基于3D卷积和自注意力机制的卫星云图预测研究

Satellite cloud image is one of the important resources of meteorological forecast. It plays a great role in meteorological analysis and forecasting by showing the generation and disappearance of clouds. Predicting cloud image in a certain period of time is helpful to grasp the movement trajectory and changes of cloud layers in time，and improve the practicability of satellite cloud image data. However，the prediction of the satellite cloud images is facing many difficulties，such as most changes in cloud clusters are non⁃stationary and nonlinear. There are many problems such as small cloud map data and poor real⁃time performance. Therefore，we propose a satellite cloud image prediction model based on 3D convolution and self⁃attention mechanism from the perspective of spatiotemporal sequence. On the basis of ST⁃LSTM (Spatiotemporal Long Short⁃Term Memory)，this model introduces 3D convolution and self attention mechanism into its unit，which enables the model to extract temporal information and spatial features at the same time，furtherly enhance the relationship between short⁃term trend and long⁃term dependence on clouds; At the same time，space and channel attention mechanisms are used in its external framework to promote the extraction of spatial features of cloud images. In this paper，the evaluation is carried out on the Fengyun⁃4 satellite cloud image. Experimental results show that the model more accurately predicts the morphological changes and movement trajectories of clouds，and is superior to the existing models in various evaluation indicators.

Keywords： Fengyun⁃4 ; cloud image prediction ; 3D convolution ; attention mechanism