The mixing layer height (MLH) indicates the change between vertical mixing of air near the surface and less turbulent air above. MLH is important for the dispersion of air pollutants and greenhouse gases and assessing the performance of numerical weather prediction systems. Existing lidar-based MLH detection algorithms typically do not use the full resolution of the ceilometer, require manual feature engineering, and often do not enforce temporal consistency of the MLH profile. Given the large-scale availability of lidar remote sensing data and the high temporal and spatial resolution at which it is recorded, this domain is very suitable for machine learning approaches such as deep learning. This paper introduces a completely new approach to estimate MLH: the Deep-Pathfinder algorithm, based on deep learning techniques for image segmentation. It is demonstrated that the algorithm can be applied in near-real-time.
Wijnands et al. (Thu,) studied this question.