Crowdsourcing urban air temperatures through smartphone battery temperatures in São Paulo, Brazil

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. We find 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 Fluxnet site, and data from 7 citizen weather stations. Daily temperature estimates are good (ρ2 of 86%), and we show they improve by optimizing model parameters for neighborhood scales (