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Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end‐users
Author(s) -
Fundel Vanessa J.,
Fleischhut Nadine,
Herzog Stefan M.,
Göber Martin,
Hagedorn Renate
Publication year - 2019
Publication title -
quarterly journal of the royal meteorological society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.744
H-Index - 143
eISSN - 1477-870X
pISSN - 0035-9009
DOI - 10.1002/qj.3482
Subject(s) - probabilistic logic , computer science , citizen journalism , bridge (graph theory) , key (lock) , end user , data science , artificial intelligence , world wide web , computer security , medicine
Today's ensemble weather prediction systems provide reliable and sharp probabilistic forecasts—yet they are still rarely communicated to outside users because of two main worries: the difficulty of communicating probabilities to lay audiences and their presumed reluctance to use probabilistic forecasts. To bridge the gap between the forecasts available and their use in day‐to‐day decision making, we encourage scientists, developers, and end‐users to engage in interdisciplinary collaborations. Here, we discuss our experience with three different approaches of introducing probabilistic forecasts to different user groups and the theoretical and practical challenges that emerged. The approaches range from quantitative analyses of users' revealed preferences online to a participatory developer–user dialogue based on trial cases and interactive demonstration tools. The examples illustrate three key points. First, to make informed decisions, users need access to probabilistic forecasts. Second, forecast uncertainty can be understood if its visual representations follow validated best practices from risk communication and information design; we highlight five important recommendations from that literature for communicating probabilistic forecasts. Third, to appreciate the value of probabilistic forecasts for their decisions, users need the opportunity to experience them in their everyday practice. With these insights and practical pointers, we hope to support future efforts to integrate probabilistic forecasts into everyday decision making.