D. Williamson

发表

Process-based climate model development harnessing machine learning: III. The Representation of Cumulus Geometry and their 3D Radiative Effects

Jacques Gautrais, Fleur Couvreux, Robin J. Hogan, 2020 .

Exploratory ensemble designs for environmental models using k-extended Latin Hypercubes

D. Williamson, D Williamson, 2015, Environmetrics.

Identifying and removing structural biases in climate models with history matching

James M. Salter, Daniel Williamson, D. Williamson, 2015, Climate Dynamics.

Efficient uniform designs for multi-wave computer experiments

Ian Vernon, Daniel Williamson, I. Vernon, 2013, 1309.3520.

How Are Emergent Constraints Quantifying Uncertainty and What Do They Leave Behind?

Philip G. Sansom, D. Williamson, 2019, Bulletin of the American Meteorological Society.

State space models for non‐stationary intermittently coupled systems: an application to the North Atlantic oscillation

Philip G. Sansom, D. Stephenson, D. Williamson, 2017, Journal of the Royal Statistical Society: Series C (Applied Statistics).

History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble

Michael Goldstein, Daniel B. Williamson, Peter G. Challenor, 2013, Climate Dynamics.

Nonstationary Gaussian Process Emulators with Kernel Mixtures

Daniel B. Williamson, Victoria Volodina, D. Williamson, 2018 .

Modelling Numerical Systems with Two Distinct Labelled Output Classes

D. Williamson, P. Challenor, Louise Kimpton, 2019, 1901.07413.

Efficient calibration for high-dimensional computer model output using basis methods

James M. Salter, D. Williamson, James Salter, 2019, International Journal for Uncertainty Quantification.

Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation

D. Williamson, T. Edwards, James Salter, 2018, SIAM/ASA Journal on Uncertainty Quantification.

Uncertainty Quantification for Computer Models With Spatial Output Using Calibration-Optimal Bases

James M. Salter, D. Williamson, J. Scinocca, 2018, Journal of the American Statistical Association.

Quantifying Spatio-Temporal Boundary Condition Uncertainty for the North American Deglaciation

James M. Salter, D. Williamson, T. Edwards, 2018, SIAM/ASA J. Uncertain. Quantification.

State-space modeling of intra-seasonal persistence in daily climate indices: a data-driven approach for seasonal forecasting

Philip G. Sansom, D. Stephenson, D. Williamson, 2018, 1807.02671.

Deep Gaussian Process Emulation using Stochastic Imputation

D. Williamson, S. Guillas, Deyu Ming, 2021, Technometrics.

Uncertainty quantification for spatio-temporal computer models with calibration-optimal bases

Daniel B. Williamson, James M. Salter, John F. Scinocca, 2018 .

A comparison of statistical emulation methodologies for multi‐wave calibration of environmental models

James M. Salter, Daniel Williamson, D. Williamson, 2016, Environmetrics.

Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models

Daniel B. Williamson, Adam T. Blaker, D. Williamson, 2014, SIAM/ASA J. Uncertain. Quantification.

Fast linked analyses for scenario‐based hierarchies

Michael Goldstein, Daniel B. Williamson, Adam T. Blaker, 2012 .

Bayesian policy support for adaptive strategies using computer models for complex physical systems

M. Goldstein, Danny Williamson, M. Goldstein, 2012, J. Oper. Res. Soc..

Policy making using computer simulators for complex physical systems : Bayesian decision support for the development of adaptive strategies

Daniel Williamson, D. Williamson, 2010 .

Cross-Validation-based Adaptive Sampling for Gaussian Process Models

Hossein Mohammadi, Marc Goodfellow, Peter Challenor, 2020, SIAM/ASA J. Uncertain. Quantification.

Emulating computer models with step-discontinuous outputs using Gaussian processes

Peter Challenor, Hossein Mohammadi, Marc Goodfellow, 2019, 1903.02071.

The Art and Science of Climate Model Tuning

Andrew Gettelman, Venkatramani Balaji, Daniel B. Williamson, 2017 .

Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model

Daniel B. Williamson, Adam T. Blaker, Bablu Sinha, 2016 .

Process‐Based Climate Model Development Harnessing Machine Learning: III. The Representation of Cumulus Geometry and Their 3D Radiative Effects

R. Hogan, J. Gautrais, F. Hourdin, 2021, Journal of Advances in Modeling Earth Systems.

Process‐Based Climate Model Development Harnessing Machine Learning: I. A Calibration Tool for Parameterization Improvement

James Salter, Florent Brient, Fleur Couvreux, 2020, Journal of Advances in Modeling Earth Systems.

Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models

Michael Goldstein, Daniel Williamson, M. Goldstein, 2015, 1512.00969.

Bayesian decision procedures based on logistic regression models for dose-finding studies.

J Whitehead, D Williamson, J. Whitehead, 1998, Journal of biopharmaceutical statistics.

Coexchangeable process modelling for uncertainty quantification in joint climate reconstruction

D. Williamson, L. Astfalck, L. Gregoire, 2021, 2111.12283.

Obtaining diverse behaviors in a climate model without the use of flux adjustments

M. Allen, D. Williamson, S. Tett, 2013 .

Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

D. Williamson, P. Challenor, A. Harper, 2021, Geoscientific Model Development.

Diagnostics-Driven Nonstationary Emulators Using Kernel Mixtures

Daniel B. Williamson, Victoria Volodina, D. Williamson, 2018, SIAM/ASA J. Uncertain. Quantification.

Emulation of high-resolution land surface models with

D. Williamson, P. Challenor, A. Harper, 2022 .

Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA

Gavin Shaddick, Kerrie L. Mengersen, Lidia Morawska, 2020, Bayesian Analysis.

Toward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections

F. Hourdin, I. Musat, D. Williamson, 2023, Science advances.

Process‐Based Climate Model Development Harnessing Machine Learning: II. Model Calibration From Single Column to Global

Fleur Couvreux, Catherine Rio, Najda Villefranque, 2020, Journal of Advances in Modeling Earth Systems.

Linked Deep Gaussian Process Emulation for Model Networks

D. Williamson, Deyu Ming, 2023, ArXiv.

Classification of Computer Models with Labelled Outputs

Peter Challenor, Daniel Williamson, Louise Kimpton, 2020, 2002.00010.