Description
Posters from followin session:
OS data acquisition, management & standardization
Processing methods
Bridging the gap
The high network density of personal weather stations (PWSs), often exceeding that of official weather stations from national meteorological agencies, offers a large potential to improve precipitation estimates. Another advantage is that PWSs have a high temporal resolution ($\sim$5~min), are available in (near) real-time and can potentially be used for now-casting, flood forecasting or early...
Commercial Microwave Links (CMLs) have emerged as a promising tool for opportunistic sensing, particularly for rainfall estimation. However, most studies have focused on high-frequency cellular network links, leaving a gap in understanding the viability of CMLs operating at lower frequencies (10 GHz – 15 GHz) and over shorter distances (≤2 km). In this study, we analyze data from a private CML...
To enhance meteorological data collection and nowcasting capabilities, the Royal Meteorological Institute (RMI) of Belgium integrated a citizen observation feature into its smartphone app in August 2019. This initiative has since accumulated over 3.3 million observations, including 56,000 user-submitted photos, significantly enriching RMI's meteorological datasets.
While the majority of...
Accurate precipitation estimation is essential for hydrology, meteorology, and climate studies. Traditional methods rely on rain gauges, weather radars, and satellite observations, each with inherent limitations. Opportunistic sensing through Commercial Microwave Links (CML) offers a promising complementary data source, especially in regions with sparse conventional observations. This work...
Landslides are rainfall induced geo-hydrological hazards that frequently occur in the mountains of Rwanda, a densely-inhabited region of the African tropics. In May 2023 alone, a heavy rainfall event triggered in a few hours a cluster of hundreds of landslides, which led to more than 100 fatalities and significant economic losses in the impacted communities. Although quite common, natural...
Compared to conventional methods, Commercial Microwave Links (CMLs) offer great spatial and temporal resolution, making them a viable opportunistic sensing technology for precipitation assessment. However, noise, ambiguity, and non-linear connections between signal attenuation and rainfall intensity make it difficult to reliably estimate precipitation using CML-derived attenuation data. In...
Accurate and continuous monitoring of precipitation and extreme weather events, such as heavy rainfall and flooding, is essential for mitigating their devastating impacts on both natural and built environments. The increasing frequency and intensity of such hydrometeorological events, exacerbated by climate change, necessitate the development of innovative and cost-effective observational...
We introduce OpenMesh, a publicly available dataset of wireless links designed for high-resolution weather monitoring in dense urban environments. Collected from NYC Mesh—an initiative primarily aimed at providing affordable internet access—this dataset demonstrates how opportunistic usage can transform existing communication infrastructure into a platform for real-time meteorological...
Accurate rainfall estimation using commercial microwave links (CMLs) is set back by wet antenna attenuation (WAA), which could lead to overestimation of rainfall intensity when not properly accounted. This study introduces a novel framework for minimizing WAA effects through an optimized calibration approach. The proposed methodological framework utilizes an effective distance metric to...
We present a dataset of commercial microwave link (CML) received signal levels for the Netherlands. This can be used to estimate path-average rainfall between telephone towers. It contains microwave frequency, end date & time of reading, minimum & maximum received power, path length, coordinates, link identifier, errored seconds, and severely errored seconds. The dataset consists of on average...
The use of so-called opportunistic rainfall sensors like Personal Weather Stations (PWS) and Commercial Microwave Links has gained much attention over the recent year, as they clearly outnumber professional rain gauges which are operated by national weather services and other. However, the data quality of such sensors is typically low and thus their information cannot be used without thorough...
Measuring natural phenomena through opportunistic sensing is crucial for maximizing the use of existing data for research and weather warning systems. This research is an addition to existing work that has been done connecting CML information with rainfield monitoring.
by integrating this signal attenuation-based rain estimation with graph signal processing techniques we can gain better...
An important step in deriving precipitation estimates from commercial microwave links (CMLs) involves separating the attenuation caused by rainfall from the baseline attenuation and wet antenna attenuation (WAA). The baseline is usually estimated from the signal loss preceding a rainfall event, making the baseline sensitive to the estimated starting time for rainfall events. A rainfall event...
Opportunistic rainfall data collected through satellite microwave links (SML), such as the ones providing TV-SAT signals from Ku-band geostationary satellites, have the potential to complement conventional sensors, due to their flexibility and the relatively low-cost of the receiving equipment. However, opportunistic data must be properly processed and validated against ground truth. To this...
There are a wide variety of opportunistic weather sensor networks, ranging from academic controlled to open source and commercial but individually owned and controlled personal weather stations. Individual participation to these networks through measurement and data sharing is a well-known method of engagement, sometimes referred to as citizen science. In addition to top-down measurement...
The focus of working group 2 of OPENSENSE is on method and software homogenisation. We have reviewed the existing software available for processing opportunistic rainfall sensor data and did provide example applications executable online in the so called OPENSENSE software sandbox. There we have identified synergies but also implementation gaps. Based on this we have set a roadmap for...
Changes in communication signals due to weather conditions are often misclassified as faults, making it challenging to differentiate between meteorological effects and actual network malfunctions, such as physical obstructions (e.g., new construction blocking the signal path) or hardware failures. In this work, we propose an unsupervised learning framework for fault detection and...
