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Alaskan North Slope Snow LiDAR Campaign: SnowSTAR-2012
Between April 8th and 21st, 2012, sixteen participants
worked in and around Toolik Lake, just north of the
Brooks Range, measuring the snow pack using a
variety of techniques, including ground and airborne
LiDAR. Five dispatches were produced during that
time and posted on the Scientific American website
(http://blogs.scientificamerican.com/expeditions/tag/al
askan-north-slope/). They have been collected here as
a informal report on the campaign.
April 8th, 2012: I had somehow missed that we were leaving on Easter Sunday…. but the
drive North across Alaska and the Brooks Range on a perfect day, clear blue skies,
pristine white snow, and majestic mountains, was ample compensation for leaving town
on a holiday, and my four companions (Fig. 1) did not seem to mind.
Figure 1: My four companions at Atigun Pass (from left: Simon, Philip, Art and Chris).
We five are the advance team for a campaign to measure the snow cover of the North
Slope of Alaska. The snow cover (Fig. 2) here lasts 8 months of the year, and it is
important for several reasons. First, somewhere between 50% and 80% of the run-off in
the rivers in this part of Alaska come from snow melt alone. Second, the snow is an
effective insulator that keeps the ground from freezing even more deeply during the
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winter than it does now. Without the snow the permafrost here might be thicker, the
summer thawing active layer thinner, and the plant life less verdant. Third, the snow is a
wonderful reflector of solar energy (it has a high albedo, reflecting about 85% of the light
that hits it), which effectively keeps Northern Alaska cooler in the winter and early spring
than it might otherwise be. If it seems like these last two are contradictory, you are right.
There is a fine balance between the insulation effect and the solar reflecting effect of
snow, and this balance plays a critical role not only in the climate of the Arctic, but also
of the entire planet. So in short, in a place like Northern Alaska, snow matters, and we
had come to measure its properties.
Figure 2: Snow blankets the slopes of an unnamed mountain south of Atigun Pass. Notice the barchan
drifts. (C. Polashenski photo).
More precisely, we have come to see if we could develop a more effective way of
measuring snow depth. Snow depth can be easily measured by pushing a ruler down
through the snow, and if the snow cover were smooth and even blanket, a few
measurements here and there would be enough to tell us about the regional snow cover,
but the snow cover of the North Slope is anything but even and smooth. Wind drifts the
snow incessantly, creating drifts and scour zones. In the space of 100-m one can measure
depths ranging from 10 cm to 500 cm. Even with GPS-enabled special ruler probes (more
on these in a later blog) it is not possible to map out the snow by hand.
Many other methods of measuring snow depth have been tried: Mono-pulse and FM-CW
radars on sleds and helicopters, passive and active microwave transmitter/receivers on
sleds, aircraft and satellites, gamma-ray detectors on aircraft, and even satellite borne
gravimeters. To date there have been some successes, but really no operational quality
methods have emerged that work for all types of snow, and the problem has been
especially acute for snow that tends to be thin (<100 cm in general) like the snow of the
North Slope of Alaska. However, the recent development of scanning LiDAR (Light
Detection And Ranging) equipment may be the game-changer. Mounted on an aircraft
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looking downward, one of these scanning devices can create a swath map of the snow
surface with near centimeter precision. If a second map of the exact same area is acquired
when there is no snow, the two surfaces can be differenced to produce a snow depth map.
DGPS (Differential Global Positioning Systems) make this precise co-registration
possible. In principle, all this could be done with sufficient accuracy to produce useful
and reliable snow maps, but is it possible in practice? That’s why we were driving North
on Easter Sunday. And this snow depth problem is not simply academic. The State of
Alaska and the Federal Government manage thousands of square kilometers of land on
the North Slope and have to make decision where and when oil, gas and mining
companies can be allowed to transit over the tundra. Open the tundra too soon with too
little snow cover and the tundra will be damaged; open it too late and the companies may
not be able to effectively explore for or develop strategic deposits (Fig. 3).
Figure 3: An oil rig near Prudhoe Bay, Alaska.
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We have been preparing for the campaign for months, and had been packing for days. In
addition to our advance team, 7 more people will be coming in at the end of this week to
help make the thousands of ground-based measurements we will need to check the
accuracy of the airborne LiDAR products. The aircraft will be coming a week from now.
Today, we are driving two rigs north pulling snowmobile trailers. Our pickup and the
SUV were packed full, and on the trailers were three snowmobiles, four plastic sleds, and
a special sled for a ground-based LiDAR (more on this later too). Everything was
covered over because we were expecting a muddy trip. We left Fairbanks at 8:30 AM.
From Fairbanks, the first part of the trip (245 km) is through the spruce- and birchcovered hills of the Yukon Tanana uplands along the Elliot Highway. Next it follows the
Dalton Highway, which runs through the Brooks Range at Atigun Pass (Fig. 4). The latter
is also know as the Haul Road, as it was built in order to haul materials north during the
building of the Trans-Alaska Pipeline. The Dalton is steeper than normal highways, much
of it is dirt (or used to be), and it is mainly transited by truckers headed to the oilfields of
Prudhoe Bay. For those who watch Ice Road Truckers (Season 3), it details the
tribulations of the truckers along this stretch of road… and exaggerates them
considerably. Personally, I think I have driven over Atigun Pass more than 40 times in
winter, and while it requires care, it is more beautiful than dangerous.
Figure 4: Looking north from Atigun Pass. Art and Philip are in the truck and trailer in front of us. (C.
Polashenski photo).
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Several hours of driving brought us to the Yukon River. During the Gold Rush in the
Klondike (1898), and for the next 40 years, this great river was the main highway for the
Alaska and Yukon Territory, plied by paddle-wheel steamers. Now it is bridged by an
unusual downhill sloping bridge (Fig. 5) built during the pipeline construction, and it is
pretty quiet. In September, moose hunters will come here to launch their boats, but the
rest of the year boat travelers would have the great river pretty much to themselves.
Figure 5: The bridge over the Yukon River (note the semi headed south…the main traffic on the road. (C.
Polashenski photo).
The trip started to get more exciting as the Brooks Range came into view. We always gas
up at Coldfoot (Fig. 6), which to me marks the south edge of this great mountain range.
Coldfoot was founded in around 1902 as a gold mining town. Legend has it that miners
who had penetrated this far got “cold feet” and turned back south, fearing the on-coming
winter and slow starvation. Many years later, the famous Iditarod musher Dick Mackie
founded the truck stop/café/tourist attraction that still exists today. Lots of visitors come
through here in the summer because it is a jumping off place for trips into the Brooks
Range. Not far away is the other old gold mining town of Wiseman, made famous by the
writings of Bob Marshall (Fig. 7). Marshall came into the country in 1929 and made a
series of long camping and exploring trips in the Brooks Range (both summer and
winter). At that time Wiseman was about as remote as any place in the world. He was
intrigued with the idea of “wilderness”, and utterly charmed by the society of white
miners and Inupiat inhabitants he found living in harmony in around Wiseman. His two
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books….both excellent reading….are Alaska Wilderness and Arctic Village. His ideas of
wilderness and the place of wildness in society still have impact today.
Figure 6: Coldfoot Camp.
Figure 7: Bob Marshall (4th from left)
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From Coldfoot we wound through the Brooks Range following the Koyukuk River
drainage toward its head, passing Sukapak (Fig. 8) and Snowden Mountain and climbing
up on to the Chandalar Shelf. Here the wildlife became spectacular….hundreds of
caribou (Fig. 9) grazing near the road, ptarmigan in full white winter plumage (Fig. 10)
enjoying the sunny day out on the tundra, and even some Dall sheep. From the Shelf, we
continued climbing up to the Pass at 4739’ (1444 m). When I was a student first starting
to work on snow on the North Slope, my mentor and friend Dr. Carl Benson would
always stop here at the summit and talk about how the Brooks Range was one of the great
climatic divides of the world, separating the taiga forests from the cold boreal tundra.
Today the Pass was living up to its reputation: to the south the roads had been muddy
and the air temperature above freezing. To the north the temperature was well below
freezing, the road snow covered, and stiff wind was blowing. Feeling the weight of Dr.
Benson’s lessons, I briefly announced to my companions that this was a “great climatic
divide”, then we got back in our rigs and descended the steep north side of the pass with
great care. Another 45 minutes of driving and we arrived at the Toolik Lake Field
Station. The camp was quiet, and as always, we received a warm welcome. We
unloaded our gear, rolled out our sleeping bags in the bunk houses, and started thinking
about the campaign to come.
Figure 8: Sukapak Mountain (C. Polashenski photo).
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Figure 9: Caribou near the road (C. Polashenski photo).
Figure 10: Ptarmigan enjoying a sunny day on the tundra (C. Polashenski photo).
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Figure 11: The long road North (C. Polashenski photo).
Figure 12:Not only gangsta’s have spinners!
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April 9th and 10th: Hoars and Drifters
Now that we have arrived the work begins. We spent the day checking out the snow,
putting our ground-based LiDAR together and testing it, and dealing with some lingering
logistical problems (like making sure ten barrels of aviation fuel arrive before the plane
does).
Checking out the snow is my favorite activity, and being the expedition leader, I got
assigned this task. I have been doing snow pits for 30 years. A snow pit is a hole you dig
in the snow (neatly and with very vertical walls and square corners if you were trained by
my mentor) in which you can look at the snow layers (Fig. 1). The layers are a
stratigraphic record of the winter’s events- - -wind events, snowfalls, and thaws. The
whole story of the winter is there if you know what to look for, but it takes a trained eye.
The layers in a snow pit are white on white ….the clues are nuanced and subtle . . . no
bright color contrasts here, unlike a soil pit or geologic rock section. Still, with practice,
anyone can learn to see these layers and understand how they came to be.
Figure 1: A snow pit near the headwaters of Imnavait Creek. The researcher is whisking
the pit face to accentuate the layers (P. Martin photo).
To help interpret the snow pit, we use a variety of tricks to delineate and accentuate the
layers. For example, using brushes, we whisk the pit face (Fig. 1). This sweeps away
material from the weaker layers, making the harder wind slabs stand out in bolder relief.
Another way is to back cut the pit, allowing sunlight to shine through the snow,
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essentially the same effect as if we were looking at a section of the snow on a light table
(Fig. 2). Both the brushing and the light tend to highlight the grain differences between
layers, and these differences arise from differences in snow metamorphism. Surprisingly
little of the layer texture is due to the nature of the initial snowfall. A third, newer method
to expose the layers, is to photograph the pit face using a near-infrared camera, which is
very sensitive to variations in light reflection due to grain size differences. Unfortunately,
this method requires post-processing, so it does not have the immediacy of the other
methods.
Figure 2: The backside (sunny side) of the snow pit has been cut away to allow light to
pass through the snow layers.
There are dozens of types of snow layers in nature (not to be confused with snowflakes,
of which there are many as well): new snow layers, recent snow layers, fine-grained
layers, melt clusters, ice layers, grauple. And there is hoar: surface hoar and depth hoar.
It turns out these latter are both very common in the Arctic, and in a moment I will
explain why, but first a disclaimer: this type of hoar is quite different from the other type,
more commonly found in gangster movies etc. The word “hoar” is related to the word
“hoary” meaning gray, old, and venerable. Apparently, the similarity between frost
feathers and an old man’s beard led to the use of the word in snow science. Frost feathers
are ornate, though usually flat or fern-like, frost crystals that condense on surfaces during
cold still periods (Fig. 3). Surface hoar is similar to frost feathers, but it forms on the
surface of snow pack on cold, clear nights. Depth hoar is equally ornate, but is more
three-dimensional (Fig. 4), and it forms throughout the winter at the bottom of the snow
pack (hence the “depth” part).
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Figure 3: Frost feathers (also known as frost flowers) on a freshwater ice surface.
Figure 4: Depth hoar from the base of the snow pack (J. Holmgren photo). The sharp
edge in the foreground was facing downward during growth.
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The reason why the Arctic snow pack has such a high percentage of depth hoar and
frequent surface hoar formation is simply because it is cold. It will come as no surprise
that this is a cold place in winter, and even when spring is arriving (as it is now), it can
still be extremely cold at night. Cold air creates temperature gradients. The snow surface
will be perhaps -30°C, while the base of the snow will be -10°C. Heat moves from warm
to cold, and moisture follows the same gradient, so moisture in the form of water
molecules are constantly moving upward from the relatively warm ground surface below
the snow through the porous snow pack, condensing on the lower sides of crystals,
causing them to grow (and have razor-sharp edges), and sublimating from the tops of the
grains, making the tops rounded. In the case of the surface hoar, on a cold, clear night the
snow surface cools by long wave radiation, and soon is the coldest surface around. Any
moisture migrating upward from below, or downward from above, condenses out as
surface hoar. All three types of crystals are ornate, with sharp edges and well-defined
facets because they grow in very moist environments. The supply of moisture for growth
is not the limiting factor: instead the crystal kinetics control the growth (a good topic for
another blog. The end result is beautiful crystals in all there cases.
These are the hoars….surface hoar, depth hoar, hoar frost. While they can be found in
many snowy locations, they can be found in their prime in the Arctic.
The other common type of snow layer in the Arctic is the wind slab, and it too can be
amazing. In a recent Guest Blog (http://blogs.scientificamerican.com/guestblog/2012/02/15/blizzard-explained/) I wrote about blizzards and how snow is moved by
the wind. The depositional results of blowing snow are dunes, wind slabs, sastrugi, and
barchans. Many people have seen a barchan without realizing what it is: a moving dune
of snow or sand, with horns or wings that point downwind. This winter I was flying back
from Western Alaska when I saw a barchan field (Fig. 5) march down a hill into a small
creek. Each barchan was about 10 m long. I mention these because today, when we
dumped the webcam at our meteorological tower, we found we had been lucky enough to
catch the march of the barchan across the field of view (Fig. 6 film clip).
Figure 5: A field of barchans (horn-shaped snow dunes) march down a hill into a creek
in Western Alaska. The photo spans about 500-m in width.
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Figure 6: Sequence of a barchan moving past our webcam. Note the horns pointing
downwind (G. Gelvin images).
Barchans seem to form early in a combined snow and wind storm when the snow is easily
transported. Later, the barchans snow will sinter (bond) and stabilize. Then if the wind
comes up sastrugi, a beautiful, sculpted erosional form of snow will form (Fig. 7).
Neither sastrugi nor barchans are easy to recognize in a snow pit. Most common in a pit
are wind slabs….hard, well bonded layers of snow……sometimes so well bonded they
can only be dug with a sharp-pointed steel spade (Fig. 8).
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Figure 7: A field of unusually large (and hard) sastrugi.
Figure 8: The end result of the snow pit with lots of depth hoar and a thick, hard wind
slab right in the middle.
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We are not the only ones in the Arctic thinking about wind slabs. Large animals like this
muskox (Fig. 9) and caribou have to work down through these hard slabs to get at their
food. A hard wind slab can make that effort too taxing, so they are excellent at finding
where the slabs are thin or non-existent…..better even than Arctic snow scientists at
doing so. But more on this process (called cratering) tomorrow.
Figure 9: A muskox has been doing some serious cratering in the snow.
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April 11th to 14th : Questionable Monuments and Widespread Cratering
It has been a busy few days getting ready for the airplane with the LiDAR. Yesterday the
remaining part of team arrived from Fairbanks. The seven are from Fairbanks and
Colorado, all veterans of Arctic snow research, with the exception of Allison, a graduate
student from the University of Colorado. We have been working at three things from the
start of the campaign, anticipating the arrival of our LiDAR-bearing aircraft tomorrow.
These have been:
1. Setting up a precise GPS control network that will allow the aircraft to navigate
with 10-cm positioning precision,
2. Operating our ground-based LiDAR to produce small “bull’s eyes” where we will
know the snow surface topography to centimeter accuracy, and
3. Measuring snow depth and snow water equivalent (SWE) along the planned flight
path without trashing too much snow before the aircraft flies.
With the full team in place and the aircraft due in a day, the pace has been quickening.
One thing we did a couple of days ago by way of preparation was to install GPS base
stations. Very early in the planning process we determined that in order to be able to
correct the GPS data that would be collected by the Airborne LIDAR, at least one or two
high order base stations would need to be established. The National Geodetic Survey
classifies benchmarks with a stability rating. There are four ratings: (A) most reliable
and expected to not move, (B) probably stable, (C) may hold an elevation but subject to
ground movement, (D) questionable or unknown stability. One of the most adventurous
aspects of working in the north is finding a survey monument that is considered Level A
and is easily accessible for the from the road system. You can find a rebar drilled into a
rock outcrop (Fig 1.), or by rooting around high places, maybe find one (as Fig. 2)
located on top of Slope Mountain where a communication tower is located
Figure 1: A rebar drilled into a rock with an aluminum cap. (M. Kaufman photo)
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Figure 2: A bent rebar was used as a temporary survey maker until a more permanent
maker was installed by Alyeska Pipeline Service Company.
Unfortunately, most of the monuments that can be found easily tend to be on a level D,
which are copperweld rods driven to about 5 feet, into loose gravel soils, and their
condition can be questionable at best. A good example would be a station call the GAL
(short for Galbraith), which is set atop a nearby hilltop near where we will be conducting
most of our work. (Fig 3). This monument was placed in 1971 and we found that the
survey cap was deformed by some unknown event. Most likely it was hit by an
adventurous snowmobiler cresting the hill.
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Figure 3: A level D monument located on top of a high hill, located on the north end of
ridge diving the Kuparuk River and the north end of Galbraith Lake. (M. Sturm photo).
The brass tablet is bent, suggesting it has been moved since it was installed.
The second most common class of monuments are Level B, which can be found all along
the Dalton Highway and were installed in 1974 (Fig 4). These monuments are copperclad steel rods that are driven to a depth of 10+ feet. Many are subjected to frost jacking.
Level A monuments offer the best stability and are placed in rock outcrops, drilled into
bedrock, or massive structures with deep foundations. These tend to be in high, hard to
get to places. In our case, we happened to know there was a beautiful monument located
just off of the highway in the farthest north rock outcrop along the road near the
Sagavanirktok (or the Sag) River. This monument is set in top of a ledge of a rock above
a creek (Fig 5). However, the crew that was sent out to install the base station had a bad
GPS position for the monument, which sent the crew digging around the bottom of the
creek looking for a non-existent rod and cap. After some time shoveling, we realized no
self-respecting surveyor would place a monument in the creek, so we looked up on the
ledge rock and found the monument in a few minutes (Fig 6).
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Figure 4: An example of Level B monuments found all along the Dalton Highway, many
are sticking well above the ground, evidence of frost jacking.
Figure 5:Monument R 167, set flush into rock. This is an Level A monument.
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Figure 6: View of rock outcrop were the level A monument is located. After a little
digging around, it was determined that the coordinates in Garmin GPS was in error after
several minutes of shoveling snow at the base of the rock outcrop.
There we set up our GPS base station, anchoring it in place using chains to keep the mast
in a vertical position. A bank of batteries and a solar panel were connected to provide
power for the GPS, which is now collecting position points at a rate of 5Hz.
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Figure 7: A GPS anntena mounted on a mast over the Sag Benchmark is chained down to
the rock to keep the mast vertical and level.
The other thing we have been doing is getting out in the field measuring a lot of snow
depths. This requires riding snowmobiles, and they can leave pretty deep tracks in the
snow (Fig. 8). Consequently, we had been concerned about not tracking up any more
snow than we had too before the airplane flew over the area mapping the snow with its
airborne LiDAR. On the other hand, we knew we needed to a get a jump on the groundbased measurements because the ground-based measurements take so much more time to
make than the airborne ones. Consequently, we had been enforcing a pretty strict
discipline on snowmobile tracks: Everyone had to ride in the same track; no one was
allowed to carve turns up pristine soft snowfields, and we drove circuitous routes around
some of our intensive of sampling areas. This doesn’t sound too restrictive, but one thing
about snowmobiling out on the open tundra is that it gives one a real is a sense of
freedom. Wide open spaces – no fences – a lot of sky. It is pretty hard to stay in line
behind another snowmobile when those wide open slopes beckon.
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Figure 8: A snowmobile climbs up through deep snow, leaving a deep furrow that the
airborne LiDAR would map instead of pristine snow.
But to our surprise, the snow was trashed already, and not by snowmobiles. It had been
trashed by the caribou. The caribou were in our area in force, and they had been feeding
on the tundra in many places (Fig. 9). When caribou feed (Fig. 10), they seek out thin
areas of snow and dig the snow away to get at the lichens underneath. This is called
cratering (Fig. 11) and it has been the subject of considerable study. The scientific
literature agrees that that caribou select where to crater based on the forage beneath the
snow (which they find by smell), the depth, and the hardness of the snow. The goal is to
dig with the least expenditure of energy for the most food value. The deeper and harder
the snow, the higher the cost. Highly packed snow takes almost four times as much
energy per stroke of a hoof than softer snow (like new or recent snow).
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Figure 9: An area of snow that has been cratered by caribou like the ones on Figure 10
(C. Polashenski photo).
Figure 10: A cow and calf caribou pair in an area of cratered snow where they have
been feeding (C. Polashenski photo).
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Figure 11: This picture provides a sense of how much area a herd of caribou can crater.
Now this is where snow science and biology intersect: Over the past few days we have
had been noticing that there was a thick wind slab buried in the middle of the snowpack
(see blog for April 9th). This wind slab had been produced by a wind storm some time
during the winter. When we finish analyzing our data we will know when that occurred.
The density of the layer was about 0.5 g/cm3, which is almost as high as seasonal snow
can be (without melting or mechanical packing). This density is equivalent to about half
air half ice. The layer was thick in some places, thin in others, and even absent in some
locations. We stopped in many of the cratered spots and it appeared to us (though without
any statistical rigor) that the caribou had generally found a thin area of snow, where they
began cratering and feeding. They had dug out from where they started, but when they
encountered the slab layer, they ceased, abandoned that cratering area, and found a new
one. Some of the literature we briefly looked at supports this idea… but other papers
were less certain.
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One thing is certain, in this cold and austere environment, where finding enough food to
stay alive and warm is difficult, small things like efficient cratering matters. This is a
land where there is a fine line between survival and death.
Figure 12: This caribou did not make it. She cratered around herself, ate the food she
could reach, and later died (C. Hiemstra photo)
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April 15th to 19th : The Pursuit of Big “N”
It has been a very busy time for us the last few days. After about
8 days of prep work, our aircraft arrived in beautiful
weather. Our pilot, Paul, and Dr. Chris Larsen started flying
airborne LiDAR immediately, and as will be seen, collecting
enormous amounts of data. Meanwhile the rest of us were
working feverishly on the ground to collect data against which the
airborne LiDAR could be compared. A schematic of the whole
campaign appears in Figure 1. Note the snow scientist labeled
‘E”. He is using a device invented by CRREL called a
MagnaProbe, which measures snow depth and a GPS position at
the same time (Fig. 2). At peak, we have had 8 of these devices
out at once, measuring snow depth in a variety of areas.
Nonetheless, despite this herculean effort, the “probers” efforts
pale in comparison to those of the LiDARians, as explained by the
two young scientists doing the ground-based LiDAR who take up
the story now.
Figure 1 – A schematic of the SnowStar 2012 Campaign by Matthew Sturm, compare to the photo
in Figure 18 to see how we did!
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Figure 2 – Sveta collects snow depth measurements with a Magnaprobe
“N” is a very important concept to scientists here on the SnowStar 2012 campaign. To us, a
person’s “N” is a measure of his/her gallantry, the quality of their character, and their general
worthiness as a scientist. “N” is the number of data points a person collects in the day and a high
“N” is a sure sign of a lion-hearted individual. The teams measuring snow depths with the
automatic snow depth probes (see Fig. 1 and 2) are engaged in a stiff competition to show their
mettle with ever higher “N”s each day. The highest reported N in the campaign so far has been
about 4000 in a day. One legendary effort, still discussed with awe by snow scientists, resulted in
an “N” that approached 11,000 over the course of a very, very long day (and on sea ice so the
snow was thin). So far in this campaign no one has come near that figure due to the large field
area, the deeper snow, and the long commute to each site.
The ground-based scanning LiDAR collects that awe-inspiring 11,000 points in less than half a
second. Hence we (the ground-based LiDAR team) have been averaging between 20 and 30
million points at each site, and we’ve knocked out as many as four sites in a day. In the world of
snow measurements, LiDAR is to a snow probe as dynamite and steam drills were to hammers
and chisels in the old railroad days. And while we shed a tear for all the John Henry’s out there
still breaking stones by hand, we have not been shy when bragging about how our LiDAR could
handily best the snow prober’s ‘N’ any* day of the week. (*Windy, foggy, or snowy days
excepted – see Figure 3). In all seriousness, Big “N” means that LiDAR holds real promise for
revolutionizing our work by making it possible to measure snow with less manpower, more
accurately, and over much larger areas, but we still have to work out the kinks in the system. This
program is all about just that – figuring out how we can better measure snow and comparing new
methods like LiDAR with the old fashioned ways.
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Figure 3 – LiDAR fails as the snow flies.
LiDAR (which stands for Light Distance And Ranging) is a technique that uses a laser
rangefinder to measure surfaces. Basically, you fire a pulse of laser light at a surface, and with an
extraordinarily precise stopwatch, time how long it takes to get back to you. Using the speed of
light, we can then use the time-of-flight to calculate the distance that the pulse traveled, divide by
two, and have the range to the surface. If we know exactly what direction the pulse was ‘fired’ at,
we can use this range to calculate the location of the snow surface as a specific point in space. By
firing the laser over and over again in slightly different directions, it is possible to collect many
samples of the snow surface. Putting all of these point measurements together gives the LiDAR’s
product: a 3-D map of the snow surface topography.
The 3D surface maps that our LiDAR produces are incredibly high resolution (at least a few
hundred points per m2) and super accurate (within about 1 cm) – over areas the size of a couple
football fields. Aside from being scientifically useful, these 3D maps are just plain awesome to
play with. Check out this video flying around in the 3D world created from scans we took at a site
in Fairbanks just before this trip (http://youtu.be/XCu9K8i0sUE). Also in the screenshot shown in
Figure 4 from this campaign, note that small twigs and even the ptarmigan tracks in the snow that
have been picked up by the LiDAR.
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Figure 4 – A screenshot of LiDAR data collected on this trip, note the snowmobile track and
footprints across the bottom of the image, and the ptarmigan tracks wandering around the snow
surface.
The LiDAR that we are using is considered a terrestrial
or ground-based LiDAR, complicated speak for the type
of LiDAR that you use from a tripod sitting on the
ground (Figure 5). Because the LiDAR works by hitting
the surface with its laser pulses, it can only scan what it
can see. Since the LiDAR is positioned about the height
of a person’s eyes off the ground, the back side of even
small hills within the scan area may be out of sight from
the LiDAR’s perspective and therefore missed in the
scans. To fill these in, we set up the LiDAR in different
positions around an area of interest, making scans first
of one side of hills, then of the other, and overlay the
scans to build a full surface. In order to overlay the
scans accurately, we put targets out around the site and
use the scanner to locate them from each position
(Figure 6). The data collected at each site is then rotated
and shifted until all the targets line up, allowing us to
very precisely tie the scans collected from different
viewpoints together into a single surface map.
Figure 5 – Simon operating the ground based LiDAR
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One of the key measures of snow that we are after is
simply its depth. The LiDAR does not, however,
measure snow depth, just the surface position. To
calculate the snow depth, we will have to come back
in June after the snowmelt and re-LiDAR the same
locations to create a second surface. Subtracting the
ground surface from the snow surface will give us a
map of the snow depth with a really big “N” and
accuracy that is likely as good or better than the snow
probes, but only if we can come back to exactly the
same spot. Accurate GPS positioning is crucial to this
process.
Figure 6 – Scanning a reflector target with the
LiDAR to overlay the scans. The green dot is the
LiDAR laser.
Despite having this new tool, Simon and I have been working some rather long days (Figure 7).
This is proof in my mind that scientists aren’t really all that smart after all. Prior to now we had a
tool that would take 11,000 points in a day with a lot of hard work. Now we have a device that
takes 30,000 points a second, and instead of letting it run for a few seconds and taking the rest of
the week off, we still scan all day and well into the night, all in the pursuit of bigger ‘N’. LiDAR
scanning isn’t such bad work though. During the data collection process, there is a period of
about 20 minutes at each scan position when the scanner is acquiring data at the speed of light,
but there really isn’t a whole lot for us to do except try to stay out of the way and not block the
scanner’s view. Some might view this free time on the tundra as little more than a good way to
get cold. Instead, it is our favorite part of the process. Simon notes that “you have to imagine
being in middle of this gigantic white landscape with few hills around and an astonishing
mountain range in the background, few herds of caribou grazing, ptarmigan flying from bush to
bush, a couple of wolves cruising, a lonely musk ox we’ve named Uncle Jacquis Alfred Dalton
(for many reasons we will not mention here…) and two young snow scientists, one American and
the other French. In this situation we usually take one of three options: admire the surrounding
nature (Figures 9-13) and explore the wonder of our favorite element (snow of course), discuss
and argue on all sorts of topics with a preference for cultural contradictions between our
respective heritages, and finally let our imaginations create little games or warm up dances
(Figure 8). Before you know it, work calls back and we move to a different site position."
SnowSTAR-2012
Figure 7 – The sun sets as Simon gets the GPS base station set up to start another site,
committing us to another 3-4 hours of work.
Figure 8 – Simon dances through LiDAR downtime.
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Figure 9 – Not a bad view from LiDAR site 9.
Figure 10 – A fox looking a bit guilty with a Ptarmigan feather in his mouth visits the LiDARians.
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Figure 11 – Uncle Jacquis Alfred Dalton demonstrates the effectiveness of long fur for showing
wind direction.
SnowSTAR-2012
Figure 12 – A pair of wolves investigate the LiDAR team just north of the Brooks Range.
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Figure 13 – The Northern Lights are a good reward for working late.
For all of our bravado about the power of our ground-based LiDAR scanner for collecting big N,
there is another type of LiDAR out there that makes our results pale in comparison. The Airborne
LiDAR! It is the carpet-bomb of all snow measurements, if it can be made to work. An airborne
LiDAR unit is flown looking down out of the belly of a plane and runs continuously as the play
flies, collecting 10-100 times as many returns in a day as our best ground-based efforts. There is a
trade-off, however: the airborne LiDAR has a lower accuracy; ~10cm rather than ~1cm.
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Nonetheless, yesterday the ground LiDAR team’s ‘N’ was humbled by this device in just 4 hours,
when the airborne team returned over 300 million points before lunch, to our 65 million all day.
Adding to our jealousy, the Airborne team also gets to trade our already pretty awesome
snowmobiles for an absolutely amazing 1000hp, turbine-powered single otter plane (Figure 14).
Figure 14 – The airborne team takes to the skies
Figure 15 – Controlling the airborne LiDAR from the cockpit.
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Figure 16 – The airborne team passes by the ground team
The struggle for data collection superiority will continue for a few more days, but for now we are
all excited to say that good weather and a stellar team is leading to ‘Big N’ for everyone on the
trip. Better still, our efforts to coordinate the different teams (Figure 17) have been going quite
well. Our goal of collecting a huge dataset of snow measurements over the same areas using a
number of different techniques is really coming together (Figure 18). We will have some mighty
data processing to do when we get home!
Figure 17 – Coordinating the teams
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Figure 18 – The whole campaign coming together.
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April 21st: Convergence SnowSTAR-2012
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 semitrucks 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: http://www.history.com/shows/ice-roadtruckers/articles/about-season-3).
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.
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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.
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
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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.
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 blogs), and of course the endless fascinating snow cover (Fig. 5).
All of these wove a spell that captured all of us.
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Figure 4: The Brooks Range at twilight.
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
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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.
Till next year, when perhaps there will be some notes from SnowSTAR-2013 . . .
Matthew Sturm
SnowSTAR-2012
About the Authors:
Matthew Sturm is the Project Leader and the Senior Scientist at
the U.S. Army Cold Regions Laboratory-Alaska where he studies
snow and climate. He is the author of Apun: The Arctic Snow
(University of Alaska Press), A Teachers Guide to Arctic Snow
(University of Alaska Press), and Finding the Arctic, a science
adventure travel book available in June from the University of
Chicago Press. He can be reached at
[email protected].
Art Gelvin is the lead Technician at CRREL-Alaska, a surveyor,
and experienced Arctic hand. He resides in Fairbanks, Alaska,
where he has lived since 1985. He can be reached at
[email protected].
Chris Polashenski has been working to better understand snow and
ice in the Arctic since 2005. He graduated with his PhD in June 2011
and now works as a researcher at the U.S. Army Cold Regions
Laboratory developing new methods to study snow and sea ice. He is a
native of Pennsylvania, but currently lives in Hanover, NH with his
partner Norah, chickens, rabbits, and a free spirited beagle named
Tracks. He can be reached at
[email protected].
Simon Filhol has previously worked in the Arctic in Norway, Svalbard,
and other parts of Alaska. He hails from Chamoix, France and is
currently working on a PhD in geology and geophysics at the University
of Alaska Fairbanks with Matthew Sturm. He can be reached at
[email protected].
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Appendix 1: Campaign Chronology
April 8th: Matthew, Art, Chris P. Simon, Philip to Toolik Lake
April 9th: Simon & Chris do Grnd LiDAR @ Toolik Lake; Philip & Matthew do snowpit
and probe Imnav. Fence drift.
th
April 10 : Simon & Chris do Grnd LiDAR @ Imnavait (2 sites); Philip, Matthew and Art
mark pull-outs and put in Sagwon base station.
th
April 11 : Simon & Chris do Grnd LiDAR @ Imnavait and Toolik River (1.5 sites);
Field excursion to large drift with cornice curl. Philip, Matthew and Art go to
snow fence and download data, then magnaprobe from fence west (Philip),
then probe the Upper Kuparuk sites (3) by Green cabin lake. Philip drives to
Prudhoe Bay.
April 12th:Art and Matthew probe the flite line up and over Slope Mountain,
snowmobiling from Toolik Lake and back. Simon and Chris finish Toolik
River site and scanned Silver Bullet. Glen Liston and April arrive about 7:00
PM. Art and Matthew arrivew Toolik camp at 8:00 PM, and LiDAR boys
arrive about 10:45 PM.
April 13th: First trailer day: April, Glen, Matthew and Art to Lake Outlet to Pump 3
flight line. Chris P. and Simon try to LiDAR at Oks Creek: too windy, moved
to Outlet1 pullout and scanned. From Fairbanks, Kelly Elder, Sveta Steufer,
Chris Hiemstra, Mark Serreze, Mark Parsons, Drew Slater and Allison Hurley
arrive at about 7:00 PM.
th
April 14 : CLPX grids with the following teams: Matthew and Mark Parsons (A4, A5,
C2 and C3); April and Glen (B1, B2, B9); Mark Serreze and Kelly (B5, B6,
B12), Sveta and Drew (B4, B7, B11), Bro-H (Brother Hiemstra) and Allison
(B3, B8 and B10). Had lunch at the Silver Bullet, then did some of the D, E
and F CLPX squares (west side, from NW corner). Simon and Chris P. went
back to Outlet 2. Art downloaded data from mini-sonics and dumped the SWE
sensor site data. He took the airplane fuel to Galbraith in the morning with
Simon and Chris P.: got stuck in the driveway due to new-blown snow. Had
to chain up.
th
April 15 : Kelly and Matthew to Green Cabin lake for snowpits. Art works out a DGPS
procedure. The rest of the crew (Glen+April, Mark P.+Mark S., Sveta+Drew
Bro-H+ Allison) return to the CLPX lines and do the G’s and the rest of the F’s
of the CLPX grid. The plane arrives at Toolik just as Kelly and Matthew come
down the hill from Imnavait. They rendezvous with the plane and about 4 PM
take off to fly the CLPX box and the 2 X 3 and 1 km grids with Paul Claus and
Chris Larsen. Finish all the grids in splendid weather. Chris P. and Simon go
to Prudhoe in the morning to do coastal sites.
April 16th:A large contingent goes to Imnavait to probe the 1 X 1 km gridlines. The
plane flies the flight line toward Prudhoe. Stopped by fog about 27 miles south
of Deadhorse, but gets the line and the Happy Valley swath. Art and Chris H.
do three spirals on the ground-based LiDAR areas using the DGPS on a plate
and WERC#2 magnaprobe. Kelly Elder, using Kelly_B helps on the last spot,
but that probe loses all the data for the day (including the 1 X 1 km grid three
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south lines). Glen and April and Allison drive to Prudhoe leaving Toolik at 8
PM.
April 17th: Mark S. plus Matthew, Drew (Boris) and Kelly (Natasha), Sveta (Squirrel)
and Mark P. (Moose) and Matthew and Mark S. (walrus) start at Sagwon and
work the flight line working south. After doing those sites, moved to Happy
Valley and worked the flight line south to a lake (Mark and Matthew) while
Drew and Kelly do a series north of that, while Sveta and Mark P. go off the
end of the Happy Valley runway and probe there.
th
April 18 :The north half of the Happy Valley swath was done the following teams: Mark
S. plus Matthew, Drew (Boris) and Kelly (Natasha), Sveta (Squirrel) and Mark
P. (Moose) and Matthew and Mark S. (walrus). Glen and Allison did sites near
Pump 2 and then arrived at Happy Valley about 4 PM and found the HV swath
team near the communication tower in murky foggy weather. Chris and Simon
do ground-based LiDAR at Franklin Bluffs and the Sagwon outhouse; arrive at
Toolik about 11:45 PM. Art and Chris H. do DGPS and close spiral
magnaprobes on 4 of the ground LiDAR sites. The plane flies north from
Toolik with Chris P. and Simon on board (after re-fueling at Galbraith). They
fly to the north edge of the swath, but get stopped by fog.
April 19th:The south half of the Happy Valley swath was done the following teams:
Mark S. plus Matthew, Drew (Boris) and Kelly (Natasha), Sveta (Squirrel) and
Mark P. (Moose) and Matthew and Mark S. (walrus) using the same
magnaprobes as yesterday. Chris P., Art, Allison and Glen go to Imnavait and
do the 2 by 3 grid in short bursts. Sonic sounder depths measured and Glen
and Art swapped the batteries at the snow fence. Simon and Chris. P. pulled the
Sagwon base station, did a scan on the Sagwon flats and another in the creek
bottom at Happy Valley
th
April 20 :In AM, Glen, Art, Kelly and Mark P. go to Imnavait to finish the 1 by 1 km
grid and the LiDAR spirals, which were done with CRREL_B probe and then
lost the data. Simon and Allison drive to Sagwon and do spirals and remove
base station.
April 21st: Entire team drives back to Fairbanks, arriving about 5:30 PM. Some people leave by
jet this evening; some the next morning.