A Deep Machine Learning Approach for Lidar Based Boundary Layer Height Detection

Inspired by the importance of Planetary Boundary Layer Heights (PBLH) for inferring Air Quality assessments and the disappointing performance of current weather forecasts of PBLH, this paper presents the proposed impact of using a Machine Learning derived PBLH (ML-PBLH) using ground-based Ceilometer observing systems. The PBLH is vital in air pollution studies to determine the extent of vertical mixing of pollutants (e.g., particles, trace gases, etc.). On the other hand, model forecasts of the nighttime collapse of the PBLH close to the surface, grossly underestimate the actual observations. We propose using machine learning methods, employing deep neural networks for denoising and image segmentation to detect PBLH from the Ceilometer backscatter signal from the LIDAR observations. The ML-PBLH detection algorithm shows promising early results when compared with conventional ground-based Ceilometer retrieval methods such as Wavelet methods used for LIDAR identification of PBLH under normal conditions. The use of conventional methods as well as the wavelet method for LIDAR backscatter retrievals is limited in the presence of dense clouds. However, the proposed machine learning approach is able to infer PBLH even under such conditions.