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

雾和相关的低能见度条件特别损害汽车、船舶和空中交通的安全。在此背景下，及早准确预测雾的形成和消解，可以为提高交通安全和管理水平做出重大贡献。然而，由于物理过程的复杂性以及局地地形和天气对雾动力学的影响，利用数值模式对地面雾进行准确预报一直是一个困难的问题。相比之下，机器学习方法在雾预测方面显示出令人振奋的结果，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只是针对选定的站点开发的，没有考虑到明确记录各个站点附近的雾发展的时间和空间方面。在这里，当地测量和空间高分辨率卫星数据的结合可以有助于改进雾预测。机器学习方法提供了以最佳方式组合具有不同属性的大量数据并使其可用于雾预测的可能性。然而，到目前为止，还没有基于机器学习的方法来利用台站测量和空间高分辨率卫星数据进行雾预测。根据申请者工作组以前在基于卫星的雾检测和降水遥感中使用机器学习方法的经验，该项目将开发一个基于站数据和MSG SEVIRI(气象卫星第二代旋转增强型可见光和红外成像仪)快速扫描雾相关变量数据的德国全国雾预测系统(形成和分辨率)。这种办法考虑到所有时间和空间上的相关变量，一方面确保最佳利用现有信息改进雾预报，另一方面为在高时空分辨率下对雾的形成和雾的分辨率进行全区域预报开辟了可能性。鉴于这种现象具有很大的社会相关性，而且对可靠的高分辨率雾预报的需求很高，该项目是对更好地模拟这一现象的重要贡献。

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.