Articles | Volume 16, issue 5
https://doi.org/10.5194/amt-16-1195-2023
https://doi.org/10.5194/amt-16-1195-2023
Research article
 | 
08 Mar 2023
Research article |  | 08 Mar 2023

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis

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Cited articles

Apke, J. M., Noh, Y.-J., and Bedka, K.: Comparison of Optical Flow Derivation Techniques for Retrieving Tropospheric Winds from Satellite Image Sequences, J. Atmos. Ocean. Tech., 39, 2005–2021, https://doi.org/10.1175/JTECH-D-22-0057.1, 2022. a
Beckman, P., Sankaran, R., Catlett, C., Ferrier, N., Jacob, R., and Papka, M.: Waggle: An open sensor platform for edge computing, in: 2016 IEEE SENSORS, Orlando, FL, USA, 30 October–3 November 2016, IEEE, 1–3, https://doi.org/10.1109/ICSENS.2016.7808975, 2016. a
Chalasinska-Macukow, K., Turon, F., Yzuel, M., and Campos, J.: Contrast performance of pure phase correlation, J. Optics, 24, 71, https://doi.org/10.1088/0150-536X/24/2/004, 1993.  a
Chow, C. W., Belongie, S., and Kleissl, J.: Cloud motion and stability estimation for intra-hour solar forecasting, Sol. Energy, 115, 645–655, 2015. a, b
Dematties, D., Raut, B. A., Park, S., Jackson, R. C., Shahkarami, S., Kim, Y., Sankarana, R., Beckmana, P., Collis, S. M., and Ferrier, N.: Let's Unleash the Network Judgement: A Self-supervised Approach for Cloud Image Analysis, Artificial Intelligence for the Earth Systems, in press, 2023. a
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Short summary
We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.