Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than...Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.展开更多
文摘利用CloudSat卫星搭载的云廓线雷达(cloud profiling radar,CPR)2007~2009年三年的观测资料,针对洋面非降水暖云有效廓线样本,分别对积云(Cu)、层云(St)、层积云(Sc)和高积云(Ac)等四类云型,分析了其在全球尺度的水平分布特征,并在此基础上特别考察了非降水暖云液相水含量(liquid water content,LWC)的垂直变化特性.研究发现,洋面非降水暖云中四类云型的样本占比从高至低依次为层积云76.46%、层云12.48%、积云7.45%、高积云3.61%,层积云在非降水暖云的总覆盖面积中占据主导作用.在样本量全球标准化后,四类云型的空间分布形式存在较大差异,层积云与层云主要集中于北美和南美大陆西侧近岸海域,积云与高积云则广泛分布于太平洋、大西洋和印度洋的洋面上,且高值位于大洋中部.尽管四类云型的生消机制和宏观形态存在很大差异,但不同云型LWC呈现出较为相似的垂直结构.对经几何厚度标准化后的LWC廓线进行比较,发现在四类典型非降水暖云中,由云底到云顶LWC一致呈现为先增后减的规律.云体中下部向上近似线性递增的结构基本反映了LWC的准绝热增长特性,而云体上部及云顶附近的向上递减结构明确反映了云顶普遍受到上空干空气侵入混合的强烈影响,由此导致了自云顶向下逐层衰减的云水蒸发.以云高和云厚两个参数分类的廓线统计结果还显示,LWC垂直结构受到云顶高度和云层几何厚度的影响.云层几何厚度增大时,LWC由云底到云中的递增结构会变厚,由云中到云顶的递减结构会变薄.几何厚度相同但云顶高度不同的云层,其LWC含量也有所不同,这表明对于特定云型,在生成及发展过程中,不同阶段所对应的LWC廓线结构也存在差异.
基金National Natural Science Foundation of China(42075075)National Key R&D Program of China(2023YFC3007700)Pre-Research Fund of USTC(YZ2082300006)。
文摘Precipitation nowcasting,as a crucial component of weather forecasting,focuses on predicting very short-range precipitation,typically within six hours.This approach relies heavily on real-time observations rather than numerical weather models.The core concept involves the spatio-temporal extrapolation of current precipitation fields derived from ground radar echoes and/or satellite images,which was generally actualized by employing computer image or vision techniques.Recently,with stirring breakthroughs in artificial intelligence(AI)techniques,deep learning(DL)methods have been used as the basis for developing novel approaches to precipitation nowcasting.Notable progress has been obtained in recent years,manifesting the strong potential of DL-based nowcasting models for their advantages in both prediction accuracy and computational cost.This paper provides an overview of these precipitation nowcasting approaches,from which two stages along the advancing in this field emerge.Classic models that were established on an elementary neural network dominated in the first stage,while large meteorological models that were based on complex network architectures prevailed in the second.In particular,the nowcasting accuracy of such data-driven models has been greatly increased by imposing suitable physical constraints.The integration of AI models and physical models seems to be a promising way to improve precipitation nowcasting techniques further.
