Validating quantitative data model

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This uncertainty typically comes from a number of sources, including: • Input uncertainty—lack of knowledge about parameters and other model inputs (initial conditions, forcings, boundary values, and so on); • Model discrepancy—the difference between model and reality (even at the best, or most correct, model input settings); • Limited evaluations of the computational model; and • Solution and coding errors.

However, FIGURE 5.1 Daily maximum temperatures for Norman, Oklahoma (left), and histograms of next-day prediction errors (right) using two prediction models.In these examples, some physical observations are used to refine or constrain uncertainties that contribute to prediction uncertainty.Estimating prediction uncertainty is a vibrant research topic whose methods vary depending on the features of the problem at hand.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).

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