Prediction model based on the Laplacian eigenmap method combined with a random forest algorithm for rainstorm satellite images during the first annual rainy season in South China.

The recent emergence of satellite detection and imaging technologies has increased the demand for the application of satellite cloud images to current weather forecasting. However, approaches based on nonlinear prediction technology to forecast satellite images are lacking, and forecasting timelines are relatively short, e.g., 1–3 h in advance. In the present study, a nonlinear dimensionality reduction approach based on Laplacian eigenmaps (LEs) was combined with a random forest (RF) algorithm to construct an intelligent computing prediction model for rainstorm satellite images obtained from the first annual rainy season (April–June) in South China from 2010 to 2018. Results showed that the proposed forecasting model based on nonlinear intelligent calculation can accurately predict the key features and trends of the development and movement of strong precipitation clouds. The predicted satellite images described by the model were also consistent with the major features of the observed satellite images. This study then used a multiple linear regression (MLR) method based on the same prediction factors to establish a model for predicting satellite images for the same modeling and forecasting samples. Comparative results of the two prediction schemes showed that the LE + RF algorithm satellite image prediction scheme yields more samples exhibiting a high correlation with observed satellite images than the MLR method. Compared with that of the proposed scheme, the amount of samples of the MLR scheme in the low-correlation area was significantly larger. In general, the nonlinear intelligent computing scheme developed in this study is superior to the MLR method for predicting satellite cloud images. Thus, the LE + RF algorithm satellite image prediction scheme provides an objective and practical method for observed satellite cloud image predictions. [ABSTRACT FROM AUTHOR]