Second Wind: Extending the official wind gust records with citizen science observations
Authors: Whan Kirien , Saunders Kate 
Published: July 2021

&lt;p&gt;Extreme wind gusts have severe socio-economic impacts, so any source of extra information on this variable is invaluable for mitigating associated damages and&lt;br /&gt;protecting vulnerable communities. Unfortunately, networks of ocial measurement stations are limited in their ability to observe wind gusts. Official stations&lt;br /&gt;are separated by vast distances, so extreme wind gusts often go unobserved due to the highly localised nature of these events. A wealth of additional observa-&lt;br /&gt;tions is available from personal weather stations (PWSs) and could be used in combination with official observations to observe extreme gust events. However,&lt;br /&gt;concerns about underlying data quality have to date prevented the usage of gust data from PWSs.&lt;/p&gt;
&lt;p&gt;Research for other meteorological variables has demonstrated that with appropriate quality control PWSs can contribute high-quality observations that complement ocial measurements. It is well known that PWSs can provide useful and reliable temperature and precipitation observations. For crowd-sourced wind variables, the situation is more dicult. Crowd-sourced wind observations have di erent sources of error that pose signi cant challenges to quality control. For example, instrumentation is non-standard which results in di erent sensor sensitivities, and non-standard station placements introduce severe spatial in-consistencies and result in censoring of low wind speeds. Chen et al. (2021) recently developed a&amp;#160; exible approach to quality control and bias adjustment (QC/BA) that addresses this for wind speeds. They incorporate QC steps for official stations and develop new QC/BA steps to address the novel challenges posed by crowd-sourced data. Chen et al. (2021) showed after QC/BA, the wind speed climatology of a network of PWSs matched well with the climatology of ocial stations, and the wind speed variability between PWSs was similar to that of official&amp;#160; tations. Additionally, subsequent analysis has shown that the quality controlled and bias adjusted data from PWSs is able to detect small scale extreme wind speeds&amp;#160; ssociated with thunderstorms, that were not observed at official stations. No attempt has yet been made to quality control crowd-sourced observations of wind gusts&amp;#160; espite how impractical it is to obtain widespread observations of wind gusts using standard techniques.&lt;/p&gt;
&lt;p&gt;In this project we will develop the necessary methods and software for the QC/BA of wind gusts. As part of this, we will develop inter-variable consistency checks between crowd-sourced wind speeds, wind gusts and wind directions. We will also produce an open-source, high-quality wind gust data set from PWSs that can be used to improve forecasts, warnings, and veri cation of extreme gusts.&lt;/p&gt;
&lt;p&gt;&lt;strong&gt;References&lt;/strong&gt;&lt;/p&gt;
&lt;p&gt;Chen, J., Saunders, K. &amp; Whan, K. (2021), `Quality control and bias correction of citizen science wind observations', &lt;em&gt;Quarterly Journal of the Royal Meteo-&lt;/em&gt;&lt;br /&gt;&lt;em&gt;rological Society (under review) &lt;/em&gt;.&lt;/p&gt;