FOrecasting radiation foG by combining station and satellite data using Machine Learning (FOG-ML)

雾和与之相关的低能见度尤其会损害汽车、船舶和空中交通的安全。在这种情况下，尽早准确地预报雾的形成和消散，对提高交通安全和管理作出了重大贡献。然而，由于物理过程的复杂性，以及局部地形和天气对雾动力学的影响，使用数值模式精确预报地面雾仍然很困难。相比之下，机器学习方法在雾预测方面显示出有希望的结果，并代表了数值雾预测的真正替代方案。然而，以前用于雾预报的机器学习方法仅针对选定的站点开发，并且没有考虑到各自站点附近雾发展的时间和空间方面的明确记录。在这种情况下，本地测量数据与空间高分辨率卫星数据的结合有助于改善雾的预报。机器学习方法提供了将具有不同属性的大量数据进行最佳组合的潜力，并使其可用于雾预测。然而，到目前为止，还没有基于机器学习的方法来使用台站测量和空间高分辨率卫星数据进行雾预测。本项目将根据申请单位工作组以往利用机器学习方法进行卫星雾探测和降水遥感的经验，结合台站数据和MSG SEVIRI （Meteosat第二代旋转增强可见光和红外成像仪）雾相关变量快速扫描数据，开发德国全国雾预报系统（形成和分辨率）。这种考虑到所有时间和空间相关变量的方法，一方面确保了对可用信息的最佳利用，以改进雾预报，另一方面开辟了在高时空分辨率下对雾形成和雾分辨率进行全区域预测的可能性。鉴于巨大的社会相关性和对可靠的高分辨率雾预报的高需求，该项目对更好地模拟这一现象作出了重要贡献。

Fog and the associated poor visibility conditions particularly impair safety in car, ship and air traffic. In this context, an accurate forecast of fog formation and resolution as early as possible can make a significant contribution to improving traffic safety and management. However, due to the complexity of the physical processes as well as the effects of local topography and weather influences on fog dynamics, the exact prediction of ground fog using numerical models remains difficult until now. In contrast, machine learning methods show promising results with regard to fog prediction and represent a real alternative to numerical fog prediction. However, the previous machine learning methods for fog forecasting were only developed for selected stations and do not take into account the explicit recording of the temporal and spatial aspects of fog development in the vicinity of the respective stations. Here the combination of local measurements and spatially high-resolution satellite data can contribute to an improvement of the fog prediction. Machine learning methods offer the potential to optimally combine large amounts of data with different properties and to make them usable for fog prediction. However, so far there is no machine learning based method for fog prediction using station measurements and spatially high-resolution satellite data.Based on the previous experiences within the working group of the applicants regarding the use of machine learning methods for satellite-based fog detection and precipitation remote sensing, a nationwide fog prediction system for Germany (formation and resolution) based on the combination of station data and MSG SEVIRI (Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager) Rapid Scan data of the fog-relevant variables shall be developed in the project. Such an approach, taking into account all temporally and spatially relevant variables, ensures on the one hand the optimal use of the available information for an improved fog prediction and on the other hand opens up the possibility of area-wide predictions of fog formation and fog resolution in high spatial-temporal resolution. In view of the great societal relevance and the high demand for reliable high-resolution fog forecasts, the project represents an essential contribution to better modelling this phenomenon.