Abstract：
Fine-mode aerosol optical depth (fAOD) is a vital proxy for the concentration of anthropogenic aerosols in the atmosphere. Currently, the limited data length and high uncertainty of the satellite-based data diminish the applicability of fAOD for climate research. Here, we propose a novel pretrained deep learning framework that can extract information underlying each satellite pixel and use it to create new latent features that can be employed for improving retrieval accuracy in regions without in situ data. With the proposed model, we developed a new global fAOD (at 0.5 μm) data from 2001 to 2020, resulting in a 10% improvement in the overall correlation coefficient (R) during site-based independent validation and a 15% enhancement in non-AERONET site areas validation. Over the past two decades, there has been a noticeable downward trend in global fAOD (-1.39 × 10-3/year). Compared to the general deep-learning model, our method reduces the global trend\'s previously overestimated magnitude by 7% per year. China has experienced the most significant decline (-5.07 × 10-3/year), which is 3 times greater than the global trend. Conversely, India has shown a significant increase (7.86 × 10-4/year). This study bridges the gap between sparse in situ observations and abundant satellite measurements, thereby improving predictive models for global patterns of fAOD and other climate factors.
摘要：
精细模式气溶胶光学深度（fAOD）是大气中人为气溶胶浓度的重要代表。目前,有限的数据长度和基于卫星的数据的高度不确定性削弱了fAOD在气候研究中的适用性。这里,我们提出了一种新颖的预训练深度学习框架，可以提取每个卫星像素的底层信息，并使用它来创建新的潜在特征，可用于提高没有原位数据的区域的检索精度。使用所提出的模型，我们从2001年到2020年开发了新的全球fAOD(0.5μm)数据,在基于站点的独立验证期间,总体相关系数(R)提高了10%,在非AERONET站点区域验证中提高了15%.在过去的二十年里，全球fAOD呈明显下降趋势(-1.39×10-3/年)。与一般的深度学习模型相比，我们的方法将全球趋势以前高估的幅度每年降低7%。中国经历了最显著的下降(-5.07×10-3/年)，是全球趋势的3倍。相反，印度表现出显著增长(7.86×10-4/年)。这项研究弥合了稀疏的原位观测和丰富的卫星测量之间的差距，从而改进了fAOD和其他气候因素的全球模式的预测模型。
