WEBVTT FILE 1 00:00:00.000 --> 00:00:05.000 [light wind] Newlin: We are in a very remote environment. 2 00:00:05.000 --> 00:00:09.050 It’s a harsh environment. You move slower, you’re tired, 3 00:00:09.050 --> 00:00:13.050 your eyes hurt because it so bright out there. You’re 4 00:00:13.050 --> 00:00:17.050 cold. You can’t remember the last time you were warm, and to 5 00:00:17.050 --> 00:00:21.050 kind of make matters worse you’re digging giant holes in the snow. So you’re working hard. 6 00:00:21.050 --> 00:00:25.050 the whole time that all of these elements are just pounding on you. 7 00:00:25.050 --> 00:00:29.050 [music, snowmobile motor sound] 8 00:00:29.050 --> 00:00:33.050 [music, snowmobile motor sound] Vuyovich: The SnowEx project is really 9 00:00:33.050 --> 00:00:37.000 a multiyear campaign to test different instruments and 10 00:00:37.000 --> 00:00:41.000 techniques for observing snow characteristics in different 11 00:00:41.000 --> 00:00:45.000 regions and different snow types. So we collect measurements on the 12 00:00:45.000 --> 00:00:49.000 ground with different ground-based instruments and observing techniques. 13 00:00:49.000 --> 00:00:53.000 We use that to validate instruments on aircraft and in the air 14 00:00:53.000 --> 00:00:57.050 and eventually, hopefully get some instrument on a satellite. 15 00:00:57.050 --> 00:01:01.050 [music] Marshall: One of the advantages of Grand Mesa is its elevation. 16 00:01:01.050 --> 00:01:05.050 We’re at over 10,000 feet here, and so, as you can see the snow is 17 00:01:05.050 --> 00:01:09.050 not wet at all. And so we want to start by really proving 18 00:01:09.050 --> 00:01:13.050 the concept in dry snow. A lot of the remote sensing approaches 19 00:01:13.050 --> 00:01:17.050 are also challenged by complex topography, so really steep topography 20 00:01:17.050 --> 00:01:21.000 and as you can see, where we’re standing here, we’re on the top of a mesa, which is relatively 21 00:01:21.000 --> 00:01:25.050 flat. This is the largest mesa in the world, and so it’s a pretty unique spot 22 00:01:25.050 --> 00:01:29.000 to do this work. Vuyovich: Within the pits, we’re looking at the vertical 23 00:01:29.000 --> 00:01:33.050 stratigraphy. So the layering of the snowpack and the different 24 00:01:33.050 --> 00:01:37.050 characteristics of those layers: temperature, density. Mason: I’m operating 25 00:01:37.050 --> 00:01:41.050 the snow micropenetrometer, which is one instrument 26 00:01:41.050 --> 00:01:45.050 that they use in the pit crews to look at the hardness of 27 00:01:45.050 --> 00:01:49.050 the snow and to look at the microstructure. Vuyovich: The microstructure is a very 28 00:01:49.050 --> 00:01:53.050 important characteristic for these active-passive microwave 29 00:01:53.050 --> 00:01:57.050 retrievals. So we wanted to get that in a lot of locations. [snowmobile motor] 30 00:01:57.050 --> 00:02:01.050 Marshall: I’ve just done a radar survey 31 00:02:01.050 --> 00:02:05.000 in a particular way. And we use this 32 00:02:05.000 --> 00:02:09.050 type of sampling strategy—we were calling a Hiemstra spiral— 33 00:02:09.050 --> 00:02:13.050 it’s a spiral pattern. I’m making a hundred measurements 34 00:02:13.050 --> 00:02:17.050 per second as I drive the snowmobile. And that is a 35 00:02:17.050 --> 00:02:21.050 very similar measurement to what’s happening on the aircraft. [music] 36 00:02:21.050 --> 00:02:25.050 Osmanoglu: SWESARR 37 00:02:25.050 --> 00:02:29.050 really stands for Snow Water Equivalent Synthetic Aperture Radar and Radiometer, 38 00:02:29.050 --> 00:02:33.050 and it’s actually two instruments in one. It has an active 39 00:02:33.050 --> 00:02:37.050 radar and a passive radiometer. Basically they both work on the microwave 40 00:02:37.050 --> 00:02:41.050 frequencies. And what they do is to penetrate the snowpack 41 00:02:41.050 --> 00:02:45.050 a little bit and give us the 42 00:02:45.050 --> 00:02:49.050 volume and scattering information, then which we can relate to how much water is inside 43 00:02:49.050 --> 00:02:53.000 the snowpack. [airplane taking off] Vuyovich: Snow water equivalent is really the volume of water 44 00:02:53.000 --> 00:02:57.050 that’s stored in the snowpack. For hydrologic applications, 45 00:02:57.050 --> 00:03:01.050 that’s really the most important characteristic. We want to know how much is available to 46 00:03:01.050 --> 00:03:05.050 melt and where it’s going to go—evaporate into our groundwater, 47 00:03:05.050 --> 00:03:09.050 reservoirs, and how much is available. Marshall: Our climate’s also 48 00:03:09.050 --> 00:03:13.050 changing and snow is playing a really big role in that, and it’s a big 49 00:03:13.050 --> 00:03:17.050 piece of the hydrologic cycle that’s quite uncertain and it’s one of the 50 00:03:17.050 --> 00:03:21.050 pieces that we really see as a very high priority 51 00:03:21.050 --> 00:03:25.000 to get more quantitative, more accurate estimates for. 52 00:03:25.000 --> 00:03:29.050 Vuyovich: Snow is a really important part of our planet. Provides water, 53 00:03:29.050 --> 00:03:33.050 hydropower. It’s a water source for agriculture and 54 00:03:33.050 --> 00:03:37.050 water supply. Mason: When we have snow in our mountains, it’s holding 55 00:03:37.050 --> 00:03:41.050 it, and it’s kind of like timing the melt in like a slow release 56 00:03:41.050 --> 00:03:45.050 versus just an onset of rain or a flood event. 57 00:03:45.050 --> 00:03:49.050 Osmanoglu: As we build these records for longer series, we 58 00:03:49.050 --> 00:03:53.050 will be able to tell how the snow accumulation is changing over a given area 59 00:03:53.050 --> 00:03:57.050 and how that might impact the agriculture in that area or how the 60 00:03:57.050 --> 00:04:01.050 people live in that area. Vuyovich: I’m really excited about the potential 61 00:04:01.050 --> 00:04:05.050 right now. I think there’s a lot of excitement in the snow community. There’s a lot 62 00:04:05.050 --> 00:04:09.050 of collaboration. I think we really are at a point 63 00:04:09.050 --> 00:04:13.050 where we can push the science forward and move 64 00:04:13.050 --> 00:04:17.050 towards a global snow product and a satellite mission 65 00:04:17.050 --> 00:04:23.104 hopefully. [music, wind]