Phase correlation on the edge for estimating cloud motion

Abstract

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.