Abstract
Abstract. The lack of observations near the surface is often cited as a limiting factor in the observation and prediction of deep convection. Recently, networks of personal weather stations (PWSs) measuring pressure, temperature and humidity in near-real time have been rapidly developing. Even if they suffer from quality issues, their high temporal resolution and their higher spatial density than standard weather station (SWS) networks have aroused interest in using them to observe deep convection. In this study, the PWS contribution to the observation of deep-convection features near the ground is evaluated. Four cases of deep convection in 2018 over France were considered using data from Netatmo, a PWS manufacturer. A fully automatic PWS processing algorithm, including PWS quality control, was developed. After processing, the mean number of observations available increased by a factor of 134 in mean sea level pressure (MSLP), of 11 in temperature and of 14 in relative humidity over the areas of study. Near-surface SWS analyses and analyses comprising standard and personal weather stations (SPWSs) were built. The usefulness of crowdsourced data was proven both objectively and subjectively for deep-convection observation. Objective validations of SWS and SPWS analyses by leave-one-out cross validation (LOOCV) were performed using SWSs as the validation dataset. Over the four cases, LOOCV root-mean-square errors (RMSEs) decreased for all parameters in SPWS analyses compared to SWS analyses. RMSEs decreased by 73 % to 77 % in MSLP, 12 % to 23 % in temperature and 17 % to 21 % in relative humidity. Subjectively, fine-scale structures showed up in SPWS analyses, while being partly, or not at all, visible in SWS observations only. MSLP jumps accompanying squall lines or individual cells were observed as well as wake lows at the rear of these lines. Temperature drops and humidity rises accompanying most of the storms were observed sooner and at a finer resolution in SPWS analyses than in SWS analyses. The virtual potential temperature was spatialized at an unprecedented spatial resolution. This provided the opportunity for observing cold-pool propagation and secondary convective initiation over areas with high virtual potential temperatures, i.e. favourable locations for near-surface parcel lifting.
Highlights
The increasing number of connected objects – i.e. with Internet access – which carry meteorological sensors has raised the interest of scientists because they are a supplementary means of observing the atmosphere
Along the strong pressure gradients revealed by the standard and personal weather stations (SPWSs) network, high wind gusts of 19 m s−1 at 12:10 UTC and 25 m s−1 at 12:38 UTC were observed in the eastern part of the mesoscale convective system (MCS)
Adding raw personal weather stations (PWSs) data in observed surface analyses strongly deteriorates the root-mean-square errors (RMSEs) calculated by leave-one-out cross validation (LOOCV) in comparison with using only standard weather station (SWS) analyses
Summary
The increasing number of connected objects – i.e. with Internet access – which carry meteorological sensors has raised the interest of scientists because they are a supplementary means of observing the atmosphere. Several publications emphasize the high potential of these sensors for the finescale observation of atmospheric phenomena, complementary to traditional sources, given the unprecedented spatiotemporal resolution of the networks constituted by these sensors (Muller et al, 2015; Chapman et al, 2017). These new observations come from smartphones (Overeem et al, 2013; Mass and Madaus, 2014; McNicholas and Mass, 2018a), connected vehicles (Mahoney and O’Sullivan, 2013) or personal weather stations (PWSs hereafter, called citizen weather stations; Bell et al, 2013), for example. Recognition of deepconvection surface features such as low-level convergence boundaries (Wilson and Schreiber, 1986; Wakimoto and Murphey, 2010), pressure, temperature and humidity fea-
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