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 was under review for the journal AMT. A final paper is not foreseen.

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

Eric A. Wendt, Bonne Ford, and John Volckens

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.

This preprint has been withdrawn.

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Eric A. Wendt, Bonne Ford, and John Volckens

Interactive discussion

Status: closed

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
  • RC2: 'Comment on amt-2022-217', Anonymous Referee #2, 10 Oct 2022

Interactive discussion

Status: closed

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
  • RC2: 'Comment on amt-2022-217', Anonymous Referee #2, 10 Oct 2022
Eric A. Wendt, Bonne Ford, and John Volckens
Eric A. Wendt, Bonne Ford, and John Volckens

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This preprint has been withdrawn.

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.