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In other settings, a more complicated statistical modeling formulation may be required to combine simulation output, various kinds of physical observations, and expert judgment to produce a prediction with accompanying prediction uncertainty, which can then be used for the assessment.
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.
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5 Model Validation and Prediction 5.1 INTRODUCTION From a mathematical perspective, validation is the process of assessing whether or not the quantity of interest (QOI) for a physical system is within some tolerance—determined by the intended use of the model—of the model prediction.
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However, FIGURE 5.1 Daily maximum temperatures for Norman, Oklahoma (left), and histograms of next-day prediction errors (right) using two prediction models.