Crowdsourcing of weather observations at national meteorological and hydrological services in Europe

Krennert, Thomas; Pistotnik, Georg; Kaltenberger, Rainer; Csekits, Christian

doi:10.5194/asr-15-71-2018

Cited by 29 publications

(18 citation statements)

References 7 publications

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“…The use of citizen science is not new in meteorology, and has even aided in the birth of the field (Eden, 2009). The emergence of PWSs has contributed to various studies, especially in urban areas lacking traditional measurements (Steeneveld et al, 2011;Wolters and Brandsma, 2012;Chapman et al, 2017;de Vos et al, 2017;Fenner et al, 2017;Chapman and Bell, 2018), but also at the level of national weather authorities (Krennert et al, 2018;Nipen et al, 2020). While PWS data provide valuable insights into undersampled urban regions, the data remain of relatively low quality compared to WMO standards.…”

Section: Use Of Pws Data In Other Studiesmentioning

confidence: 99%

Assessing the potential and application of crowdsourced urban wind data

Droste

Heusinkveld

Fenner

et al. 2020

Quart J Royal Meteoro Soc

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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.

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Section: Use Of Pws Data In Other Studiesmentioning

confidence: 99%

Assessing the potential and application of crowdsourced urban wind data

Droste

Heusinkveld

Fenner

et al. 2020

Quart J Royal Meteoro Soc

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“…As predicted, data derived from social weather are playing an increasingly important role in meteorological research, especially in public discourse (Kirilenko & Stepchenkova, ; Auer et al , ; Krennert et al , ) and in intensive and cost‐efficient data acquisition. Also known as ‘crowdsourcing for atmospheric sciences’, social weather data consist of temporal, spatial, textual, behavioural and other features.…”

Section: Review Of Previous Work and Analysismentioning

confidence: 99%

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Zhu

Zhang

et al. 2019

Geoscience Data Journal

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Crowdsourcing has significantly motivated the development of meteorological services. Starting from the beginning of 2010s and highly motivating after 2014, crowdsourcing-driven meteorological services have evolved from a single collection and observation of data to the systematic acquisition, analysis and application of these data. In this review, by focusing on papers and databases that have combined crowdsourcing methods to promote or implement meteorological knowledge services, we analysed the relevant literature in three dimensions: data collection, information analysis and meteorological knowledge applications. First, we selected the potential data sources for crowdsourcing and discussed the characteristics of the collected data in four dimensions: consciousness, objectiveness, mobility and multidisciplinary.Second, based on the purpose of these studies and the extent of utilizing data as well as knowledge, we categorize the crowdsourcing-based meteorological analysis into three levels: relationship discovery, knowledge generalization and systemized service. Third, according to the application scenario, we discussed the applications that have already been put into use, and we suggest current challenges and future research directions. These previous studies show that the use of crowdsourcing in social space can expand the coverage as well as enhance the performance of meteorological 62 | ZHU et al.

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“…A comprehensive overview of current crowdsourcing activities in NWP is given by Hintz et al (2019b), and Krennert et al (2018) give a more general overview of recent crowdsourcing activities within Europe.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Wind Speed and Roughness Length Using Smartphones: Method and Quality Assessment

Hintz

Vedel

Kaas

et al. 2020

Journal of Atmospheric and Oceanic Technology

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Crowdsourced data is now seen as a potential source of high-resolution observations in the atmospheric sciences. In this paper we investigate a potential data source, wind observations obtained using anemometers connected to handheld smartphones. The aim of this paper is twofold: to assess the quality of raw and height extrapolated wind measurements from the handheld anemometer against professional-grade SYNOP stations, and to use this data of opportunity to infer a more accurate estimation of terrain roughness lengths. Roughness lengths are essential in numerical weather prediction; however, they are often poorly determined. Roughness lengths are also necessary when correcting near-surface wind observations for height offsets. For the analysis we performed a series of field experiments measuring wind profiles using handheld anemometers at roughly 2 m above ground. These raw measurements were then extrapolated to 10 m height using roughness lengths from three different sources. The extrapolation enabled us to compare the quality of roughness lengths estimated from smartphone measurements to those from traditional sources, as well as assess the quality of these wind measurements against the professional-grade stations. We find the handheld wind measurements are comparable in quality to wind measurements from SYNOP stations at 10 m height, and that for some cases the handheld measurements can be more representative than SYNOP stations only about a kilometre away. To determine the roughness lengths, we examine a method based on the turbulent intensity derived from the high-frequency signal of the smartphone wind measurements. Under certain circumstances, the roughness lengths obtained with the approach presented here are superior to traditional sources.

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Crowdsourcing of weather observations at national meteorological and hydrological services in Europe

Cited by 29 publications

References 7 publications

Assessing the potential and application of crowdsourced urban wind data

Assessing the potential and application of crowdsourced urban wind data

Social weather: A review of crowdsourcing‐assisted meteorological knowledge services through social cyberspace

Estimation of Wind Speed and Roughness Length Using Smartphones: Method and Quality Assessment

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