Commercial cellular telecommunication networks can be used for rainfall estimation by measuring the attenuation of electromagnetic signals transmitted between antennas from microwave links. However, as the received link signal may also decrease during dry periods, a method to separate wet and dry periods is required. Methods utilizing ground-based radar rainfall intensities or nearby link data cannot always be used. Geostationary satellites can provide a good alternative. A combination of two Meteosat Second Generation satellite precipitation products, Precipitating Clouds and Cloud Physical Properties, is employed to decide whether a 15-min time interval for a given link is rainy or not. A 12-d dataset of link-based rainfall maps for the Netherlands is validated against gauge-adjusted radar rainfall maps. Results clearly improve upon the case when no wet–dry classification is applied and thus the method shows potential for application to large areas of the world where the other methods cannot be applied.

Crowdsourcing as a method to obtain and apply vast datasets is rapidly becoming prominent in meteorology, especially for urban areas where routine weather observations are scarce. Previous studies showed that smartphone battery temperature readings can be used to estimate the daily and citywide air temperature via a direct heat transfer model. This work extends model estimates by studying smaller temporal and spatial scales. The study finds the number of battery readings influences the accuracy of temperature retrievals. Optimal results are achieved for 700 or more retrievals. An extensive dataset of over 10 million battery temperature readings for estimating hourly and daily air temperatures is available for São Paulo, Brazil. The air temperature estimates are validated with measurements from a WMO station, an Urban Flux Network site, and data from seven citizen weather stations. Daily temperature estimates are good (coefficient of determination ρ² of 86%), and the study shows they improve by optimizing model parameters for neighborhood scales (< 1 km²) as categorized in local climate zones (LCZs). Temperature differences between LCZs can be distinguished from smartphone battery temperatures. When validating the model for hourly temperature estimates, the model requires a diurnally varying parameter function in the heat transfer model rather than one fixed value for the entire day. The results show the potential of large crowdsourced datasets in meteorological studies, and the value of smartphones as a measuring platform when routine observations are lacking.