Head of Earth System Modelling
Research, Earth System Modelling, Coupled Processes
Professional interests:
- All aspects of Earth system modelling.
- Km-scale weather and climate simulations.
- High-performance computing for weather and climate models.
- Machine learning for weather and climate prediction.
- Model error, model uncertainty and predictability.
Career background:
EDUCATION AND WORK EXPERIENCE
- 2024 - today: Honorary Professor at the University of Cologne
- 2022 - today: Head of the Earth System Modelling Section at the European Centre for Medium Range Weather Forecasts (ECMWF)
- 2019 - 2022: Coordinator of Machine Learning and AI activities at ECMWF.
- 2017 - 2022: 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.
- 2025
- Benoît Vannière, Irina Sandu, Peter Dueben, Richard Forbes, Inna Polichtchouk, Annelize Van Niekerk, Birgit Sützl, Michail Diamantakis, Jasper Denissen, Estibaliz Gascon, Michael Maier-Gerber, Llorenç Lledo, Ivan Bastak-Duran, Aristofanis Tsiringakis, Tobias Becker, Josef Schröttle, Ziga Zaplotnik (January 2025) The Global Extremes Digital Twin of Destination Earth: successes and challenges. DOI: 10.5194/egusphere-egu24-20097
- Jasper Denissen, Gabriele Arduini, Ervin Zsoter, Michel Wortmann, Maliko Tanguy, Estíbaliz Gascón, Cinzia Mazzetti, Christel Prudhomme, Oisín Morrison, Peter Düben, Irina Sandu, Christoph Rüdiger, Benoît Vannière (March 2025) The role of sub-grid orography for global high-resolution flood forecasting. DOI: 10.5194/egusphere-egu25-7097
- Jasper Denissen, Gabriele Arduini, Ervin Zsoter, Cinzia Mazzetti, Christel Prudhomme, Shaun Harrigan, Gianpaolo Balsamo, Iria Ayan-Miguez, Peter Dueben, Irina Sandu, Benoit Vanniere (January 2025) Towards a global km-scale flood forecasting system. DOI: 10.5194/egusphere-egu24-15657
- Christian Lessig, Peter Lean, Tony McNally, Mihai Alexe, Simon Lang, Matthew Chantry, Peter Dueben (January 2025) Towards a machine learning model for data assimilation and forecasting directly trained from observations. DOI: 10.5194/egusphere-egu24-11724
- Tim Reichelt, Juniper Tyree, Milan Kloewer, Peter Dueben, Bryan Lawrence, Dorit Hammerling, Alisson Baker, Sara Faghih-Naini, Philip Stier (March 2025) ClimateBenchPress: A Benchmark for Compression of Climate Data. DOI: 10.5194/egusphere-egu25-15912
- Sara Faghih-Naini, Till Ehrengruber, Christian Kühnlein, Lukas Papritz, Peter Dueben (March 2025) Developing ECMWF’s portable next-generation atmospheric dynamical core. DOI: 10.5194/egusphere-egu25-16827
- Maliko Tanguy, Gabriele Arduini, Matthieu Chevallier, Jasper M.C. Denissen, Peter Dueben, Estibaliz Gascon, Thomas Haiden, Cinzia Mazzetti, Nikolaos Mastrantonas, Gwyneth Matthews, Oisin Morrison, Christel Prudhomme, Christoph Rüdiger, Irina Sandu, Benoît Vannière, Michel Wortmann, Ervin Zsoter (March 2025) Enhancing Global Flood Forecasting: A Methodological Framework for Assessing High-Resolution Simulations in the DestinE G-EDT. DOI: 10.5194/egusphere-egu25-17195
- Karsten Peters-von Gehlen, Juniper Tyree, Sara Faghih-Naini, Peter Dueben, Jannek Squar, Anna Fuchs (March 2025) Lossy Data Compression Exploration in an Online Laboratory and the Link to HPC Design Decisions. DOI: 10.5194/egusphere-egu25-19418
- Juniper Tyree, Sara Faghih-Naini, Peter Dueben, Karsten Peters-von Gehlen, Heikki Järvinen (March 2025) Exploring Lossy Data Compression in an Online Laboratory for Climate Science and Meteorology. DOI: 10.5194/egusphere-egu25-15003
- 2024
- Dmitrii Kochkov, Janni Yuval, Ian Langmore, Peter Norgaard, Jamie Smith, Griffin Mooers, Milan Klöwer, James Lottes, Stephan Rasp, Peter Düben, Sam Hatfield, Peter Battaglia, Alvaro Sanchez-Gonzalez, Matthew Willson, Michael P. Brenner, Stephan Hoyer (August 2024) Neural general circulation models for weather and climate, Nature. DOI: 10.1038/s41586-024-07744-y
- 2023
- Gianpaolo Balsamo, Florence Rabier, Magdalena Balmaseda, Peter Bauer, Andy Brown, Peter Dueben, Steve English, Tony McNally, Florian Pappenberger, Irina Sandu, Jean-Noël Thepaut, Nils Wedi (May 2023) Recent progress and outlook for the ECMWF Integrated Forecasting System. DOI: 10.5194/egusphere-egu23-13110
- Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, Tim Palmer (December 2023) Deep learning for quality control of surface physiographic fields using satellite Earth observations, Hydrology and Earth System Sciences. DOI: 10.5194/hess-27-4661-2023
- Matthew Chantry, Peter Ukkonen, Robin Hogan, Peter Dueben (May 2023) Emulating radiative transfer in a numerical weather prediction model. DOI: 10.5194/egusphere-egu23-3256
- Margarita Choulga, Tom Kimpson, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, Tim Palmer (May 2023) Deep Learning for Verification of Earth's surfaces. DOI: 10.5194/egusphere-egu23-8777
- Anton Pershin, Matthew Chantry, Peter Dominik Dueben, Robin J Hogan, Tim Palmer (September 2023) Computing the ecRad radiation scheme with half-precision arithmetic. DOI: 10.22541/essoar.169603511.10575870/v1
- 2022
- David Meyer, Robin J. Hogan, Peter D. Dueben, Shannon L. Mason (March 2022) Machine Learning Emulation of 3D Cloud Radiative Effects, Journal of Advances in Modeling Earth Systems n. 3. DOI: 10.1029/2021MS002550
- Patrick Laloyaux, Thorsten Kurth, Peter Dominik Dueben, David Hall (January 2022) Deep learning to estimate model biases in an operational NWP assimilation system. DOI: 10.1002/essoar.10510309.1
- David Meyer, Sue Grimmond, Peter Dueben, Robin Hogan, Maarten van Reeuwijk (March 2022) Machine Learning Emulation of Urban Land Surface Processes, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2021MS002744
- Jan Ackmann, Peter Dominik Dueben, Tim N Palmer, Piotr K. Smolarkiewicz (April 2022) Mixed-precision for Linear Solvers in Global Geophysical Flows. DOI: 10.1002/essoar.10511194.1
- Tom Kimpson, Margarita Choulga, Matthew Chantry, Gianpaolo Balsamo, Souhail Boussetta, Peter Dueben, Tim Palmer (December 2022) Deep Learning for Verification of Earth-System Parametrisation of Water Bodies. DOI: 10.5194/egusphere-2022-1177
- Patrick Laloyaux, Thorsten Kurth, Peter Dominik Dueben, David Hall (June 2022) Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2022MS003016
- Milan Klöwer, Miha Razinger, Juan J. Dominguez, Peter D. Düben, Tim Palmer (March 2022) Compressing atmospheric data into its real information content. DOI: 10.5194/egusphere-egu22-3109
- Zied Ben Bouallègue, Fenwick Cooper, Matthew Chantry, Peter Dueben, Peter Bechtold, irina sandu (April 2022) Statistical modelling of 2m temperature and 10m wind speed forecast errors, ECMWF Technical Memoranda n. 896. DOI: 10.21957/vdcccja3f
- Jan Ackmann, Peter D. Dueben, Tim Palmer, Piotr K. Smolarkiewicz (September 2022) Mixed‐Precision for Linear Solvers in Global Geophysical Flows, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2022MS003148
- Lucy Harris, Andrew T. T. McRae, Matthew Chantry, Peter D. Dueben, Tim N. Palmer (October 2022) A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2022MS003120
- Milan Klöwer, Samuel Hatfield, Matteo Croci, Peter D. Düben, Tim Palmer (March 2022) Fluid simulations accelerated with 16 bits: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16. DOI: 10.5194/egusphere-egu22-3095
- 2021
- Simon T. K. Lang, Andrew Dawson, Michail Diamantakis, Peter Dueben, Samuel Hatfield, Martin Leutbecher, Tim Palmer, Fernando Prates, Christopher D. Roberts, Irina Sandu, Nils Wedi (October 2021) More accuracy with less precision, Quarterly Journal of the Royal Meteorological Society n. 741, pp. 4358-4370. DOI: 10.1002/qj.4181
- Milan Klöwer, Sam Hatfield, Matteo Croci, Peter Düben, Tim Palmer (August 2021) Fluid simulations accelerated with 16 bit: Approaching 4x speedup on A64FX by squeezing ShallowWaters.jl into Float16. DOI: 10.1002/essoar.10507472.2
- Samuel Edward Hatfield, Matthew Chantry, Peter Dominik Dueben, Philippe Lopez, Alan Jon Geer, Tim N Palmer (February 2021) Building tangent-linear and adjoint models for data assimilation with neural networks. DOI: 10.1002/essoar.10506310.1
- Mark John Rodwell, Michail Diamantakis, Peter Dueben, Martin Janousek, Simon Lang, Inna Polichtchouk, Fernando Prates, Chris Roberts, Filip Vána (July 2021) IFS upgrade provides more skilful ensemble forecasts, ECMWF Newsletter, issue 168, pp. 18-23. DOI: 10.21957/m830hnz27r
- Peter Dueben, Umberto Modigliani, Alan Geer, Stephan Siemen, Florian Pappenberger, Peter Bauer, Andy Brown, Martin Palkovic, Baudouin Raoult, Nils Wedi, Vasileios Baousis (January 2021) Machine learning at ECMWF: A roadmap for the next 10 years, ECMWF Technical Memoranda n. 878. DOI: 10.21957/ge7ckgm
- Jan Ackmann, Peter Düben, Tim Palmer, Piotr Smolarkiewicz (March 2021) Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows. DOI: 10.5194/egusphere-egu21-5507
- Niraj Agarwal, D. Kondrashov, P. Dueben, E. Ryzhov, P. Berloff (September 2021) A Comparison of Data‐Driven Approaches to Build Low‐Dimensional Ocean Models, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2021MS002537
- Matthew Chantry, Hannah Christensen, Peter Dueben, Tim Palmer (April 2021) Opportunities and challenges for machine learning in weather and climate modelling: hard, medium and soft AI, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences n. 2194, pp. 20200083. DOI: 10.1098/rsta.2020.0083
- Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler (April 2021) Deep learning for post-processing ensemble weather forecasts, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences n. 2194, pp. 20200092. DOI: 10.1098/rsta.2020.0092
- Sam Hatfield, Kristian Mogensen, Peter Dueben, Nils Wedi, Michail Diamantakis (March 2021) Operational Single-Precision Earth-System Modelling at ECMWF. DOI: 10.5194/egusphere-egu21-733
- David Meyer, Robin J. Hogan, Peter D. Dueben, Shannon L. Mason (March 2021) Machine Learning Emulation of 3D Cloud Radiative Effects. DOI: 10.5194/egusphere-egu21-3342
- Matthew Chantry, Sam Hatfield, Peter Duben, Inna Polichtchouk, Tim Palmer (March 2021) Machine learning emulation of gravity wave drag in numerical weather forecasting. DOI: 10.5194/egusphere-egu21-7678
- Thomas Rackow, Nils Wedi, Kristian Mogensen, Peter Dueben, Helge F. Goessling, Jan Hegewald, Christian Kühnlein, Lorenzo Zampieri, Thomas Jung (March 2021) DYAMOND-II simulations with IFS-FESOM2. DOI: 10.5194/egusphere-egu21-9672
- Falko JUDT, Daniel KLOCKE, Rosimar RIOS-BERRIOS, Benoit VANNIERE, Florian ZIEMEN, Ludovic AUGER, Joachim BIERCAMP, Christopher BRETHERTON, Xi CHEN, Peter DÜBEN, Cathy HOHENEGGER, Marat KHAIROUTDINOV, Chihiro KODAMA, Luis KORNBLUEH, Shian-Jiann LIN, Masuo NAKANO, Philipp NEUMANN, William PUTMAN, Niklas RÖBER, Malcolm ROBERTS, Masaki SATOH, Ryosuke SHIBUYA, Bjorn STEVENS, Pier Luigi VIDALE, Nils WEDI, Linjiong ZHOU (May 2021) Tropical Cyclones in Global Storm-Resolving Models, Journal of the Meteorological Society of Japan. Ser. II n. 3, pp. 579-602. DOI: 10.2151/jmsj.2021-029
- Maike Sonnewald, Redouane Lguensat, Daniel C Jones, Peter D Dueben, Julien Brajard, V Balaji (July 2021) Bridging observations, theory and numerical simulation of the ocean using machine learning, Environmental Research Letters n. 7, pp. 073008. DOI: 10.1088/1748-9326/ac0eb0
- Sam Hatfield, Matthew Chantry, Peter Dueben, Philippe Lopez, Alan Geer, Tim Palmer (September 2021) Building Tangent‐Linear and Adjoint Models for Data Assimilation With Neural Networks, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2021MS002521
- Christian Zeman, Nils P. Wedi, Peter D. Dueben, Nikolina Ban, Christoph Schär (February 2021) Model intercomparison of COSMO 5.0 and IFS 45r1 at kilometer-scale grid spacing. DOI: 10.5194/gmd-2021-31
- Jérôme Barré, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, Antje Inness, Alba Lorente, Joe McNorton, Vincent-Henri Peuch, Gabor Radnoti, Roberto Ribas (April 2021) Systematic detection of local CH4 anomalies by combining satellite measurements with high-resolution forecasts, Atmospheric Chemistry and Physics. DOI: 10.5194/acp-21-5117-2021
- 2020
- Peter Düben (July 2020) Review "Necessary conditions for algorithmic tuning of weather prediction models using OpenIFS as an example". DOI: 10.5194/gmd-2020-56-RC1
- Stephan Rasp, Peter D. Dueben, Sebastian Scher, Jonathan A. Weyn, Soukayna Mouatadid, Nils Thuerey (November 2020) WeatherBench: A Benchmark Data Set for Data‐Driven Weather Forecasting, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2020MS002203
- Nils P. Wedi, Inna Polichtchouk, Peter Dueben, Valentine G. Anantharaj, Peter Bauer, Souhail Boussetta, Philip Browne, Willem Deconinck, Wayne Gaudin, Ioan Hadade, Sam Hatfield, Olivier Iffrig, Philippe Lopez, Pedro Maciel, Andreas Mueller, Sami Saarinen, Irina Sandu, Tiago Quintino, Frederic Vitart (November 2020) A Baseline for Global Weather and Climate Simulations at 1 km Resolution, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2020MS002192
- Peter Düben, Nils Wedi, Sami Saarinen, Christian Zeman (March 2020) Global simulations of the atmosphere at 1.45 km grid-spacing with the Integrated Forecasting System. DOI: 10.5194/egusphere-egu2020-19344
- M. Klöwer, P. D. Düben, T. N. Palmer (October 2020) Number Formats, Error Mitigation, and Scope for 16‐Bit Arithmetics in Weather and Climate Modeling Analyzed With a Shallow Water Model, Journal of Advances in Modeling Earth Systems n. 10. DOI: 10.1029/2020MS002246
- Jérôme Barré, Ilse Aben, Anna Agustí-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Peter Dueben, Richard Engelen, Antje Inness, Alba Lorente, Joe McNorton, Vincent-Henri Peuch, Gabor Radnoti, Roberto Ribas (September 2020) Systematic detection of local CH4 emissions anomalies combining satellite measurements and high-resolution forecasts. DOI: 10.5194/acp-2020-550
- Hatfield S., McRae A., Palmer T., Düben P. (May 2020) Single-precision in the tangent-linear and adjoint models of incremental 4D-VAr, Monthly Weather Review. DOI: 10.1175/MWR-D-19-0291.1
- Cooper F.C., Düben P.D., Denis C., Dawson A., Ashwin P. (May 2020) The relationship between numerical precision and forecast lead time in the Lorenz'95 system, Monthly Weather Review. DOI: 10.1175/MWR-D-18-0200.1
- Saffin L., Hatfield S., Düben P., Palmer T. (May 2020) Reduced-precision parametrization: lessons from an intermediate-complexity atmospheric model, Quarterly Journal of the Royal Meteorological Society. DOI: 10.1002/qj.3754
- Peter Bauer, Tiago Quintino, Nils Wedi, Antonino Bonanni, Marcin Chrust, Willem Deconinck, Michail Diamantakis, Peter Dueben, Stephen English, Johannes Flemming, Paddy Gillies, Ioan Hadade, James Hawkes, Mike Hawkins, Olivier Iffrig, Christian Kühnlein, Michael Lange, Peter Lean, Olivier Marsden, Andreas Müller, Sami Saarinen, Domokos Sarmany, Michael Sleigh, Simon Smart, PIOTR SMOLARKIEWICZ, Daniel Thiemert, Giovanni Tumolo, Christian Weihrauch, Cristiano Zanna, Pedro Maciel (February 2020) The ECMWF Scalability Programme: Progress and Plans, ECMWF Technical Memoranda n. 857. DOI: 10.21957/gdit22ulm
- 2019
- Chantry M., Thornes T., Palmer T., Düben P. (May 2019) Scale-selective precision for weather and climate forecasting, Monthly Weather Review. DOI: 10.1175/MWR-D-18-0308.1
- Hatfield S., Chantry M., Düben P., Palmer T. (May 2019) Accelerating high-resolution weather models with deep-learning hardware, Proceedings of the Platform for Advanced Scientific Computing Conference, PASC 2019. DOI: 10.1145/3324989.3325711
- Klöwer M., Düben P.D., Palmer T.N. (May 2019) Posits as an alternative to floats for weather and climate models, ACM International Conference Proceeding Series. DOI: 10.1145/3316279.3316281
- Düben P.D., Leutbecher M., Bauer P. (May 2019) New methods for data storage of model output from ensemble simulations, Monthly Weather Review. DOI: 10.1175/MWR-D-18-0170.1
- Stevens B., Satoh M., Auger L., Biercamp J., Bretherton C.S., Chen X., Düben P., Judt F., Khairoutdinov M., Klocke D., Kodama C., Kornblueh L., Lin S.-J., Neumann P., Putman W.M., Röber N., Shibuya R., Vanniere B., Vidale P.L., Wedi N., Zhou L. (May 2019) DYAMOND: the DYnamics of the Atmospheric general circulation Modeled On Non-hydrostatic Domains, Progress in Earth and Planetary Science. DOI: 10.1186/s40645-019-0304-z
- Satoh M., Stevens B., Judt F., Khairoutdinov M., Lin S.-J., Putman W.M., Düben P. (May 2019) Global Cloud-Resolving Models, Current Climate Change Reports. DOI: 10.1007/s40641-019-00131-0
- Neumann P., Düben P., Adamidis P., Bauer P., Brück M., Kornblueh L., Klocke D., Stevens B., Wedi N., Biercamp J. (May 2019) Assessing the scales in numerical weather and climate predictions: Will exascale be the rescue?, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. DOI: 10.1098/rsta.2018.0148
- Peter Düben (November 2019) Review. DOI: 10.5194/gc-2019-22-RC1
- Peter Düben (July 2019) Review of Combining data assimilation and machine learning to emulate a dynamical model from sparse and noisy observations: a case study with the Lorenz 96 model. DOI: 10.5194/gmd-2019-136-RC1
- Peter Düben (April 2019) Review "Scalability and some optimization of the Finite-volumE Sea ice-Ocean Model, Version 2.0 (FESOM2)". DOI: 10.5194/gmd-2018-334-RC1
- Peter Düben (March 2019) Review of "How to use mixed precision in Ocean Models". DOI: 10.5194/gmd-2019-20-RC1
- Peter Düben (March 2019) Review of weather and climate forecasting with neural networks. DOI: 10.5194/gmd-2019-53-RC1
- 2018
- Thornes T., Düben P., Palmer T. (May 2018) A power law for reduced precision at small spatial scales: Experiments with an SQG model, Quarterly Journal of the Royal Meteorological Society. DOI: 10.1002/qj.3303
- Hatfield S., Subramanian A., Palmer T., Düben P. (May 2018) Improving weather forecast skill through reduced-precision data assimilation, Monthly Weather Review. DOI: 10.1175/MWR-D-17-0132.1
- Dawson A., Düben P.D., MacLeod D.A., Palmer T.N. (May 2018) Reliable low precision simulations in land surface models, Climate Dynamics. DOI: 10.1007/s00382-017-4034-x
- Peter Dueben, Michail Diamantakis, Simon Lang, Sami Saarinen, irina sandu, Nils Wedi, Tomas Wilhelmsson (October 2018) Progress in using single precision in the IFS, ECMWF Newsletter, issue 157, pp. 26-31. DOI: 10.21957/ps2y9gfa2d
- Peter D. Düben (November 2018) A New Number Format for Ensemble Simulations, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2018MS001420
- Sam Hatfield, Peter Düben, Matthew Chantry, Keiichi Kondo, Takemasa Miyoshi, Tim Palmer (September 2018) Choosing the Optimal Numerical Precision for Data Assimilation in the Presence of Model Error, Journal of Advances in Modeling Earth Systems. DOI: 10.1029/2018MS001341
- Peter Düben (August 2018) Response to the first round of reviews. DOI: 10.5194/gmd-2018-148-AC1
- Peter D. Dueben, Peter Bauer (June 2018) Supplementary material to "Challenges and design choices for global weather and climate models based on machine learning". DOI: 10.5194/gmd-2018-148-supplement
- Peter D. Dueben, Peter Bauer (June 2018) Challenges and design choices for global weather and climate models based on machine learning. DOI: 10.5194/gmd-2018-148
- 2017
- Düben P.D., Dawson A. (May 2017) An approach to secure weather and climate models against hardware faults, Journal of Advances in Modeling Earth Systems. DOI: 10.1002/2016MS000816
- Targett J.S., Düben P., Luk W. (May 2017) Validating optimisations for chaotic simulations, 2017 27th International Conference on Field Programmable Logic and Applications, FPL 2017. DOI: 10.23919/FPL.2017.8056793
- Váňa F., Düben P., Lang S., Palmer T., Leutbecher M., Salmond D., Carver G. (May 2017) Single precision in weather forecasting models: An evaluation with the IFS, Monthly Weather Review. DOI: 10.1175/MWR-D-16-0228.1
- Russell F.P., Düben P.D., Niu X., Luk W., Palmer T.N. (May 2017) Exploiting the chaotic behaviour of atmospheric models with reconfigurable architectures, Computer Physics Communications. DOI: 10.1016/j.cpc.2017.08.011
- Düben P.D., Subramanian A., Dawson A., Palmer T.N. (May 2017) A study of reduced numerical precision to make superparameterization more competitive using a hardware emulator in the OpenIFS model, Journal of Advances in Modeling Earth Systems. DOI: 10.1002/2016MS000862
- Tobias Thornes, Peter Düben, Tim Palmer (January 2017) On the use of scale‐dependent precision in Earth System modelling, Quarterly Journal of the Royal Meteorological Society. DOI: 10.1002/qj.2974
- 2016
- Targett J.S., Niu X., Russell F., Luk W., Jeffress S., Duben P. (May 2016) Lower precision for higher accuracy: Precision and resolution exploration for shallow water equations, 2015 International Conference on Field Programmable Technology, FPT 2015. DOI: 10.1109/FPT.2015.7393152
- Filip Vána, Glenn Carver, Simon Lang, Martin Leutbecher, Deborah Salmond, Peter Dueben, Tim Palmer (May 2016) Single-precision IFS, ECMWF Newsletter, issue 148, pp. 20-23. DOI: 10.21957/avq2i3tp
- Andrew Dawson, Peter Düben (November 2016) rpe v5: An emulator for reduced floating-point precision in large numerical simulations. DOI: 10.5194/gmd-2016-247
- 2015
- Düben P.D., Dolaptchiev S.I. (May 2015) Rounding errors may be beneficial for simulations of atmospheric flow: results from the forced 1D Burgers equation, Theoretical and Computational Fluid Dynamics. DOI: 10.1007/s00162-015-0355-8
- Duben P., Parishkrati, Yenugula S., Augustine J., Palem K., Schlachter J., Enz C., Palmer T.N. (May 2015) Opportunities for energy efficient computing: A study of inexact general purpose processors for high-performance and big-data applications, Proceedings -Design, Automation and Test in Europe, DATE. DOI: 10.7873/date.2015.1116
- Düben P.D., Russell F.P., Niu X., Luk W., Palmer T.N. (May 2015) On the use of programmable hardware and reduced numerical precision in earth-system modeling, Journal of Advances in Modeling Earth Systems. DOI: 10.1002/2015MS000494
- Russell F.P., Düben P.D., Niu X., Luk W., Palmer T.N. (May 2015) Architectures and precision analysis for modelling atmospheric variables with chaotic behaviour, Proceedings - 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines, FCCM 2015. DOI: 10.1109/FCCM.2015.52
- 2014
- Düben P.D., McNamara H., Palmer T.N. (May 2014) The use of imprecise processing to improve accuracy in weather & climate prediction, Journal of Computational Physics. DOI: 10.1016/j.jcp.2013.10.042
- Düben P.D., Joven J., Lingamneni A., McNamara H., De Micheli G., Palem K.V., Palmer T.N. (May 2014) On the use of inexact, pruned hardware in atmospheric modelling, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. DOI: 10.1098/rsta.2013.0276
- Düben P.D., Palmer T.N. (May 2014) Benchmark tests for numerical weather forecasts on inexact hardware, Monthly Weather Review. DOI: 10.1175/MWR-D-14-00110.1
- Palmer T., Düben P., McNamara H. (May 2014) Stochastic modelling and energy-efficient computing for weather and climate prediction, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences. DOI: 10.1098/rsta.2014.0118
- Düben P.D., Korn P. (May 2014) Atmosphere and ocean modeling on grids of variable resolution-A 2D case study, Monthly Weather Review. DOI: 10.1175/MWR-D-13-00217.1
- 2012
- Düben P.D., Korn P., Aizinger V. (May 2012) A discontinuous/continuous low order finite element shallow water model on the sphere, Journal of Computational Physics. DOI: 10.1016/j.jcp.2011.11.018
- 2009
- Düben P., Homeier D., Jansen K., Mesterhazy D., Münster G. (May 2009) Monte carlo approach to turbulence, Proceedings of Science.
- 2008
- Düben P., Homeier D., Jansen K., Mesterhazy D., Münster G., Urbach C. (May 2008) Monte Carlo simulations of the randomly forced Burgers equation, EPL. DOI: 10.1209/0295-5075/84/40002
Contact Details:
peter.dueben
ecmwf.int
ecmwf.int