High-Resolution Tropical Cyclone Rainfall Detection From C-Band SAR Imagery With Deep Learning

This article introduces an innovative deep-learning approach for retrieving tropical cyclone (TC) rainfall information from C-band Sentinel-1 synthetic aperture radar (SAR) imagery. We collected 17 SAR images under TC conditions from 2016 to 2021 and matched them with synchronous observational Next Generation Weather Radar (NEXRAD) Level-III data, forming a dataset of 302689 data pairs for model development. The model inputs include SAR-measured physical parameters in normalized radar cross section (NRCS), texture features represented by the gray-level co-occurrence matrix (GLCM), and statistical parameters of VV-polarized NRCS. A deep-learning-based TC rain rate retrieval (TC3R) model, combining a convolutional network and a fully connected (FC) network, was developed to retrieve quantitative TC rainfall information effectively. The test results demonstrate that the TC3R model can offer reasonable and stable quantitative rainfall estimation, particularly effectively detecting areas with medium-to-heavy rainfall events (2.5-40 mm/h) in SAR images where the NRCS is significantly affected by rain. Furthermore, to offer valuable insights into the performance of the TC3R model, we analyzed results across TC events of different intensities as case studies. Our results show high structural similarity (SSIM) in rainfall patterns between SAR and NEXRAD across all cases, consistently achieving SSIM values above 0.67. Moreover, in areas where SAR signals are notably affected by rainfall, the SSIM index even exceeds 0.80. Finally, our model's performance was evaluated by comparing its results with the independent global precipitation measurement (GPM) data, demonstrating effective rainfall prediction, particularly for the primary spiral rain band, in the two cases analyzed.