km resolution. Extensive experiments conducted in the Seattle region demonstrate significant improvements in accuracy, bias reduction, and spatial detail. Importantly, our approach achieves these results using only precipitation data, showcasing the potential of a purely computer vision-based approach for enhancing satellite precipitation products and paving the way for further advancements in this domain.

Problem

• Satellite Rainfall Estimation
• Accuracy Improvement
• Global Real-time Monitoring

Innovation

• PrecipDiff
• Image Diffusion Model
• Residual Learning