Assessing the potential and application of crowdsourced urban wind data

The use of crowdsourcing – obtaining large quantities of data through the Internet – has been of great value in urban meteorology. Crowdsourcing has been used to obtain urban air temperature, air pressure, and precipitation data from sources such as mobile phones or personal weather stations (PWSs), but so far wind data have not been researched. Urban wind behaviour is highly variable and challenging to measure, since observations strongly depend on the location and instrumental set‐up. Crowdsourcing can provide a dense network of wind observations and may give insight into the spatial pattern of urban wind. In this study, we evaluate the skill of the popular “Netatmo” PWS anemometer against a reference for a rural and an urban site. Subsequently, we use crowdsourced wind speed observations from 60 PWSs in Amsterdam, the Netherlands, to analyse wind speed distributions of different Local Climate Zones (LCZs). The Netatmo PWS anemometer appears to systematically underestimate the wind speed, and episodes with rain or high relative humidity degrade the measurement quality. Therefore, we developed a quality assurance (QA) protocol to correct PWS measurements for these errors. The applied QA protocol strongly improves PWS data to a point where they can be used to infer the probability density distribution of wind speed of a city or neighbourhood. This density distribution consists of a combination of two Weibull distributions, rather than the typical single Weibull distribution used for rural wind speed observations. The limited capability of the Netatmo PWS anemometer to measure near‐zero wind speed causes the QA protocol to perform poorly for periods with very low wind speeds. However, results for a year‐long wind speed climatology of the wind speed are satisfactory, as well as for a shorter period with higher wind speeds. We research the value of crowdsourced urban wind observations from Personal Weather Stations. From comparison measurements against known wind speeds at two sites, we construct a Quality Assurance protocol to improve data quality. After applying this protocol, the crowdsourced data can successfully be used to calculate the urban wind speed probability distribution.