Extreme wind gusts have severe socio-economic impacts, so any source of extra information on this variable is invaluable for mitigating associated damages and
protecting vulnerable communities. Unfortunately, networks of ocial measurement stations are limited in their ability to observe wind gusts. Official stations
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-
tions is available from personal weather stations (PWSs) and could be used in combination with official observations to observe extreme gust events. However,
concerns about underlying data quality have to date prevented the usage of gust data from PWSs.
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 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 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 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 espite how impractical it is to obtain widespread observations of wind gusts using standard techniques.
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.
Chen, J., Saunders, K. & Whan, K. (2021), `Quality control and bias correction of citizen science wind observations', Quarterly Journal of the Royal Meteo-
rological Society (under review) .
How to cite: Whan, K. and Saunders, K.: Second Wind: Extending the official wind gust records with citizen science observations, EMS Annual Meeting 2021, online, 6–10 Sep 2021, EMS2021-510, https://doi.org/10.5194/ems2021-510, 2021.