Sorry for missing yesterday. I had a good excuse. Honestly:
3… You see, I was at AbGradCon, the Astrobiology Graduate Conference. It’s a place where graduate students and early-career postdocs in astrobiology can get together inside a “closed ecosystem” that is free from the stresses imposed by the presence of established scientists. It’s a big part of the astrobiology community, and to be totally honest one of the major reasons I’m still in the field. I have many colleagues I consider to be friends thanks to this meeting…. and now I have more. Thanks to all that attended for a wonderful time and some absolutely exhilarating conversations. I honestly haven’t been this excited about our work for a long, long time. For those that did NOT attend, you will be able to check out the archives of the talks at the conference website soon.
2… I never cease to be amazed at how much we can learn of the subsurface of planets with remote observations. In this case, a paper in Science shows how magnetometer data from the Galileo mission can be used to infer the presence of a subsurface magma ocean. (Say it like Dr. Evil.)
1… One of the attendees at AbGradCon was Tyler Robinson, who is also the lead author on an excellent paper that came out in Astrobiology yesterday. In the paper, Ty worked with colleagues to validate the 3-D Earth Spectral Model from the NAI’s Virtual Planetary Laboratory team, using observations from NASA’s EPOXI and Aqua missions. The correlation between the models and observations is incredible. They’re even more amazing when one realizes the model was *not* tuned to match the observations. Instead, the model was built using inputs from many other NASA missions to prescribe the surface type, cloud thickness/distribution, and atmospheric properties for the Earth. The models matched over wide wavelength ranges and over three 24-hour periods. Why is this level of accuracy important? Well, once one has an accurate Earth model you can start simulating what the Earth looks like from angles and at wavelengths for which you do *not* have observations. To put it another way: they’ve created the most accurate model of our “pale blue dot.”