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 2) 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 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