How is avalanche danger described in textual descriptions in avalanche forecasts in Switzerland? Consistency between forecasters and avalanche danger

Effective and efficient communication of expected avalanche conditions and danger to the public is of great importance, especially where the primary audience of forecasts are recreational, non-expert users. In Europe, avalanche danger is communicated using a pyramid, starting with ordinal levels of avalanche danger and progressing through avalanche-prone locations and avalanche problems to a danger description. In many forecast products, information relating to the trigger required to release an avalanche, the frequency or number of potential triggering spots, and the expected avalanche size is described exclusively in a textual danger description. These danger descriptions are, however, the least standardized part of avalanche forecasts. Taking the perspective of the avalanche forecaster and focusing particularly on terms describing these three characterizing elements of avalanche danger, we investigate first which meaning forecasters assign to the text characterizing these elements and second how these descriptions relate to the forecast danger level. We analyzed almost 6000 danger descriptions in avalanche forecasts published in Switzerland and written using a structured catalogue of phrases with a limited number of words. Words and phrases representing information describing these three elements were labeled and assigned to ordinal classes by Swiss avalanche forecasters. These classes were then related to avalanche danger. Forecasters were relatively consistent in assigning labels to words and phrases with Cohen's kappa values ranging from 0.64 to 0.87. Avalanche danger levels were also described consistently using words and phrases, with for example avalanche size classes increasing monotonically with avalanche danger. However, especially for danger level 2 (moderate), information about key elements of avalanche danger, for instance the frequency or number of potential triggering spots, was often missing in danger descriptions. In general, the analysis of the danger descriptions showed that extreme conditions are described in more detail than intermediate values, highlighting the difficulty of communicating conditions that are neither rare nor frequent or neither small nor large. Our results provide data-driven insights that could be used to refine the ways in which avalanche danger could be communicated. Furthermore, through the perspective of the semiotic triangle, relating a referent (the avalanche situation) through thought (the processing process) to symbols (the textual danger description), we provide an alternative starting point for future studies of avalanche forecast consistency and communication.

Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

Even today, the assessment of avalanche danger is by large a subjective, yet data-based decision-making process. Human experts analyze heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowadays, modern machine learning methods and the rise in computing power in combination with physical snow cover modelling open up new possibilities for developing decision support tools for operational avalanche forecasting. Therefore, we developed a fully data-driven approach to predict the regional avalanche danger level, the key component in public avalanche forecasts, for dry-snow conditions in the Swiss Alps. Using a large data set of more than 20 years of meteorological data measured by a network of automated weather stations, which are located at the elevation of potential avalanche starting zones, and snow cover simulations driven with these input weather data, we trained two random forest (RF) classifiers. The first classifier (RF #1) was trained relying on the forecast danger levels published in the avalanche bulletin. Given the uncertainty related to a forecast danger level as a target variable, we trained a second classifier (RF #2), relying on a quality-controlled subset of danger level labels. We optimized the RF classifiers by selecting the best set of input features combining meteorological variables and features extracted from the simulated profiles. The accuracy of the danger level predictions ranged between 74 % and 76 % for RF #1, and between 72 % and 78 % for RF #2, with both models achieving better performance than previously developed methods. To assess the accuracy of the forecast, and thus the quality of our labels, we relied on nowcast assessments of avalanche danger by well-trained observers. The performance of both models was similar to the accuracy of the current experience-based Swiss avalanche forecasts (which is estimated to 76 %). The models performed consistently well throughout the Swiss Alps, thus in different climatic regions, albeit with some regional differences. A prototype model with the RF classifiers was already tested in a semi-operational setting by the Swiss avalanche warning service during the winter 2020-2021. The promising results suggest that the model may well have potential to become a valuable, supplementary decision support tool for avalanche forecasters when assessing avalanche hazard.

Distributed coverage optimisation for a fleet of unmanned maritime systems

<p>Unmanned maritime systems (UMS) can provide important benefits for maritime law enforcement agencies for tasks such as area surveillance and patrolling, especially when they are able to work together as one coordinated system. In this context, this paper proposes a methodology that optimises the coverage of a fleet of UMS, thereby maximising the opportunities for identifying threats. Unlike traditional approaches to maritime coverage optimisation, which are also used, for example, in search and rescue operations when searching for victims at sea, this approach takes into consideration the limited seaworthiness of small UMS, compared with traditional large ships, by incorporating the danger level into the design of the optimiser.</p>

Avalanche danger level characteristics from field observations of snow instability

Avalanche danger levels are described in qualitative terms that mostly are not amenable to measurements or observations. However, estimating and improving forecast consistency and accuracy require descriptors that can be observed or measured. Therefore, we aim to characterize the avalanche danger levels based on expert field observations of snow instability. We analyzed 589 field observations by experienced researchers and forecasters recorded mostly in the region of Davos (Switzerland) during 18 winter seasons (2001–2002 to 2018–2019). The data include a snow profile with a stability test (rutschblock, RB) and observations on snow surface quality, drifting snow, signs of instability and avalanche activity. In addition, observers provided their estimate of the local avalanche danger level. A snow stability class (very poor, poor, fair, good, very good) was assigned to each profile based on RB score, RB release type and snowpack characteristics. First, we describe some of the key snowpack characteristics of the data set. In most cases, the failure layer included persistent grain types even after a recent snowfall. We then related snow instability data to the local avalanche danger level. For the danger levels 1–Low to 4–High, we derived typical stability distributions. The proportions of profiles rated poor and very poor clearly increased with increasing danger level. For our data set, the proportions were 5 %, 13 %, 49 % and 63 % for the danger levels 1–Low to 4–High, respectively. Furthermore, we related the local avalanche danger level to the occurrence of signs of instability such as whumpfs, shooting cracks and recent avalanches. The absence of signs of instability was most closely related to 1–Low and the presence of them to 3–Considerable. Adding the snow stability class and the 3 d sum of new snow depth improved the discrimination between the lower three danger levels. Still, 2–Moderate was not well described. Nevertheless, we propose some typical situations that approximately characterize each of the danger levels. Obviously, there is no single easily observable set of parameters that would allow us to fully characterize the avalanche danger levels. One reason for this shortcoming is the fact that the snow instability data we analyzed usually lack information on spatial frequency, which is needed to reliably assess the danger level.

ANALISIS TINGKAT BAHAYA EROSI PADA VEGETASI KEBUN KARET PADA BERBAGAI KELERENGAN DI SUB DAS BATI-BATI DAS MALUKA PROVINSI KALIMANTAN SELATAN

The danger level of erosion at each land closure and Slopes has different results. It is important to know the handling of erosion hazards later. The purpose of this study is to calculate the amount of erosion due to changes in land closures and to know the level of erosion hazard (TBE) of rubber plantations on various slopes in Sub Das Bati – Bati Das Maluka. The research method is purposive random sampling. Sample points taken based on soil type, slopes class, vegetation, and land cover are adjusted to the land units of the land unit map (overlay). Soil sampling using ring samples and soil drills will then be tested. Land cover and marbles are closely related to erosion values. The highest erosion value is in Land Unit (LU) 38 with an erosion value of 73.64 tons/ha/yr, while the lowest value is at LU 7 with an erosion value of 6.34 tons/ha/yr. The degree of erosion hazard is related to the soil solum. Erosion hazard level in all land units and land cover indicates grade II-S (medium) is present at LU 38 while light (I-SR) is on, LU 37, LU 50, and LU 59, and very light (0-SR) is on LU 7 and LU 34.Tingkat bahaya erosi pada masing-masing penutupan lahan dan kelerengan mempunyai hasil yang berbeda. Hal ini penting untuk mengetahui penanganan bahaya erosi nantinya. Tujuan dari penelitian ini ialah untuk menghitung besarnya jumlah erosi akibat perubahan penutupan lahan serta mengetahui tingkat bahaya erosi (TBE) vegetasi kebun karet pada berbagai kelerengan di Sub Das Bati-Bati Das Maluka. Metode penelitian dilakukan secara purposive random sampling. Titik sampel yang diambil berdasarkan jenis tanah, kelas kelerengan, vegetasi, dan tutupan lahan yang disesuaikan dengan unit lahan dari peta satuan lahan (overlay). Pengambilan sampel tanah menggunakan ring sample dan bor tanah yang kemudian akan dilakukan pengujian. Tutupan lahan dan kelerengan erat kaitannya dengan nilai erosi. Nilai erosi tertinggi berada pada Unit Lahan (UL) 38 dengan nilai erosi sebesar 73,64 ton/ha/thn, sedangkan nilai terendah ada pada UL 7 dengan nilai erosi sebesar 6,34 ton/ha/thn. Tingkat bahaya erosi berhubungan dengan solum tanah. Tingkat bahaya erosi pada semua unit lahan dan tutupan lahan, menunjukkan TBE kelas II-S (sedang) terdapat pada UL 38 sedangkan TBE ringan (I-SR) ada pada, UL 37, UL 50, dan UL 59 serta TBE sangat ringan (0-SR) ada pada UL 7 dan UL 34.

Generating automatic warning proposals

&lt;p&gt;A warning system is a complex chain, which builds on different applications leading to a customer friendly product. The goal of the product is to deliver useful information to the end-user, giving indication of the severity of the event and what best to do in order to avoid damages and/or injuries/fatalities. In-between the different production steps are a number of processes, which can be altered to improve the products; for example by including probabilistic information or by producing impact-oriented warnings.&lt;/p&gt;&lt;p&gt;As MeteoSwiss is renewing its warning system, it opens up the possibility to include the above-mentioned information. Furthermore, it also offers the option to automatize the warning generation chain. One key part of this process are the automatically generated first guesses of warning regions. These regions display the danger level of any given hazard based only on the meteorological situation; hence, no predefined regions will be used to generate the warning products. As of now, MeteoSwiss used a set of predefined regions on which the danger level was indicated. These regions were not necessarily defined to best represent weather phenomena but rather often municipal boundaries.&lt;/p&gt;&lt;p&gt;However, how to produce meaningful regions is not trivial and it requires discussions with the forecasters as there are a number of parameters to tune. Tuning the regions is needed as no forecasting system is perfect and ideally, the automatically generated first guesses compensate for these short-comings. However, realistically speaking, before achieving a fully automatic warning system, there will be an intermediate phase when first guesses will likely have to be manually adjusted by the forecasters.&lt;/p&gt;&lt;p&gt;We will present our work and first results of automatic warning proposals based on COSMO-2E and feedbacks thereof we got from discussions with the forecasters.&lt;/p&gt;

How is avalanche danger described in public avalanche forecasts? Analyzing textual descriptions of avalanche forecasts in Switzerland

Efficient communication in public avalanche forecasts is of great importance to clearly inform and warn the public about expected avalanche conditions. In Europe, avalanche danger is communicated using a pyramid, starting with ordinal categories of avalanche danger, and progressing through avalanche-prone locations and avalanche problems to a danger description. In many forecast products, information relating to the trigger required to release an avalanche, the frequency or number of potential triggering locations, and the expected avalanche size, are described exclusively in the danger description. These danger descriptions are, however, the least standardized part of avalanche forecasts. Taking the perspective of the avalanche forecaster, and focusing particularly on terms describing these three characterizing elements of avalanche danger, we investigate firstly which text symbols are used to describe these elements, and secondly how these descriptions relate to the forecast danger level. We do so through the perspective of the semiotic triangle, relating a referent (the avalanche situation) through thought (the processing process) to symbols (the textual danger description). We analyzed almost 6000 danger descriptions in avalanche forecasts published in Switzerland and written using a structured catalog of phrases with a limited number of words. Text symbols representing information describing these three elements were labeled and assigned to ordinal classes by Swiss avalanche forecasters. These classes were then related to avalanche danger. Forecasters were relatively consistent in assigning labels to words and phrases with Cohen's Kappa values ranging from 0.67 to 0.87. Nonetheless, even experts were not in complete agreement about the labeling of terms and were less likely to agree on terms not used in official definitions. Avalanche danger levels were categorized relatively consistently using words and phrases, with for example avalanche size classes increasingly monotonically with avalanche danger. However, especially for danger level 2-Moderate, information about key elements was often missing in danger descriptions. In general, the analysis of the danger descriptions showed that extreme conditions are more frequently described in detail than intermediate values, highlighting the difficulty of communicating conditions that are neither rare nor frequent, or neither small nor large. Our results provide data-driven insights that could be used to refine the ways in which avalanche danger could and should be communicated, especially to recreationalists, and provide a starting point for future studies of how users interpret avalanche forecasts.