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.

DustNet 是一个快速而准确的模型，使用纳秒级训练和秒级预测，在粗糙的 1x1 度分辨率情况下，超过了基于物理模型的最先进模型的 95% 格点位置上的地面真实卫星数据，具有潜力改变我们对天气模式影响的理解。