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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2019-229
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2019-229
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 17 Jul 2019

Submitted as: research article | 17 Jul 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Natural Hazards and Earth System Sciences (NHESS).

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

Marc Mandement and Olivier Caumont Marc Mandement and Olivier Caumont
  • CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France

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 PWSs 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 (SPWS) 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 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. 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.

Marc Mandement and Olivier Caumont
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Status: open (until 21 Sep 2019)
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Marc Mandement and Olivier Caumont
Marc Mandement and Olivier Caumont
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Short summary
The number of connected personal weather stations has dramatically increased in the last years. These weather stations produce massive amounts of data that need a thorough quality control to unleash their potential. A novel quality control algorithm now allows to take full advantage of these data and observe thunderstorms with fine-scale details that cannot be caught by standard networks. These results pave the way to tremendous advances in thunderstorm understanding and forecasting.
The number of connected personal weather stations has dramatically increased in the last years....
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