Back to Search
Start Over
Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network.
- Source :
-
Geophysical Research Letters . 6/28/2019, Vol. 46 Issue 12, p7035-7044. 10p. - Publication Year :
- 2019
-
Abstract
- We demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two‐dimensional cloud vertical structures that would be observed by the CloudSat satellite‐based radar, using only the collocated Moderate‐Resolution Imaging Spectrometer measurements as input. The CGAN is usually able to generate reasonable guesses of the cloud structure and can infer complex structures such as multilayer clouds from the Moderate‐Resolution Imaging Spectrometer data. This network, which is formulated probabilistically, also estimates the uncertainty of its own predictions. We examine the statistics of the generated data and analyze the response of the network to each input parameter. The success of the CGAN in solving this problem suggests that generative adversarial networks are applicable to a wide range of problems in atmospheric science, a field characterized by complex spatial structures and observational uncertainties. Key Points: We trained a generative adversarial network (GAN) to generate cloud vertical structuresThe network generates plausible CloudSat scenes, given MODIS data as an inputThis demonstrates the potential usefulness of GANs in atmospheric science [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00948276
- Volume :
- 46
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Geophysical Research Letters
- Publication Type :
- Academic Journal
- Accession number :
- 137775478
- Full Text :
- https://doi.org/10.1029/2019GL082532