Sea surface rainfall inversion model based on the fusion of heterogeneous FY-3E GNSS-R data

Rainfall (especially moderate to heavy rain) will change sea surface roughness, leading to alter Global Navigation Satellite System Reflection (GNSS-R) signal characteristics. This study utilizes L1-level GNSS-R data from Fengyun-3E (FY-3E) Global Navigation Satellite System Occultation Sounder II (GNOS-II) to study the classification and regression tasks for moderate to heavy rain (with rainfall intensity greater than 0.4 mm/h). In the 0classification task, three multi-parameter classification models were developed based on GPS/BDS feature data and environmental characteristic data, the experimental results from a 2-month validation dataset indicated that the logistic regression (LR) model performed best, achieving an accuracy of 0.97 and a precision of 0.80. In the regression task for moderate to heavy rain, the Proportional-Integral-Derivative-based Search Algorithm (PSA)-Fusion-Convolutional Neural Networks (CNN)-Transformer-RainNet (PFCT-RainNet) sea surface rain regression inversion model was proposed. This model integrates Delay Doppler Map (DDM) images for multi-modal and multi-parameter fusion training, and incorporates the PSA for automatic hyperparameter optimization. The results from the 2-month validation dataset showed that the PFCT-RainNet model achieved a coefficient of determination ( R 2 ) of 0.50, a root mean square error (RMSE) of 0.59 mm/h, and a mean absolute error (MAE) of 0.39 mm/h, which demonstrate the effectiveness of the PFCT-RainNet model in inversing moderate to heavy rain intensity under conditions of low wind speed (WS) and low significant wave height (SWH).