Practical application of some new technologies, and the light they shed on model design and model problem decomposition

Practical application of some new technologies, and the light they shed on model design and model problem decomposition

New robust efficient modelling technologies are available to quantify the uncertainties associated with model predictions.  We adopt a decision support modelling framework which uses a combination of two of these new technologies, Data Space Inversion and Ensemble Space Inversion.  Using this framework helps answer model design and deployment questions that are critical for a specified decision.  These questions include:

• What contributes to the uncertainty of what could go wrong with this decision?
• Where is the information that may reduce this uncertainty?
• How can this information be best harvested – what model structure, parameterisation, observation weighting strategy, and technologies are most appropriate?
• How are the consequences of information insufficiency best expressed?

We demonstrate how this modelling framework reveals the predictive accuracy costs of over-fitting to some types of data.  We also identify for a specific prediction which alternative model structures and inversion methods are more appropriate given alternative data sets.