Contribution of personal weather stations to the observation of deep-convection features near the ground

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.

A numerical study to investigate the roles of former Hurricane Leslie, orography and evaporative cooling in the 2018 Aude heavy-precipitation event

“…Simulations are performed with the non-hydrostatic numerical research model Meso-NH version 5.4.2 (Lac et al, 2018), extensively used to study Mediterranean MCSs (Bouin et al, 2017;Martinet et al, 2017;Duffourg et al, 2018).…”

The heavy precipitation event of 14–15 October 2018 in the Aude catchment: A meteorological study based on operational numerical weather prediction systems and standard and personal observations

“…Surface observations result from the combination of standard weather stations (SWSs), which are Météo-France operational weather stations sampling atmospheric parameters at a time step of 1 min on the one hand, and crowdsourced personal weather stations (PWSs), on the other hand. The PWS time series of mean sea level pressure (MSLP), temperature and relative humidity are processed following the method presented by Mandement and Caumont (2020). Gridded analyses of surface pressure, mean sea level pressure, temperature, relative humidity, and virtual potential temperature derived from observations near the ground are built at a 5 min time step and 0.01°resolution in latitude and longitude.…”

“…Virtual potential temperature fields are built from the previous fields. Details are given by Mandement and Caumont (2020).…”

Thunderstorms and extreme rainfall in south Norfolk, 16 August 2020: meteorological analysis

“…However, utilising observations from privately-owned automatic weather stations (AWS) to significantly increase the spatial density of observations can be very beneficial when analysing surface parameters within and near thunderstorms (e.g. Clark et al, 2018;Mandement and Caumont, 2020).…”