New Study on the Application of Convolutional Neural Network to Vertical CALIPSO Profile Measurements
Abstract
The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), on-board the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) is a satellite-borne polarization sensitive lidar. It has been providing the vertical distributions of clouds and aerosols along with their microphysical and optical properties since 2006. One of its important Level 2 products, feature classification, has been determined using the lidar information from 532 nm parallel and perpendicular channels, and 1064 nm channel measurements of layer integrated backscatter. Deep machine learning methods which combine both the channel and texture information to recognize feature patterns can be uniquely beneficial when applied to this data. In the CALIOP integrated layer properties, the texture information has been masked due to averaging. In this study, we use Convolutional Neural Network (CNN), a deep machine learning method, to classify lidar aerosol subtypes by using the CALIOP profile observations. This method uses additional information from the vertical texture of the feature instead of using only the layer information. Our results will show how the texture information plays a role in the classification. This preliminary work explores the benefits and potential of deep machine learning methods for lidar retrievals and focuses on the aerosol subtype classification. The broader application extends to the classification of other feature types. Future applications include the development of deep machine learning methods with neural networks to retrieve properties of the features, and studies of indirect effect of cloud-aerosol interaction from lidar measurements.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2018
- Bibcode:
- 2018AGUFM.A31P3169Z
- Keywords:
-
- 0365 Troposphere: composition and chemistry;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 0394 Instruments and techniques;
- ATMOSPHERIC COMPOSITION AND STRUCTUREDE: 3311 Clouds and aerosols;
- ATMOSPHERIC PROCESSESDE: 3360 Remote sensing;
- ATMOSPHERIC PROCESSES