scholarly journals Supplementary material to "Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating of snowpack model output for avalanche forecasting"

2020 ◽  
Author(s):  
Florian Herla ◽  
Simon Horton ◽  
Patrick Mair ◽  
Pascal Haegeli
Keyword(s):  
Model Output ◽  
Avalanche Forecasting ◽  
Similarity Assessment ◽  
Supplementary Material ◽  
Profile Alignment

scholarly journals Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting

2021 ◽  
Vol 14 (1) ◽  
pp. 239-258
Author(s):  
Florian Herla ◽  
Simon Horton ◽  
Patrick Mair ◽  
Pascal Haegeli
Keyword(s):  
Hazard Assessment ◽  
Assessment Process ◽  
Model Output ◽  
Time Warping ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Snow Stratigraphy ◽  
Operational Application ◽  
Dynamic Time ◽  
Profile Alignment

Abstract. Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant for their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models, which consist of multidimensional sequences describing the snow characteristics of grain type, hardness, and age. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. By emulating aspects of the human avalanche hazard assessment process, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build an understanding of how to interpret and trust operational snowpack simulations.

scholarly journals Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating of snowpack model output for avalanche forecasting

2020 ◽  
Author(s):  
Florian Herla ◽  
Simon Horton ◽  
Patrick Mair ◽  
Pascal Haegeli
Keyword(s):  
Hazard Assessment ◽  
Model Output ◽  
Time Warping ◽  
Avalanche Forecasting ◽  
Avalanche Hazard ◽  
Snow Stratigraphy ◽  
Operational Application ◽  
Assessment Approach ◽  
Dynamic Time ◽  
Profile Alignment

Abstract. Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant to their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. Through emulating a human avalanche hazard assessment approach, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build understanding in how to interpret and when to trust operational snowpack simulations.

A tracking algorithm to identify slab and weak layer combinations for assessing snow instability and avalanche problem type

2021 ◽  
Author(s):  
Benjamin Reuter ◽  
Léo Viallon-Galinier ◽  
Stephanie Mayer ◽  
Pascal Hagenmuller ◽  
Samuel Morin
Keyword(s):  
Snow Cover ◽  
Winter Season ◽  
Essential Element ◽  
Problem Type ◽  
Model Output ◽  
Avalanche Forecasting ◽  
Weak Layer ◽  
Problem Types ◽  
Snow Stratigraphy ◽  
Wet Snow

<p>Snow cover models have mostly been developed to support avalanche forecasting. Recently developed snow instability metrics can help interpreting modeled snow cover data. However, presently snow cover models cannot forecast the relevant avalanche problem types – an essential element to describe avalanche danger. We present an approach to detect, track and assess weak layers in snow cover model output data to eventually assess the related avalanche problem type. We demonstrate the applicability of this approach with both, SNOWPACK and CROCUS snow cover model output for one winter season at Weissfluhjoch. We introduced a classification scheme for four commonly used avalanche problem types including new snow, wind slabs, persistent weak layers and wet snow, so different avalanche situations during a winter season can be classified based on weak layer type and meteorological conditions. According to the modeled avalanche problem types and snow instability metrics both models produced weaknesses in the modeled stratigraphy during similar periods. For instance, in late December 2014 the models picked up a non-persistent as well as a persistent weak layer that were both observed in the field and caused widespread instability in the area. Times when avalanches released naturally were recorded with two seismic avalanche detection systems, and coincided reasonably well with periods of low modeled stability. Moreover, the presented approach provides the avalanche problem types that relate to the observed natural instability which makes the interpretation of modeled snow instability metrics easier. As the presented approach is process-based, it is applicable to any model in any snow avalanche climate. It could be used to anticipate changes in avalanche problem type due to changing climate. Moreover, the presented approach is suited to support the interpretation of snow stratigraphy data for operational forecasting.</p>

scholarly journals Verified combination of numerical weather- and avalanche-prediction models at Kootenay Pass, British Columbia, Canada

2004 ◽  
Vol 38 ◽  
pp. 215-222
Author(s):  
Claudia Roeger ◽  
David M. McClung ◽  
Roland Stull
Keyword(s):  
British Columbia ◽  
Prediction Models ◽  
Weather Prediction ◽  
Meteorological Variables ◽  
Weather Data ◽  
Forecasting Model ◽  
Forecast Errors ◽  
Model Output ◽  
Avalanche Forecasting ◽  
Numerical Weather

AbstractThe combination of numerical weather prediction (NWP) and snow avalanche forecasting has been performed using the output of two weather models run at the University of British Columbia, Canada, and a local numerical avalanche-forecasting model developed for Kootenay Pass (McClung and Tweedy, 1994). The main motivations for this work are. (1) to extend the lead time of avalanche forecasts by using NWP forecasts of meteorological variables as input to statistical avalanche-threat models (instead of the traditional method of using current/past observed meteorological variables as input); and (2) to create another tool to help avalanche forecasters in their daily decision-making by making true forecasts instead of “nowcasts”. Therefore, verified weather-forecast model output was used as input for the local avalanche-forecasting model at Kootenay Pass. The resulting 24 hour avalanche forecast was compared to observed avalanche occurrences and to the 12 hour avalanche forecast with current weather observations. As a result, the avalanche-model output for the test runs with numerically predicted weather data is comparable in accuracy to the runs with observed weather data. The results also suggest that avalanches may be predicted statistically for 24 hours into the future when high-resolution NWP is used as input, weather- and avalanche-forecast errors taken into account during operational use.

Designing Workbook for Teaching Writing Descriptive Text Based on Scientific Approach

2019 ◽  
Vol 1 (2) ◽  
pp. 59
Author(s):  
Indah Pratiwi ◽  
Yanti Sri Rezeki
Keyword(s):  
Teaching Writing ◽  
English Teacher ◽  
Design Development ◽  
Seventh Grade ◽  
Internal Evaluation ◽  
Writing Ability ◽  
Good Book ◽  
Scientific Approach ◽  
Supplementary Material ◽  
Analysis Design

This research aims to design workbook based on the scientific approach for teaching writing descriptive text. This research was conducted on the seventh-grade students of SMPN 24 Pontianak. The method of this research is ADDIE (Analysis, Design, Development, Implementation, and Evaluation) with the exclusion of Implementation and Evaluation phases. This material was designed as supplementary material to support the course book used especially in teaching writing of descriptive text. The respondents in this research were the seventh-grade students and an English teacher at SMPN 24 Pontianak. In this research, the researchers found that workbook based on scientific approach fulfilled the criteria of the good book to teach writing descriptive text. The researchers conducted an internal evaluation to see the usability and the feasibility of the workbook. The result of the evaluation is 89%. It showed that the workbook is feasible to be used by students as the supplementary material to support the main course book and help the students improve their writing ability in descriptive text.

Catalytic Fluorination of Boron Compounds at a Bismuth Redox Platform

2019 ◽  
Author(s):  
Oriol Planas ◽  
Feng Wang ◽  
Markus Leutzsch ◽  
Josep Cornella
Keyword(s):  
Transition Metal ◽  
Main Group ◽  
Group Element ◽  
Oxidation States ◽  
Boron Compounds ◽  
Main Text ◽  
Main Group Element ◽  
Rational Ligand Design ◽  
Supplementary Material

The ability of bismuth to maneuver between different oxidation states in a catalytic redox cycle, mimicking the canonical organometallic steps associated to a transition metal, is an elusive and unprecedented approach in the field of homogeneous catalysis. Herein we present a catalytic protocol based on bismuth, a benign and sustainable main-group element, capable of performing every organometallic step in the context of oxidative fluorination of boron compounds; a territory reserved to transition metals. A rational ligand design featuring hypervalent coordination together with a mechanistic understanding of the fundamental steps, permitted a catalytic fluorination protocol based on a Bi(III)/Bi(V) redox couple, which represents a unique example where a main-group element is capable of outperforming its transition metal counterparts.<br>A main text and supplementary material have been attached as pdf files containing all the methodology, techniques and characterization of the compounds reported.<br>

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