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A Deep Machine Learning Approach for Lidar Based Boundary Layer Height Detection
- Source :
- IGARSS
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- 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.
- Subjects :
- 010504 meteorology & atmospheric sciences
Backscatter
Planetary boundary layer
business.industry
Deep learning
02 engineering and technology
Atmospheric model
Image segmentation
Machine learning
computer.software_genre
01 natural sciences
Ceilometer
020202 computer hardware & architecture
Lidar
Wavelet
0202 electrical engineering, electronic engineering, information engineering
Environmental science
Artificial intelligence
business
computer
0105 earth and related environmental sciences
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium
- Accession number :
- edsair.doi...........d1587471aab97cfb4af6d618641b54aa