Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments

@article{Ma2019ComputerME,
  title={Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments},
  author={Pulong Ma and Anirban Mondal and Bledar A. Konomi and Jonathan Hobbs and Joon Jin Song and Emily Lei Kang},
  journal={Technometrics},
  year={2019},
  volume={64},
  pages={65 - 79}
}
Abstract Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission. One important component in the OCO-2 retrieval algorithm is a full-physics forward model that describes the mathematical relationship between atmospheric variables such as carbon dioxide and radiances measured by the remote sensing instrument. This complex forward model is… 

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References

SHOWING 1-10 OF 54 REFERENCES

Computer Model Calibration Using High-Dimensional Output

This work focuses on combining observations from field experiments with detailed computer simulations of a physical process to carry out statistical inference, and makes use of basis representations to reduce the dimensionality of the problem and speed up the computations required for exploring the posterior distribution.

Dimension Reduction for Gaussian Process Emulation: An Application to the Influence of Bathymetry on Tsunami Heights

A joint framework merging emulation with dimension reduction is introduced in order to overcome this hurdle and provide an answer to the dimension reduction issue in emulation for a wide range of simulation problems that cannot be tackled using existing methods.

Simulation-Based Uncertainty Quantification for Estimating Atmospheric CO2 from Satellite Data

NASA's Orbiting Carbon Observatory-2 (OCO-2) collects tens of thousands of observations of reflected sunlight daily, and the mission's retrieval algorithm processes these indirect measurements into estimates of atmospheric carbon dioxide, an inverse problem that consists of a physical forward model for the transfer of radiation through the atmosphere.

An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations

ABSTRACT In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design

Multivariate Gaussian Process Emulators With Nonseparable Covariance Structures

Nonseparable covariance structures for Gaussian process emulators are developed, based on the linear model of coregionalization and convolution methods, finding that only emulators with nonseparable covariances structures have sufficient flexibility both to give good predictions and to represent joint uncertainty about the simulator outputs appropriately.

Functional emulation of high resolution tsunami modelling over Cascadia

A novel statistical emulation of the input-output dependence of these computer models is introduced, and the coseismic representation in this analysis is novel, and more realistic than in previous studies.

Quantification of uncertainties in OCO-2 measurements of XCO 2 :simulations and linear error analysis

Abstract. We present an analysis of uncertainties in global measurements of the column averaged dry-air mole fraction of CO2 (XCO2) by the NASA Orbiting Carbon Observatory-2 (OCO-2). The analysis is

The Orbiting Carbon Observatory-2: first 18 months of science data products

The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO_2) with the accuracy,

Computer model validation with functional output

A six-step process for computer model validation is set out in Bayarri et al. (2007) based on comparison of computer model runs with field data of the process being modeled, which is particularly suited to treating the major issues associated with the validation process.
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