Regional wind monitoring system based on multiple sensor networks: A crowdsourcing preliminary test

Agüera-Pérez, Agustín; Palomares-Salas, José Carlos; Rosa, Juan José González de la; Sierra-Fernández, José María

doi:10.1016/j.jweia.2014.02.006

Cited by 9 publications

(9 citation statements)

References 25 publications

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“…Snow cover and depth data can be used as input for hydrological modeling of snow‐fed rivers (Parajka & Blöschl, ), and they can also be used to estimate snow erosion on mountain ridges (Parajka et al, ). Moreover, wind data are used extensively in the efficient management and prediction of wind power production (Agüera‐Pérez et al, ).…”

Section: Review Methodologymentioning

confidence: 99%

“…For example, in the United Kingdom and Ireland, the weather observation website and Weather Underground have been developed to accept weather reports from public amateurs, and in early spring 2012, over 400 and 1,350 amateurs have been regularly uploading their weather data (temperature, wind, pressure, and so on) to weather observation website and Weather Underground, respectively (Bell et al, ). Agüera‐Pérez et al () compiled wind data from 198 citizen‐owned weather stations and successfully estimated the regional wind field with high accuracy, while a high density of temperature data was collected through citizen‐owned automatic weather stations (Chapman et al, ; Wolters & Brandsma, ; Young et al, ), which have been used in urban climate research in recent years (Meier et al, ).…”

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

“…In relation to the use of crowdsourcing methods for the collection of weather data, measurements from amateur gauges and weather stations can now be assimilated in real time (Agüera‐Pérez et al, ; Bell et al, ), and new, low‐cost sensors have been developed and integrated to allow a larger number of citizens to be involved in the monitoring of weather (Muller et al, ). Similarly, other geophysical data can now be collected more cheaply and with a greater spatial and temporal resolution with the assistance of citizens, including data on ecological variables (Chandler et al, ; Donnelly et al, ), temperature (Meier et al, ), and other atmospheric observations (McKercher et al, ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

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Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing‐based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.

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Section: Review Methodologymentioning

confidence: 99%

Section: Review Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Zheng

Tao

Maier

et al. 2018

Reviews of Geophysics

110

View full text Add to dashboard Cite

show abstract

“…street canyons, forests) and crowdsourcing may prove useful. However, as was found to be the case for amateur weather stations, in order for data to be reliable, details about the site of the instrumentation need to be known (Steeneveld et al, 2011;Wolters and Brandsma, 2012;Bell et al, 2013), although Agüera-Pérez et al (2014) did find that useful wind descriptions could be generated using high-density stations -run by various public institutions -based on quantity rather than quality. Other variables may only benefit significantly from supplementary crowdsourced data for certain applications; for example pressure does not tend to vary significantly over short distances except during the passage of a front or convective bands.…”

Section: Quality Assurance/quality Controlmentioning

confidence: 99%

“…Smart Streets project: www.smartstreethub.com; Chapman et al, 2014); energy (e.g. Farhangi, 2010;Agüera-Pérez et al, 2014); other societal uses (e.g. Urban Atmospheres: http://www.urban-atmospheres.net) -and therefore real opportunities for utilizing it to improve our way of life.…”

Section: Applications and Potential Innovationsmentioning

confidence: 99%

Crowdsourcing for climate and atmospheric sciences: current status and future potential

Muller

Chapman

Johnston³

et al. 2015

Intl Journal of Climatology

298

259

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Crowdsourcing is traditionally defined as obtaining data or information by enlisting the services of a (potentially large) number of people. However, due to recent innovations, this definition can now be expanded to include ‘and/or from a range of public sensors, typically connected via the Internet.’ A large and increasing amount of data is now being obtained from a huge variety of non‐traditional sources – from smart phone sensors to amateur weather stations to canvassing members of the public. Some disciplines (e.g. astrophysics, ecology) are already utilizing crowdsourcing techniques (e.g. citizen science initiatives, web 2.0 technology, low‐cost sensors), and while its value within the climate and atmospheric science disciplines is still relatively unexplored, it is beginning to show promise. However, important questions remain; this paper introduces and explores the wide‐range of current and prospective methods to crowdsource atmospheric data, investigates the quality of such data and examines its potential applications in the context of weather, climate and society. It is clear that crowdsourcing is already a valuable tool for engaging the public, and if appropriate validation and quality control procedures are adopted and implemented, it has much potential to provide a valuable source of high temporal and spatial resolution, real‐time data, especially in regions where few observations currently exist, thereby adding value to science, technology and society.

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Gauging Through the Crowd: A Crowd‐Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications

Yang

2017

Water Resources Research

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Accurate rainfall measurement at high spatial and temporal resolutions is critical for the modeling and management of urban storm water. In this study, we conduct computer simulation experiments to test the potential of a crowd‐sourcing approach, where smartphones, surveillance cameras, and other devices act as precipitation sensors, as an alternative to the traditional approach of using rain gauges to monitor urban rainfall. The crowd‐sourcing approach is promising as it has the potential to provide high‐density measurements, albeit with relatively large individual errors. We explore the potential of this approach for urban rainfall monitoring and the subsequent implications for storm water modeling through a series of simulation experiments involving synthetically generated crowd‐sourced rainfall data and a storm water model. The results show that even under conservative assumptions, crowd‐sourced rainfall data lead to more accurate modeling of storm water flows as compared to rain gauge data. We observe the relative superiority of the crowd‐sourcing approach to vary depending on crowd participation rate, measurement accuracy, drainage area, choice of performance statistic, and crowd‐sourced observation type. A possible reason for our findings is the differences between the error structures of crowd‐sourced and rain gauge rainfall fields resulting from the differences between the errors and densities of the raw measurement data underlying the two field types.

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Regional wind monitoring system based on multiple sensor networks: A crowdsourcing preliminary test

Cited by 9 publications

References 25 publications

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions

Crowdsourcing for climate and atmospheric sciences: current status and future potential

Gauging Through the Crowd: A Crowd‐Sourcing Approach to Urban Rainfall Measurement and Storm Water Modeling Implications

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