Generation of AI-based proxy radar data from GK2A/AMI data in East Asia

Remote sensing data such as weather satellites and radars with the high spatio-temporal resolution are importantly used for severe weather monitoring. Geostationary meteorological satellites can observe clouds in a wide area, and the weather radar can obtain rainfall intensity information close to the ground truth through raindrop particle observation. These remote sensing data are collected as real-time images, which are suitable for application to image convolution-based artificial intelligence (AI) models. In this study, we developed an AI-based model for the generation of proxy radar data from the Geo-Kompsat-2A (GK2A) satellite data. As an AI model structure, an image-to-image translation technique called Pix2PixCC which is based on a conditional Generative Adversarial Network (cGAN) was used. For the training model, GK2A satellite data and composite data of radar reflectivity with Korea, Japan, and China were used. In order to solve the memory issue of AI model learning using high-performance GPU, the model was trained by dividing the image into 256x256 patch sizes. This AI model made it possible to produce real-time proxy radar data without gaps in the East Asian region, and this proxy data showed a similar shape to the real radar data compared to precipitation information from geostationary meteorological satellites in traditional methods. In future studies, we plan to improve the accuracy by applying a custom loss function that applies a high weight to the high reflectivity of radar.