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

Bell, Z., Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338 and Waller, J. A. (2022) Exploring the characteristics of a vehicle-based temperature dataset for kilometre-scale data assimilation. Meteorological Applications, 29 (3). e2058. ISSN 1469-8080 doi: 10.1002/met.2058

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/104199
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Royal Meteorological Society
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