Preprints
https://doi.org/10.5194/amt-2022-217
https://doi.org/10.5194/amt-2022-217
 
18 Aug 2022
18 Aug 2022
Status: this preprint is currently under review for the journal AMT.

A cloud screening algorithm for ground-based sun photometry using all-sky images and deep transfer learning

Eric A. Wendt1, Bonne Ford2, and John Volckens1 Eric A. Wendt et al.
  • 1Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado, USA, 80523
  • 2Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA, 80523

Abstract. Aerosol optical depth (AOD) is used to characterize aerosol loadings within Earth’s atmosphere. Sun photometers measure AOD from the Earth’s surface based on direct-sunlight intensity readings by spectrally narrow light detectors. However, when the solar disk is partially obscured by cloud cover, sun photometer measurements can be biased due to the interaction of sunlight with cloud constituents. We present a novel deep transfer learning model on all-sky images to support more accurate AOD retrievals. We used three independent image datasets for training and testing: the novel Northern Colorado All-Sky Image (NCASI), the Whole Sky Image SEGmentation (WSISEG), and the METCRAX-II datasets from the National Center for Atmospheric Research (NCAR). We visually partitioned all-sky images into three categories: 1) clear sky around the solar disk, 2) thin cirrus obstructing the solar disk, and 3) thick, non-cirrus clouds obstructing the solar disk. Two-thirds of the images were allocated for training and one-third were allocated for testing. We trained models based on all possible combinations of the training sets. The best-performing model successfully classified 95.5 %, 96.9 %, and 89.1 % of testing images from NCASI, METCRAX-II and WSISEG datasets, respectively. Our results demonstrate that all-sky imaging with deep transfer learning can be applied toward cloud screening, which would aid ground-based AOD measurements.

Eric A. Wendt et al.

Status: open (until 12 Oct 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-217', Anonymous Referee #1, 27 Sep 2022 reply

Eric A. Wendt et al.

Eric A. Wendt et al.

Viewed

Total article views: 211 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
156 50 5 211 19 2 3
  • HTML: 156
  • PDF: 50
  • XML: 5
  • Total: 211
  • Supplement: 19
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 18 Aug 2022)
Cumulative views and downloads (calculated since 18 Aug 2022)

Viewed (geographical distribution)

Total article views: 217 (including HTML, PDF, and XML) Thereof 217 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 28 Sep 2022
Download
Short summary
Outdoor air pollution poses global public health and environmental risks. One method to quantify outdoor air pollution is sun photometery, a technique that measures how much airborne particles affects sunlight intensity. Clouds obscuring the sun can bias sun photometer measurements. Here we propose an image-based deep learning framework for automatic quality control of sun photometer measurements. We show our algorithm is effective at classifying images of the sun as cloud-contaminated or not.