How good are citizen weather stations? Addressing a biased opinion

Bell, Simon J.; Cornford, Dan; Bastin, Lucy

doi:10.1002/wea.2316

Cited by 99 publications

(125 citation statements)

References 14 publications

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“…It is therefore considered to be a passive crowdsourcing method (Muller et al , ). While there are a large number of similar citizen science weather stations available (Bell et al , , ), none have proven to be as prolific in adoption as the Netatmo, particularly in urban settings.…”

Section: Methodsmentioning

confidence: 99%

Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations

Chapman

Bell

2016

Intl Journal of Climatology

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140

115

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Crowdsourcing techniques are frequently used across science to supplement traditional means of data collection. Although atmospheric science has so far been slow to harness the technology, developments have now reached the point where the benefits of the approaches simply cannot be ignored: crowdsourcing has potentially far‐reaching consequences for the way in which measurements are collected and used in the discipline. To illustrate this point, this paper uses air temperature data from the prolific, low‐cost, Netatmo weather station to quantify the urban heat island of London over the summer of 2015. The results are broadly comparable with previous studies, and indeed standard observations (albeit with a warm bias, a likely consequence of non‐standard site exposure), showing a range of magnitudes of between 1 and 6 °C across the city depending on atmospheric stability. However, not all the results can be easily explained by physical processes and therefore highlight quality issues with crowdsourced data that need to be resolved. This paper aims to kickstart a step‐change in the use of crowdsourcing in urban meteorology by encouraging atmospheric scientists to more positively engage with the new generation of manufacturers producing mass market sensors.

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Section: Methodsmentioning

confidence: 99%

Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations

Chapman

Bell

2016

Intl Journal of Climatology

Self Cite

140

115

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“…A growing number of weather enthusiasts measure their local weather with automatic personal weather stations (PWSs). PWS accuracy on measuring temperature, relative humidity, radiation, pressure, rainfall, wind speed and direction has been evaluated for popular high-end expensive weather stations (Jenkins, 2014;Bell et al, 2015), as well as for the cheaper, user-friendly Netatmo type (temperature only) (Meier et al, 2015), which have grown rapidly in number over the past years. So far, weather stations have been used to obtain air temperature data to examine the urban heat island effect (Steeneveld et al, 2011;Wolters and Brandsma, 2012), although other meteorological variables, such as rainfall, are measured by some of these stations as well.…”

Section: Introductionmentioning

confidence: 99%

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

Vos

Leijnse

Overeem

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam ( ∼ 575 km 2 ) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.

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“…The effectiveness of the radiation shield was tested against a Bureau of Meteorology weather station at half‐hourly resolution at the Adelaide Airport, leading to a correlation coefficient of 0.997 and root‐mean‐squared difference of 0.24°C. This degree of accuracy is comparable to, or better than, commercially available radiation shields [ Bell et al , ].…”

Section: Methodsmentioning

confidence: 72%

Temporal and spatial patterns of air temperature in a coastal city with a slope base setting

et al. 2016

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In coastal cities with a slope base setting, effects of sea breeze and cold air drainage superpose on top of the urban heat island (UHI) effect. It is difficult to articulate the urbanization effect which, however, is important in developing summer heat mitigation solutions. The aim of this study is to address this challenge by investigating the temporal and spatial distribution of air temperature in the Adelaide metropolitan area of South Australia based on data from a monitoring network in 2011–2013. The results indicate that the dominant influence on the spatial variability of air temperature in winter is the urban heat island effect, followed by the prevailing nighttime northeasterly wind and cold air drainage. In summer, the dominant influence comes from daytime sea breezes, followed by the urban heat island effect, and the prevailing southeasterly nighttime wind. The urban heat island intensity is estimated based on various references where the influence of other factors is minimal. A clear nighttime urban heat island is observed over the Adelaide metropolitan area, with a mean intensity of 2.5°C for both summer and winter. The daytime urban heat island is not clear. On hot days, the city center develops a dominant nighttime urban heat island about 4°C warmer than the coast, stronger than an average summer night. Given multiple influences on the thermal climate in the Adelaide metropolitan area, it is suggested that the unique 500 m wide Adelaide parkland belt surrounding the city center be used as a reference to evaluate the Adelaide city center urban heat island in summer. A rural station, Edinburgh, can be used to evaluate the nighttime urban heat island intensity in the study area.

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How good are citizen weather stations? Addressing a biased opinion

Cited by 99 publications

References 14 publications

Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations

Can the crowdsourcing data paradigm take atmospheric science to a new level? A case study of the urban heat island of London quantified using Netatmo weather stations

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

Temporal and spatial patterns of air temperature in a coastal city with a slope base setting

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