Data Interpretation

Unlike other types of training, modeling technology training does not focus only on technical skills, such as how to run or use available modeling software. It also requires specific expertise in interpreting modeling results. The purpose is to understand the numerical processes within the modeling program so that users can determine whether the model outputs are accurate after the model verification and validation process has been completed. If the results are not accurate, users must be able to identify which parameters or boundary conditions are not appropriate and need to be adjusted.

After the model has passed the verification and validation stages and has produced reliable outputs, users must also be able to understand the cause-and-effect processes reflected in the model results. This ability is especially important when the model uses many modeling scenarios. Another important aspect of interpretation is the ability to connect modeling outputs with their possible impacts on the aquatic environment. This allows the model results to be explained logically, and the interactions with the environment to be described through clear cause-and-effect relationships.

In addition, model output data can be analyzed further using other methodological approaches, such as spatial-based statistical methods, including Empirical Orthogonal Function, Kalman Filter, Spectral Density, and others.