The ongoing urbanization and climate change urges further understanding and monitoring of weather in cities. Two case studies during a 17-day period over the Amsterdam metropolitan area, the Netherlands, are used to illustrate the potential and limitations of hydrometeorological monitoring using nontraditional and opportunistic sensors. We employ three types of opportunistic sensing networks to monitor six important environmental variables: 1) air temperature estimates from smartphone batteries and personal weather stations, 2) rainfall from commercial microwave links and personal weather stations, 3) solar radiation from smartphones, 4) wind speed from personal weather stations, 5) air pressure from smartphones and personal weather stations, and 6) humidity from personal weather stations. These observations are compared to dedicated, traditional observations where possible, although such networks are typically sparse in urban areas. First, we show that the passage of a front can be successfully monitored using data from several types of nontraditional sensors in a complementary fashion. Also, we demonstrate the added value of opportunistic measurements in quantifying the urban heat island (UHI) effect during a hot episode. The UHI can be clearly determined from personal weather stations, though UHI values tend to be high compared to records from a traditional network. Overall, this study illustrates the enormous potential for hydrometeorological monitoring in urban areas using nontraditional and opportunistic sensing networks.

Commercial microwave links are installed and maintained for the purpose of telecommunication. Hydrometeors between transmitting and receiving antennas cause the microwave signal to be attenuated. From signal attenuation, the path-averaged rainfall intensity can be calculated. A 7-month dataset of instantaneously logged signal powers from almost 2000 unique links in the Netherlands is analyzed. Rainfall intensities are calculated with the RAINLINK package with a novel preprocessing module, enabling the package to be applied on instantaneously logged data from now on. Rainfall intensities per link are validated with the path-averaged rainfall intensities according to a gauge-adjusted radar product. Both the overall performance and the dependence of errors on link characteristics and measurement conditions are evaluated. The coefficient of variation decreases from 3.70 to 2.32 and the correlation increases from 0.30 to 0.63 from instantaneous to daily estimates of rainfall accumulations. The coefficient of variation is also smaller during heavy rainfall. Errors are largest for pathlengths shorter than 2 km, for observations during the late night and early morning, and for observations during colder months (when solid or melting precipitation could occur and dew is more likely to form on the antennas). Comparison of our results with those of earlier studies shows that minimum/maximum sampling (widely employed in network management systems) outperforms instantaneous sampling regarding detection of both quantity and occurrence of rain at a 15-min sampling rate in the Dutch climate.

Many applications in urban areas require high-resolution rainfall measurements. Typical operational weather radars can provide rainfall intensities at 1-km ² grid cells every 5 min. Opportunistic sensing with commercial microwave links yields path-averaged rainfall intensities (typically 0.1–10 km) within urban areas. Additionally, large amounts of urban in situ rainfall measurements from amateur weather observers are obtainable in real-time. The accuracy of these three techniques is evaluated for an urban study area of 20 × 20 km, taking into account their respective network layouts and sampling characteristics. We use two simulated rainfall events described in terms of drop size distributions on a 100-m grid and with a temporal resolution of 30 s. Accurate radar rainfall estimation with the Z-R relationship relies heavily on an appropriate choice of parameters, and a dual-polarization strategy is more suitable for higher intensities. Under ideal measurement conditions, the weather station network is the most promising, with a Pearson correlation coefficient above 0.86 and a relative bias below 4% for 100-m rainfall estimates at 5-min resolution. Microwave link rainfall observations contain the largest error, shown by a consistently larger coefficient of variation. The accuracy of all techniques improves when considering rainfall at larger scales, especially by increasing time intervals, with the strongest improvements found for microwave links for which errors are largely caused by their temporal sampling. Sparser networks are examined, showing that the decline in measurement accuracy only becomes significant when the link and station network density are reduced to less than half their levels in Amsterdam.