Note: Aurorasaurus is currently a prototype. Expect bugginess. :)

Aurorasaurus

Our aim is to create a highly accurate easy-to-use real-time map of confirmed aurora sightings to increase your chances of seeing the rare beautiful northern lights during the upcoming maximum of the solar cycle (2012-2014). This will be the first solar maximum with social media and the chance to Tweet about aurora sightings is a powerful way to inform others also. We aim to build a predictive capability for the lights' visibility based on your positive and negative sightings and social media. We are also using your volunteered geographic information for space science and computer science related research. We welcome suggestions on improving the site and volunteers of any sort are also very appreciated.

What do I do here?

When auroras (northern or southern lights) are visible, we’d like to know your location so we can map visibility for others to see. The aim of aurorasaurus is to help as many people as possible know when the aurora is visible in their neighborhood, accurately and in real-time. No scientific knowledge or jargon is required, just the ability to navigate the map and enter simple observations. If you went looking for aurora and weren’t able to see it, that is also valuable information.

How do I know when auroras are visible?

This is more complicated. The short answer is you can watch our map, especially the auroral oval forecast for one hour into the future. If you have more scientific knowledge about auroral behavior, you can watch the main forecasting websites and their space data, e.g. spaceweather.com and spaceweather.gov. But the bottom line is that when the Sun is very active and emits large amounts of energy towards Earth, we don’t have enough observations to tell when that energy will hit Earth to better than ~half a day (+/- 7 hrs). Until the energy hits the ACE satellite upstream of Earth then we have about an hour of warning. An estimate for the extent of the auroral oval based on the ACE satellite data and a neural network model is shown on our map, for the current time and about an hour into the future.

Disclaimer

Space weather and terrestrial weather can move very quickly and auroral visibility is not guaranteed by Aurorasaurus.org.

Our team

Elizabeth MacDonald

Liz MacDonald has a Ph.D. in Physics (focusing on Space Science) from the University of New Hampshire. She attended the University of Washington for her undergrad where she was introduced to the aurora and the exciting research field of experimental space science.

As a scientist at Los Alamos National Laboratory (LANL), Liz works primarily on space weather instrument teams; for example she is a Co-Investigator for the NASA Radiation Belt Storm Probes (RBSP) mission focusing on the HOPE instrument for the last several years. She is also actively involved in educational outreach efforts aimed at increasing diversity in STEM fields and communicating the beauty of science (especially the aurora!). You can find out more professional information here and follow Liz on Twitter @spaceyliz.

Outside of work she likes to hike, climb, and ski in the beautiful sunny environs of northern New Mexico.

Niels van Hecke

Niels van Hecke graduated from Rochester Institute of Technology in 2012 with a BS in Game Design and Development and is seeking employment in the games software industry. His strengths are in games networking and server-side development. Niels is currently working as an indie developer on the math puzzle game Chromathud and another unannounced math puzzle game.

Outside of work, Niels runs and does multiple martial arts.

You can learn more about Niels at his website.

Gordon McDonough and Elizabeth Martineau

Liz Martineau and Gordon McDonough are the science educators at Los Alamos National Lab’s Bradbury Science Museum. They work with thousands of students from all over northern New Mexico and the surrounding region, and specialize in interactive programming. While both have taught several grades in public and private schools, Liz focused on elementary grades and Gordon on middle school. They are both enthusiasts in science and history. Their programs involve a wide range of science topics, from the nano-scale to cosmology, and they love working with scientists to bring their research to a broad public.

Liz is a mom and a skilled fiber artist. She likes to hike and camp, and is deeply involved in several community organizations. She is currently pursuing her Master’s degree in educational leadership.

Gordon is a dad, hikes and bikes, likes to camp, and is a working automaton artist with a small but growing local reputation. A sampling of his work resides atgordonmcd.tumblr.com. Gordon is trying to learn the ins and outs of micro-controllers, especially Arduino, for possible use in his art.

Among many other things, Liz and Gordon enjoy Argentine Tango lessons together.

Team Members Emeriti

Reid Priedhorsky

Reid Priedhorsky is a postdoctoral research associate at Los Alamos National Laboratory. He holds a Ph.D. from the University of Minnesota in computer science.

As a researcher focusing on human-centered computing, he works to empower communities to make better decisions in pursuit of a sustainable, secure, and just global future. He does this by exploring the tools and algorithms which facilitate human communication, collaboration, and shared creation of knowledge, with a current focus on the use of such tools and algorithms to answer quantitative questions about the real world.

Reid’s past projects include Cyclopath, a web map serving the bicycling community of the Minneapolis-St. Paul metro area. This researcher is a “geowiki”: any user can edit both the base map as well as points of interest and other annotations, and changes are live immediately (i.e., they are peer-reviewed after publication, not before). The Cyclopath map has been revised over 13,000 times.

You can find out more about Reid on his personal research page or at LANL.

Yan Cao

Yan Cao is pursuing her Ph.D. degree in Information Sciences and Technology (IST, focusing on Human Computer Interaction) at Penn State University. Interestingly, Yan obtained a master’s degree in Physics, concentrating on condensed matter physics. Her undergraduate was in Physics as well in the University of Science and Technology of China.

This unusual transfer from Physics to IST was encouraged by Yan’s strong interest in behavioral science and information technologies. Her current research involves participatory risk management, risk games (with a purpose), and crowdsourcing. This citizen science aurora project is a great fit for her interests.

Outside of work, Yan enjoys running and plans to do more marathons and half-marathons. She travels a lot and loves camping and hiking as well.

Please find more about Yan here.

Acknowledgements

Funding for our project is provided by Los Alamos National Laboratory’s LDRD program in conjunction with the IGPP Institutes Office, the New Mexico Consortium, and the Radiation Belt Storm Probes Education/Public Outreach program.

Support is also provided by the Bradbury Museum. Our Twitter data partner is Social Flow. Space physics real-time data are provided by the Space Weather Prediction Center at NOAA, in particular the Wing-Kp Predicted Geomagnetic Activity Index (Wing et al., 2005, J. Geophys. Res). Dirk Lummerzheim at the UAF Geophysical Institute provided code for the auroral oval prediction using Kp, an idea, partially begun by the International Space Apps challenge team from Dublin. Matt Gray provided the formatted tweet option, first mapping, and key input (www.auroramap.com).

Special thanks to Katharine Chartrand, Pam Hundley, Lora Suther, NOAA NGDC folks, Twitter, and beta testers everywhere.

*Image from Wikipedia