Snow

In my last blog post, I tried to answer the question: How has snowfall at Sandia Peak Ski Area changed over the years? Ultimately, I want to know how climate change is affecting the future of skiing in the Sandias and to develop some sense of what weather patterns are associated with particularly good or bad years. With that in mind, I focused my initial research on assessing daily SWE values at the Ski Area. Unlike daily snowfall or total seasonal snowfall, daily SWE represents the actual amount of snow on the ground, including the effects of melting, sublimation, and redistribution from wind. Furthermore, since SWE is fundamentally a measure of water quantity, this analysis not only describes the overall quality of the snow for skiing, it is also equally useful from a hydrologic perspective. ...

My graduate degree research was focused on glacial hydrology, which is basically trying to figure out how water moves above, below, and through glaciers and ice sheets. Water is important because it affects things like sliding, melting, sub-glacial erosion, and geochemistry. My research utilized temperature measurements from snow on the Greenland ice sheet, and I was lucky enough to travel to SW Greenland in the summer of 2010. Figure 1 - SW Greenland Ice Sheet ...

This is the second part of my two part series on a machine learning competition to predict snow water equivalent (SWE). In Part 1, I describe the competition, as well as, my process for coming up with an approach for making SWE predictions at 9,067 locations across the Western US. That approach, sometimes called the “hypsometric” method (Fassnacht et al., 2003, see Part 1 for an overview of the method), is one of the easiest I could find, and it therefore seemed doable given personal time constraints. My expectations were low - I just wanted to see how a simple approach compared to others in the competition. To my surprise, out of about 1000 predictions submitted to the competition, my predictions ranked 62. Here I describe how I computed the SWE predictions and assess the results. ...

Part 1: Competition Overview Late last December I ran across a machine learning competition hosted by Driven Data. The goal of the competition is to predict snow water equivalent at high spatial resolution across the western US. I had never before thought of participating in a machine learning competition, although I had heard of the idea via another platform, Kaggle. However, a machine learning competition involving snow is more up my alley, as I have both professional and personal experience with snow science. Furthermore, I had been wanting to enhance my familiarity with machine learning techniques. I decided to give it a shot. ...