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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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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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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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