  Spatial prediction of weather-elements like temperature, precipitation, and
barometric pressure are generally based on satellite imagery or data collected
at ground-stations. None of these data provide information at a more granular
or "hyper-local" resolution. On the other hand, crowdsourced weather data,
which are captured by sensors installed on mobile devices and gathered by
weather-related mobile apps like WeatherSignal and AccuWeather, can serve as
potential data sources for analyzing environmental processes at a hyper-local
resolution. However, due to the low quality of the sensors and the
non-laboratory environment, the quality of the observations in crowdsourced
data is compromised. This paper describes methods to improve hyper-local
spatial prediction using this varying-quality noisy crowdsourced information.
We introduce a reliability metric, namely Veracity Score (VS), to assess the
quality of the crowdsourced observations using a coarser, but high-quality,
reference data. A VS-based methodology to analyze noisy spatial data is
proposed and evaluated through extensive simulations. The merits of the
proposed approach are illustrated through case studies analyzing crowdsourced
daily average ambient temperature readings for one day in the contiguous United
States.
