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Description
While traditional rainfall retrieval from CMLs relies on physics-based models, these often struggle with real-world complexities like signal noise and nonlinear attenuation. To address these limitations, this study proposes using AI algorithms for rainfall detection and estimation. This work first presents an unsupervised framework for classifying the wet–dry periods of CML based on reconstruction using deep learning models, specifically an autoencoder (AE), a variational autoencoder (VAE), and a
hybrid VAE–AE model. Compared to the supervised approach, the proposed framework does not require labeled rainfall data for training, and it is more generalizable. A LSTM model and a hybrid deep learning architecture based on the Transformer and a Bidirectional Gated Recurrent Unit (BiGRU) are desgined for rain rate estimation. The design synergistically captures both long-term dependencies and local sequential features in the CML signal data.