Sometimes in science everything just comes together, but not often. This time it did. What could have gone wrong in the campaign? Lots. The two biggest possibilities were that the weather could have been bad (Fig. 1) during the five-day window when the airplane was with us, grounding the plane and making it impossible to obtain the crucial airborne LiDAR data. Or a big snowstorm could have occurred between when we made thousands of hard-won ground-based measurements and when the airborne measurements were made, making the ground measurements null and void. Either would have been enough to sink the project.

Other bad things that didn’t happen: serious equipment malfunctions, frostbite, incidents while trailering and un-trailering heavy snowmobiles, and accidents on the narrow and icy Dalton Highway, a dirt road mainly driven by semi-trucks in a big hurry to get to Prudhoe Bay, or back home to Fairbanks. Cracked windshields from flying rocks on all four of our trucks testify to the rugged nature of this road (see also Season 3 of Ice Road Truckers).

Figure 1: This is what bad weather looks like on the North Slope of Alaska. Three snowmobiles can barely be seen in the middle distance through the ground blizzard, but we had nothing like this during the campaign, thankfully.

Figure 1: This is what bad weather looks like on the North Slope of Alaska. Three snowmobiles can barely be seen in the middle distance through the ground blizzard, but we had nothing like this during the campaign, thankfully.

First the numbers: The 16 of us took more than a 100,000 ground-based snow depths spread across a 200-km swath of Northern Alaska, dug 20 detailed snow pits, obtained 286 snow cores that were weighed for snow water equivalent (ave. density was 283 kg/m3), produced 19 ground-based LiDAR maps, and collected more than a hundred square kilometers of airborne LiDAR mapping in a swath about 200-m wide (Fig. 2).

Figure 2: Maps showing where data were collected.

Figure 2: Maps showing where data were collected.

If all goes well as we begin to crunch the data we will find that when we subtract the summer LiDAR surfaces (which we will measure in June) from the winter snow-covered surfaces, we will produce maps of snow depth that compare well with the depth measurements we made on the ground. Using the snow cores, we will then develop a simple regression equation that will let us convert these snow depths into snow water equivalents, and when all is done, we will have maps of snow pack across the study region.

The challenging part is that the snow cover in this Arctic region is thin….ranging from a few to about 150 centimeters. . .so the signal-to-noise ratio for the airborne LiDAR must be kept as high as possible. The noise in the airborne LiDAR measurements is on the order of tens of centimeters, but by using the road surface and other fixed targets like metal sheds (Fig. 3), concrete structures, and the Alaska Pipeline (also Fig. 3), we believe we can reduce this noise, giving us extremely useful results.

Figure 3: A fixed green metal shed near the Trans-Alaska pipeline (lower edge of photo), one of the ad hoc control points for the airborne LiDAR.

Figure 3: A fixed green metal shed near the Trans-Alaska pipeline (lower edge of photo), one of the ad hoc control points for the airborne LiDAR.

What will it mean if all the analysis proves successful? I would like to think that using our results we might be able to convince oil companies, State and Federal regulators, agencies, wildlife biologists, and hydrologists to collaborate in developing an operational program that uses LiDAR technology to better map and assess the North Slope snow cover year after year. This could produce both management-useful maps and a climate record of snow cover that will allow us to understand how climate change is altering winter precipitation, as well as the patterns of that change.

Of course, there were many intangible results too, these more personal than scientific. The first was the spell the Arctic cast over all of the team, veterans and newcomers alike. The sky, the aurora, the Brooks Range looming to the south day after day (Fig. 4), the animals (see previous posts), and of course the endless fascinating snow cover (Fig. 5). All of these wove a spell that captured all of us.

Figure 4: The Brooks Range at twilight.

Figure 4: The Brooks Range at twilight.

Figure 5: Frost feathers against a background of surface hoar.

Figure 5: Frost feathers against a background of surface hoar.

The second was the camaraderie of our group. The cold and hard work made for big appetites and lots of good-natured barracks-room humor. We introduced Sveta, our Russian colleague, to the Rocky and Bullwinkle Show (remember Natasha and Boris?), competed for the highest ‘N’ (number of measurements) each day, and taught two novices to drive snowmobiles, a learning process that never fails to delight all involved. Kelly and I got to compare notes on the snow crystals found in an Arctic snow pack as opposed to one in Colorado, where he normally works, and Mark S. got recharged for another year of running the National Snow and Ice Data Center in Boulder, Colorado, where he is the director. Perhaps Allison, the newest member of our team, started a scientific relationship with snow that will influence what she does on her Ph.D. thesis. For myself, in my 28th year of working in this area, and my 39th year in the Arctic, I have to say it still feels special to work in the North, and it is still a privilege to get to do this sort of work with such fine companions (Fig. 6).

Figure 6: The SnowSTAR-2012 team.

Figure 6: The SnowSTAR-2012 team.

Till next year, when perhaps there will be some notes from SnowSTAR-2013...

Previously in this series:

Alaskan North Slope Snow LiDAR Campaign: SnowSTAR-2012

SnowSTAR-2012: Hoars and Drifters

SnowSTAR-2012: Questionable Monuments and Widespread Cratering

SnowSTAR-2012: Big “N” – The pursuit of snow data and high honor