DustNet: skillful neural network predictions of Saharan dust

TL;DR

DustNet is a rapid, neural network-based model that predicts Saharan dust aerosol optical depth with high accuracy, outperforming traditional physics-based models at a fraction of the computational cost.

Contribution

This paper introduces DustNet, a novel neural network model capable of fast, accurate 24-hour aerosol optical depth predictions, significantly reducing computation time compared to existing models.

Findings

DustNet trains in less than 8 minutes.

DustNet predictions outperform physics-based models at 95% of grid locations.

DustNet creates predictions in 2 seconds on a desktop computer.

Abstract

Suspended in the atmosphere are millions of tonnes of mineral dust which interacts with weather and climate. Accurate representation of mineral dust in weather models is vital, yet remains challenging. Large scale weather models use high power supercomputers and take hours to complete the forecast. Such computational burden allows them to only include monthly climatological means of mineral dust as input states inhibiting their forecasting accuracy. Here, we introduce DustNet a simple, accurate and super fast forecasting model for 24-hours ahead predictions of aerosol optical depth AOD. DustNet trains in less than 8 minutes and creates predictions in 2 seconds on a desktop computer. Created by DustNet predictions outperform the state-of-the-art physics-based model on coarse 1 x 1 degree resolution at 95% of grid locations when compared to ground truth satellite data. Our results show DustNet has a potential for fast and accurate AOD forecasting which could transform our understanding of dust impacts on weather patterns.