Wireless communication links are point-to-point radio connections between two fixed locations. Terrestrial links, often employed by mobile operators and enterprises for backhaul, are also widely used in smart-city applications and community-driven initiatives (e.g., NYC Mesh), offering both commercial and public connectivity. These links span meters to kilometers and operate across a wide spectrum, from 1 up to 100 GHz, depending on usage, and are used mainly for point-to-point interconnection, with coverage and capacity determined by the frequency band and environment. Network Management Systems (NMS) typically collect link performance data to automate network monitoring and quality management, but storing or archiving these datasets is not always mandated and often depends on specific network standards and operator practices.

The following describes the structure of terrestrial wireless link elements : a “site” is the physical location hosting one or more antennas; a “hop” denotes a site-to-site segment; a “sublink” is a one-directional path between antennas; and a “link” comprises the two opposite-direction sublinks between the same antenna pair, as shown in Fig. a.

Wireless link measurements can originate from diverse providers, including cellular telecommunication companies, municipal smart-city operators, and community-driven networks (e.g., NYC Mesh), and each features distinct topologies and coverage densities.

Next generation networks are expected to shift to higher bands to access wide, underutilized spectrum – specifically mmWave bands such as the V-band (including 57–64 GHz) and the E-band (71–76, 81–86, 92–95 GHz) – which enable wider channels and higher capacity, supporting multi-gigabit rates for high-bandwidth applications . At higher bands, signals suffer significantly greater path loss and weather-induced attenuation (see Fig. b). This favors short line-of-sight links and dense site deployments, particularly in urban areas.

Wireless networks typically measure signals strength using two key metrics: the transmitted signal level (TSL) and the received signal level (RSL), both expressed in dBm. The difference between them, referred to as total attenuation (in dB), is given by 1Atot(t)=TSL(t)-RSL(t). Depending on the operator’s protocols, TSL and RSL are often logged at intervals ranging from seconds to minutes, hours, or even days, stored either as instantaneous values or aggregated statistics (e.g., mean, minimum, or maximum) for each sampling window. In addition, these network protocols typically quantize measurements which affects data precision. Together, these factors ultimately define the dataset’s resolution and precision. The total attenuation, Atot(t), arises from multiple factors such as free-space path loss; scattering, reflection, and diffraction by obstacles (e.g., buildings or terrain) further affect the signal. Atmospheric variables (e.g., humidity, temperature inversions, gaseous absorption) further increase the specific attenuation, and when precipitation is present along the propagation path, hydrometeors add excess attenuation. In Fig. b, the influence of several atmospheric phenomena on the expected attenuation (in dBkm-1) is illustrated . While rainfall increasingly dominates overall attenuation at most frequencies,especially at higher bands, a pronounced absorption peak occurs near 60 GHz due to oxygen resonance .

Power measurements collected from communication links can effectively function as environmental sensors , leveraging their sensitivity to atmospheric phenomena. Within the OS approach, WCN infrastructure (e.g., CMLs) is repurposed as a cost-effective virtual sensor network for environmental monitoring . Notably, rainfall is the most established application, with efforts worldwide leveraging WCN datasets to retrieve urban-scale and country-wide rainfall fields . Beyond rain, wireless communication sensing has been explored for other atmospheric variables. For humidity, CML attenuation shows potential for near-surface humidity retrieval , with links near the 22.235 GHz water-vapor line (K-band) exhibiting increased sensitivity to vapor density , and at higher frequencies (E-band, 71–86 GHz) paths are typically shorter, yet water-vapor sensitivity per kilometer is higher, making them attractive for humidity sensing . Furthermore, CML-based retrievals for additional atmospheric variables represent emerging applications: for air quality, signals respond to stable stratification and inversion layers that can trap pollutants, yielding indirect indicators . For fog, detection has been shown in analytical and event-based observations and is expected to improve with denser high-frequency deployments . Still, CML sensing of non-rain phenomena suffers from low SNR and confounding losses (wet-antenna, drift, multipath), coarse quantization, reliance on auxiliary data (e.g., temperature), and path/frequency/height mismatches, so estimates are indirect, uncertain, and often detect only strong events (e.g., dense fog) . With the rapid development of dense urban deployments and greater use of higher-frequency spectrum with increased atmospheric sensitivity, emerging opportunities arise for urban OS applications – from high-resolution precipitation mapping to rain-cell front tracking .

The variety of OS dataset sources and methods – including wireless links – has prompted global initiatives to establish a unified reference community and standards. One such effort is the Global Microwave Link Data Collection Initiative (GMDI) , established under the COST Action OPENSENSE, which serves a specific role of cataloging microwave-link datasets and harmonizing data formats to facilitate their collection and sharing.

The k-R relationship describes rain-induced attenuation γ as a function of the rainfall rate R, typically expressed as : γr(R)=kRα, where k is an attenuation coefficient and α is an exponent that depends on factors such as signal frequency, polarization and the drop-size distribution. The total rain-induced attenuation Ar(t) along the link’s propagation path can then be written as: 2Ar(t)=∫pathγr(R(ℓ,t))dℓ≈kR‾(t)αL,

Here, γr(R(ℓ,t)) is the local specific attenuation (dBkm-1) at rain rate R(ℓ,t), and the integral accumulates the total attenuation along the path. Assuming uniform rainfall along the path, the integral simplifies to a power-law (PL) relation in which the path-averaged rainfall rate R‾(t) and effective link length L govern attenuation – an approximation widely used in radio communications and meteorological estimation. While the link length L is typically taken as the distance from site to site and the coefficients are derived from ITU-R standards , methods have been proposed to calibrate them, including path-length corrections . Specifically, standard models can exhibit larger prediction errors on short paths (≲1km) . This limitation is highly relevant for dense urban deployments, such as beyond 5G/6G networks and smart-city infrastructures. This challenge further motivates active research into methods for rainfall retrieval from wireless links and underscores the value of studying datasets in urban topologies, such as the OpenMesh dataset.

NYC Mesh is a community-driven network led by volunteers that builds and maintains a member supported network for internet connectivity in NYC. By deploying nodes on existing urban infrastructure, such as rooftops and balconies, and using a hybrid mesh, it reduces reliance on traditional internet service providers and expands local connectivity, without logging personal data. NYC Mesh is a non-commercial ISP whose participant installations form direct node-to-node links, enabling dynamic routing and resilience. Its backbone connects hubs and supernodes via point-to-point wireless (the focus of this study), while selected supernodes provide fiber uplinks and peering for upstream connectivity. Community members host local services including a node database (MeshDB), network monitoring dashboards (UISP), a community wiki, and a Slack workspace for coordination . Wireless coverage is currently concentrated in Brooklyn and Lower Manhattan, with an ongoing expansion to the Upper West Side, Harlem, and the Bronx.

NYC Mesh employs a three-tiered network architecture that combines mesh principles with a star topology (hub-and-spoke), blending the resilience of a mesh with the simplicity of a star – local nodes connect to nearby hubs, while hubs interlink to maintain redundant, mesh-like connectivity across the network. Figure  illustrates this architecture, showing fiber-connected Supernodes in data centers interfacing with internet exchange points and forming a partial mesh with wirelessly interconnected Hubs that serve as intermediate nodes: (i) Supernodes (Tier 1) serving as primary public Internet gateways, typically connected via fiber uplinks; (ii) Hub Nodes (Tier 2) are neighborhood level relay points, spanning basic Omni Hubs, intermediate Sector Hubs, and major Backbone Nodes, that aggregate traffic from multiple rooftop installations; and (iii) Member Nodes (Tier 3) represent the network edge, comprising both standard nodes that maintain direct uplinks to Hubs and “Omni-Only” nodes that rely on short-range mesh connections with nearby neighbors. This is a hybrid topology of fiber optic and wireless links in a distributed architecture that dynamically reroutes traffic, with fiber connections between Supernodes and central Hubs providing more reliable pathways than wireless segments, especially during weather disruptions. Wireless links, which are the core of our dataset, serve two distinct roles in the NYC Mesh network: (i) high-capacity links interconnect major hubs and supernodes to form a partially meshed backhaul, and (ii) access point links, which constitute the network's “last mile,” provide the final hop from a neighborhood hub to individual members in a point-to-multipoint configuration.

NYC Mesh employs a wide range of commercial hardware from vendors such as Ubiquiti and MikroTik to build a resilient urban network and uses licensed and unlicensed spectrum bands. NYC Mesh uses available spectrum in multiple bands that balances the trade-offs between different frequency bands. The 5 GHz band is used for reliable, weather-resilient connections; the 24 GHz and unlicensed 60 GHz bands provide gigabit speeds but typically over short distances due to oxygen absorption. The 70 and recently deployed 80 GHz bands are reserved for critical backbone connections with the highest capacity. Figure  displays the OpenMesh deployment of the NYC Mesh network in the published dataset. Figure a maps OpenMesh links in the Brooklyn area, which presents the core of the NYC Mesh network, color-coded by carrier frequency (5 GHz in blue, 24 GHz in green, 60 GHz in orange, and 70 GHz in red), demonstrating the spectrum diversity of the multi-band deployment across a dense urban area (∼ 10 km²). Unlike traditional CMLs (Fig. b), which rely on dedicated telecommunication towers, NYC Mesh leverages existing urban infrastructure, primarily rooftops, as shown in Fig. a, with members mounting hardware with clear line of sight. Moreover, NYC Mesh deploys dual-band link configurations for critical hub connections, pairing high-frequency bands with 5 GHz backup links to maintain connectivity during adverse weather conditions, trading bandwidth for link availability.

Wireless link metrics are collected through NYC Mesh's monitoring system, which archives telemetry from network devices. These feeds supply 1 min measurements that comprise the OpenMesh dataset. An API, maintained by NYC Mesh, is reachable from any node within the mesh's private IP space; each device's JSON payload is refreshed every minute, with a timestamp indicating the time of measurement. To fetch from NYC Mesh, we use a fully automated pipeline that runs daily: it queries the NYC Mesh inventory for active devices, fetches link metrics, including signal strength (dBm), capacity, latency, and throughput, to perform standard reliability checks, flagging and retrying failed or incomplete requests before uploading validated records to a cloud database. The pipeline interfaces with MeshDB and UISP monitoring tools, with MeshDB additionally tracking network infrastructure updates such as new node installations and topology changes.

The OpenMesh dataset spans a period of eight months (29 October 2023–1 July 2024), with all timestamps in UTC. The dataset comprises two main components:

Raw measurements. Time-series data from NYC Mesh wireless links, providing RSL measurements at 1 min time resolution spanning the study period.

Table   summarizes the OpenMesh data characteristics that provide open-access RSL measurements for 103 sublinks across 75 hops, all sampled with 1 dBm quantization and vertical polarization. Among these hops, 24 contain 2 or more sublinks: 22 employ bidirectional link pairs for two-way communication between NYC Mesh nodes, and 6 use same direction dual-band configurations pairing two directional links to the same sites. This results in hops in the dataset that contain more than 2 sublinks, as they combine both dual-band and bidirectional configurations.

The OpenMesh dataset is a curated subset of the broader NYC Mesh network, encompassing several hundred wireless links and continuously expanding. Figure  depicts carrier frequencies and path lengths (transmitter–receiver distances), highlighting the published OpenMesh sublinks within all active NYC Mesh wireless links. The released dataset comprises links selected according to these criteria: (i) Data completeness and continuity: As a community-run, evolving network, NYC Mesh is dynamic, where links are added and reconfigured over time. We prioritized links with stable logging and fewer gaps across the study window, excluding data streams with substantial outages or irregular sampling. (ii) Representativeness and relevance: To preserve weather-sensing relevance, we selected sublinks across multiple bands, prioritizing higher frequencies. These include V-band links, covering both the oxygen resonance range (∼ 60 GHz band), typically avoided in commercial backhaul due to high attenuation, and upper V-band frequencies (e.g., 69 GHz). We also selected for diversity in link lengths and included co-located dual-band pairs, offering frequency-dependent comparisons along identical paths that are rarely available in existing open microwave link datasets. Conversely, we de-prioritized very short 5 GHz links, which represent a large fraction of the network but contribute little atmospheric signal.

Missing data appear as gaps caused by link outages, which can arise from hardware failures or due to strong weather attenuation. All such intervals are encoded as NaN in the raw RSL series and aligned to a unified 1 min sampling grid. Two notable missing-data windows are present: (i) Late startup: about 30 % of sublinks began data collection on 7 November, leaving an initial gap of 10 d after the nominal start date; and (ii) Hardware outage: during the first week of March, the NYC Mesh data collection system experienced a hardware failure lasting one week, resulting in missing or corrupted samples. NYC Mesh devices typically record RSL at 56 or 64 s intervals, with occasional missing samples or transmission errors. To ensure consistency in the published data with unified time stamps for all raw measurements: (i) Time series were resampled to a fixed 1 min interval to provide consistent temporal resolution across all data. Minor gaps (up to two minutes) were linearly interpolated to maintain continuity due to irregular time sampling, while longer sampling gaps remained as missing data and were filled with NaN.

PWS, deployed by individuals or local organizations typically with low-cost instruments, collect localized meteorological data. They fill observational gaps in urban areas where official WS coverage is limited, providing near real-time data critical for capturing rapidly changing conditions. Key applications include urban climate research, urban hydrology, and potentially weather prediction model improvement through data assimilation and model bias correction . PWS have become increasingly popular for collecting precipitation data, with observations shared in real-time through online platforms such as WUnderground, Netatmo, WOW, and Weathercloud that provide access to dense international networks; however, commercial platform ownership creates third-party dependencies and uncertainties in data processing for scientific use . The major drawback of PWS data is their reliability: quality can vary greatly, with common problems including faulty zeros, missing data, and bias, often stemming from non-standardized, user-operated installations and inadequate maintenance. Moreover, PWS typically use unheated tipping bucket rain gauges, which limits their utility to liquid precipitation and reduces accuracy during snow or frozen events compared to heated standard gauges. Therefore, automated Quality Control (QC) is a critical step to filter out erroneous measurements before the data is used. For precipitation, initial QC methodologies focus on detecting errors like faulty zeros and outliers through spatial consistency checks with neighboring stations . This is often followed by a separate post-processing step, such as bias correction against reference gauge and radar products, to address the systematic errors. While data quality varies, PWS networks provide complementary local observations, with recent open-source QC algorithms  and ongoing standardization efforts expanding their usability.

WUnderground hosts an extensive global PWS network, currently exceeding 250 000 stations, with more than 180 000 located in the United States. The online platform aggregates real-time weather observations, forecasts, and historical records from these stations, providing users with hyperlocal forecasts, data visualizations, and other tools through its website and mobile applications. This study uses WUnderground PWS data to obtain temporally aligned weather records with OpenMesh network performance metrics, enabling examination of how meteorological conditions influence wireless signals in the NYC area. PWS in the WUnderground network typically use a self-emptying tipping bucket design, often capturing rainfall in increments of 0.25 mm. Standard, unheated PWS devices often fail to differentiate precipitation types and may lead to delayed or underreported data for non-liquid forms (snow, hail). As a result, snow or frozen-precipitation measurements may be incomplete or imprecise. We highlight these constraints in our study and emphasize the need for data validation when analyzing precipitation-related processes. Although precipitation is our primary focus, WUnderground PWS typically measure additional variables including temperature, humidity, wind, pressure, and often solar radiation, with derived quantities such as dew point.

This study primarily focuses on precipitation data, combining observations from standard WS and PWS across NYC. We leverage meteorological data from two main sources: WUnderground, offering a dense PWS network with broad spatial coverage and high sampling rates, and the NOAA, which provides officially QC measurements. We categorize two types of stations used in this study:

PWS (WUnderground). Data from a total of 37 PWS distributed across Brooklyn and Manhattan provide detailed meteorological variables, including precipitation measurements. We sourced PWS data via the WUnderground API; stations generally report at 5 min intervals, with observations available in near real time. Reporting times can be irregular and are not necessarily synchronized across stations, so timestamps may differ between PWS records. The WUnderground PWS network includes only basic automated quality checks, and measurements are not as rigorously controlled as those from professional stations. Nevertheless, their fine-grained temporal and spatial coverage enables analysis of urban meteorological processes at the micro-scale, supporting correlation with NYC Mesh link measurements and exploration of local weather impacts on communication links.

Let L (km) denote the link length, i.e., the distance between the transmitter and receiver. The path-averaged attenuation ΔA‾(t) is defined by normalizing the total measured attenuation above the baseline by L: 3ΔA‾(t)=ΔA(t)L, where ΔA(t) is the total measured attenuation at time t. Specifically, the total attenuation A(t) along the path represents the cumulative effects experienced by the signal along its propagation path. Here, we simplify these accumulated effects by letting the effective link length Leff approximate the direct distance L, thereby facilitating statistical comparisons among links by minimizing distance-dependent biases.

Our dataset comprises RSL measurements, from which we derive attenuation metrics. To quantify the added attenuation beyond baseline conditions, we define ΔA(t)=RSLbaseline(t)-RSL(t), where RSL(t) is the measured power at time t and RSLbaseline(t) denotes the nominal dry-weather signal level. We use a dynamic baseline approach to determine the baseline level by using 12 h windows of past measurements.

In our analysis, we paired signal measurements from sublinks with precipitation collected by nearby PWS. Each sublink is paired with spatially averaged PWS rainfall measurements from stations located within 3 km distance.

To capture both short-term fluctuations and longer-term rainfall trends, we aggregate meteorological and attenuation data over different time intervals. Instantaneous measurements at time t refer to the native sensor temporal scales (e.g., 1 min for signal measurements and 5 min for PWS data). We define ΔAm(t) as the attenuation averaged over an m-min interval ending at t. For example, we use 15 min ΔA15(t) and 60 min ΔA60(t) aggregations to reflect different insights into observed patterns. Similarly, we define σm(t) as the corresponding standard deviation (STD) of the signal within the m-min window, which can be used to quantify signal variability and has been suggested for rainfall detection .

Figure  provides a histogram of data availability after preprocessing, showing the percentage of valid records relative to the total possible measurements over the study period. Most published sublinks and PWS achieved around ∼ 90 % or higher availability, with mean availabilities of 88.9 % and 88.7 %. Data availability accounts for all sources of missing data over the study period, marked by NaN values. For OpenMesh sublinks, missing data is categorized into three main categories: (i) Known system events (detailed in Sect. ): delayed startup and week-long outage in early March; (ii) Operational issues: sporadic link outages from hardware failures or mid-period shutdowns; (iii) Environmental causes: external signal blockages or weather-induced attenuation (the focus of this study), where RSL drops below detectable levels. Despite these gaps, most OpenMesh sublinks maintain high availability, with 92 sublinks achieving above 80 % coverage (including 58 sublinks above 90 %). For PWS, availability is computed for precipitation measurements at each station's sampling interval (5 min for 34 stations, 15 min for 3 stations). The majority of PWS stations (28 of 37) achieve above 80 % availability, with 21 stations exceeding 90 % coverage.

Figures  and  characterize RSL properties and baseline variability in the dataset. Figure a shows the distribution of median RSL across sublinks, concentrated around -60 dBm and ranging from -80 to -35 dBm due to variations in transmitted power, path length, antenna configuration, and operating frequency. The median RSL represents the nominal dry-weather signal level for each sublink and serves as a simple static baseline for rain-induced attenuation estimation. Figure b shows the distribution of all RSL measurements (logarithmic scale), ranging from -20 to -96 dBm, with lower RSL values being more susceptible to outages that can occur during heavy rain events. Figure  assesses the dynamic baseline's stability, which is a prerequisite for accurate rain estimation. Figure a–b quantify this stability by showing the distributions of the baseline's STD and robust range (5–95 percentile), which are calculated using a 12 h window. Most sublinks exhibit a STD of less than 3 dB and a robust range of less than 5 dB, indicating the baseline is stable in most cases. High STD values can indicate non-rain attenuation from factors like atmospheric absorption or other RSL drifts and changes. Figure c exemplifies such an event, where the corresponding baseline (red) tracks a sudden ∼ 5 dB RSL drop around 13 May and remains low, distinguishing it from the normal, smaller-scale deviations during the series. These results are particularly relevant for the OpenMesh dataset, which contains 60 GHz links that may cause more significant dry-weather attenuation and that lack TSL values, implying that, more processing may be needed for reliable rainfall estimation.

Figure  presents the cumulative rainfall from the PWS and ASOS networks over the study period. Figure a displays all PWS stations with available data, categorized by total accumulation. The lowest group (<400 mm) shows notable gaps and flat periods, which are due to missing data or equipment issues rather than a true absence of rain. Figure b compares the cumulative rainfall from the GHCN-Daily ASOS stations with the mean and median of the PWS network, omitting the low-availability group (<400 mm). This comparison shows that the PWS and ASOS networks capture similar overall trends and accumulation totals of 850 to 900 mm. Although individual PWS stations exhibit high variability and data gaps, the network’s high spatial density is a valuable asset that encourages the future application of QC methods to further refine these rainfall estimates.

Different frequency bands inherently experience varying attenuation characteristics, so we analyze multiple bands in the NYC Mesh sublinks to capture these differences. The OpenMesh dataset allows for this analysis, as it contains sublinks from four distinct frequency bands (5, 24, 60, and 70 GHz) co-located within the same urban environment.

Figure  details the trends between precipitation, carrier frequency, and attenuation. For a fair comparison we show attenuation statistics for links of a similar length and normalized values.

Figure a provides an example comparing sublinks of approximately 1.5 km in length, plotting the 15 min attenuation (ΔA15) against WMO-defined precipitation categories , which are determined by the mean of paired PWS data. A clear and expected trend emerges: attenuation increases significantly with both precipitation intensity and carrier frequency. Specifically, the 60 and 70 GHz bands show stronger attenuation during moderate to heavy rain compared to the 5 and 24 GHz bands. Figure b shows the Complementary Cumulative Distribution Function (CCDF) of hourly aggregated statistics (mean and median) of path-normalized attenuation ΔA‾60(t). The plot highlights the percentage of time each attenuation threshold is surpassed. Notably, the 5 GHz mean attenuation appears to exceed the 24 GHz band, an artifact that can be attributed to normalization effects from short sublinks, which artificially increase the normalized attenuation. Moreover, this band's large size (55 sublinks) provides broader temporal and spatial coverage.

Figure  shows a three-day interval (22–24 March) of a dual-band link from the City College of New York to a northern endpoint in Harlem, illustrating RSL attenuation across frequency bands under identical rain conditions as measured by a co-located PWS. This channel consists of two sublinks operating at a high frequency (69 GHz) and a low frequency (5.7 GHz), respectively, which follow similar paths and experience the same weather conditions yet display markedly different attenuation levels. During this period, heavy rainfall was recorded on 23 March, corresponding to substantial attenuation in both sublinks. At the peak of the rain event, the 69 GHz sublink undergoes nearly 30 dB of additional path loss – enough to cause sublink outage, while the 5.7 GHz sublink incurs only around 5 dB of loss.

We adopt the rolling (STD) method , applying a 30 min window to flag significant RSL fluctuations indicative of rain-induced attenuation using a 70 GHz band NYC Mesh sublink spanning 6.9 km. Figure  shows a continuous record for June 2024, comparing sublink measurements with the average rainfall intensity from paired PWS stations. The top plot shows the RSL (black line); the middle plot displays its 30 min rolling STD,σ30, (purple line); and the bottom plot indicates PWS-derived rain-intensity categories (drizzle to heavy , shown in Fig. ). Red bars mark “wet” intervals based on the rolling-STD threshold. Overall, wet flags align well with periods of higher PWS rainfall intensities, especially during intense events. Still, some periods of inconsistencies arise, including false alarms and missed detection periods, suggesting that additional preprocessing and alignment are needed when using and comparing opportunistic sensors.

We present NYC Mesh link responses during Winter 2024 snowstorms, cross-referenced with local PWS and ASOS observations. Table  summarizes daily snowfall records for Winter 2024 at KJFK, KLGA, and KNYC. The table shows snowfall depths from six specific days in early 2024. The January events were generally light, with most measurements at 5.08 cm or less. The February events were much heavier, with a significant snowfall of 8–11 cm recorded at all three stations on 13 February, and a peak accumulation of 15.75 cm at KJFK on 17 February. Figure  shows two of these events during Winter 2024: 16 January, with an average daily accumulation across stations of 3.9 cm, and 13 February, with heavier accumulations of 9.6 cm. The bottom panel displays 5 min ASOS precipitation records for each event. Above, RSL measurements from two sublinks per frequency band (5, 60, and 70 GHz) in the Brooklyn region are shown alongside mean and median precipitation and air temperature from the local WUnderground PWS network.

All plots are color shaded by ASOS NOAA station category to reflect the reported condition at each interval: dry (white), wet (rain), wintry mix (freezing rain, freezing drizzle, or sleet), and snow, using the majority category within each hourly window. Figure a corresponds to the 16 January 2024 event, with ASOS stations recording snow conditions mainly in the early morning (up to 09:00 a.m.) with recorded precipitation. At the same time, no significant effect on the microwave signal was observed, and no precipitation was registered by the WUnderground PWS. These observations can be associated with the recorded subzero temperatures (–2 to –4 °C), which may suggest the presence of predominantly dry snow. Such conditions are typically transparent to microwave signals and are often not recorded by unheated PWS tipping-bucket gauges, which can be susceptible to freezing. Around 09:00, as the air temperature rose toward 0 °C, ASOS station reports shifted from “snow” to “wintry mix,” indicating mixed-phase conditions. Concurrently, NYC Mesh wireless links exhibited marked attenuation, exceeding 10 dB at higher frequencies, while co-located PWS reported only low precipitation levels. Figure b illustrates the 13 February event, characterized by heavier snowfall, averaging about 9 cm of snow depth across the three ASOS stations. In the morning hours, ASOS stations began recording precipitation with present weather codes indicating snow. At the same time, wireless links showed substantial attenuation across all frequency bands, including more than 20 dB on high-frequency (60–70 GHz) sublinks and some link outages. Concurrently, PWS also registered precipitation, but in low amounts, with a majority of stations reporting zero.

After 13:00, wireless links showed signal recovery, returning to baseline attenuation levels that aligned with reported dry conditions. Simultaneously, the PWS network recorded spikes with high precipitation levels, even as official ASOS stations reported zero precipitation. This significant peaking lag in the PWS data, which is delayed relative to the earlier RSL attenuation and ASOS observations, may suggest it is not a new precipitation event. A possible reason for this behavior is a “melting lag,” where the unheated PWS gauges may be measuring the previously uncaptured snowpack as it melted, long after the actual snowfall had ended. Overall, OpenMesh wireless links exhibited frequency-dependent attenuation during snow storms, showing potential to complement sparse official stations for snow monitoring, and to discriminate between dry and wet snow, as suggested in .