  Crowdsourced vehicle-based observations have the potential to improve
forecast skill in convection-permitting numerical weather prediction (NWP). The
aim of this paper is to explore the characteristics of vehicle-based
observations of air temperature. We describe a novel low-precision
vehicle-based observation dataset obtained from a Met Office proof-of-concept
trial. In this trial, observations of air temperature were obtained from
built-in vehicle air-temperature sensors, broadcast to an application on the
participant's smartphone and uploaded, with relevant metadata, to the Met
Office servers. We discuss the instrument and representation uncertainties
associated with vehicle-based observations and present a new quality-control
procedure. It is shown that, for some observations, location metadata may be
inaccurate due to unsuitable smartphone application settings. The
characteristics of the data that passed quality-control are examined through
comparison with United Kingdom variable-resolution model data, roadside weather
information station observations, and Met Office integrated data archive system
observations. Our results show that the uncertainty associated with
vehicle-based observation-minus-model comparisons is likely to be
weather-dependent and possibly vehicle-dependent. Despite the low precision of
the data, vehicle-based observations of air temperature could be a useful
source of spatially-dense and temporally-frequent observations for NWP.
