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.

翻译：大气中悬浮着数百万吨矿物尘埃，这些尘埃与天气和气候相互作用。在天气模型中准确表征矿物尘埃至关重要，但仍具挑战性。大规模天气模型需使用高性能超级计算机，耗时数小时才能完成预报。这种计算负担使其仅能采用月平均气候态矿物尘埃数据作为输入状态，从而限制了预报精度。本文提出DustNet——一个简单、准确且超高速的预报模型，用于未来24小时气溶胶光学厚度（AOD）预测。DustNet在台式计算机上仅需不到8分钟完成训练，并在2秒内生成预测结果。与地面实况卫星数据对比显示，在1×1度粗分辨率网格上，DustNet的预测性能在95%的网格点超越最先进的物理模型。我们的研究表明，DustNet具备实现快速精准AOD预报的潜力，或将彻底改变我们对沙尘影响天气模式的理解。