Boundary layer meteorology is made challenging by the built urban environment affecting the intensity and distribution of precipitation. The cost of land is prohibitive for having enough gauges to represent locally significant precipitation gradients. It is also very difficult to locate class A weather stations within guidelines for site exposure and uniformity. However, the value of urban precipitation data for improving decision-making is elevated because of the consequence of some decisions  (e.g.  planning expensive long-term sewer and drainage projects) and to the volume and frequency of other decisions (e.g. planning a morning commute). Crowdsourcing data from private weather stations may reveal insight that is unknowable from high quality data that is not fully representative of the true spatial and temporal variability. While the very nature of crowdsourcing defies the concept of standards compliance for quality and provenance, the development of defensible processes for aggregating, analyzing and reporting on crowdsourced data and its uncertainties is a challenge we should all embrace.

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