Research on Precipitation Estimation Algorithm from Fengyun-4 Satellite Based on Improved U-Net

Aiming at the problems of low accuracy and low spatial-temporal resolution of precipitation estimation using satellite images under the condition of severe convective weather, an improved U-Net precipitation estimation algorithm is proposed. Firstly the encoder of the U-Net model is combined with the decoder through the residual module, so that the model parameters can be shared to avoid the disappearance of deep network model gradients. Based on this structure, the spatial pyramid module is introduced for multi-scale feature extraction to retain more image features and strengthen the feature extraction ability of small precipitation cloud information. The attention mechanism module is added to extract important precipitation feature information. The experimental results show that probability of detection, false alarm ration, critical success index of the proposed algorithm are 0.84, 0.48 and 0.59, respectively. The root mean square error and mean absolute error are 1.354 mm/h and 0.432 mm/h respectively. Compared with PERSIANN-CNN and U-Net, the proposed algorithm effectively improves the accuracy of precipitation estimation. Compared with other precipitation products, it also has certain advantages. Therefore, the algorithm can achieve near-real-time results and effectively improve the accuracy of precipitation estimation, which is valuable for the research of precipitation estimation with low tempotal resolution. © 2023 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.