Wed,19 Sep, 4 pm: Migration of calibration software
Challenge: Providing a widely-used environment (such as Python) and a user-friendly GUI for existing calibration software (ecPoint-PyCal).
ec-Point-Cal is a software that compares numerical model outputs against point observations to identify biases/errors at local scale. The software had two different processes and both steps are written in two different programming languages, which cannot be easily integrated. The challenge proposes to migrate the existing code into Python and develop a user-friendly GUI for the software.
The developed software ecPoint-PyCal provides a dynamic environment in Python and could be ultimately used to help steer model developments and to post-process ECMWF model parameters to produce probabilistic products for geographical locations.
Thu, 20 Sep, 4 pm: Web Crawler
Challenge: Develop of a tool to search the web systematically, identifying data sources for observed environmental data.
Globally, new sources of raw data are being made available via the web all the time, however, ECMWF often isn’t aware of these. Manually identifying and gathering information on these new sources is both time consuming and error prone. The challenge aims at developing a tool that searches the web systematically, identifying data sources for observed environmental data. The software automates the discovery, analysis and assessment of the candidate web pages in order to find new data sets. The resulting data can be used to improve global predictive weather forecasting models.
Fri, 21 Sep, 4 pm: Innovative Visualisations
Challenge: Implementation of a gallery of novel visualisation approaches to communicate weather information to non-experts.
ECMWF’s visualisations are developed having weather forecasters in mind. The challenge focuses on developing new visualisations that help communicating weather information to non-experts. The goal is to develop an innovative visualisation to present ensemble weather forecasts. A new design of a meteogram (a graphical presentation of multiple weather variables for a particular location) has been developed with value-suppressing uncertainty (VSU) icons. They allocate a larger range of icons when the uncertainty is low and a smaller range when the uncertainty is high. This helps to make ensemble forecasts more accessible and to increase the overall trust in 15-day forecasts.
