The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework-Reference-Cited by-同舟云学术

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The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework

Published:2019-03 Issue:3 Volume:55 Page:2407-2431
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Abbaszadeh Peyman¹^ORCID,Moradkhani Hamid¹^ORCID,Daescu Dacian N.²

Affiliation:

1. Center for Complex Hydrosystems Research, Department of Civil, Construction and Environmental Engineering University of Alabama Tuscaloosa AL USA

2. Fariborz Maseeh Department of Mathematics and Statistics Portland State University Portland OR USA

Funder

National Science Foundation

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2018WR023629

Reference73 articles.

1. Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo

2. Uncertainty Quantification in Land Surface Hydrologic Modeling: Toward an Integrated Variational Data Assimilation Framework

3. An Ensemble Adjustment Kalman Filter for Data Assimilation

4. A Monte Carlo Implementation of the Nonlinear Filtering Problem to Produce Ensemble Assimilations and Forecasts

5. Particle Markov chain Monte Carlo methods

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