Abstract
Abstract The information content, that is, the predictive capability, of a forecast system is often quantified with skill scores. This paper introduces two ranked mutual information skill (RMIS) scores, RMISO and RMISY, for the evaluation of probabilistic forecasts. These scores are based on the concept of mutual information of random variables as developed in information theory. Like the ranked probability skill score (RPSS)—another and often applied skill score—the new scores compare cumulative probabilities for multiple event thresholds. The RMISO quantifies the fraction of information in the observational data that is explained by the forecasts. The RMISY quantifies the amount of useful information in the forecasts. Like the RPSS, the new scores are biased, but they can be debiased with a simple and robust method. This and additional promising characteristics of the scores are discussed with ensemble forecast assessment experiments.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
