A GUI platform for uncertainty quantification of complex dynamical models

Wang, Chen; Duan, Qingyun; Tong, Charles; Di, Zhenhua; Gong, Wei

doi:10.1016/j.envsoft.2015.11.004

Cited by 50 publications

(20 citation statements)

References 40 publications

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“…The Uncertainty Quantification Python Laboratory (UQ‐PyL) software platform developed at Beijing Normal University was used for the sensitivity analysis. UQ‐PyL is an uncertainty analysis and optimization tool for highly complex dynamic system models (Wang et al ., ). It provides a large number of functions, including experimental design, sensitivity analysis, statistical analysis, surrogate modelling, and numerical optimization.…”

Section: Numerical Experimental Designmentioning

confidence: 99%

An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model

Quan

Duan

et al. 2016

Quart J Royal Meteoro Soc

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The specification of model parameters in numerical weather prediction (NWP) models has great influence on model performance. However, how to specify model parameters properly is not a trivial task because a typical NWP model like the Weather Research and Forecasting (WRF) model contains many model parameters and many model outputs. This article presents the results of an investigation into the sensitivities of different WRF model outputs to the specification of its model parameters. Using a global sensitivity analysis method, the sensitivities are evaluated for surface meteorological variables such as precipitation, surface air temperature, humidity and wind speed, as well as for atmospheric variables such as total precipitable water, cloud cover, boundary‐layer height and outgoing long‐wave radiation at the top of the atmosphere, all simulated by the WRF model using different model parameters. The goal of this study is to identify the parameters that exert most influence on the skill of short‐range meteorological forecasts. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parametrization schemes were considered. The results indicate that parameter sensitivities vary with different model outputs. However, some of the 23 model parameters considered are shown to be sensitive to all model outputs evaluated, while other parameters may be sensitive to a particular output. The sensitivity results from this research are a basis for further optimizations of the WRF model parameters.

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Section: Numerical Experimental Designmentioning

confidence: 99%

An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model

Quan

Duan

et al. 2016

Quart J Royal Meteoro Soc

Self Cite

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“…Sixteen parameters are involved in the SAC‐SMA model and Table summarizes the description, variation range as well as the synthetic true value to generate the synthetic streamflow and soil moisture for each parameter. Thirteen parameters are considered for data assimilation with the other three ones fixed at prespecified values according to some literates [ Gan et al ., ; Wang et al ., ]. Similar to the synthetic experiment of Hymod, Gaussian noise is employed to account for uncertainties in evapotranspiration and the synthetic “true” observations, while lognormal noise is used for precipitation.…”

Section: Case Study Iii: Sacramento Soil Moisture Accounting Model (Smentioning

confidence: 99%

Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

Fan

Huang

Baetz

et al. 2017

Water Resources Research

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In this study, a copula-based particle filter (CopPF) approach was developed for sequential hydrological data assimilation by considering parameter correlation structures. In CopPF, multivariate copulas are proposed to reflect parameter interdependence before the resampling procedure with new particles then being sampled from the obtained copulas. Such a process can overcome both particle degeneration and sample impoverishment. The applicability of CopPF is illustrated with three case studies using a twoparameter simplified model and two conceptual hydrologic models. The results for the simplified model indicate that model parameters are highly correlated in the data assimilation process, suggesting a demand for full description of their dependence structure. Synthetic experiments on hydrologic data assimilation indicate that CopPF can rejuvenate particle evolution in large spaces and thus achieve good performances with low sample size scenarios. The applicability of CopPF is further illustrated through two real-case studies. It is shown that, compared with traditional particle filter (PF) and particle Markov chain Monte Carlo (PMCMC) approaches, the proposed method can provide more accurate results for both deterministic and probabilistic prediction with a sample size of 100. Furthermore, the sample size would not significantly influence the performance of CopPF. Also, the copula resampling approach dominates parameter evolution in CopPF, with more than 50% of particles sampled by copulas in most sample size scenarios. However, the EnKF can achieve good performances with small ensembles; but related studies have claimed Key Points: A copula-based particle filter approach is developed for hydrological data assimilation Synthetic experiments and real-case studies demonstrate performance of the CopPF method Results suggest that CopPF provides greater opportunities to access new samples which in turn lead to more accurate predictions

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“…If the check result is that there is no fault in the current switch hardware structure, the switch will work normally and the state of the switch changes correspondingly. Otherwise, the corresponding fault phenomenon appear (for instance, the fault indicator light turns on) and submit the detailed information on fault to the human computer interface [4]. Fig.…”

Section: Electrical Switch System Function Modeling Of the Hydropowermentioning

confidence: 99%

Graphical Modeling and simulation of the Hydropower Plant Switch

Zhou¹,

Tang-xian²,

Zhang³

2016

Proceedings of the 5th International Conference on Mechanical Engineering, Materials and Energy (5th ICMEME2016)

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The circuit breaker and the relevant disconnecting switches of the hydropower plant have different varieties in large quantity. Due to the different specific operation processes of the switches, the simulation is quite complex. Adopting the graphical and modular modeling approach, classify the switch system into various sub-modules to set up the universal models, and put forward the new methods of the logical interlock judgment for the switch action process on the basis of the connecting line merging topology analysis algorithm, improving the generality of the modeling and simulation of the electrical switch system. The modeling method provides an effective way for the large-scale customization of the hydropower plant simulation system.

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A GUI platform for uncertainty quantification of complex dynamical models

Cited by 50 publications

References 40 publications

An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model

An evaluation of parametric sensitivities of different meteorological variables simulated by the WRF model

Development of a copula‐based particle filter (CopPF) approach for hydrologic data assimilation under consideration of parameter interdependence

Graphical Modeling and simulation of the Hydropower Plant Switch

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