Back to Search
Start Over
Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data
- Source :
- Atmosphere, Volume 12, Issue 5, Atmosphere, Vol 12, Iss 606, p 606 (2021)
- Publication Year :
- 2021
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2021.
-
Abstract
- Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.
- Subjects :
- Atmospheric Science
Daytime
010504 meteorology & atmospheric sciences
Backscatter
aerosol
Cloud computing
Environmental Science (miscellaneous)
Machine learning
computer.software_genre
01 natural sciences
Convolutional neural network
03 medical and health sciences
Meteorology. Climatology
International Space Station
cloud
Image resolution
lidar
030304 developmental biology
0105 earth and related environmental sciences
0303 health sciences
business.industry
Aerosol
Lidar
machine learning
Environmental science
Artificial intelligence
QC851-999
business
Algorithm
computer
Subjects
Details
- Language :
- English
- ISSN :
- 20734433
- Database :
- OpenAIRE
- Journal :
- Atmosphere
- Accession number :
- edsair.doi.dedup.....14da94baf0d35a179d84652674c9d065
- Full Text :
- https://doi.org/10.3390/atmos12050606