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.
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.
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.
##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.
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.
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.