Uncertainty Quantification of Flame Transfer Function under a Bayesian Framework

2018 ◽  
Author(s):  
Yixing Li ◽  
Xingjian Wang ◽  
Simon Mak ◽  
Chih-Li Sung ◽  
Jeff Wu ◽  
...  
Keyword(s):  
Transfer Function ◽  
Uncertainty Quantification ◽  
Bayesian Framework

Uncertainty Quantification for Multiscale Simulations1

2001 ◽  
Vol 124 (1) ◽  
pp. 29-41 ◽  
Author(s):  
B. DeVolder ◽  
J. Glimm ◽  
J. W. Grove ◽  
Y. Kang ◽  
Y. Lee ◽  
...  
Keyword(s):  
Shock Wave ◽  
Numerical Simulations ◽  
Uncertainty Quantification ◽  
Confidence Intervals ◽  
Petroleum Reservoirs ◽  
Bayesian Framework ◽  
Simulation Studies ◽  
Error Statistics ◽  
Key Issues ◽  
Leading Term

A general discussion of the quantification of uncertainty in numerical simulations is presented. A principal conclusion is that the distribution of solution errors is the leading term in the assessment of the validity of a simulation and its associated uncertainty in the Bayesian framework. Key issues that arise in uncertainty quantification are discussed for two examples drawn from shock wave physics and modeling of petroleum reservoirs. Solution error models, confidence intervals and Gaussian error statistics based on simulation studies are presented.

scholarly journals A Bayesian Approach for Uncertainty Quantification of Extreme Precipitation Projections Including Climate Model Interdependency and Nonstationary Bias

2014 ◽  
Vol 27 (18) ◽  
pp. 7113-7132 ◽  
Author(s):  
Maria Antonia Sunyer ◽  
Henrik Madsen ◽  
Dan Rosbjerg ◽  
Karsten Arnbjerg-Nielsen
Keyword(s):  
Climate Change ◽  
Uncertainty Quantification ◽  
Extreme Precipitation ◽  
Climate Change Impact ◽  
Climate Models ◽  
Climate Model ◽  
Bayesian Framework ◽  
Change Impact ◽  
Multimodel Ensembles ◽  
Precipitation Projections

Abstract Climate change impact studies are subject to numerous uncertainties and assumptions. One of the main sources of uncertainty arises from the interpretation of climate model projections. Probabilistic procedures based on multimodel ensembles have been suggested in the literature to quantify this source of uncertainty. However, the interpretation of multimodel ensembles remains challenging. Several assumptions are often required in the uncertainty quantification of climate model projections. For example, most methods often assume that the climate models are independent and/or that changes in climate model biases are negligible. This study develops a Bayesian framework that accounts for model dependencies and changes in model biases and compares it to estimates calculated based on a frequentist approach. The Bayesian framework is used to investigate the effects of the two assumptions on the uncertainty quantification of extreme precipitation projections over Denmark. An ensemble of regional climate models from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project is used for this purpose. The results confirm that the climate models cannot be considered independent and show that the bias depends on the value of precipitation. This has an influence on the results of the uncertainty quantification. Both the mean and spread of the change in extreme precipitation depends on both assumptions. If the models are assumed independent and the bias constant, the results will be overconfident and may be treated as more precise than they really are. This study highlights the importance of investigating the underlying assumptions in climate change impact studies, as these may have serious consequences for the design of climate change adaptation strategies.

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