Articles | Volume 18, issue 8
https://doi.org/10.5194/essd-18-5817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-5817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
12 Aug 2026
Data description article |
| 12 Aug 2026
| 12 Aug 2026
OpenMesh: wireless signal dataset for opportunistic urban weather sensing in New York City
School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
Shuyue Yu
Department of Computer Science, Columbia University, New York, NY, USA
Qianfei Hu
Department of Computer Science, Columbia University, New York, NY, USA
Zachary Hine
Department of Computer Science, Columbia University, New York, NY, USA
Rob Johnson
NYC Mesh, New York, NY, USA
Jonatan Ostrometzky
School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
Igor Kadota
Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, USA
Gil Zussman
Department of Electrical Engineering, Columbia University, New York, NY, USA
Hagit Messer
School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
Cited articles
Ahuna, M. N., Afullo, T. J., and Alonge, A. A.: Rain Attenuation Prediction Using Artificial Neural Network for Dynamic Rain Fade Mitigation, SAIEE Africa Research Journal, 110, https://doi.org/10.23919/SAIEE.2019.8643146, 2019. a
Andersson, J. C. M., Olsson, J., van de Beek, R. (C. Z.), and Hansryd, J.: OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden, Earth Syst. Sci. Data, 14, 5411–5426, https://doi.org/10.5194/essd-14-5411-2022, 2022. a
Arvas, M. and Alsunaidi, M.: Analysis of oxygen absorption at 60 GHz frequency band, in: 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, IEEE, 2127–2128, https://doi.org/10.1109/APUSNCURSINRSM.2019.8888884, 2019. a
Ashraf, S., Sheikh, J. A., Ashraf, A., and Rasool, U.: 5g millimeter wave technology: An overview, Intelligent signal processing and RF energy harvesting for state of art 5G and B5G networks, Springer, Singapore, 97–112, https://doi.org/10.1007/978-981-99-8771-9_6, 2024. a
Bárdossy, A., Seidel, J., and El Hachem, A.: The use of personal weather station observations to improve precipitation estimation and interpolation, Hydrol. Earth Syst. Sci., 25, 583–601, https://doi.org/10.5194/hess-25-583-2021, 2021. a
Berne, A., Delrieu, G., Creutin, J.-D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, https://doi.org/10.1016/j.jhydrol.2004.08.002, 2004. a
Brake, D.: 5G and next generation wireless: Implications for policy and competition, Information Technology & Innovation Foundation, 22 pp., https://www2.itif.org/2016-5g-next-generation.pdf (last access: 22 July 2026), 2016. a
Brousse, O., Simpson, C. H., Kenway, O. A., Martilli, A., Krayenhoff, E. S., Zonato, A., and Heaviside, C.: Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data, J. Appl. Meteorol. Clim., 62, 1539–1572, https://doi.org/10.1175/JAMC-D-22-0142.1, 2023. a
Chwala, C. and Kunstmann, H.: Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges, Wiley Interdisciplinary Reviews: Water, 6, e1337, https://doi.org/10.1002/wat2.1337, 2019. a, b, c
Covi, E. and Roversi, G.: OpenRainER, Zenodo [data set], https://doi.org/10.5281/zenodo.10593848, 2024. a
Covi, E., Roversi, G., and Nebuloni, R.: OpenRainER: an open-source dataset for studying the opportunistic sensing of rainfall in Emilia-Romagna, Italy, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2026-160, in review, 2026. a
Cristiano, E., ten Veldhuis, M.-C., and van de Giesen, N.: Spatial and temporal variability of rainfall and their effects on hydrological response in urban areas – a review, Hydrol. Earth Syst. Sci., 21, 3859–3878, https://doi.org/10.5194/hess-21-3859-2017, 2017. a, b
Csurgai-Horváth, L. and Bitó, J.: Fog Attenuation on V Band Terrestrial Radio and a Low-Cost Measurement Setup, in: Future Network and Mobile Summit, IEEE, 9 pp., ISBN 978-1-905824-16-8, 2010. a
da Silva Mello, L. and Pontes, M.: Prediction of rain attenuation in terrestrial links with the full rainfall rate distribution, in: 2nd European Conference on Antennas and Propagation (EuCAP 2007), IET, https://doi.org/10.1049/ic.2007.0872, 2007. a
David, N. and Gao, H. O.: Using cellular communication networks to detect air pollution, Environ. Sci. Technol., 50, 9442–9451, https://doi.org/10.1021/acs.est.6b00681, 2016. a
David, N., Sendik, O., Messer, H., and Alpert, P.: Cellular network infrastructure: The future of fog monitoring?, B. Am. Meteorol. Soc., 96, 1687–1698, https://doi.org/10.1175/BAMS-D-13-00292.1, 2015. a, b
de Vos, L. W., Leijnse, H., Overeem, A., and Uijlenhoet, R.: Quality control for crowdsourced personal weather stations to enable operational rainfall monitoring, Geophys. Res. Lett., 46, 8820–8829, https://doi.org/10.1029/2019GL083731, 2019. a, b
Dong, F., Liu, F., Cui, Y., Wang, W., Han, K., and Wang, Z.: Sensing as a Service in 6G Perceptive Networks: A Unified Framework for ISAC Resource Allocation, IEEE T. Wirel. Commun., 22, 3522–3536, https://doi.org/10.1109/TWC.2022.3219463, 2022. a, b
El Hachem, A., Seidel, J., O'Hara, T., Villalobos Herrera, R., Overeem, A., Uijlenhoet, R., Bárdossy, A., and de Vos, L.: Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations, Hydrol. Earth Syst. Sci., 28, 4715–4731, https://doi.org/10.5194/hess-28-4715-2024, 2024. a
Fencl, M., Dohnal, M., and Bareš, V.: Retrieving Water Vapor From an E-Band Microwave Link With an Empirical Model Not Requiring In Situ Calibration, Earth and Space Science, 8, e2021EA001911, https://doi.org/10.1029/2021EA001911, 2021. a
Fencl, M., Nebuloni, R., Andersson, J. C. M., Bareš, V., Blettner, N., Cazzaniga, G., Chwala, C., Colli, M., de Vos, L., El Hachem, A., Galdies, C., Giannetti, F., Graf, M., Jacoby, D., Habi, H. V., Musil, P., Ostrometzky, J., Roversi, G., Sapienza, F., Seidel, J., Špačková, A., van de Beek, R. C. Z., Walraven, B., Wilgan, K., and Zheng, X.: Data formats and standards for opportunistic rainfall sensors, Open Research Europe, 3, 169, https://doi.org/10.12688/openreseurope.16068.2, 2024. a, b, c, d, e, f, g
Fencl, M., Bareš, V., Ostrometzky, J., Messer, H., Winterrath, T., Priestley, A., and Chwala, C.: A new initiative to facilitate the global collection of microwave link data and its use in hydrometeorology, B. Am. Meteorol. Soc., https://doi.org/10.1175/BAMS-D-24-0346.1, 2025. a, b, c, d
Graf, M., Chwala, C., Polz, J., and Kunstmann, H.: Rainfall estimation from a German-wide commercial microwave link network: optimized processing and validation for 1 year of data, Hydrol. Earth Syst. Sci., 24, 2931–2950, https://doi.org/10.5194/hess-24-2931-2020, 2020. a
Habi, H. V. and Messer, H.: Uncertainties in short commercial microwave links fading due to rain, in: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 9006–9010, https://doi.org/10.1109/ICASSP40776.2020.9053814, 2020. a
Hadar, M., Ostrometzky, J., and Messer, H.: Parameter estimation of in-city frontal rainfall propagation, in: ICASSP 2020–2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 4910–4914, https://doi.org/10.1109/ICASSP40776.2020.9053572, 2020. a
Hahn, C., Garcia-Marti, I., Sugier, J., Emsley, F., Beaulant, A.-L., Oram, L., Strandberg, E., Lindgren, E., Sunter, M., and Ziska, F.: Observations from personal weather stations – EUMETNET interests and experience, Climate, 10, 192, https://doi.org/10.3390/cli10120192, 2022. a
Hu, Q., Li, Z., Wang, L., Huang, Y., Wang, Y., and Li, L.: Rainfall spatial estimations: A review from spatial interpolation to multi-source data merging, Water, 11, 579, https://doi.org/10.3390/w11030579, 2019. a
Imhoff, R., Overeem, A., Brauer, C., Leijnse, H., Weerts, A., and Uijlenhoet, R.: Rainfall nowcasting using commercial microwave links, Geophys. Res. Lett., 47, e2020GL089365, https://doi.org/10.1029/2020GL089365, 2020. a
ITU-R P.530: Propagation data and prediction methods required for the design of terrestrial line-of-sight systems, ITU-R Recommendation P.530-17, https://www.itu.int/rec/R-REC-P.530-17-201712-S/en (last access: 22 July 2026), 2017. a
ITU-R P.837: Characteristics of precipitation for propagation modelling, ITU-R Recommendation P.837-7, https://www.itu.int/rec/R-REC-P.837-7-201706-S/en (last access: 22 July 2026), 2017. a
Jacoby, D.: OpenMesh: Code Repository, Zenodo [code], https://doi.org/10.5281/zenodo.18510358, 2025. a
Jacoby, D., Ostrometzky, J., and Messer, H.: Hybrid hierarchical models for ISAC predictions with wireless links, in: 2024 32nd European Signal Processing Conference (EUSIPCO), IEEE, 1982–1986, https://doi.org/10.23919/eusipco63174.2024.10715001, 2024. a
Jacoby, D., Messer, H., and Ostrometzky, J.: Spatio-temporal model for predicting multivariate weather-induced attenuation in wireless networks, IEEE T. Instrum. Meas., https://doi.org/10.1109/TIM.2025.3555716, 2025a. a
Jacoby, D., Yu, S., Ostrometzky, J., Kadota, I., Zussman, G., and Messer, H.: OpenMesh Dataset – Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City, Zenodo [data set], https://doi.org/10.5281/zenodo.15287692, 2025b. a, b
Jacoby, D., Li, Y., Yu, S., Di Cicco, N., Messer, H., Zussman, G., and Kadota, I.: AWaRe-SAC: Proactive Slice Admission Control under Weather-Induced Capacity Uncertainty, arXiv [preprint], https://doi.org/10.48550/arXiv.2601.05978, 2026. a
Janco, R., Ostrometzky, J., and Messer, H.: Rain estimation from smart city’s E-band links, in: 2022 IEEE 14th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), IEEE, 5 pp., https://doi.org/10.1109/IVMSP54334.2022.9816243, 2022. a
Janco, R., Ostrometzky, J., and Messer, H.: In-City Rain Mapping from Commercial Microwave Links – Challenges and Opportunities, Sensors, 23, 4653, https://doi.org/10.3390/s23104653, 2023. a, b, c
Kadota, I., Jacoby, D., Messer, H., Zussman, G., and Ostrometzky, J.: Switching in the rain: Predictive wireless x-haul network reconfiguration, Proceedings of the ACM on Measurement and Analysis of Computing Systems, 6, 1–26, https://doi.org/10.1145/3570616, 2022. a
Kaushik, A., Singh, R., Dayarathna, S., Senanayake, R., Di Renzo, M., Dajer, M., Ji, H., Kim, Y., Sciancalepore, V., Zappone, A., et al.: Toward Integrated Sensing and Communications for 6G: Key Enabling Technologies, Standardization, and Challenges, IEEE Communications Standards Magazine, 8, 52–59, https://doi.org/10.1109/MCOMSTD.0007.2300043, 2024. a, b
Mandement, M. and Caumont, O.: Contribution of personal weather stations to the observation of deep-convection features near the ground, Nat. Hazards Earth Syst. Sci., 20, 299–322, https://doi.org/10.5194/nhess-20-299-2020, 2020. a
Messer, H. and Gazit, L.: From cellular networks to the garden hose: Advances in rainfall monitoring via cellular power measurements, in: 2016 IEEE Global Conference on Signal and Information Processing (GlobalSIP), IEEE, 1012–1016, https://doi.org/10.1109/GlobalSIP.2016.7905994, 2016. a
Messer, H., Zinevich, A., and Alpert, P.: Environmental monitoring by wireless communication networks, Science, 312, 713–713, https://doi.org/10.1126/science.1120034, 2006. a, b, c
Michelon, A., Benoit, L., Beria, H., Ceperley, N., and Schaefli, B.: Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment, Hydrol. Earth Syst. Sci., 25, 2301–2325, https://doi.org/10.5194/hess-25-2301-2021, 2021. a
National Research Council: Observing weather and climate from the ground up: A nationwide network of networks, National Academies Press, https://doi.org/10.17226/12540, 2009. a
NOAA National Weather Service, U.S. Federal Aviation Administration, U.S. Department of Defense, and NOAA National Centers for Environmental Information: 5-Minute Surface Weather Observations from the Automated Surface Observing Systems (ASOS) Network, https://www.ncei.noaa.gov/access/metadata/landing-page/bin/iso?id=gov.noaa.ncdc:C00418 (last access: 22 July 2026), 2005. a
NYC Mesh: NYC Mesh, GitHub [code], https://github.com/nycmeshnet (last access: 15 October 2025), 2025a. a
Olsen, R., Rogers, D., and Hodge, D.: The aRb relation in the calculation of rain attenuation, IEEE T. Antenn. Propag., 26, 318–329, https://doi.org/10.1109/TAP.1978.1141845, 1978. a
OpenSense Action: Opportunistic Hydrology Codebase, https://github.com/OpenSenseAction (last access: 11 November 2025), 2024. a
Ostrometzky, J.: Statistical signal processing of extreme attenuation measurements taken by commercial microwave links for rain monitoring, PhD thesis, Tel Aviv University, https://web2.eng.tau.ac.il/wtest/rainlinklab/wp-content/uploads/2020/07/Jonathan_Ostrometzky_Phd_Dissertation_Nov17_FINALSmallFileSize.pdf (last access: 22 July 2026), 2017. a
Ostrometzky, J. and Messer, H.: Dynamic determination of the baseline level in microwave links for rain monitoring from minimum attenuation values, IEEE J. Sel. Top. Appl., 11, 24–33, https://doi.org/10.1109/JSTARS.2017.2752902, 2017. a
Ostrometzky, J. and Messer, H.: Opportunistic Weather Sensing by Smart City Wireless Communication Networks, Sensors, 24, 7901, https://doi.org/10.3390/s24247901, 2024. a
Ostrometzky, J., Raich, R., Eshel, A., and Messer, H.: Calibration of the attenuation-rain rate power-law parameters using measurements from commercial microwave networks, in: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 3736–3740, https://doi.org/10.1109/ICASSP.2016.7472375, 2016. a
Overeem, A.: Commercial microwave link data for rainfall monitoring, 4TU.ResearchData [data set], https://doi.org/10.4121/7a692e36-c32f-4916-813b-c62d2566e8d8, 2023. a, b
Overeem, A., Leijnse, H., and Uijlenhoet, R.: Measuring urban rainfall using microwave links from commercial cellular communication networks, Water Resour. Res., 47, https://doi.org/10.1029/2010WR010350, 2011. a
Øydvin, E., Hagland, R. F., Nilsen, V., Wolff, M. A., and Kitterød, N.-O.: Classification of snow and rainfall using commercial microwave links, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12061, https://doi.org/10.5194/egusphere-egu23-12061, 2023. a
Øydvin, E., Gaban, R., Andersson, J., (C. Z.) van de Beek, R., Wolff, M. A., Kitterød, N.-O., Chwala, C., and Nilsen, V.: Combining commercial microwave links and weather radar for classification of dry snow and rainfall, Atmos. Meas. Tech., 18, 2279–2293, https://doi.org/10.5194/amt-18-2279-2025, 2025. a, b
Rubin, Y., Rostkier-Edelstein, D., Chwala, C., and Alpert, P.: Challenges in diurnal humidity analysis from cellular microwave links (CML) over Germany, Remote Sens., 14, 2353, https://doi.org/10.3390/rs14102353, 2022. a, b
Rubin, Y., Sohn, S., and Alpert, P.: High-resolution humidity observations based on commercial microwave links (cml) data – case of tel aviv metropolitan area, Remote Sens., 15, 345, https://doi.org/10.3390/rs15020345, 2023. a
Schilling, W.: Rainfall data for urban hydrology: what do we need?, Atmos. Res., 27, 5–21, https://doi.org/10.1016/0169-8095(91)90003-F, 1991. a, b
Schleiss, M. and Berne, A.: Identification of dry and rainy periods using telecommunication microwave links, IEEE Geosci. Remote S., 7, 611–615, https://doi.org/10.1109/LGRS.2010.2043052, 2010. a, b
Špačková, A., Bareš, V., Fencl, M., Schleiss, M., Jaffrain, J., Berne, A., and Rieckermann, J.: 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, 13, 4219–4240, https://doi.org/10.5194/essd-13-4219-2021, 2021. a
Stewart, B.: Measuring what we manage – the importance of hydrological data to water resources management, P Int. Ass. Hydrol. Sci., 366, 80–85, https://doi.org/10.5194/piahs-366-80-2015, 2015. a
Tapiador, F. J., Turk, F. J., Petersen, W., Hou, A. Y., García-Ortega, E., Machado, L. A. T., Angelis, C. F., Salio, P., Kidd, C., Huffman, G. J., and de Castro, M.: Global precipitation measurement: Methods, datasets and applications, Atmos. Res., 104, 70–97, https://doi.org/10.1016/j.atmosres.2011.10.021, 2012. a
Upton, G., Holt, A., Cummings, R., Rahimi, A., and Goddard, J.: Microwave links: The future for urban rainfall measurement?, Atmos. Res., 77, 300–312, https://doi.org/10.1016/j.atmosres.2004.10.009, 2005. a
Weather Underground: Weather Underground, https://www.wunderground.com/ (last access: 29 October 2025), 2025. a
Yaghoubi, F., Furdek, M., Rostami, A., Öhlén, P., and Wosinska, L.: Consistency-aware weather disruption-tolerant routing in SDN-based wireless mesh networks, IEEE T. Netw. Serv. Man., 15, 582–595, https://doi.org/10.1109/TNSM.2018.2795748, 2018. a
Zawadzki, I.: Factors affecting the precision of radar measurements of rain, in: 22nd Conference on Radar Meteorology, Zurich, Switzerland, 251–256, https://www.ametsoc.org/ (last access: 22 July 2026), 1984. a
Zhang, P., Liu, X., and Pu, K.: Precipitation monitoring using commercial microwave links: current status, challenges and prospectives, Remote Sens., 15, 4821, https://doi.org/10.3390/rs15194821, 2023a. a
Zhang, P., Liu, X., and Zou, M.: Reconstructing and nowcasting the rainfall field by a CML network, Earth and Space Science, 10, e2023EA002909, https://doi.org/10.1029/2023EA002909, 2023b. a
Zhang, Q. S.: Environment Pollution Analysis on Smart Cities Using Wireless Sensor Networks, Strategic Planning for Energy and the Environment, 42, 239–262, https://doi.org/10.13052/spee1048-5236.42112, 2023. a
Short summary
We present a dataset of signal strength measurements from 103 wireless links in a community internet network in New York City, paired with data from 37 personal weather stations. By tracking how rain and snow weaken these signals at one-minute intervals across Manhattan and Brooklyn, we show that community networks can serve as low-cost weather sensing systems. The dataset and analysis demonstrate its use for real-time precipitation monitoring in cities.
We present a dataset of signal strength measurements from 103 wireless links in a community...
Altmetrics
Final-revised paper
Preprint