Fine‐scale analysis of a severe hailstorm using crowd‐sourced and conventional observations

Abstract

On 1 July 2015, severe hailstorms developed over northern England. One storm tracked across an area with a dense network of privately owned (i.e. 'home') automatic weather stations (AWSs), permitting analysis of surface wind, pressure and temperature fields on the storm scale. The home AWS data were filtered and corrected by comparison with data from the nearest United Kingdom Met Office AWS, where measurements are made with calibrated sensors having known error characteristics. A time-compositing technique was applied to the corrected home AWS data, before interpolation onto a 1 km grid using Delaunay triangulation. The resulting analyses were compared with radar data to assess their quality and provide insights into storm evolution and structure. Surface analyses resolved a pressure anomaly couplet on the right, rear flank of the storm, gust fronts, and regions of inflow and outflow. The pressure couplet was closely collocated with the radar-observed mid-level updraught position, with the mesolow (mesohigh) situated underneath the downshear (upshear) flank. The mesolow was also collocated with the strongest inflow winds. Structural features in the surface analyses (e.g. forward-and rear-flank gust fronts and a prominent inflow notch) compared well with radar-observed structures, and conformed closely to established conceptual models of supercell storm morphology. Further insight into storm structure was gained by synthesis of the surface analyses, Doppler radar data, crowd-sourced hail reports and eyewitness photographs. Collectively, the results demonstrate that the gridded home AWS data may be of sufficient quality for use in post-event studies of severe thunderstorms and, potentially, in the operational forecasting environment.Fine-scale analysis of a hailstorm

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

“…A fully automatic PWS processing algorithm based on comparisons with SWSs was developed. The features near the ground of isolated storms, multicellular 5 systems or supercell storms are observed, extending the work of Clark et al (2018) which focused on a sole supercell storm.…”

“…SPWS analysis is also more coherent with surface wind observations than SWS analysis. A rise in MSLP under the 5 supercell evolving ahead of the squall line is also exhibited by the SPWS analysis, but is not observed in the SWS analysis whereas MSLP rise is usually observed under supercellular storms (Clark et al, 2018). Effects of this cell near the ground are confirmed by a nearby SWS which recorded a 22 m s −1 gust.…”

“…It includes a quality control in pressure, temperature and humidity which is designed to be simple and efficient whatever the meteorological situation is. The algorithm is mainly based on comparisons with a quality-controlled reference network as it was done by Meier et al (2017) and Clark et al (2018). The data processing is performed during the periods of time indicated in Table 1.…”

Collecting and processing of barometric data from smartphones for potential use in numerical weather prediction data assimilation

“…Currently only a few preliminary studies on the use of crowd-sourced data in meteorology exist. Chapman et al (2017) used Netatmo data to quantify the urban heat island effect of London, Overeem et al (2013) quantified urban air temperatures using battery temperatures from smartphones and Clark et al (2018) used private weather stations to do a fine-scale analysis of a severe hailstorm.…”

Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–5 December 2018

“…Clark et al . () examined the use of PWSs and made considerable progress in the quality control and use of such data. Examples of successful assimilation of such observations into operational Numerical Weather Prediction (NWP) models are currently few and far between.…”