Engineering Commons^™

Toward Reduction Of Model Uncertainty: Integration Of Bayesian Model Averaging And Data Assimilation, Mark A. Parrish, Hamid Moradkhani, Caleb Matthew Dechant

Civil and Environmental Engineering Faculty Publications and Presentations

Multimodeling in hydrologic forecasting has proved to improve upon the systematic bias and general limitations of a single model. This is typically done by establishing a new model as a linear combination or a weighted average of several models with weights on the basis of individual model performance in previous time steps. The most commonly used multimodeling method, Bayesian model averaging (BMA), assumes a fixed probability distribution around individual models' forecast in establishing the prior and uses a calibration period to determine static weights for each individual model. More recent work has focused on a sequential Bayesian model selection technique …