A Radar Reflectivity Image Prediction Method: The Spatial MIM + Pix2Pix

Abstract

Radar reflectivity images have the potential to provide vital information on the development of convective cloud interiors, which can play a critical role in precipitation prediction. However, traditional prediction methods face challenges in preserving the high-frequency component, leading to blurred prediction results. To address this issue and accurately estimate radar reflectivity intensity, this paper proposes a novel reflectivity image prediction approach based on the Spatial Memory in Memory (Spatial MIM) networks and the Pix2Pix networks. Firstly, a rough radar reflectivity image prediction is made using the Spatial MIM network. Secondly, the prediction results from the Spatial MIM network are fed into the Pix2pix network, which improves the high-frequency component of the predicted image and solves the image blurring issue. Finally, the proposed approach is evaluated using data from Oklahoma in the United States during the second and third quarters of 2021. The experimental results demonstrate that the proposed approach yields more accurate radar reflectivity prediction images.

Keywords

• radar applications
• spatiotemporal sequence
• radar reflectivity images
• deep learning
• Pix2Pix