Exploring the characteristics of a vehicle‐based temperature dataset for kilometre‐scale data assimilation

Abstract

Abstract 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 in the context of data assimilation. 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.

Keywords

• crowdsourced data
• data assimilation
• dataset of opportunity
• km‐scale numerical weather prediction
• quality control
• road‐surface energy balance