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Prof Daniel Williamson

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Publications

Published

Volodina, V., Williamson, D. B. (2020) Diagnostics-driven nonstationary emulators using kernel mixtures. SIAM Journal of Uncertainty Quantification 8(1) 1-26 DOI 10.1137/19M124438X 

 

Williamson, D. B., Sansom, P. G. (2020) How are emergent constraints quantifying uncertainty and what do they leave behind? BAMS, 100, 2571-2588, https://doi.org/10.1175/BAMS-D-19-0131.1 

 

Dawkins, L. C. Williamson, D. B. Mengersen, K. L., Morawska, L.; Jayaratne, R., Shaddick, G. (2020) Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA. Bayesian Analysis, doi:10.1214/19-BA1193. 

 

Dawkins, L. C., Williamson, D. B., Barr, S. W., Lampkin, S. R. (2020). What drives commuter behaviour? A Bayesian clustering approach for understanding opposing behaviours in social surveys." J. Roy Stat. Soc. Ser. A, 183, 251-280, https://doi.org/10.1111/rssa.12499

 

Sansom, P.G., Williamson, D. B., Stephenson, D. B. (2019) State space models for intermittently coupled systems, J. Roy. Stat. Soc. Ser. C, 68, 1259-1280. doi:10.1111/rssc.12354

 

Salter, J.M., Williamson, D.B., Scinocca, J., Kharin, S. (2019) "Uncertainty quantification for spatio-temporal computer models with calibration-optimal bases" Journal of the American Statistical Association 114:528, 1800-1814, DOI: 10.1080/01621459.2018.1514306. 

 

Dawkins, L. C., Barr, S. W., Williamson, D. B., and Lampkin, S. R. (2018). "Influencing transport behaviour: A Bayesian modelling approach for segmentation of social surveys." Journal of Transport Geography, 70 91-103. 

 

Williamson, D. B., Blaker, A. T., Sinha, B. (2017) "Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model.", Geoscientific Model Development, 10(4) 1789-1816.

 

Screen, J., Williamson, D. (2017) "Ice-free Arctic at $1.5^o$C?", Nature Climate Change 7(4) 230-231.

 

Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J-C., Balaji, V., Duan, Q., Folini, D,. Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L., Watanabe, M., Williamson, D. (2017) "The art and science of climate model tuning" BAMS 589-602. 

 

Salter, J. M., Williamson, D. (2016) ``A comparison of statistical emulation methodologies for multi-wave calibration'', Environmetrics 27(8), 507-523.

 

Williamson, D., Goldstein M. (2015), "Posterior belief assessment: extracting meaningful subjective judgements from Bayesian analyses with complex statistical models", Bayesian Analysis, 10(4) 877-908. 

 

Williamson, D., Blaker, A. T., Hampton, C., Salter, J. (2015) ``Identifying and removing structural biases in climate models with history matching'',  Climate Dynamics, 45, 1299-1324.

 

Williamson, D. (2015), "Exploratory designs for computer experiments using k-extended Latin Hypercubes", Environmetrics 26(4) 268-283.

 

Williamson, D., Blaker, A.~T. (2014), ``Evolving Bayesian emulators for structured chaotic time series, with application to large climate models'', SIAM Journal of Uncertainty Quantification, 2(1).

 

Williamson, D., Vernon, I.R., (2013), ``Efficient uniform designs for multi-wave computer experiments'', arXiv:1309.3520

 

Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P. Jackson, L., Yamazaki, K., (2013), ``History matching for exploring and reducing climate model parameter space using observations and a large perturbed physics ensemble'', Climate Dynamics, 41 1703--1729. 

 

Williamson, D., Goldstein, M. and Blaker, A. (2012), ``Fast Linked Analyses for Scenario based Hierarchies'', Journal of the Royal Statistical Society Series C. 61(5), 665-693.

 

Yamazaki, K., Rowlands, D.~J., Aina, T., Blaker, A., Bowery, A., Massey, N., Miller, J., Rye, C., Tett, S.~F.~B., Williamson, D., Yamazaki, Y.~H., Allen, M.~R. (2012), ``Obtaining diverse behaviours in a climate model without the use of flux adjustments'', Journal of Geophysical Research - Atmospheres, 118(7), 2781--2793.

 

Williamson, D. and Goldstein, M. (2012), ``Bayesian policy support for

  adaptive strategies using computer models for complex physical systems,'' Journal of the Operational Research Society, 63, 1021--1033.

  

Williamson, D. (2011) Comment on Gramacy, R. B. and Lee, H. K. H. Optimization under unknown constraints Bayesian Statistics 9, Oxford: Oxford University Press.

 

Williamson, D. (2010), ``Policy making using computer simulators for

  complex physical systems; Bayesian decision support for the development of

  adaptive strategies'', Ph.D. thesis, University of Durham,

  http://etheses.dur.ac.uk/348/

   

Submitted and In Revision

Salter, J. M., Williamson, D.B., Gregoire, L.J., Edwards, T. L. (2020) "Quantifying spatio-temporal boundary condition uncertainty for the North American deglaciation". In Revision for SIAM Journal of Uncertainty Quantification.   Kimpton, L., Challenor, P., Williamson, D.B., (2020) Classification of computer models with labelled outputs. In Submission   Xu, W., Williamson, D.B., Challenor, P. (2020) Local Voronoi tessellations for robust multi-wave calibration of computer models. In Submission.   Couvreux, F., Hourdin, F., Williamson, D. B., Roehrig, R., Volodina, V., Villefranque, N., Rio, C., Audouin, O., Salter, J., Bazile, E., Brient, F., Favot, F., Honnert, R., Lefebvre, MP, Madeleine, JB, Rodier, Q. (2020) Process-based climate model development harnessing machine learning: I. a calibration tool for parameterization improvement.    Hourdin, F., Williamson, D. B., Rio, C., Couvreux, F., Roehrig, R., Villefranque, N., Musat, I., Fairhead, L., Diallo, F. B., Volodina, V. (2020) Process-based climate model development harnessing machine learning: II. model calibration from single column to global. In Submission to JAMES.