  Connected vehicles are poised to transform the field of environmental sensing
by enabling acquisition of scientific data at unprecedented scales. Drawing on
a real-world dataset collected from almost 70 connected vehicles, this study
generates improved rainfall estimates by combining weather radar with
windshield wiper observations. Existing methods for measuring precipitation are
subject to spatial and temporal uncertainties that compromise high-precision
applications like flash flood forecasting. Windshield wiper measurements from
connected vehicles correct these uncertainties by providing precise information
about the timing and location of rainfall. Using co-located vehicle dashboard
camera footage, we find that wiper measurements are a stronger predictor of
binary rainfall state than traditional stationary gages or radar-based
measurements. We introduce a Bayesian filtering framework that generates
improved rainfall estimates by updating radar rainfall fields with windshield
wiper observations. We find that the resulting rainfall field estimate captures
rainfall events that would otherwise be missed by conventional measurements. We
discuss how these enhanced rainfall maps can be used to improve flood warnings
and facilitate real-time operation of stormwater infrastructure.
