You may be wondering what I’ll be doing for the rest of the ATom 1 mission now that I’m back at base in Colorado. As I watched the plane leave Hawaii and went from the mission-bubble surrounded by scientists and crew to interact with, daily maintenance, scheduled access and preparations for the next flight etc. to suddenly being alone on an island I wondered the same thing briefly.
Then I quickly got on xchat, a program we use to live-chat with the scientist on board the plane from the ground, and guided my colleagues through their first flight of running my instruments for me. There were last minute problems to diagnose remotely and fix, questions about optimal performance to figure out and, of course, once the flight was complete, data to process. I’ll also be remotely assisting mu colleague, Agnieszka, the other expert on our set-up, who takes over from New Zealand onwards, on the remaining flights and for maintenance on down-days.
And actually, collecting and initial processing of the data is only the tip of the iceberg. One of the first things I need to do is write a full inversion for the concentrations of particles we produce, to get a size distribution, that is, how many particles within every given size range are present at each point in time. This is something I geek out on. It’s mathematically complex and there are many different, competing theories of how best to do it. The coding of it is intense. I have some experience of writing inversions from my doctoral work on the CLOUD experiment at CERN, and have been applying this to our ATom data. With my feet on the ground for a week or so, I can really optimize and test this.
After that, there are two ways to look at these size distributions we’ll produce. The first is to look at singular, interesting events e.g. evidence of possible new particle formation, pollution layers we pass through, plumes from sources that need to be identified. The second, and arguably, more important, is to look at the times where nothing in particular is happening. I’ve mentioned this before, a lot of the value of ATom data is getting the background atmosphere better understood. How do things look like on average in different regions and at different altitudes, what does this mean for our understanding of the basic processes and how well do our current models match up with what we’re seeing. This means systematically going through and spotting the trends in aerosol concentrations with respect to latitude, longitude, altitude, meteorology and then other variables measured by instruments on the plane such as concentrations of different gases.
This may sound less glamorous that hopping around the world on a plane, but it brings out a deeper, quieter excitement in me. I have in front of me the beginnings of a treasure-trove dataset and the value I can extract from it is now just down to how well I can apply my scientific skills to it. The challenge is on!