Making Probabilities Work (SPARK session)

For this session we are aiming to organise it according to the SPARK concept:
https://www.ems2020.eu/programme_and_abstracts/on_the_programme/spark_sessions.html

Weather forecasts have matured substantially in providing reliable and sharp predictions and consequently the associated forecast uncertainty. This information can be integrated in downstream models and used to support decision-making processes.

The raw uncertainty information, e.g. as members of one or multiple ensemble prediction systems or as statistically derived probability distributions, has to be postprocessed, combined or visualized before it can serve as input for impact models such as hydrological models, or as decision support for weather forecasters, and for lay or professional end-users, such as emergency managers or energy providers.

In this session, we would like to support a holistic perspective on issues that arise when making use of uncertainty information of weather forecasts in decision processes and applications. To this end, we encourage contributions that investigate the application and interpretation of uncertainty information along any of the following questions:

How does the quality of the final decision depend on forecast uncertainty and uncertainty from other parts of the decision process (e.g., missing information, weather impact assessment, other sources, interactions, misinterpretations)?
Where, along the chain from raw forecast uncertainty to the final decision, do the largest uncertainties arise?
How is the uncertainty information (e.g., from ensemble prediction systems, multi-models etc.) propagated through the production chain up to the final decision?
How can we tailor information about forecast uncertainty to a given decision process or application?
How is uncertainty represented best in a given case (e.g., as ensemble members, PDFs, or worst/best case) to reduce complexity and computational or cognitive cost?
How can we identify the most suitable representation for different user-groups and decision processes?
How can we incorporate vulnerability and exposure data in a risk-based decision framework?
How can we evaluate and quantify the value of uncertainty information for the final decision?
What strategies help the end-user to the right interpretation of the uncertainty forecasts to make informed decisions?
What are the benefits of impact-based or risk-based forecasts and warnings in decision-making (including for disaster risk reduction)?
How can the interaction between scientists and end-users help to overcome reservations about uncertainty forecasts?