Winter 2020/21

Figure 1: San Bernardino Figure 2: Lugano Figure 3: Chaumont Der Winter 2019/20 war in allen Regionen der Schweiz äusserst mild. Beständige Westwindlagen sorgten für mehrere Sturmtiefdruckgebiete auf der Alpennordseite. In mittleren und tiefen Lagen herrschte grosse Schneearmut. Teilweise lag weniger Schnee als im Winter 1989/90 (Zwischenstand: 14. März 2020). Anhand der Tagesdaten der NBCN-Stationen von MeteoSchweiz soll anhand von einigen Grafiken ein Rückblick auf die Schneeverhältnisse im vergangenen Winter gemacht werden.

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Summary I’ve built a number of Shiny apps in the past already. Even though they had a lot of common features, I found it difficult to migrate functionality from one to the next project. Often times, different data structure and variable names made it necessary it to re-write a large portion of the code. Fortunately, there are a number of tools available within the R framework (packages and concepts) which allow for a high level of reusability.

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Homogeneous data series since 1864 are provided by MeteoSwiss through the opendata.swiss platform. The series consist of monthly means of temperature and precipitation for a total of 14 weather stations in Switzerland. To allow for a comparison of different months and stations a standard score is applied to these series. For a reference period (e. g. the climatological normal period 1980 - 2010) mean and standard deviation are determined per month of the year and station.

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ERA-20C reanalysis data was used to reconstruct the weather situation during a historical flash flood event in the Jona river basement (Canton of Zürich, Switzerland) back in 1939 (see report). Vertical temperature and moisture profiles are crucial in the search for relevant convective storm processes. Thus, for a periode between 1980 and 2010 radiosonde observations from Payerne, Switzerland were compared with reanalysis data. ERA-20C tends to overestimate relative humdity in the lower and upper troposphere.

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Am 25. August 1939 kam es im Einzugsgebiet der Jona zu einer Hochwasserkatastrophe. Durch Überflutung, Hangrutschungen und Übermurung entstanden grosse Schäden an Gebäuden, Infrastruktur und landwirtschaftlichen Flächen. Die Schweiz lag damals in einem Gebiet flacher Druckverteilung mit schwachen Winden. Im Bereich einer Tiefdruckrinne entstanden praktisch ortsfeste Regenzellen, die sich aus grossen Feuchtigskeitreserven spiesen. Eine solche Zelle sorgte am Abend des 25. August für extrem intensive Niederschläge im Einzugsgebiet der Jona.

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The goal of this reporting tool was to provide a visual overview of the numerous runs from numerical weather prediction models (NWP) during a tropical storm event. Output (PNG or PDF) is suited for insurance client reporting purposes. Figure 1: Example report for Major Hurricane Irma issued on 9 September 2017. Tracks from various models (black solid lines), isoatchs of gust wind speed (colour lines) and forecasted storm intensity (markers) are shown here.

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##Project Description MeteoSwiss weather observations are available through the opendata.swiss platform. Current project is about a simple R Shiny user interface that produces customised time series plots of various meteorological parameters. It features a dropdown menu with pre-defined reports (see figure 1). A change in this selector triggers an update of the whole UI, filling in the report selections. Go to Demo Figure 1: Screenshot of the UI.

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Project Description Understanding the local terrain features is crucial when it comes to interpretation of weather observations (e. g. comparison of wind speeds during a storm event or validation of temperature extrema). The landform classification algorithm described above is applied to the set of automated weather stations run owned by MeteoSwiss. A simple classification by altitude and local landform (figure 1) provides a good overview of station characteristics. Detailed maps allow for a better understanding of local weather/climate processes.

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Jasiewicz and Stepinkski (2013) provided a novel method for classification and mapping of landform elements from a digital elevation model (DEM) based on the principle of pattern recognition rather than differential geometry. At the core of the method is the concept of a geomorphon (geomorphologic phonotypes) — a simple ternary pattern that serves as an archetype of a particular terrain morphology. From a total of 498 possible combinations a set of ten distinct patterns is formed (see figure 1).

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Project Description Current project features a static markdown report that is updated every ten minutes and rendered into a HTML file. The reports shows the data completeness status of MeteoSwiss’ observation network (SwissMetNet). Data is made available through the opendata.swiss platform. A leaflet map provides an overview of current situtation, the colour scheme stands for the duration of the interruption. Customised map markers provide detailed information on the duration by type of sensor.

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Author's picture

M. Saenger

Meteorologist (MSc University of Zürich/ETH Zürich) with more than 10 years of experience in traditional reinsurance and ILS industry: Reinsurance Pricing and Catastrophe Model validation. Development of pricing and portfolio reporting tools. Profound knowledge of statistical methods and software (MySQL/R/Shiny/Leaflet/Markdown)

Natural Hazard and Data Scientist

Zürich - Switzerland