
CLIMIS4AVAL
Real-time cleansing of snow and weather data for operational avalanche forecasting

Abstract
Avalanche forecasting relies on snow, snow cover and weather data – for expert evaluation as well as for machine learning based support tools. The data need to be accessible in high quality as soon as these become available. Any measurement errors, anomalies and data gaps diminish forecast accuracy.
Avalanche forecasting, physical snow models, hydrological predictions and many other Alpine scientific activities are largely data-driven; therefore, consistent and accurate data are fundamental for high-quality outputs. With increasing data volumes and the increased need for timely and accurate forecasts, preferably automated location-based forecasts, it becomes imperative to clean these essential data on the fly.
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Corinne joined the SDSC as a senior data scientist in December 2020. She graduated with a PhD in Statistics from the University of Washington in 2020. Her doctoral research focused on representation learning for partitioning problems. Prior to her PhD, she obtained bachelor's degrees in Math, Statistics, and Economics, along with a master's degree in Economics, from Penn State University. Her research interests include deep learning and kernel-based methods, with applications in fields ranging from computer vision to oceanography.


Michele received a Ph.D. in Environmental Sciences from the University of Lausanne (Switzerland) in 2013. He was then a visiting postdoc in the CALVIN group, Institute of Perception, Action and Behaviour of the School of Informatics at the University of Edinburgh, Scotland (2014-2016). He then joined the Multimodal Remote Sensing and the Geocomputation groups at the Geography department of the University of Zurich, Switzerland (2016-2017). His main research activities were at the interface of computer vision, machine and deep learning for the extraction of information from aerial photos, satellite optical images and geospatial data in general.
SLF / WSL:
- Prof. Dr. Jürg Schweizer
- Dr. Massimiliano Zappa
- Dr. Jan Svoboda
- Marc Ruesch
- David Liechti
- Dr. Frank Techel
- Florian Lustenberger
description
Introduction:
The backbone of the Swiss avalanche forecast infrastructure are the data of the Intercantonal Measurement and Information System (IMIS), which currently consists of 187 automated snow and weather stations. They are distributed throughout the Swiss Alps and in most cases are situated in the high alpine region above the tree line. The stations continuously record the snow and weather conditions, transmit data to the SLF every hour and provide the national avalanche warning service of the SLF as well as local avalanche services responsible for public safety in settlements and on roads with the crucial information for danger assessment. Public products such as new snow or snow height maps also rely on these data.
Therefore, the objective is the development of algorithms that allow real-time detection of anomalies in the time series, but also the detection of outliers, and impute missing data by applying state-of-the-art machine learning approaches.
This real-time data cleansing will solve a long-standing issue with the IMIS data that are known to be contaminated with data anomalies and has hindered automated processing. Hence, the completion of the proposed research will have a major impact, in particular for the application of numerical avalanche prediction models such as we recently developed in collaboration with the SDSC.
Problem:
This project aims to perform anomaly detection, outlier detection, and imputation on IMIS station data both retrospectively and in real time. In particular, the project focuses on the measurements of snow depth, wind speed and direction, air temperature, precipitation, and a maintenance model.
Proposed approach:
The SDSC is working with SLF to apply time-series-based statistical and machine learning-based methods for anomaly detection, outlier detection, and imputation in the given context.
Impact:
Numerical avalanche prediction and other models used at SLF will be more accurate with the cleaned data. The cleaned data will also be made openly available in the data portal of SLF, and will therefore benefit the numerous downstream users of the data.

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Additionnal resources
Bibliography
- Bavay, M. and Egger, T.: MeteoIO 2.4.2: a preprocessing library for meteorological data, Geosci. Model Dev., 7, 3135–3151, https://doi.org/10.5194/gmd-7-3135-2014, 2014.
- Pérez-Guillén, C., Techel, F., Hendrick, M., Volpi, M., van Herwijnen, A., Olevski, T., Obozinski, G., Pérez-Cruz, F., and Schweizer, J.: Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland, Nat. Hazards Earth Syst. Sci., 22, 2031–2056, https://doi.org/10.5194/nhess-22-2031-2022, 2022.
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