Overview

Research Interests: I am a Senior Lecturer (50%) and Senior Research Fellow (50%). I specialise in applied statistics and data science, particularly the development of statistical and computational models for environmental statistics and forecasting using computer simulation models. The motivation of my research is to evaluate and improve accuracy and uncertainty quantification in complex, dynamic models of the real world. My research has broad implications for environmental policy, public health, and disaster preparedness. My research is funded through different organizations, including UKRI, ESRC, EPSRC, and external partners like the UK Met Office and IBM. My research projects span from improving weather forecasts using machine learning to designing long-term environmental monitoring programs. My research outputs include journal publications, book chapters and software packages.

Teaching: In my academic roles, I design and deliver undergraduate and postgraduate courses that integrate fundamental mathematical concepts with timely applications. I aim to demystify computational methods and improve real-world problem-solving skills. My courses and workshops are largely research-led to ensure practical relevance and encourage student engagement. I believe in the importance of bridging the gap between theory and practice, equipping students with the skills they need to address pressing challenges of our time.

External Partners: I am the Director of Business Engagement and Innovation of the Department for Mathematics and Statistics. I work closely with external partners from industry and government. I currently hold a UKRI Policy Fellowship to work with Defra on agricultural system modelling to inform future UK food policy. I have previously worked with UKHSA on time series modelling for Covid prevalence. I have provided statistical and data science consultancies for organizations such as UK Met Office, ECMWF, United Nations FAO, and intelligentAI.

Selected Research Grants:

• GW4+ PhD studentship with Met Office CASE funding on "Improving urban weather forecasts using Machine Learning" (10/2023)
• UKRI Policy Fellowship: Defra Building a Green Future (10/2023 - 03/2025)
• UKRI Knowledge Transfer Partnership with Agile Applications "AI Solutions for local government" (01/2024 - 02/2026)
• Design of Environmental Long-Term Monitoring Program, NEOM, Saudi Arabia (12/2022 - 02/2024)
• Estimating worst-case storms and seasons using seasonal forecasts - Contract Research with Guy Carpenter (11/2022 - 02/2023)
• UK Joint Biosecurity Centre, Department for Health & Social Care - Secondment (Statistics for wastewater epidemiology) (11/2021 - 09/2022)
• UKRI EPSRC Industrial CASE PhD studentship "Bayesian Methods for Climate Impact Uncertainty Quantification" (10/2022 - 09/2026)
• EPSRC "Uncertainty Quantification for Expensive COVID-19 Simulation Models - UQ4Covid" (01/2021 - 11/2022)
• UKRI Knowledge Transfer Partnership with SRK Explorations "Statistical modelling for mineral systems exploration" (09/2020 - 08/2022)
• UKRI Knowledge Transfer Partnership with Agile Applications "AI for automated processing of planning applications" (09/2020 - 08/2022)
• Geospatial Commission and Innovate UK "Crowdsourcing for a Digital Geospatial Joint Strategic Needs Assessment" (05/2019 - 03/2020)
• EPSRC ReCoVER feasibility fund “Fast statistical inference for climate projections with INLA” (09/2017 - 12/2017)

Student supervision and PhD projects: I am interested in supervising students at all stages, from 1st year up to and including PhD students, on projects related to mathematical-statistical methodology (including machine learning, data science, artificial intelligence), in application areas related to environmental science (such as weather forecasting, climate modelling, environmental extremes). If you have an idea for a project in these areas that you would like to work on, please feel free to contact me.

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Publications

_{Copyright Notice: Any articles made available for download are for personal use only. Any other use requires prior permission of the author and the copyright holder.}

| 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 |

2023

• Lovegrove J, Siegert S. (2023) Improving Numerical Weather Forecasts by Bayesian Hierarchical Modelling, Statistical Modeling Using Bayesian Latent Gaussian Models, Springer Nature, 193-218, DOI:10.1007/978-3-031-39791-2_6.
• Lewis-Borrell L, Irving J, Lilley CJ, Courbariaux M, Nuel G, Danon L, O'Reilly KM, Grimsley JMS, Wade MJ, Siegert S. (2023) Robust smoothing of left-censored time series data with a dynamic linear model to infer SARS-CoV-2 RNA concentrations in wastewater, AIMS Mathematics, volume 8, no. 7, pages 16790-16824, DOI:10.3934/math.2023859.

2022

• Thomas ML, Shaddick G, Topping D, Morrissey K, Brannan TJ, Diessner M, Bowyer RCE, Siegert S, Coe H, Evans J. (2022) A Data Integration Approach to Estimating Personal Exposures to Air Pollution, 2022 IEEE International Conference on Big Data (Big Data), 17th - 20th Dec 2022, 2022 IEEE International Conference on Big Data (Big Data), DOI:10.1109/bigdata55660.2022.10020701. [PDF]
• Siegert S, Hooper B, Lovegrove J, Thomson T, Hrafnkelsson B. (2022) Spatial forecast postprocessing: The Max-and-Smooth approach. [PDF]
• Jóhannesson ÁV, Siegert S, Huser R, Bakka H, Hrafnkelsson B. (2022) Approximate Bayesian inference for analysis of spatiotemporal flood frequency data, The Annals of Applied Statistics, volume 16, no. 2, DOI:10.1214/21-aoas1525. [PDF]
• Aguirre Perez R. (2022) Areal Downscaling - A spatial downscaler for population counts.

2021

• Sansom PG, Cummins D, Siegert S, Stephenson DB. (2021) Towards Reliable Probabilistic Time-Series Projections of Global Mean Surface Temperature, DOI:10.21203/rs.3.rs-1103739/v1. [PDF]
• Challen R, Dyson L, Overton CE, Guzman-Rincon LM, Hill EM, Stage HB, Brooks-Pollock E, Pellis L, Scarabel F, Pascall DJ. (2021) Early epidemiological signatures of novel SARS-CoV-2 variants: establishment of B.1.617.2 in England, DOI:10.1101/2021.06.05.21258365.
• Sansom PG, Cummins D, Siegert S, Stephenson DB. (2021) Towards reliable projections of global mean surface temperature. [PDF]

2020

• Hemri S, Bhend J, Liniger MA, Manzanas R, Siegert S, Stephenson DB, Gutiérrez JM, Brookshaw A, Doblas-Reyes FJ. (2020) How to create an operational multi-model of seasonal forecasts?, Climate Dynamics, volume 55, no. 5-6, pages 1141-1157, DOI:10.1007/s00382-020-05314-2.
• Siegert S. (2020) SpecsVerification: Forecast Verification Routines for Ensemble Forecasts of Weather and Climate.
• Siegert S. (2020) SpecsVerification: Forecast Verification Routines for Ensemble Forecasts of Weather and Climate.
• Robin X. (2020) pROC: Display and analyze ROC curves in R. [PDF]

2019

• Johannesson ÁV, Siegert S, Huser R, Bakka H, Hrafnkelsson B. (2019) Approximate Bayesian inference for analysis of spatio-temporal flood frequency data, DOI:10.48550/arxiv.1907.04763.
• Hrafnkelsson B, Siegert S, Huser R, Bakka H, Jóhannesson ÁV. (2019) Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models, DOI:10.48550/arxiv.1907.11969.
• Siegert S, Stephenson DB. (2019) Chapter 15 Forecast Recalibration and Multimodel Combination, Sub-Seasonal to Seasonal Prediction, Elsevier, 321-336, DOI:10.1016/b978-0-12-811714-9.00015-2.
• Hrafnkelsson B, Siegert S, Huser R, Bakka H, Jóhannesson ÁV. (2019) Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models. [PDF]
• Johannesson ÁV, Siegert S, Huser R, Bakka H, Hrafnkelsson B. (2019) Approximate Bayesian inference for analysis of spatio-temporal flood frequency data. [PDF]

2018

• Siegert S, Stephenson D. (2018) Forecast recalibration and multi-model combination, Sub-seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting, Elsevier.

2017

• Bellprat O, Massonnet F, Siegert S, Prodhomme C, Macias-Gómez D, Guemas V, Doblas-Reyes F. (2017) Uncertainty propagation in observational references to climate model scales, Remote Sensing of Environment, volume 203, pages 101-108, DOI:10.1016/j.rse.2017.06.034.
• Siegert S. (2017) Simplifying and generalising Murphy's Brier score decomposition, Quarterly Journal of the Royal Meteorological Society, volume 143, no. 703, pages 1178-1183, DOI:10.1002/qj.2985.
• Siegert S, Bellprat O, Ménégoz M, Stephenson DB, Doblas-Reyes FJ. (2017) Detecting improvements in forecast correlation skill: Statistical testing and power analysis, Monthly Weather Review, volume 145, no. 2, pages 437-450, DOI:10.1175/MWR-D-16-0037.1.

2016

• Siegert S, Kantz H. (2016) Prediction of complex dynamics: Who cares about chaos?, Lecture Notes in Physics, 249-269, DOI:10.1007/978-3-662-48410-4_8.
• Siegert S, Sansom PG, Williams RM. (2016) Parameter uncertainty in forecast recalibration, Quarterly Journal of the Royal Meteorological Society, volume 142, no. 696, pages 1213-1221, DOI:10.1002/qj.2716.
• Siegert S, Stephenson DB, Sansom PG, Scaife AA, Eade R, Arribas A. (2016) A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?, Journal of Climate, volume 29, no. 3, pages 995-1012, DOI:10.1175/JCLI-D-15-0196.1.
• Siegert S, Broecker J, Kantz H. (2016) Skill of data based predictions versus dynamical models -- case study on extreme temperature anomalies, Extreme Events: Observations, Modeling, and Economics.

2015

• Siegert S, Sansom PG, Williams R. (2015) Parameter uncertainty in forecast recalibration, DOI:10.48550/arxiv.1509.07102.
• Siegert S, Stephenson DB, Sansom PG, Scaife AA, Eade R, Arribas A. (2015) A Bayesian framework for verification and recalibration of ensemble forecasts: How uncertain is NAO predictability?, DOI:10.48550/arxiv.1504.01933.

2014

• Siegert S, Ferro CAT, Stephenson DB. (2014) Evaluating ensemble forecasts by the Ignorance score -- Correcting the finite-ensemble bias, DOI:10.48550/arxiv.1410.8249.
• Siegert S. (2014) Variance estimation for Brier Score decomposition, Quarterly Journal of the Royal Meteorological Society, volume 140, no. 682, pages 1771-1777, DOI:10.1002/qj.2228.
• Gundermann J, Siegert S, Kantz H. (2014) Improved predictions of rare events using the Crooks fluctuation theorem, Phys Rev E Stat Nonlin Soft Matter Phys, volume 89, no. 3, DOI:10.1103/PhysRevE.89.032112. [PDF]
• Gundermann J, Siegert S, Kantz H. (2014) Improved predictions of rare events using the Crooks fluctuation theorem, PHYSICAL REVIEW E, volume 89, no. 3, article no. ARTN 032112, DOI:10.1103/PhysRevE.89.032112.
• Siegert S. (2014) Variance estimation for Brier Score decomposition, Quarterly Journal of the Royal Meteorological Society, volume 140, pages 1771-1777, DOI:10.1002/qj.2228.

2013

• Siegert S. (2013) Variance estimation for Brier Score decomposition, DOI:10.48550/arxiv.1303.6182.
• Siegert S, Broecker J, Kantz H. (2013) Skill of data based predictions versus dynamical models -- case study on extreme temperature anomalies, DOI:10.48550/arxiv.1312.4323.

2012

• Siegert S, Broecker J, Kantz H. (2012) On the predictability of outliers in ensemble forecasts, ADVANCES IN SCIENCE AND RESEARCH, volume 8, pages 53-57, DOI:10.5194/asr-8-53-2012. [PDF]
• Siegert S, Bröcker J, Kantz H. (2012) Rank Histograms of Stratified Monte Carlo Ensembles, Monthly Weather Review, volume 140, no. 5, pages 1558-1571, DOI:10.1175/MWR-D-11-00302.1.

2011

• Bröcker J, Siegert S, Kantz H. (2011) Comments on "conditional exceedance probabilities", Monthly Weather Review, volume 139, no. 10, pages 3322-3324, DOI:10.1175/2011MWR3658.1.
• Siegert S, Bröcker J, Kantz H. (2011) Predicting outliers in ensemble forecasts, Quarterly Journal of the Royal Meteorological Society, volume 137, no. 660, pages 1887-1897, DOI:10.1002/qj.868.

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