Speaker
Description
Opportunistic climate monitoring has emerged as a critical frontier in meteorological research, leveraging existing telecommunication infrastructures to sense environmental phenomena. Analysing the attenuation of Satellite Microwave Links (SML) offers a highly promising avenue for high-resolution, global rainfall measurement. In an era of escalating climate volatility, the ability to rapidly deploy pervasive monitoring networks utilizing ubiquitous commercial receivers can provide vital, real-time data for early warning systems and flood mitigation. However, conventional rainfall retrieval algorithms from SMLs rely heavily on parametric physics-based models, employing power-law relationships such as those available in the ITU recommendations. These traditional approaches necessitate extensive prior knowledge of link topology, receiver hardware specifications, and localized environmental parameters—including operating frequency and signal polarization—which inherently limits their scalability and global applicability.
To address these limitations, this paper presents an innovative, data-driven machine learning approach for direct rain rate estimation using SML attenuation. The proposed architecture employs a hybrid 1D-Convolutional Neural Network (1D-CNN) and Long Short-Term Memory (LSTM) network to autonomously learn the non-linear relationship between signal attenuation and precipitation. The 1D-CNN block acts as a spatial feature extractor, utilizing sliding learnable filters, Batch Normalization, and ReLU activations to map complex attenuation patterns and reliably distinguish true rain fades from anomalous signal fluctuations. Concurrently, the LSTM network models the long-term temporal dependencies inherent in time-correlated signal attenuation, effectively maintaining a dynamic "clear-sky" reference baseline that adapts during precipitation events. Evaluated on a comprehensive two-year dataset collected in Israel, the proposed deep learning model achieves rainfall estimation performance comparable to established parametric models, demonstrating its viability as a robust, hardware-agnostic solution for opportunistic meteorological monitoring.