SBIR Phase I: Icy Road Forecast and Alert Product Using Road-Physics-Enhanced Weather Forecast Model and Remote Sensing Technologies

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将是减少冬季结冰道路上的风险，从而提高运输安全。积雪和结冰的道路导致死亡和商业运输中断。政府和行业的决策者需要实时观测和可靠的天气预报来支持及时决策，以降低与结冰道路状况相关的风险。该SBIR项目旨在开发和商业化一种创新技术，以检测，预测和提醒最终用户结冰的道路状况。这个小型企业创新研究（SBIR）第一阶段项目将把多卫星降水（液态和冰冻）和遥感云观测与一个新的基于物理的道路状况预测模型相结合，以及时、安全和有效地预测结冰的道路状况。具体而言，拟议的技术将使用NOAA降雨观测和NASA云观测服务来评估路面的表面温度和水的可用性，这是道路结冰的两个关键参数。此外，还将开发一个模型，其中包括一个新的表面能量预算和代表道路能量运输的物理和热力学过程的参数，以便提前24小时预测结冰的道路状况。此外，还将开发安装在无人机上的高光谱摄像机，利用可见光和近红外波段探测路面上的冰。从这些相机收集的黑冰数据将与道路天气信息系统（RWIS）的地面测量结果进行比较，以进行验证。通过结合这三种新方法，该技术预计将达到约85%的准确度结冰道路预测。这个奖项反映了NSF的法定使命，并已被认为是值得支持的评估使用基金会的知识价值和更广泛的影响审查标准。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will be to reduce risks on icy winter roads and thus enhance transportation safety. Snow-covered and icy roads result in fatalities and disruption of commercial transportation. Decision-makers in government and industry need real-time observations and reliable weather forecasts to support timely decisions to reduce risks associated with icy road conditions. This SBIR project aims to develop and commercialize an innovative technology to detect, forecast and alert end users to icy road conditions. This Small Business Innovation Research (SBIR) Phase I project will integrate multi-satellite precipitation (liquid and frozen) and remote cloud sensing observations with a new physics-based road condition forecasting model to produce timely, secure, and efficient prediction of icy road conditions. Specifically, the proposed technology will use NOAA rainfall observations and NASA cloud observation services to assess surface temperature and water availability at a road surface, the two key parameters for ice formation on a road. In addition, a model incorporating a new surface energy budget and parameters to represent the physical and thermodynamic processes of road energy transportation will be developed to forecast icy road conditions up to 24 hours in advance. Furthermore, drone-mounted hyperspectral cameras using visible and near-infrared bands to detect ice on road surfaces will be developed. Collected black ice data from such cameras will be compared with the ground measurements from Road Weather Information Systems (RWIS) for validation. By combining these three new approaches, the technology is expected to reach an accuracy of approximately 85% for icy road forecasts.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.