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Results for “atmospheric motion vectors”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Satellite-Based Observations of Nonlinear Relationships between Vertical Wind Shear and Intensity Changes during the Life Cycle of Hurricane Joaquin (2015)

A CIMSS vertical wind shear (VWS-C) dataset based on reprocessed GOES-East atmospheric motion vectors (AMVs) at 15-min intervals has a −0.36 correlation with the CIMSS Satellite Consensus (SATCON) intensity changes at 30-min intervals over the life cycle of Hurricane Joaquin (2015). Correlations are then calculated for four intensity change events including two rapid intensifications (RIs) and two decays, and four intensity change segments immediately before or after these events. During the first RI, the peak intensity increase of 16 kt (6 h) −1 (1 kt ≈ 0.51 m s −1 ) follows a small VWS-C decrease to a moderate 8 m s −1 value (negative correlation). A 30-h period of continued RI following the first peak RI occurred under moderate magnitude VWS-C (negative correlation), but with a rotation of the VWS-C direction to become more aligned with the southwestward heading of Joaquin. During the second RI, the peak intensity increase of 15 kt (6 h) −1 leads the rapid VWS-C increase (positive correlation), which the horizontal plots of VWS-C vectors demonstrate is related to an upper-tropospheric cyclone to the northeast of Joaquin. A conceptual model of ocean cooling within the anticyclonic track loop is proposed to explain a counterintuitive decreasing intensity when the VWS-C was also decreasing (positive correlation) during the Joaquin track reversal. These alternating negative and positive correlations during the four events and four segments of intensity change demonstrate the nonlinear relationships between the VWS-C and intensity changes during the life cycle of Joaquin that must be understood, analyzed, and modeled to improve tropical cyclone intensity forecasts, and especially RI events.

Elsberry, Russell L.↗
DOE OSTI2023

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

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) 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 blockwise 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 CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs 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. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

54 ENVIRONMENTAL SCIENCES↗