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Crowdsourced vehicle-based observations have the potential to improveforecast skill in convection-permitting numerical weather prediction (NWP). Theaim of this paper is to explore the characteristics of vehicle-basedobservations of air temperature. We describe a novel low-precisionvehicle-based observation dataset obtained from a Met Office proof-of-concepttrial. In this trial, observations of air temperature were obtained frombuilt-in vehicle air-temperature sensors, broadcast to an application on theparticipant's smartphone and uploaded, with relevant metadata, to the MetOffice servers. We discuss the instrument and representation uncertaintiesassociated with vehicle-based observations and present a new quality-controlprocedure. It is shown that, for some observations, location metadata may beinaccurate due to unsuitable smartphone application settings. Thecharacteristics of the data that passed quality-control are examined throughcomparison with United Kingdom variable-resolution model data, roadside weatherinformation station observations, and Met Office integrated data archive systemobservations. Our results show that the uncertainty associated withvehicle-based observation-minus-model comparisons is likely to beweather-dependent and possibly vehicle-dependent. Despite the low precision ofthe data, vehicle-based observations of air temperature could be a usefulsource of spatially-dense and temporally-frequent observations for NWP.

Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical weather prediction