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, Lee; Bell, Cassandra; Bell, Simon J.

doi:10.1002/joc.4940

Cited by 138 publications

(135 citation statements)

References 39 publications

(72 reference statements)

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“…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%

“…On the data collection side, some of the challenges related to the deployment of low‐cost and mobile sensors may be solved through improving the reliability of the sensors in the future (McKercher et al, ). However, an ongoing challenge that hinders the wider collection of atmospheric observations from the public is that outdoor measurement facilities are often vulnerable to environmental damage (Chapman et al, ; Melhuish & Pedder, ). There are technical challenges arising from the lack of data standards and interoperability for data sharing (Panteras & Cervone, ), particularly in domains where multiple types of data are collected and integrated within a single application.…”

Section: Review Of Issues Associated With Crowdsourcing Applicationsmentioning

confidence: 99%

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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 Of Crowdsourcing Data Acquisition Methods Usedmentioning

confidence: 99%

Section: Review Of Issues Associated With Crowdsourcing Applicationsmentioning

confidence: 99%

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

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“…Using only these stations, it would not be possible to assess how the different LCZ descriptions affected WRF predictions across Vienna's varied urban land cover typologies. Therefore, the use of crowdsourced data from the PWSN seemed to be relevant for this study Muller et al, 2015;Chapman et al, 2017). The Weather Underground maintains a database of weather data voluntarily provided by stations in their PWSN.…”

Section: Observational Data Sourcesmentioning

confidence: 99%

Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria

Hammerberg

Brousse

Martilli

et al. 2018

Intl Journal of Climatology

119

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ABSTRACT:One of the major obstacles to using numerical weather prediction models for guidance on mitigating urbanization's impact on local and regional climate is the lack of detailed and model ready morphological data at urban scale. The World Urban Database and Access Portal Tool (WUDAPT) is a recent project developed to extract climate relevant information on urban areas, in the form of local climate zones (LCZs), out of remote sensing imagery. This description of the urban landscape has been tested and used for parameterization of different urban canopy models (UCM) for mesoscale studies. As detailed information is usually bounded within cities' centres, crowdsourced and remote sensing data offer the possibility to move beyond the old barriers of urban climate investigations by studying the full range of variation from the urban core to the periphery and its related impacts on local climate. Thus, for this study we sought to compare the relative impact of using the WUDAPT methodology versus a simplified definition of the urban morphology extracted out of detailed GIS information to initialize a regional weather model and compare the output against official and crowdsourced weather station networks. A case study over Vienna, Austria was conducted using the weather research forecasting (WRF) model, coupled with the building effect parameterization and building energy models (BEP-BEM) in five distinct seasonal periods. Results demonstrated that using detailed GIS data to derive morphological descriptions of LCZs for mesoscale studies provided only a marginal overall improvement over using the default WUDAPT parameters based on the ranges proposed by Stewart and Oke (2012). The findings also highlighted the importance of developing techniques that are better at capturing the morphological heterogeneity across the entire urban landscape and thus improve our understandings of UCM performance over urban areas.

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“…Currently only a few preliminary studies on the use of crowd-sourced data in meteorology exist. Chapman et al (2017) used Netatmo data to quantify the urban heat island effect of London, Overeem et al (2013) quantified urban air temperatures using battery temperatures from smartphones and Clark et al (2018) used private weather stations to do a fine-scale analysis of a severe hailstorm.…”

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confidence: 99%

Collecting and processing of barometric data from smartphones for potential use in numerical weather prediction data assimilation

Hintz

Vedel

Kaas

2019

Meteorological Applications

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The potential for use of crowd‐sourced data in the atmospheric sciences is vastly expanding, including observations from smartphones with barometric sensors. Smartphone pressure observations can potentially help improve numerical weather prediction and aid forecasters. In this contribution a method of collecting data from smartphones is presented, other methods are discussed and guidelines are derived from the experience. Quality control is vital when using crowd‐sourced data. Screening methods aimed at smartphone pressure observations are presented. Results from previous studies, showing a substantial but long‐term stable bias in combination with high relative accuracy, are confirmed. The collection of Danish smartphone pressure observations has been very successful, with over 6 million observations during a 7 week period. Case studies show that distinct weather patterns can be seen in unprocessed data. The screening method developed reduces the observational noise but filters out the majority of observations. Assimilating smartphone pressure observations in a single case study, using the 3D variational data assimilation system of the HARMONIE numerical weather prediction system, proved to decrease the bias of surface pressure in the model without increasing the root mean square error and the skill of accumulated precipitation increased. It is found that the altitude assignment of smartphones needs improvement.

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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

Cited by 138 publications

References 39 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

Implications of employing detailed urban canopy parameters for mesoscale climate modelling: a comparison between WUDAPT and GIS databases over Vienna, Austria

Collecting and processing of barometric data from smartphones for potential use in numerical weather prediction data assimilation

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