Crowdsourcing for climate and atmospheric sciences: current status and future potential (original) (raw)

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.

Co-observing the Weather, Co-predicting the Climate: Human Factors in Building Infrastructures for Crowdsourced Data

This paper investigates the embodied performance of 'doing citizen science'. It examines how 'citizen scientists' produce scientific data using the resources available to them, and how their socio-technical practices and emotions impact the construction of a crowdsourced data infrastructure. We found that conducting citizen science is highly emotional and experiential, but these individual experiences and feelings tend to get lost or become invisible when user-contributed data are aggregated and integrated into a big data infrastructure. While new meanings can be extracted from big data sets, the loss of individual emotional and practical elements denotes the loss of data provenance and the marginalisation of individual efforts, motivations, and local politics which might lead to disengaged participants and unsustainable communities of citizen scientists. The challenges of constructing a data infrastructure for crowdsourced data therefore lie in the management of both technical and social issues which are local as well as global.

mPING: Crowd-Sourcing Weather Reports for Research

Bulletin of the American Meteorological Society, 2014

The Weather Service Radar-1988 Doppler (WSR-88D) network within the United States has recently been upgraded to include dual-polarization capability. Among the expectations that have resulted from the upgrade is the ability to discriminate between different precipitation types in winter precipitation events. To know how well any such algorithm performs and whether new algorithms are an improvement, observations of winter precipitation type are needed. Unfortunately, the automated observing systems cannot discriminate between some of the more important types. Thus, human observers are needed. Yet, to deploy dedicated human observers is impractical because the knowledge needed to identify the various precipitation types is common among the public. To most efficiently gather such observations would require the public to be engaged as citizen scientists using a very simple, convenient, nonintrusive method. To achieve this, a simple “app” called mobile Precipitation Identification Near t...

Exploitation of Crowdsourcing Tools and Earth Observation data: A Systematic Literature Review

Global NEST International Conference on Environmental Science & Technology

Crowdsourcing is a method gaining ever wider use in practice and leverages human intelligence to solve problems in a considerable number of study fields. Howe (Howe 2006) coined the concept defining: “Crowdsourcing represents the act of a company or institution taking a function once performed by employees and outsourcing it to an undefined network of people in the form of an open call.” This systematic review aims to understand the Crowdsourcing tools and Earth Observation (Satellite, aerial & in-situ) data and their contribution to environmental conservation and sustainability. The review involved 29 papers with particular focus on Technology Readiness Level (TRL), data fusion methods applied and topics such as types of users and their incentives and tools used to engage users and to collect the data. This article provides a glimpse of the Citizen Science (CS) data collection combined with Earth Observation data and explores the development of this swiftly emerging and evolving su...

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