Home > Research > Publications & Outputs > Using citizen science and crowdsourcing via Aur...

Associated organisational unit

View graph of relations

Using citizen science and crowdsourcing via Aurorasaurus as a near real time data source for space weather applications

Research output: Contribution to conference Abstract

Published
  • Elizabeth MacDonald
  • Matt Heavner
  • Andrea Tapia
  • Nicolas Lalone
  • Jessica Clayton
  • Nathan Case
Close
Publication date12/2014
Number of pages7
<mark>Original language</mark>English
EventAGU Fall Meeting 2014 - San Francisco, United States

Conference

ConferenceAGU Fall Meeting 2014
CountryUnited States
CitySan Francisco
Period15/12/1419/12/14

Abstract

Aurorasaurus is on the cutting edge of space science, citizen science, and computer science simultaneously with the broad goals to develop a real-time citizen science network, educate the general public about the northern lights, and revolutionize real-time space weather nowcasting of the aurora for the public. We are currently in the first solar maximum with social media, which enables the technological roots to connect users, citizen scientists, and professionals around a shared global, rare interest. We will introduce the project which has been in a prototype mode since 2012 and recently relaunched with a new mobile and web presence and active campaigns. We will showcase the interdisciplinary advancements which include a more educated public, disaster warning system applications, and improved real-time ground truth data including photographs and observations of the Northern Lights. We will preview new data which validates the proof of concept for significant improvements in real-time space weather nowcasting. Our aim is to provide better real-time notifications of the visibility of the Northern Lights to the interested public via the combination of noisy crowd-sourced ground truth with noisy satellite-based predictions. The latter data are available now but are often delivered with significant jargon and uncertainty, thus reliable, timely interpretation of such forecasts by the public are problematic. The former data show real-time characteristic significant rises (in tweets for instance) that correlate with other non-real-time indices of auroral activity (like the Kp index). We will discuss the source of 'noise' in each data source. Using citizen science as a platform to provide a basis for deeper understanding is one goal; secondly we want to improve understanding of and appreciation for the dynamics and beauty of the Northern Lights by the public and scientists alike.