TY - CONF

T1 - Using citizen science and crowdsourcing via Aurorasaurus as a near real time data source for space weather applications

AU - MacDonald, Elizabeth

AU - Heavner, Matt

AU - Tapia, Andrea

AU - Lalone, Nicolas

AU - Clayton, Jessica

AU - Case, Nathan

PY - 2014/12

Y1 - 2014/12

N2 - 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.

AB - 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.

M3 - Abstract

T2 - AGU Fall Meeting 2014

Y2 - 15 December 2014 through 19 December 2014

ER -