Preprints
https://doi.org/10.5194/essd-2025-238
https://doi.org/10.5194/essd-2025-238
25 Jun 2025
 | 25 Jun 2025
Status: this preprint is currently under review for the journal ESSD.

OpenMesh: Wireless Signal Dataset for Opportunistic Urban Weather Sensing in New York City

Dror Jacoby, Shuyue Yu, Qianfei Hu, Zachary Hine, Rob Johnson, Jonatan Ostrometzky, Igor Kadota, Gil Zussman, and Hagit Messer

Abstract. We introduce OpenMesh, a publicly available dataset of wireless signal measurements from a community-run communication network in New York City. While originally designed for affordable internet access, these links can be used opportunistically for high-resolution weather monitoring in dense urban areas, providing 1-minute sampling and dense spatial coverage. Spanning eight months of measurements (November 2023 to June 2024), the dataset comprises 103 directional links in Lower Manhattan and Brooklyn, operating in three primary frequency ranges: 5–6 GHz (C-band), 24 GHz (K-band), and 58–70 GHz (V-band)—part of the millimeter-wave (mmWave) spectrum.

Our analysis incorporates meteorological records from the study period, including precipitation from local weather stations, thereby enabling real-time analysis of signal-weather relationships and expanding in-city applications through opportunistic sensor networks. During the study period, diverse weather events—ranging from intense rainfall that caused link attenuations of up to 30 dB and occasional outages, to snowstorms in Winter 2024—demonstrated the network’s potential for broader meteorological sensing. Analyzing multi-band observations provides valuable insights into emerging 5G/6G challenges and uncovers new opportunities for urban environmental monitoring. The OpenMesh dataset is available at https://doi.org/10.5281/zenodo.15268340 (Jacoby et al., 2025). By publishing both the datasets and our preliminary analyses, we hope to encourage further research that leverages wireless networks in dense urban areas for real-time sensing.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
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Dror Jacoby, Shuyue Yu, Qianfei Hu, Zachary Hine, Rob Johnson, Jonatan Ostrometzky, Igor Kadota, Gil Zussman, and Hagit Messer

Status: open (until 01 Aug 2025)

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Dror Jacoby, Shuyue Yu, Qianfei Hu, Zachary Hine, Rob Johnson, Jonatan Ostrometzky, Igor Kadota, Gil Zussman, and Hagit Messer

Data sets

OpenMesh Dataset Dror Jacoby https://zenodo.org/records/15268341

Model code and software

read_OpenMesh_nc.py Dror Jacoby https://zenodo.org/records/15268341

Dror Jacoby, Shuyue Yu, Qianfei Hu, Zachary Hine, Rob Johnson, Jonatan Ostrometzky, Igor Kadota, Gil Zussman, and Hagit Messer
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Latest update: 25 Jun 2025
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Short summary
Using more than one hundred wireless links in New York City, we convert signal drops into minute-scale, real-time rainfall estimates. The eight-month dataset spans five-to seventy-gigahz bands and captures precipitation from light rain to snow. Analyzed alongside meteorological observations, it aligns with local weather stations and offers added value. We show that emerging new-generation networks—especially high-frequency links—thus function as sustainable sensors for future urban monitoring.
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