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. 1: Report selection, 2: Station selection, 3: Parameter, 4: Time picker, 5: Time inverval, 6: Aggregation function, 6: Perspective (absolute or change by time), 7: Time lag when perspective = change, 8: Download button Figure 2: Example of a time series plot for two stations (Altdorf and Magadino) Technology MySQL DB R packages: data.

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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 2) provides a good overview of station characteristics. Detailed maps allow for a better understanding of local weather/climate processes.

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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