Scientists have developed a system that uses structure-borne sound sensors and machine learning to measure rainfall without moving parts.The technology could complement conventional rain gauges. The system achieved an average error of 1.88 millimeters per hour,Scientists have developed a new artificial intelligence (AI)-powered rainfall monitoring system that can estimate light rainfall with an average error of just 1.88 millimeters (mm) per hour.The new method, developed by researchers at Germany’s Fraunhofer Institute for Digital Media Technology (IDMT) and Pan Acoustics GmbH, analyzes the tiny vibrations produced when raindrops strike solar panels. It offers a low-maintenance alternative for expanding weather monitoring networks.The system uses structure-borne sound sensors and machine learning to measure rainfall without moving parts. The technology could complement conventional rain gauges by creating dense networks of inexpensive measurement points that capture highly localized rainfall.The researchers demonstrated the approach through the recently completed lokalRAIN project, testing the system in both laboratory experiments and outdoor installations. They are now seeking industry partners for additional pilot deployments to evaluate the technology under a wider range of operating conditions.“We have proven the feasibility of our approach for light rain,” said Jakob Bergner, the project’s scientific director at Fraunhofer IDMT. “Now we want to collect further data for the less frequent heavy rain events and optimize the procedure for different operating conditions.”Turning solar panels into rainfall sensorsThe system attaches structure-borne sound sensors to photovoltaic (PV) modules. Every raindrop that hits the panel generates microscopic vibrations, creating unique acoustic signatures that vary with rainfall intensity.Instead of sending raw sensor data to remote servers, an AI model running on an energy-efficient microcontroller processes the information directly at the measurement site. Only the calculated rainfall values are transmitted, significantly reducing power consumption and wireless bandwidth requirements.Pan Acoustics developed the hardware and software for the wireless communication network. During field testing, the system achieved transmission ranges of about 2.5 miles (4 kilometers) in urban environments, and more than 22 miles (35 kilometers) in rural areas with an unobstructed line of sight, the statement reported.Data collected from multiple sensor nodes were then combined within a central data infrastructure to generate localized precipitation maps.Filling gaps in existing weather networksConventional precipitation monitoring depends on weather stations equipped with dedicated rain gauges, which can be costly to install and maintain. As a result, many regions have relatively sparse monitoring networks that struggle to capture localized storms, where rainfall can vary dramatically over short distances.To validate the new approach, researchers first conducted experiments inside a rain simulator, testing different droplet sizes, rainfall intensities, photovoltaic modules, and sensor placements.They later deployed three outdoor measurement systems at Fraunhofer IDMT in Ilmenau, Pan Acoustics in Wolfenbüttel, and the weather garden at the Technical University of Ilmenau. The collected data were compared with reference rainfall measurements to train and validate the machine learning models.For rainfall rates between 2 and 5 millimeters per hour, the system achieved an average error of 1.88 millimeters per hour, meeting the World Meteorological Organization’s (WMO) Class C accuracy requirement for low-intensity precipitation measurements.From weather forecasts to risk managementThe researchers stress that the technology is designed to supplement rather than replace high-precision precipitation gauges.A denser network of rainfall sensors could improve real-time monitoring of localized downpours and provide valuable data for urban digital twins, allowing municipalities and infrastructure operators to respond more quickly to flood risks. The information could also support drainage planning, insurance risk assessments, and weather forecasting models.Fraunhofer IDMT is also exploring agricultural applications through another project that integrates the sensor network into agrivoltaic systems, enabling farmers to make more targeted irrigation decisions using site-specific rainfall data.Before commercial deployment, the researchers plan additional pilot installations to collect more data during heavy rainfall events and further refine the sensor housing, electronics, and quality-assurance processes needed for production-ready systems.