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

TL;DR

This paper introduces PrecipDiff, a diffusion model-based method that improves satellite precipitation data accuracy, bias, and resolution, enabling better global water disaster monitoring especially in low-income regions.

Contribution

It presents the first diffusion model for correcting inconsistencies in precipitation data and demonstrates effective downscaling from 10 km to 1 km resolution using only precipitation data.

Findings

Significant improvements in accuracy and bias reduction.

Effective downscaling from 10 km to 1 km resolution.

Potential for purely computer vision-based enhancement of satellite precipitation data.

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.