10 Jun 2022
10 Jun 2022
Status: this preprint is currently under review for the journal AMT.

Phase correlation on the edge for estimating cloud motion

Bhupendra A. Raut1,2, Scott Collis1,2, Nicola Ferrier1,2, Paytsar Muradyan2, Rajesh Sankaran1,2, Robert Jackson1,2, Sean Shahkarami2,3, Seongha Park1,2, Dario Dematties1,2, Yongho Kim1,2, Joseph Swantek1,2, Neal Conrad1,2, Wolfgang Gerlach1,2, Sergey Shemyakin2,3, and Pete Beckman1,2 Bhupendra A. Raut et al.
  • 1Northwestern-Argonne Institute of Science and Engineering, Northwestern University, Evanston, Illinois, USA
  • 2Argonne National Laboratory, Lemont, Illinois, USA
  • 3University of Chicago, Chicago, Illinois, USA

Abstract. Phase Correlation (PC) is a well-known method for estimating cloud motion vectors (CMV) from infrared and visible spectrum images. Commonly phase-shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the block-wise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMV, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMV was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure testbed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMV with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. The raindrop contaminated images were excluded by identifying the rotation of the raindrop contaminated TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing enabled sensor systems.

Bhupendra A. Raut et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2022-159', Anonymous Referee #1, 30 Jun 2022
  • RC2: 'Comment on amt-2022-159', Anonymous Referee #2, 20 Sep 2022
    • AC3: 'Reply on RC2', Bhupendra Raut, 03 Nov 2022
  • AC2: 'Reply on RC2', Bhupendra Raut, 03 Nov 2022

Bhupendra A. Raut et al.

Video supplement

Examples of cloud motion vectors and raindrop contamination in TSI Bhupendra A. Raut

Bhupendra A. Raut et al.


Total article views: 454 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
354 84 16 454 4 4
  • HTML: 354
  • PDF: 84
  • XML: 16
  • Total: 454
  • BibTeX: 4
  • EndNote: 4
Views and downloads (calculated since 10 Jun 2022)
Cumulative views and downloads (calculated since 10 Jun 2022)

Viewed (geographical distribution)

Total article views: 452 (including HTML, PDF, and XML) Thereof 452 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 14 Nov 2022
Short summary
We tested the quality of cloud motion from sky camera images using the phase correlation method to optimize the algorithm in real time. Increased framerate and large image block considerably improved the quality, but image resolution and the color channel had a minor effect. In images from the revolving camera system, raindrop contamination is identified using the rotational motion of the drops.