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Abstract Urbanization affects atmospheric boundary layer dynamics by altering cloud formation and precipitation patterns through the urban heat island (UHI) effect, perturbed wind flows, and urban aerosols, that overall contribute to the urban rainfall effect (URE). This study analyzes an ensemble of numerical simulations with the Weather Research and Forecasting (WRF) model and its version with coupled chemistry and aerosols (WRF-Chem) through a Functional ANalysis Of VAriance (FANOVA) approach to isolate the urban signature from the regional climatology and to investigate the relative contributions of various mechanisms and drivers to the URE. Different metropolitan areas across the United States are analyzed and their urban land cover and anthropogenic emissions are replaced with dominant land-use categories such as grasslands or croplands and biogenic only emissions, as in neighboring regions. Our findings indicate a significant role of the urban land cover in impacting surface temperature and turbulent kinetic energy over the city, and precipitation patterns, both within and downwind of the urban environment. Moreover, simulations of a deep convection event suggest that the aerosols impact dominates the sign and spatial extent of the changes in the simulated precipitation compared to the UHI effect, leading to a significant precipitation enhancement within the urban borders and suppression in downwind regions. 

Abstract Citizen science data for monitoring air pollution have recently emerged as a powerful yet under-explored resource to complement expensive and sparse national air quality monitors. In urban environments, these new data have the potential to allow for high-resolution and high-frequency forecasts, and thereby to provide an assessment of population exposure at neighbourhood level. The complex spatio-temporal structure of these data, however, requires new flexible methods that are also able to provide timely forecasts. In this work, we propose a novel method that first provides forecasts with a reservoir computing approach, an echo-state network, adjusts the forecast with a transformer network with attention mechanism and then merges the echo-state and transformer forecast into a combined network. The stochastic nature of the method allows for a fast and more accurate forecast then individual predictors as well as standard statistical methods. Simulation and application to San Francisco air pollution show how the proposed method is able to produce high-resolution urban maps of air quality. Additionally, we show how these forecasts can be used to provide neighbour-level exposure assessment using population data, a task that would not be achievable with sparse government-sponsored air quality networks.