Forecasting influenza primes public health systems to respond, reducing transmission, morbidity and mortality. Most influenza forecasts to date have, by necessity, relied on spatially course-grained data (e.g. state-or country-level incidence), and have operated at time horizons of 12 weeks or less. If influenza outbreaks could be predicted farther in advance and with increased spatial precision, then limited public health resources could be adaptively managed to minimize spread and improve health outcomes. Here, we use real-time syndromic data from a distributed network of thermometers to construct city-specific forecasts of influenza-like illness (ILI) with a horizon of 30 weeks. Daily geolocated ILI data from the network allows for estimates of recurrent city-specific patterns in ILI transmission rates. These “transmission templates” are used to parameterize an ensemble of ILI forecasts that differ randomly in three parameters, representing city- and season-specific rates of susceptible depletion and reporting, as well as differences in influenza season onset timing. For nine cities across the US, the best-in-hindsight model matches the observed data, and the best forecast variants can be identified in the early season.