How close are opportunistic rainfall observations to providing societal benefit?

tigation of water-related hazards as well as sustainable water resource management is conditioned on accurate and detailed spatiotemporal rainfall observations. Today, water authorities like National Meteorological and Hydrological Services (NMHS) in developed countries operate observation systems consisting of meteorological stations and weather radars. These observations provide state-of-the-art precipitation products, but they remain error prone due to device-specific limitations. This has driven growing interest in opportunistic sensors (OS) of rainfall, primarily commercial microwave links (CML) and personal weather stations (PWS). In the Global South, where meteorological station networks are usually very sparse, OS rainfall data conceivably have an even higher potential to provide an added value. However, although numerous studies have demonstrated the capability and potential of accurate rainfall estimation by OS, no dedicated investigation has been made with regard to their application for operational monitoring and prediction. How close are OS rainfall data to providing societal benefit, e.g., by widespread integration in existing hydrometeorological observation and prediction systems? We address this question by 1) making a review of studies that use OS rainfall data in applications (rainfall mapping, nowcasting, and hydrological prediction), 2) providing a status report on the transition from research to operational usage from the perspective of European Cooperation in Science and Technology (EU COST) Action Opportunistic Precipitation Sensing Network (OpenSense), and 3) discussing the challenges NMHS face in deploying OS rainfall data in operational services. We conclude that while distinct challenges still remain, in terms of both access and processing, the applicability of OS rainfall data is well scientifically supported and operation is under way in several countries.