Retrieval of Hurricane Rain Rate From SAR Images Based on Artificial Neural Network

Spaceborne synthetic aperture radar (SAR) is gradually being applied to hurricane observation because of its all-weather, high-resolution observation capability. In particular, the retrieval of rain rate using SAR images holds significant scientific and practical importance. However, accurately retrieving rain rate over the sea surface, particularly for high rain rate events under hurricane conditions, remains a significant challenge. The study proposes a new method for rain rate retrieval from hurricane SAR images. We have developed a cascaded feedforward neural network model based on Sentinel-1’s double-polarized C-band SAR images of 46 hurricanes to retrieve rain rate under hurricane conditions. In order to overcome the problem of local optimal solution of neural network, the genetic algorithm is employed for optimized model parameter selection. Preliminary results indicate that this approach not only enhances the neural network's iteration speed but also improves its prediction accuracy. Compared with the rain rate of the Stepped-Frequency microwave radiometers, the root mean squared error of retrieved rain rate is 3.05 mm/h and the correlation coefficient is 0.88. Furthermore, we independently verify the rain rate during Hurricane Douglas and compared with global precipitation mission 2-level dual-frequency precipitation radar rain rate product, the results demonstrate that our model can effectively retrieve rain rate in the range of 0–60 mm/h under hurricane conditions. The encouraging results prove the feasibility of the method in SAR rain rate retrieval.