Tracking bits of information through forecasting systems: from source to decision

Probabilistic forecasts are essential for good decision making, because they communicate the forecaster's best attempt at representation of both information available and the remaining uncertainty of a variable of interest. The amount of information provided, which can be measured in bits using information theory, would then be a natural measure of success for the forecast in a verification exercise. On the other hand, it may seem rational to tune the forecasting system to provide maximum value to users. Somewhat counter-intuitively, there are arguments against tuning for maximum value. When the design of the forecasting system also includes the choice of the sources of information, monitoring network optimization becomes part of the problem to solve.  
In this presentation, we give a brief overview of the different roles information theory can have in these different aspects of probabilistic forecasting. These roles range from analysis of predictability, model selection, forecast verification, monitoring network design, and data assimilation by ensemble weighting. Using the same theoretical framework for all these aspects has the advantage that some connections can be made that may eventually lead to a more unified perspective on forecasting.