Speaker
Description
Accurate and continuous rainfall measurements remain a challenging task, particularly in regions with sparse ground-based observations. In recent years, opportunistic approaches have emerged as a means of improving precipitation monitoring. In this study, we adapt a methodology originally developed for rainfall detection using Commercial Microwave Links to a framework based on passive acoustic recorders. Our approach consists of obtaining a dry-weather baseline of the acoustic Power Spectral Density (PSD) for a given environment. Audio spectra are then normalized and integrated (mean) over a selected frequency band, followed by a percentile-based approach for rainfall detection. We evaluated the method across datasets from tropical (Brazil and Côte d’Ivoire) and temperate (France) regions. Sensitivity tests yielded two distinct frequency bands suitable for rainfall monitoring: 0.2-0.5 kHz for tropical regions and 3.2-3.5 kHz for temperate regions. The main sources of error are associated with wind, technophonic, and biophonic interference. Results demonstrate strong temporal coherence between our method and rainfall rate (mm/5 min), with a mean correlation of 0.7 across all sites and values approaching 0.9 in best-case scenarios, with rainfall detection errors of approximately 0.02. Although further work is needed to improve background noise filtering, our proposed approach provides potential for a low-cost, scalable complement to established monitoring systems, including satellite rainfall products.