Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

Opportunistic sensors, devices primarily intended not intended for sensing, are increasingly used for rainfall measurement. The lack of conventions defining which data should be stored and how, makes it difficult to automatically process the data and integrate these observations into standard monitoring networks. This paper reviews current practice of collecting and storing precipitation opportunistic sensing (OS) data based on the experience of more than 100 members of the OpenSense Cost Action and suggest common data format standards. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML), Satellite Microwave Links (SML), and Personal Weather Stations (PWS). The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

Artys srl Genova Italy

Brno University of Technology Brno Czech Republic

Chair of Regional Climate and Hydrology Institute of Geography University Augsburg Augsburg Germany

Department of Hydraulics and Hydrology Czech Technical University Prague Prague 6 16629 Czech Republic

Department of Information Engineering University of Pisa Pisa Italy

Department of Physics and Astronomy Augusto Righi University of Bologna Bologna Italy

Department of Water Management Delft University of Technology Delft Netherlands Antilles

GFZ Potsdam Potsdam Germany

IEIIT CNR Milano Italy

Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Stuttgart Germany

Institute of Earth Systems University of Malta Msida Malta

Institute of Meteorology and Climate Research Campus Alpin Karlsruhe Institute of Technology Garmisch Partenkirchen Germany

ISAC CNR Roma Italy

Politecnico di Milano Milan Lombardy Italy

Royal Netherlands Meteorological Institute de Bilt Netherlands Antilles

School of Electrical Engineering Tel Aviv University Tel Aviv Yafo Tel Aviv District Israel

State Key Laboratory of Hydrology Water Resources and Hydraulic Engineering Center for Global Change and Water Cycle Hohai University Nanjing Jiangsu China

Swedish Meteorological and Hydrological Institute Gothenburg Sweden

Zobrazit více v PubMed

Ahrens CD: Meteorology Today: An Introduction to Weather, Climate, and the Environment, 10th edition.ed. Brooks Cole, Belmont, CA,2012. Reference Source

Andersson JCM, Olsson J, van de Beek R, et al. : OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden. Earth System Science Data. 2022;14:5411–5426. 10.5194/essd-14-5411-2022 DOI

Bárdossy A, Seidel J, El Hachem A: The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrol Earth Syst Sci. 2021;25:583–601. 10.5194/hess-25-583-2021 DOI

Brenot H, Neméghaire J, Delobbe L, et al. : Preliminary signs of the initiation of deep convection by GNSS. Atmospheric Chemistry and Physics. 2013;13:5425–5449. 10.5194/acp-13-5425-2013 DOI

Chwala C, Keis F, Kunstmann H: Real-time data acquisition of commercial microwave link networks for hydrometeorological applications. Atmos Meas Tech. 2016;9:991–999. 10.5194/amt-9-991-2016 DOI

Chwala C, Kunstmann H: Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges. WIREs Water. 2019;6: e1337. 10.1002/wat2.1337 DOI

Colli M, Cassola F, Martina F, et al. : Rainfall Fields Monitoring Based on Satellite Microwave Down-Links and Traditional Techniques in the City of Genoa. IEEE Trans Geosci Remote Sens. 2020;58:6266–6280. 10.1109/TGRS.2020.2976137 DOI

Colli M, Stagnaro M, Caridi A, et al. : A Field Assessment of a Rain Estimation System Based on Satellite-to-Earth Microwave Links. IEEE Trans Geosci Remote Sens. 2019;57:2864–2875. 10.1109/TGRS.2018.2878338 DOI

David N, Sendik O, Messer H, et al. : Cellular Network Infrastructure: The Future of Fog Monitoring? Bull Am Meteorol Soc. 2015;96(10):1687–1698. 10.1175/BAMS-D-13-00292.1 DOI

de Vos LW, Leijnse H, Overeem A, et al. : The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam. Hydrol Earth Syst Sci. 2017;21:765–777. 10.5194/hess-21-765-2017 DOI

de Vos LW, Leijnse H, Overeem A, et al. : Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring. Geophys Res Lett. 2019a;46:8820–8829. 10.1029/2019GL083731 DOI

de Vos LW: Rainfall observations datasets from Personal Weather Stations. 2019b. 10.4121/uuid:6e6a9788-49fc-4635-a43d-a2fa164d37ec DOI

Dong R, Liao J, Li B, et al. : Measurements of rainfall rates from videos.In: 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). Presented at the 2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI).2017. 10.1109/CISP-BMEI.2017.8302066 DOI

Douša J, Dick G, Kacmarík M, et al. : Benchmark campaign and case study episode in central Europe for development and assessment of advanced GNSS tropospheric models and products. Atmospheric Measurement Techniques. 2016;9:2989–3008. 10.5194/amt-9-2989-2016 DOI

ECC: ECC Report 173 - Fixed Service in Europe Current use and future trends post 2016.2012. Reference Source

Elgered G: An overview of COST Action 716: exploitation of ground-based GPS for climate and numerical weather prediction applications. Phys Chem Earth. Proceedings of the First COST Action 716 Workshop Towards Operational GPS Meteorology and the Second Network Workshop of the International GPS Service (IGS).2001;26:399–404. 10.1016/S1464-1895(01)00073-4 DOI

Ericsson: Ericsson Microwave Outlook Report - 2023.2023. Reference Source

Ernst & Young - Parthenon: Satellite internet: The next big wave - Market study report.2023. Reference Source

Fencl M, Dohnal M, Bareš V: Retrieving Water Vapor From an E-Band Microwave Link With an Empirical Model Not Requiring In Situ Calibration. Earth Space Sci. 2021;8(11): e2021EA001911. 10.1029/2021EA001911 DOI

Fencl M, Rieckermann J, Sýkora P, et al. : Commercial microwave links instead of rain gauges: fiction or reality? Water Sci Technol. 2015;71(1):31–37. 10.2166/wst.2014.466 PubMed DOI

Giannetti F, Moretti M, Reggiannini R, et al. : The NEFOCAST System for Detection and Estimation of Rainfall Fields by the Opportunistic Use of Broadcast Satellite Signals. IEEE Aerospace and Electronic Systems Magazine. 2019;34:16–27. Reference Source

Giannetti F, Reggiannini R: Opportunistic Rain Rate Estimation from Measurements of Satellite Downlink Attenuation: A Survey. Sensors (Basel). 2021;21(7):5872. 10.3390/s21175872 PubMed DOI PMC

Giannetti F, Vaccaro A, Sapienza F, et al. : Multi-Satellite Rain Sensing: Design Criteria and Implementation Issues.In: 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC).Presented at the 2022 3rd URSI Atlantic and Asia Pacific Radio Science Meeting (AT-AP-RASC).2022;1–4. 10.23919/AT-AP-RASC54737.2022.9814405 DOI

Graf M, Chwala C, Polz J, et al. : Rainfall estimation from a German-wide commercial microwave link network: Optimized processing and validation for one year of data. Hydrol Earth Syst Sci. 2019;1–23. 10.5194/hess-2019-423 DOI

Guerova G, Jones J, Douša J, et al. : Review of the state of the art and future prospects of the ground-based GNSS meteorology in Europe. Atmos Meas Tech. 2016;9(11):5385–5406. 10.5194/amt-9-5385-2016 DOI

Hassell D, Gregory J, Blower J, et al. : A data model of the Climate and Forecast metadata conventions (CF-1.6) with a software implementation (cf-python v2; 1). Geosci Model Dev. 2017;10(12):4619–4646. 10.5194/gmd-10-4619-2017 DOI

ITU-R: Report ITU-R F.2323-1, Fixed service use and future trends (No. F.2323-1). International Telecommunication Union.2017. Reference Source

ITU-R: RECOMMENDATION ITU-R P.839-4 - Rain height model for prediction methods.2013. Reference Source

ITU-R: RECOMMENDATION ITU-R P.838-3, Specific attenuation model for rain for use in prediction methods.2005. Reference Source

Jones J, Guerova G, Douša J, et al. : Advanced GNSS Tropospheric Products for Monitoring Severe Weather Events and Climate: COST Action ES1206 Final Action Dissemination Report.Springer International Publishing, Cham.2020. 10.1007/978-3-030-13901-8 DOI

Li H, Wang X, Wu S, et al. : An Improved Model for Detecting Heavy Precipitation Using GNSS-Derived Zenith Total Delay Measurements. IEEE J Sel Top Appl Earth Obs Remote Sens. 2021;14:5392–5405. 10.1109/JSTARS.2021.3079699 DOI

Liu F, Cui Y, Masouros C, et al. : Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond. IEEE J Sel Areas Commun. 2022;40(6):1728–67. 10.1109/JSAC.2022.3156632 DOI

Lorenz C, Kunstmann H: The Hydrological Cycle in Three State-of-the-Art Reanalyses: Intercomparison and Performance Analysis. J Hydrometeor. 2012;13:1397–1420. 10.1175/JHM-D-11-088.1 DOI

Metsälä EM, Salmelin JTT: LTE Backhaul: Planning and Optimization.John Wiley & Sons,2015. Reference Source

Muller CL, Chapman L, Johnston S, et al. : Crowdsourcing for climate and atmospheric sciences: current status and future potential. Int J Climatol. 2015;35(11):3185–3203. 10.1002/joc.4210 DOI

Ochoa-Rodriguez S, Wang LP, Gires A, et al. : Impact of spatial and temporal resolution of rainfall inputs on urban hydrodynamic modelling outputs: A multi-catchment investigation. J Hydrol (Amst). Hydrologic Applications of Weather Radar,2015;531:389–407. 10.1016/j.jhydrol.2015.05.035 DOI

Olsen R, Rogers D, Hodge D: The aR brelation in the calculation of rain attenuation. IEEE Transactions on Antennas and Propagation. 1978;26(2):318–329. 10.1109/TAP.1978.1141845 DOI

Ostrometzky J, Cherkassky D, Messer H: Accumulated Mixed Precipitation Estimation Using Measurements from Multiple Microwave Links. Adv Meteorol. 2015;2015(2): e707646. 10.1155/2015/707646 DOI

Overeem A: Commercial microwave link data for rainfall monitoring.2023. 10.4121/7a692e36-c32f-4916-813b-c62d2566e8d8 DOI

Overeem A, Leijnse H, Uijlenhoet R: Country-wide rainfall maps from cellular communication networks. Proc Natl Acad Sci U S A. 2013;110(8):2741–2745. 10.1073/pnas.1217961110 PubMed DOI PMC

Roversi G, Alberoni PP, Fornasiero A, et al. : Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy. Atmos Meas Tech. 2020;13(11):5779–5797. 10.5194/amt-13-5779-2020 DOI

Rubin Y, Rostkier-Edelstein D, Chwala C, et al. : Challenges in Diurnal Humidity Analysis from Cellular Microwave Links (CML) over Germany. Remote Sens. 2022;14(10):2353. 10.3390/rs14102353 DOI

Špačková A, Bareš V, Fencl M, et al. : A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations. Earth Syst Sci Data. 2021;13(8):4219–4240. 10.5194/essd-13-4219-2021 DOI

Tauro F, Selker J, van de Giesen N, et al. : Measurements and Observations in the XXI century (MOXXI): innovation and multi-disciplinarity to sense the hydrological cycle. Hydrol Sci J. 2018;63(2):169–196. 10.1080/02626667.2017.1420191 DOI

UniData: NetCDF Users Guide: The NetCDF User’s Guide.[WWW Document],2021. (accessed 11.22.22). Reference Source

van Leth TC, Overeem A, Uijlenhoet R, et al. : Wageningen Urban Rainfall experiment.2018. 10.4121/UUID:1DD45123-C732-4390-9FE4-6E09B578D4FF DOI

WMO: Guide to Instruments and Methods of Observation (WMO-No. 8). 2018th and 2021 editions ed WMO., WMO, Geneva.2021. Reference Source

Zhao Q, Yao Y, Yao W, et al. : Real-time precise point positioning-based zenith tropospheric delay for precipitation forecasting. Sci Rep. 2018;8(1): 7939. 10.1038/s41598-018-26299-3 PubMed DOI PMC

Zheng F, Tao R, Maier HR, et al. : Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions. Rev Geophys. 2018;56(4):698–740. 10.1029/2018RG000616 DOI