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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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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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