How close are opportunistic rainfall observations to providing societal benefit?

Olsson, Jonas (author), Horváth-Varga, Laura (author), Van de Beek, Remco (author), Graf, Maximilian (author), Overeem, Aart (author), Szaton, Magdalena (author), Bareš, Vojtěch (author), Bezak, Nejc (author), Chwala, Christian (author), De Michele, Carlo (author), Fencl, Martin (author), Seidel, Jochen (author), Todorović, Andrijana (author)

Mitigation of water-related hazards as well as sustainable water resources management are conditioned on accurate and detailed spatio-temporal rainfall observations. Today, water authorities like National Meteorological and Hydrological Services (NHMS) 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 has 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 hydro-meteorological 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 EU COST Action OpenSense, and (3) discussing the challenges NHMS 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 underway in several countries.

• Language: English
• Keywords: rainfall data, opportunistic sensor, review study, hydrological prediction
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FGG - Faculty of Civil and Geodetic Engineering
• Publication status: Published
• Publication version: Author Accepted Manuscript
• Year: 2025
• Number of pages: Str. 1585-1602
• Numbering: iss. 11, Letn. 26
• PID: 20.500.12556/RUL-176191
• UDC: 502/504:556
• ISSN on article: 1525-755X
• DOI: 10.1175/JHM-D-25-0043.1
• COBISS.SI-ID: 249021955
• Publication date in RUL: 24.11.2025

Record is a part of a journal

• Title: Journal of hydrometeorology
• Shortened title: J. hydrometeorol.
• Publisher: American Meteorological Society
• ISSN: 1525-755X
• COBISS.SI-ID: 728341

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Keywords: podatki o padavinah, oportunistični senzorji, pregledna študija, hidrološke napovedi

Projects

• Funder: SPARC project (project ID 2021-02380_Formas) funded by the Swedish Research Council Formas

• Funder: COST Action “Opportunistic Precipitation Sensing Network” CA20136, supported by European Cooperation in Science and Technology (COST)