论文标题
对基于雷达的降水和适用机器学习技术的审查
A review of radar-based nowcasting of precipitation and applicable machine learning techniques
论文作者
论文摘要
“现象”是一种天气预报,在短期内做出预测,通常不到两个小时 - 在这个时期,传统的数值天气预测可以受到限制。这种天气预测对商业航空有重要的应用;公共和户外活动;以及建筑行业,电力公用事业和地面运输服务,这些服务在户外进行大部分工作。重要的是,现有系统的关键需求之一是在此类情况下对不利天气事件的准确警告,例如大雨和洪水,以保护生命和财产。典型的现象方法基于应用于观测的简单外推模型,主要是降雨雷达。在本文中,我们回顾了来自环境科学的基于雷达的现有技术,以及根据机器学习领域适用的统计方法。 Nowcasting仍然是运营系统的重要组成部分,我们认为通过环境科学与机器学习社区之间的新合作伙伴关系是可能的。
A 'nowcast' is a type of weather forecast which makes predictions in the very short term, typically less than two hours - a period in which traditional numerical weather prediction can be limited. This type of weather prediction has important applications for commercial aviation; public and outdoor events; and the construction industry, power utilities, and ground transportation services that conduct much of their work outdoors. Importantly, one of the key needs for nowcasting systems is in the provision of accurate warnings of adverse weather events, such as heavy rain and flooding, for the protection of life and property in such situations. Typical nowcasting approaches are based on simple extrapolation models applied to observations, primarily rainfall radar. In this paper we review existing techniques to radar-based nowcasting from environmental sciences, as well as the statistical approaches that are applicable from the field of machine learning. Nowcasting continues to be an important component of operational systems and we believe new advances are possible with new partnerships between the environmental science and machine learning communities.