Snow Profile Alignment and Similarity Assessment for Aggregating, Clustering, and Evaluating of Snowpack Model Output for Avalanche Forecasting
Abstract
Detailed snowpack models that simulate the evolution of the snow stratigraphy in support of operational avalanche forecasting have been around for close to 20 years. Despite their ability to complement the traditional data streams used in avalanche forecasting , the adoption of snowpack models has been limited . I nformal conversations with forecasters highlight two main issues: 1) the overwhelming volume of data produced by the models, and 2) validity concerns due to cumulative impact of potentially inaccurate weather inputs . Hence, e nhancing the operational value of snowpack models critically re quires the development of processing tools that reduce data volum e , and allow forecaster s to explore the simulations in a way that builds trust .
In this presentation, we introduce a numerical method for process ing and summariz ing large number s of snow profile s that emulates the data assimilation process of avalanche forecasters . Our approach exploits Dynamic Time Warping, a well-established data science method , to align profiles by matching layers between them based on hardness, grain type and optionally deposition date . The similarity of the aligned profiles is then evaluated with an independent similarity measure that focuses on snowpack features relevant for avalanche forecasting , which creates new opportunities for automated summarizing and validating of snowpack model output. First , t he similarity measure provide s the necessary quantitative link to data clustering and aggregating methods, which can be used to meani ngfully group and summariz e snowpack information . Second, t he similarity measure can be used to computationally compare model output with human snow profile observation s and objectively quantify the degree of agreement. O ur algorithm aims to promote the operational application of snowpack model s in avalanche warning services by providing analysis and validation tools that enable forecasters to continuous ly i nteract with snowpack simulations in familiar and accessible way s that offer direct operational value . This allow s forecasters to develop an in-depth understanding of how to interpret the simulations and when to trust them , which is critical for a meaningful integration of snowpack models into operational avalanche forecasting .- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2020
- Bibcode:
- 2020AGUFMC068...07H
- Keywords:
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- 0736 Snow;
- CRYOSPHERE;
- 0740 Snowmelt;
- CRYOSPHERE;
- 0798 Modeling;
- CRYOSPHERE