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Estimating prediction uncertainty requires the combination of computational models, physical observations, and possibly other information sources.Exactly how this estimation is carried out can range from very direct, as in the weather forecasting example in Figure 5.1, to quite complicated, as described in the case studies in this chapter.While the communication component is not fundamentally mathematical, effective communication may depend on mathematical aspects of the assessment.The various tasks mentioned in the preceding paragraphs give a broad outline of validation and prediction.
In addition to physical observations, information may come from the literature or expert judgment that may incorporate historical data or known physical behavior.
Estimating how different forms of additional information would improve predictions or the validation assessment can be an important component of the validation effort, guiding decisions about where to invest resources in order to maximize the reduction of uncertainty and/ or an increase in reliability.
Communicating the results of the prediction or validation assessment includes both quantitative aspects (the predicted QOI and its uncertainty) and qualitative aspects (the strength of the assumptions on which the assessment is based).
The top histogram shows residuals from the persistence model, predicting tomorrow’s high temperature with today’s high temperature.
The bottom histogram shows residuals from the National Weather Service (NWS) forecast.