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Full-Text Articles in Engineering
The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu
The Quest For Model Uncertainty Quantification: A Hybrid Ensemble And Variational Data Assimilation Framework, Peyman Abbaszadeh, Hamid Moradkhani, Dacian Daescu
Civil and Environmental Engineering Faculty Publications and Presentations
This article presents a novel approach to couple a deterministic four‐dimensional variational (4DVAR) assimilation method with the particle filter (PF) ensemble data assimilation system, to produce a robust approach for dual‐state‐parameter estimation. In our proposed method, the Hybrid Ensemble and Variational Data Assimilation framework for Environmental systems (HEAVEN), we characterize the model structural uncertainty in addition to model parameter and input uncertainties. The sequential PF is formulated within the 4DVAR system to design a computationally efficient feedback mechanism throughout the assimilation period. In this framework, the 4DVAR optimization produces the maximum a posteriori estimate of state variables at the beginning …
Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani
Toward A Reliable Prediction Of Seasonal Forecast Uncertainty: Addressing Model And Initial Condition Uncertainty With Ensemble Data Assimilation And Sequential Bayesian Combination, Caleb Matthew Dechant, Hamid Moradkhani
Civil and Environmental Engineering Faculty Publications and Presentations
Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate …
Improving Robustness Of Hydrologic Parameter Estimation By The Use Of Moving Block Bootstrap Resampling, Hamid Moradkhani, Mohammad Ebtehaj, Hoshin V. Gupta
Improving Robustness Of Hydrologic Parameter Estimation By The Use Of Moving Block Bootstrap Resampling, Hamid Moradkhani, Mohammad Ebtehaj, Hoshin V. Gupta
Civil and Environmental Engineering Faculty Publications and Presentations
Modeling of natural systems typically involves conceptualization and parameterization to simplify the representations of the underlying process. Objective methods for estimation of the model parameters then require optimization of a cost function, representing a measure of distance between the observations and the corresponding model predictions, typically by calibration in a static batch mode and/or via some dynamic recursive optimization approach. Recently, there has been a focus on the development of parameter estimation methods that appropriately account for different sources of uncertainty. In this context, we introduce an approach to sample the optimal parameter space that uses nonparametric block bootstrapping coupled …
Uncertainty Assessment Of Hydrologic Model States And Parameters: Sequential Data Assimilation Using The Particle Filter, Hamid Moradkhani, Kuo-Lin Hsu, Hoshin V. Gupta, Soroosh Sorooshian
Uncertainty Assessment Of Hydrologic Model States And Parameters: Sequential Data Assimilation Using The Particle Filter, Hamid Moradkhani, Kuo-Lin Hsu, Hoshin V. Gupta, Soroosh Sorooshian
Civil and Environmental Engineering Faculty Publications and Presentations
Two elementary issues in contemporary Earth system science and engineering are (1) the specification of model parameter values which characterize a system and (2) the estimation of state variables which express the system dynamic. This paper explores a novel sequential hydrologic data assimilation approach for estimating model parameters and state variables using particle filters (PFs). PFs have their origin in Bayesian estimation. Methods for batch calibration, despite major recent advances, appear to lack the flexibility required to treat uncertainties in the current system as new information is received. Methods based on sequential Bayesian estimation seem better able to take advantage …
Data Assimilation In Models With Convective Adjustment, Robert N. Miller, Edward D. Zaron, Andrew F. Bennett
Data Assimilation In Models With Convective Adjustment, Robert N. Miller, Edward D. Zaron, Andrew F. Bennett
Civil and Environmental Engineering Faculty Publications and Presentations
Practical hydrostatic ocean models are often restricted to statically stable configurations by the use of a convective adjustment. A common way to do this is to assign an infinite boat conductivity to the water at a given level if the water column should become statically unstable. This is implemented in the form of a switch. When a statically unstable configuration is detected, it is immediately replaced with a statically stable one in which heat is conserved. In this approach, the model is no longer governed by a smooth set of equations, and usual techniques of variational data assimilation must be …