Peter Dueben

Royal Society Fellow

Research Department, Earth System Modelling Section, Numerical Methods Group

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Profile

Professional interests:

High-resolution weather and climate simulations.
High-performance computing for weather and climate models.
Machine learning for weather and climate predictions.
Model error, model uncertainty and predictability of chaotic systems.
New hardware for computational fluid dynamics such as field programmable gate arrays, machine learning accelerators, and stochastic processors.
Reduced numerical precision and hardware faults.

Career background:

EDUCATION AND WORK EXPERIENCE

2019-today: Coordinator of Machine Learning and AI activities at the European Centre for Medium Range Weather Forecasts (ECMWF).
2017-today: Royal Society University Research Fellow in the Research Department of ECMWF.
2016 - 2017: Scientist working in the Research Department of ECMWF on the ESiWACE project, Reading, UK.
2012 - 2016: Postdoctoral Research Assistant working with Tim Palmer at the sub-department for Atmospheric, Oceanic and Planetary Physics, University of Oxford, UK. Postdoctoral MCR Member of Jesus College.
2009 - 2012: PhD supervised by Peter Korn and Jochem Marotzke at the Max Planck Institute for Meteorology, Hamburg, Germany. Thesis: “Finite element methods, grid refinement, and boundary currents in geophysical modeling”. Member of the International Max Planck Research School on Earth System Modelling.
2004 - 2009: Diploma in Physics supervised by Dirk Homeier and Gernot Münster at the Institute for Theoretical Physics, Westfälische Wilhelms-Universität, Münster, Germany.

TEACHING EXPERIENCE

2017 - today: Lecturer at training courses at ECMWF.
2020: Lecturer at the Summer School on "Effective HPC for Climate and Weather", Reading, UK.
2016: Lecturer at the ISSAOS 2016 summer school on "Advanced Programming Techniques for the Earth System Science" hosted by the Gran Sasso Science Institute in L’Aquila, Italy.
2016: Organiser and Lecturer at the "Chaos and Uncertainty in Weather Forecasts" advanced course for PhD students of the Environmental Research Doctoral Training Partnership at the University of Oxford.
2013 - 2016: Tutor for the "Fluid Flows, Fluctuations and Complexity" course for undergraduate students in Physics at New College and Somerville College, University of Oxford.
2011 - 2015: Lecturer at the "Introduction to Earth System Science and Modelling" course for PhD students at the International Max Planck Research School on Earth System Modelling.
2007 - 2009: Tutor for undergraduate students in Physics at the University of Münster, Germany.

STUDENT SUPERVISION

2017 - today: PhD supervisor (together with Tim Palmer) of the PhD project of Milan Klöwer at the University of Oxford.
2018-today: PhD supervisor (together with Pavel Berloff) of the PhD project of Niraj Agarwal at Imperial College London.
2017 - 2018: Supervisor of Master Projects at the University of Bristol and Imperial College London.
2015 - 2019: PhD supervisor (together with Tim Palmer) of the PhD project of Samuel Hatfield at the University of Oxford.
2014 - 2018: PhD supervisor (together with Tim Palmer) of the PhD project of Tobias Thornes at the University of Oxford.
2015: Evaluator of MPhys projects at the Department of Physics, University of Oxford.
2013 - 2014: Supervisor of MPhys Projects in the Department of Physics, University of Oxford.

SPECIAL ISSUES AND MEETING ORGANISATION FROM 2019

2021: Organiser of the IS-ENES3-ESiWACE2 Workshop on new Opportunities for ML and AI in Weather and Climate Modelling.
2019 and 2021: Convener of several Minisymposia at PASC on high resolution modelling and machine learning in weather and climate science.
2015-2021: Convener at EGU General Assembly for several sessions on high-resolution simulations, high-performance computing and machine learning in Earth System modelling.
2020: Organiser of the Machine Learning Seminar Series at ECMWF with recorded and live-streamed seminar talks of international speakers.
2020: Guest Editor for a special issue of the Philosophical Transactions of the Royal Society A.
2020: Organiser of the 6th ENES HPC workshop in Hamburg, Germany.
2020: Organiser of the ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction at ECMWF.
2020: Reviewer of the Frontier Development Lab.
2019: Guest Editor for a special issue of the Journal of the Meteorological Society of Japan.
2019: Organiser of the "1st Artificial Intelligence for Copernicus Workshop" at ECMWF.
2019: Organiser of the "Machine Learning for Weather and Climate Modelling" workshop at Corpus Christi College, University of Oxford.
2019: Committee member for the Climate and Weather Domain of PASC 2019.
2019: Member of the Program Committee of the ”AI in HPC” workshop at ISC.

External recognitions:

AWARDS, PROPOSALS AND GRANTS

2021: Coordinator of the MAELSTROM EuroHPC Joint Undertaking project which involves seven partners within Europe and a budget of 4,3 million Euro.
2021: Co-Author of the AI4Copernicus ICT proposal.
2018-today: Work-package leader of the ESiWACE-2 Centre of Excellence H2020 project (www.esiwace.eu).
2020 and 2021: Co-Pi of an US INCITE grant to perform seasonal predictions with IFS at 1.45 km resolution for a full season on Summit - the second fastest supercomputer in the world (PI Nils Wedi). The simulation of 2020 has won the "Reader's Choice Best Use of HPC in Physical Science" by HPCwire in 2020.
2020: Best Paper Award at ICLR2020.
2020: Co-Pi of an Alan Turing Institute pilot project for a six-months Postdoctoral Research Assistant position at Warwick University (PI Ritabrata Dutta).
2019: Best Paper Award at PASC2019.
2018: Co-author of the ExtremeEarth Preparatory Project proposal for the preparation of a EU Flagship proposal on high-resolution modeling of weather, climate and solid Earth (http://www.extremeearth.eu/).
2017: Royal Society University Research Fellowship on "Uncertainty in Earth System Modelling".
2017: Co-author of the ESCAPE-2 FET H2020 project (www.hpc-escape.eu).
2016: Main author of an accepted proposal for a three year Postdoctoral Research Assistant position, funded by the Office of Naval Research (PI Tim Palmer).
2016: Principal Investigator of a special project worth 45 million billing units at the high-performance computing centre of ECMWF.
2015: Main author of an accepted proposal for a four-month Postdoctoral Research Assistant position, funded by the Recover network (PI Tim Palmer).
2015: Award for Excellence from the Department of Physics at the University of Oxford. One-off lump sum of 965 GBP.

SELECTION OF TALKS IN 2020 (mostly virtual):

Invited talk at the symposium on "Implementing Data-Driven Science: The challenges and opportunities for Earth and Space Research", Hamburg, Germany.
Invited talk at the Environmental Intelligence conference, Exeter, UK.
Invited talk at the TRUSTinAI Conference 2020, Kaiserslautern, Germany.
Invited talk at the 2nd NOAA Workshop on Leveraging AI in Environmental Sciences, College Park, USA.
Invited talk at the Stanford Meeting of the HPC Advisory Council, Stanford, US.
Invited talk at the Oxford Machine Learning and Physics Seminar Series, Oxford, UK.
Invited talk at the CCI ESA meeting, Oxford, UK.
Invited talk at the ORAP User Forum, Paris, France.
Invited talk at the ESiWACE conference on Emerging Technologies, Hamburg, Germany.
Invited talk at the Machine Learning in Meteorology and ClimateML Symposium in Wageningen, Netherlands.
Invited seminar talk at the Max Planck Institute for Biogeochemistry, Jena, Germany and the Ellis network.
Invited seminar talk at the University of Cape Town, South Africa.
Invited seminar talk at MARUM in Kiel, Germany.
Invited (short) talk at the ESA PhiWeek, Rome, Italy.
Talk at the ECMWF-ESA Workshop on Machine Learning for Earth System Observation and Prediction, Reading, UK.
Talk at the Climate Informatics 2020 conference, Oxford, UK.
Talk at the Multi-core 2020 meeting, Boulder, USA.
Talk at the 6th ENES HPC workshop in Hamburg, Germany.

OUTREACH ACTIVITIES FROM 2019

2019: Invited panel member at a Euronews live with Jeremy Wilks on "How can artificial intelligence help to fight climate change?" (more than 100k views on Youtube).
2020: Invited committee member at the NVIDIA HPC Summit Digital conference, USA.
2020: Article on “AI and machine learning at ECMWF” in the ECMWF Newsletter.
2020: Support and funding for PhD students to build a Raspberry Pi “supercomputer” for weather forecasts for a science fair in Oxford.
2019: Article about machine learning for weather predictions for the EU Parliament Magazine.
2019: ECMWF science blog on “What next for machine learning in weather forecasting?”.

Peter gave invited seminar talks at the following institutions:

Alfred Wegener Institute for Polar and Marine Research (AWI)
Deutsches Klimarechenzentrum (DKRZ)
European Centre for Medium-Range Weather Forecasts (ECMWF)
Free University of Berlin
Imperial College London
Los Alamos National Laboratory
Ludwig Maximilian University Munich
Max Planck Institute for Biogeochemistry
Max Planck Institute for Meteorology
MARUM in Kiel
MPE CDT Reading
Pacific Northwest National Laboratory
Science & Technology Facilities Council (STFC) Daresbury
University of Cambridge
University of Exeter
University of Frankfurt
University of Hamburg
University of Leeds
University of Oxford
University of Reading

SCIENTIFIC REVIEWER

Peter has written referee reports for articles in the following scientific journals:

Bulletin of the American Meteorological Society, Geoscience Communication, Geosciences, Geoscientific Model Development, IEEE Transactions on Parallel and Distributed Systems, Journal of Advances in Modeling Earth Systems, Journal of Applied Meteorology and Climatology, Journal of Computational Physics, Nature Communications, Nonlinear Processes in Geophysics, Nonlinearity, Monthly Weather Review, Science Advances and Tellus A.

Publications

Submitted or pre-published on arxiv

J. Ackmann, P. D. Dueben, T. Palmer, and P. Smolarkiewicz. Machine-learned preconditioners for linear solvers in geophysical fluid flows. https://arxiv.org/abs/2010.02866, 2020.

R. Adewoyin, P. D. Dueben, P. Watson, Y. He, and R. Dutta. Tru-net: A deep learning approach to high resolution prediction of rainfall. https://arxiv.org/abs/2008.09090, 2020.

J. Barre, I. Aben, A. Agusti-Panareda, G. Balsamo, N. Bousserez, P. D. Dueben, R. Engelen, A. Inness, A. Lorente, J. McNorton, et al. Systematic detection of local CH 4 emissions anomalies combining satellite measurements and high-resolution forecasts. Atmospheric Chemistry and Physics Discussions, pages 1–25, 2020.

Sherman Lo, Peter Watson, Peter Dueben, and Ritabrata Dutta. High-resolution probabilistic precipitation prediction for use in climate simulations, https://arxiv.org/abs/2012.09821, 2020.

Peer-reviewed publications

[46] P. Bauer, P. D. Dueben, T. Hoefler, T. Quintino, T. Schulthess, and N. Wedi. The digital revolution of earth-system science. Accepted in Nature Computational Science, 2020.

[45] P. Groenquist, C. Yao, T. Ben-Nun, N. Dryden, P. Dueben, S. Li, T. Hoefler: Deep Learning for Post-Processing Ensemble Weather Forecasts, Accepted in Phil. Trans. A, 2020

[44] S. Rasp, P. D. Dueben, S. Scher, J.A. Weyn, S. Mouatadid, N. Thuerey: WeatherBench: A benchmark dataset for data-driven weather forecasting, JAMES, 12(11):e2020MS002203, 2020

[43] N. P. Wedi, I. Polichtchouk, P. Dueben, V. G. Anantharaj, P. Bauer, S. Boussetta, P. Browne, W. Deconinck, W. Gaudin, I. Hadade, S. Hatfield, O. Iffrig, P. Lopez, P. Maciel, A. Mueller,S. Saarinen, I. Sandu, T. Quintino, F. Vitart: A baseline for global weather and climate simulations at 1.4 km resolution, JAMES, e e2020MS002192, 2020

[42] M. Kloewer, P. D. Dueben, T. N. Palmer: Number Formats, Error Mitigation, and Scope for 16‐Bit Arithmetics in Weather and Climate Modeling Analyzed With a Shallow Water Model, JAMES, 12(10):e2020MS002246, 2020

[41] P. D. Dueben, N. Wedi, S. Saarinen, C. Zeman. Global simulations of the atmosphere at 1.45 km grid-spacing with the Integrated Forecasting System. JMSJ, Ser. II, 2020

[40] L. Saffin, S. Hatfield, P. Dueben, T. N. Palmer. Reduced-precision parametrization: lessons from an intermediate-complexity atmospheric model. QJMRS, 146(729):1590–1607, 2020

[39] S. Hatfield, P. D. Dueben, A. McRae, T. N. Palmer. Single-precision in the tangent-linear and adjoint models of incremental 4D-Var. Monthly Weather Review, 148(4):1541–1552, 2020

[38] F. Cooper, P. D. Dueben, C. Denis, A. Dawson, P. Ashwin. The relationship between numerical precision and forecast lead time in the Lorenz’95 system. Monthly Weather Review, 148(2):849–855, 2020

[37] T. Benacchio, L. Bonaventura, M. Altenbernd, C. D. Cantwell, P. D. Dueben, M. Gillard, L. Giraud, D. Goddeke, E. Raffin, K. Teranishi, et al. Resilience and fault-tolerance in high-performance computing for numerical weather and climate prediction. Accepted in the International Journal of High Performance Computing Applications, 2020

[36] M. Bonavita, R. Arcucci, A. Carrassi, P. D. Dueben, A. J. Geer, B. Le Saux, N. Longepe, P.-P. Mathieu, and L. Raynaud. Machine learning for earth system observation and prediction. Bulletin of the American Meteorological Society, pages 1 – 13, 28 Dec., 2020

[35] B. Stevens, M. Satoh, L. Auger, J. Biercamp, C. Bretherton, X. Chen, P. Düben, F. Judt, M. Khairoutdinov, D. Klocke, C. Kodama, L. Kornblueh, S.-J. Lin, W. Putman, S. Ryosuke, P. Neumann, N. Röber, B. Vannier, P.-L. Vidale, N. Wedi, L. Zhou. DYAMOND: The DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains. Progress in Earth and Planetary Science, 6(1):61, 2019

[34] M. Satoh, B. Stevens, F. Judt, M. Khairoutdinov, S.-J. Lin, W. Putman, P. D. Dueben. Global Cloud-Resolving Models. Current Climate Change Reports, 5(3):172–184, 2019

[33] S. Hatfield, M. Chantry, P. D. Dueben, T. N. Palmer. Accelerating high-resolution weather and climate models with deep-learning hardware. PASC Proceedings, Best Paper Award, 2019

[32] M. Kloewer, P. D. Dueben, T. N. Palmer. Posits as an alternative to floats for weather and climate models. Proceedings of the Conference for Next Generation Arithmetic CoNGA, 2019

[31] A. C. Subramanian, S. Juricke, P. D. Dueben and T. N. Palmer. A Stochastic Representation of Sub-Grid Uncertainty for Dynamical Core Development. Bulletin of the American Meteorological Society, 100(6):1091–1101, 2019

[30] P. Neumann, P. D. Dueben, P. Adamidis, P. Bauer, M. Bruck, L. Kornblueh, D. Klocke, B. Stevens, N. Wedi, and J. Biercamp. Assessing the scales in numerical weather and climate predictions: will exascale be the rescue? Philosophical Transactions of the Royal Society A, 377(2142):20180148, 2019

[29] M. Chantry, T. Thornes, T. Palmer, and P. D. Dueben. Scale-selective precision for weather and climate forecasting. Monthly Weather Review, 147(2):645–655, 2019

[28] P. D. Dueben, M. Leutbecher, and P. Bauer. New methods for data storage of model output from ensemble simulations. Monthly Weather Review, 147(2):677–689, 2019

[27] P. D. Dueben. A new number format for ensemble simulations. Accepted in Journal of Advances in Modeling Earth Systems, 10(11):2983–2991, 2018.

[26] P. D. Dueben and P. Bauer. Challenges and design choices for global weather and climate models based on machine learning. Geoscientific Model Development, 11, 10, 3999-4009, 2018

[25] S. E. Hatfield, P. D. Dueben, M. Chantry, K. Kondo, T. Miyoshi, T. N. Palmer. Choosing the optimal numerical precision for data assimilation in the presence of model error. Journal of Advances in Modeling Earth Systems, 10, 2177-2191, 2018

[24] T. Thornes, P. Dueben, T. N. Palmer. A Power Law for Reduced Precision at Small Spatial Scales: Experiments with an SQG Model. Quarterly Journal of the Royal Meteorological Society, 144:1179-1188, 2018

[23] S. E. Hatfield, A. Subramanian, T. N. Palmer, P. D. Dueben. Improving weather forecast skill through reduced precision data assimilation. Monthly Weather Review, 146 (1), 49-62, 2018

[22] A. Dawson, P. D. Dueben, David MacLeod and Tim Palmer. Reliable low precision simulations in land surface models. Climate Dynamics, 51(7-8):2657–2666, 2018

[21] F. P. Russell, P. D. Dueben, X. Niu, W. Luk, T. N. Palmer. Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures. Computer Physics Communications, 221, 160-173, 2017

[20] J. S. Targett, P. D. Dueben, W. Luk. Validation optimisations for chaotic simulations, Field Programmable Logic and Application (FPL), pages 1-4, 2017

[19] A. Dawson and P. D. Dueben. rpe v5: An emulator for reduced floating-point precision in large numerical simulations. Geoscientific Model Development, 10 (6), 2221-2230, 2017

[18] S. Jeffress, P. D. Dueben, T. N. Palmer. Bitwise Efficiency in Chaotic Models. Proceedings A of the Royal Society A, 473 (2205), 20170144, 2017

[17] P. D. Dueben and A. Dawson. An approach to secure weather and climate models against hardware faults. Journal of Advances in Modeling Earth Systems, 9 (1), 501-513, 2017

[16] P. D. Dueben, A. Subramanian, A. Dawson and T. N. Palmer. A study of reduced precision to make superparametrisation more competitive using a hardware emulator in the OpenIFS model. Journal of Advances in Modeling Earth Systems, 9 (1), 566-584, 2017

[15] T. Thornes, P. D. Dueben, and T. Palmer. On the use of scale-dependent precision in Earth System modelling. Quarterly Journal of the Royal Meteorological Society, 143: 897-908, 2017

[14] F. Váňa, P. D. Dueben, S. Lang, T. Palmer, M. Leutbecher, D. Salmond, and G. Carver. Single precision in weather forecasting models. Monthly Weather Review, 145 (2), 495-502, 2017

[13] P. D. Dueben, F. P. Russell, X. Niu, W. Luk, and T. N. Palmer. On the use of programmable hardware and reduced numerical precision in earth-system modeling. Journal of Advances in Modeling Earth Systems, 7(3):1393–1408, 2015

[12] P. D. Dueben and S. I. Dolaptchiev. Rounding errors may be beneficial for simulations of atmospheric flow: Results from the forced 1D Burgers equation. Theoretical and Computational Fluid Dynamics, 29(4):311–328, 2015

[11] P. D. Dueben, J. Schlachter, Parishkrati, S. Yenugula, J. Augustine, C. Enz, K. Palem, and T. N. Palmer. Opportunities for energy efficient computing: A study of inexact general purpose processors for high-performance and big-data applications. Design Automation and Test in Europe (DATE), pages 764–769, 2015

[10] F. P. Russell, P. D. Dueben, X. Niu, W. Luk, and T. N. Palmer. Architectures and precision analysis for modelling atmospheric variables with chaotic behaviour. IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM), 171–178, 2015

[9] J. Targett, S. Jeffress, X. Niu, F. Russell, P. D. Dueben, and W. Luk. Lower precision for higher accuracy: Precision and resolution exploration for shallow water equations. Proceedings of the International Conference on Field Programmable Technology (FPT), 2015

[8] P. D. Dueben and T. N. Palmer. Benchmark tests for numerical weather forecasts on inexact hardware. Monthly Weather Review, 142:3809–3829, 2014

[7] P. D. Dueben, J. Joven, A. Lingamneni, H. McNamara, G. De Micheli, K. V. Palem, and T. N. Palmer. On the use of inexact, pruned hardware in atmospheric modelling. Philosophical Transactions of the Royal Society A, 372(2018), 2014

[6] P. D. Dueben and P. Korn. Atmosphere and ocean modeling on grids of variable resolution - a 2d case study. Monthly Weather Review, 142:1997–2017, 2014

[5] T. Palmer, P. D. Dueben, and H. McNamara. Stochastic modelling and energy-efficient computing for weather and climate prediction. Philosophical Transactions of the Royal Society A, 372(2018), 2014

[4] P. D. Dueben, H. McNamara, and T.N. Palmer. The use of imprecise processing to improve accuracy in weather & climate prediction. Journal of Computational Physics, 271(0):2–18, 2014

[3] P. D. Dueben, P. Korn, and V. Aizinger. A discontinuous/continuous low order finite element shallow water model on the sphere. Journal of Computational Physics, 231(6):2396–2413, 2012

[2] P. D. Dueben, D. Homeier, G. Münster, K. Jansen, and D. Mesterhazy. Monte Carlo approach to turbulence. 27. International Symposium on Lattice Field Theory, Beijing, China, 41, 2009

[1] P. D. Dueben, D. Homeier, K. Jansen, D. Mesterhazy, G. Münster, and C. Urbach. Monte Carlo simulations of the randomly forced burgers equation. Europhysics Letters, 84(4):40002, 2008