使用机载激光雷达对云和气溶胶层进行机器学习实时检测

M. McGill, P. Selmer, A. Kupchock, J. Yorks
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引用次数: 1

摘要

激光雷达大气剖面提供了关于云层和气溶胶层的存在以及这些层的高度和结构的信息。特征边界的知识是同化模型的关键输入。此外,以最小的延迟识别特征边界对于影响操作同化和实时决策至关重要。使用先进的卷积神经网络算法,我们演示了使用机载后向散射激光雷达实时确定大气特征边界。结果表明,该方法与传统的处理方法吻合较好,并具有比传统方法更高的水平分辨率。通过机载激光雷达的演示,该算法和过程可扩展到未来星载传感器的实时数据产品生成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine learning-enabled real-time detection of cloud and aerosol layers using airborne lidar
Lidar profiling of the atmosphere provides information on existence of cloud and aerosol layers and the height and structure of those layers. Knowledge of feature boundaries is a key input to assimilation models. Moreover, identifying feature boundaries with minimal latency is essential to impact operational assimilation and real-time decision making. Using advanced convolution neural network algorithms, we demonstrate real-time determination of atmospheric feature boundaries using an airborne backscatter lidar. Results are shown to agree well with traditional processing methods and are produced with higher horizontal resolution than the traditional method. Demonstrated using airborne lidar, the algorithms and process are extendable to real-time generation of data products from a future spaceborne sensor.
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