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

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 nearreal 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 PWSs contribution to the observation of deep-convection features near the ground is evaluated. Four cases 5 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 (SPWS) were built. The usefulness of crowdsourced data was proven both objectively and subjectively for deep convection observation. 10Objective 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 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 15 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. It gave the opportunity to observe cold pool propagation and secondary convective initiation over areas with high virtual potential temperatures, i.e. favorable locations for near surface parcel lifting.

How useful are crowdsourced air temperature observations? An assessment of Netatmo stations and quality control schemes over the United Kingdom

“…Evidence of a nighttime urban heat island (UHI) effect of up to 5.5°C was shown in London using 287 Netatmo and 7 Met Office stations, utilizing the Met Office data to remove anomalous Netatmo observations (Chapman et al, 2017). Netatmo stations were also used by Mandement and Caumont (2019) to analyse deep convection in France, showing that rapid changes in atmospheric pressure were detected around squall lines. Data from 63 citizen rain gauges around Amsterdam were correlated with rain radar observations (De Vos et al, 2017).…”

Rainfall monitoring with opportunistic sensors

“…The measured reflectivity can be translated to rainfall amounts. One commonly used relation between reflectivity (Z in mm 6 m −3 ) and rainfall intensity (R in mm h −1 ) is the Marshall-Palmer relation Z = 200R 1.6 (Marshall et al, 1955), but as the actual Z-R-relation is dependent on rainfall type and local climate many other relations have been proposed (e.g. Uijlenhoet, 2001;Raghavan, 2013).…”

“…Given the Rayleigh scattering assumption, Z can be determined from the sixth-order moment of the DSDs (Bringi & Chandrasekar, 2001): For each time step, simulated Z values in the 100 pixels that correspond with a single 1 km 2 pixel are replaced by their averaged value. Subsequently the rainfall for each 100 m × 100 m grid cell is set to the rainfall intensity computed from this averaged Z using the Z-R relationship for stratiform rainfall by Marshall et al (1955):…”

“…Beekhuis & Mathijssen (2018) provide detailed characteristics on the radars and the processing of their data. Reflectivity factors Z (mm 6 m −3 ) are converted to rainfall intensities R (mm h −1 ) with a fixed Z-R relationship (Marshall et al, 1955), Z = 200R 1.6 , and, subsequently, accumulated to rainfall depths for different durations. The two KNMI rain gauge networks are employed to adjust the radar-based accumulated rainfall depths: an automatic network with 1-h rainfall depths for each hour (∼1 station per 1000 km 2 ) and a manual network with 24-h 08:00-08:00 UTC rainfall depths (∼1 station per 100 km 2 ).…”

Understanding the urban atmosphere through conceptual modelling and opportunistic sensing

“…The shown forecast is 7 hours ahead, and by ingesting PWS data the location and intensity of the precipitation event correspond better to radar observations, though especially location is still somewhat inaccurate. Other applications of opportunistic sensing data include the use of pressure data obtained through smartphones to improve location of frontal zones (Mass and Madaus, 2014;Madaus and Mass, 2017;Hintz et al, 2019), or using PWS measurements for the validation of local processes such as deep convection (Mandement and Caumont, 2019). Increasingly, crowdsourced data are finding their way into the established meteorological institutes: the ECMWF acknowledges the value of crowdsourced data, which can "[capture] rapid temporal/spatial variations in weather parameters; enable new quality control methods (e.g.…”