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

摘要: AbstractCrowdsourcing 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 estimate the daily citywide air via direct heat transfer model. This work extends model estimates by studying smaller temporal spatial scales. The study finds number of influences accuracy retrievals. Optimal results achieved 700 or more An extensive dataset over 10 million estimating hourly temperatures available Sao Paulo, Brazil. validated with measurements from WMO station, an Urban Flux Network site, data seven citizen stations. Daily good (coefficient determination ρ2 86%), shows they improve opt...