Deep learning for radar beam blockage correction
This thesis aims to propose a deep learning framework based on generative adversarial networks (GANs) for correcting partial beam blockage regions in polarimetric radar observations. The correction of such data is an essential step in radar data quality control and subsequent quantitative applications, especially in complex terrain environments. The proposed methodology is demonstrated using two S-band operational Weather Surveillance Radar - 1988 Doppler (WSR-88D) located in different regions of the western United States, characterized by different precipitation types. To train the GAN model, observation sectors of both radars are manually cropped to simulate partial beam blockage situations. The effectiveness of the trained models is demonstrated using independent precipitation events in Texas and California, and their generalization capacity is examined by cross-testing the data with different precipitation features. The beam blockage correction performance is compared with a traditional linear interpolation approach, and the results show that the proposed approach significantly improves the continuity of precipitation observations in both domains. While visible discrepancies exist between the models trained based on convective and stratiform precipitation events in Texas and California, respectively, both models outperform the traditional interpolation method. The repaired observations demonstrate great potential for improved quantitative applications, despite the unavailability of ground truth for real blocked radar data.
Includes bibliographical references.
radar data inpainting