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

使用机器学习 (FOG-ML) 结合站和卫星数据来预测辐射 FoG

• 批准号: 424021288
• 负责人: Professor Dr. Jörg Bendix
• 金额: --
• 依托单位: Fachgebiet Klimageographie und Umweltmodellierung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2019
• 资助国家: 德国
• 起止时间: 2018-12-31 至 2023-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/424021288?language=en
• 关键词: FOrecasting; radiation; foG; combining; station

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.

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。在这种情况下，尽早准确预测雾的形成和解决方案可以为改善交通安全和管理做出重大贡献。然而，由于雾的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，迄今为止，利用数值模式对雾进行准确预报仍然是困难的。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实的替代方案。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，并且没有考虑到明确记录各个站点附近雾发展的时间和空间方面。在这里，当地的测量和空间高分辨率的卫星数据的组合可以有助于改善雾的预测。机器学习方法提供了最佳的联合收割机组合大量数据与不同属性，使它们可用于雾预测的潜力。然而，到目前为止，还没有基于机器学习的方法用于使用站点测量和空间高分辨率卫星数据进行雾预测。基于申请人工作组内关于将机器学习方法用于基于卫星的雾检测和降水遥感的先前经验，德国全国雾预报系统基于台站数据和MSG SEVIRI的组合（形成和分辨率）（气象卫星第二代旋转增强型可见光和红外成像仪）应在本项目中开发与雾有关的变量的快速扫描数据。这种方法，考虑到所有的时间和空间相关的变量，确保一方面的可用信息的最佳使用，以改善雾的预测，另一方面开辟了雾的形成和雾的分辨率在高时空分辨率的区域范围内的预测的可能性。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，该项目代表了对更好地模拟这一现象的重要贡献。