Articles | Volume 9, issue 7
https://doi.org/10.5194/amt-9-2813-2016
https://doi.org/10.5194/amt-9-2813-2016
Research article
 | 
07 Jul 2016
Research article |  | 07 Jul 2016

Top-of-the-atmosphere shortwave flux estimation from satellite observations: an empirical neural network approach applied with data from the A-train constellation

Pawan Gupta, Joanna Joiner, Alexander Vasilkov, and Pawan K. Bhartia

Viewed

Total article views: 3,298 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,705 1,436 157 3,298 185 118 106
  • HTML: 1,705
  • PDF: 1,436
  • XML: 157
  • Total: 3,298
  • Supplement: 185
  • BibTeX: 118
  • EndNote: 106
Views and downloads (calculated since 01 Feb 2016)
Cumulative views and downloads (calculated since 01 Feb 2016)

Cited

Latest update: 19 Nov 2024
Download
Short summary
The A-train constellation of satellites provides a unique opportunity to analyze near-simultaneous data from several of these sensors. In this paper, retrievals of cloud/aerosols parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been used to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data as inputs. Application of our method to other ultraviolet sensors may provide unique estimates of TOA SWF.