Abstract
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.
Highlights
IntroductionCloud observations are mainly divided into space-based satellites, air-based radiosondes and ground-based remote sensing observations
In order to facilitate the arrangement, images of the different sizes were linearly scaled. It can be seen from the figure that the original prediction results can restore detailed information to a certain extent after being enhanced by the super-resolution model
It can be seen from the figure that the original prediction results can restore detailed information to aresults certain extent after being enhanced by reconstruction the super-resolution model
Summary
Cloud observations are mainly divided into space-based satellites, air-based radiosondes and ground-based remote sensing observations. Air-based radiosonde observation has advantages in detecting the vertical structure of the cloud, but it is difficult to meet the requirements of actual cloud detection due to its high cost and low detection frequency [1]. The deficiency of air-based radiosonde and space-based satellite remote sensing observation leads to the popularization of ground-based remote sensing observations, especially cloud image observations, which can provide strong support for satellite and radiosonde observation. These images are low-cost and high-resolution, which can provide accurate local sky cloud information [1]
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