MAchinE Learning for Scalable meTeoROlogy and cliMate

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

First version of workflow tools published that allows to perform quarterly benchmarks of ML solutions
Hackathon I
Dissemination workshop I
Benchmarking and test infrastructure
Dissemination workshop II
Hackathon II
First version of customised ML solutions for the datasets of D1.2
Final version of workflow tools published
Revised version of customised ML solutions for the updated datasets of D1.2 that are based on the workflow tools of WP2 and optimised based on the feedback from WP3 regarding performance and energy consumption
Performance model for hardware configurations

Final report on hardware performance benchmarking for ML solutions with the full implementation of the workflow tools of D2.2
Report on tests with a tangent linear and adjoint version of ML emulators with 4DVar
Report on software performance benchmarking for ML solutions from deliverable D1.4
Report on the survey of the workflow, the MAELSTROM protocol and ML requirements

Report on the survey of the workflow the MAELSTROM protocol and ML requirements

Report on hardware performance benchmarking for ML solutions from D1.3 on a number of hardware configurations
Report on hardware performance benchmarking for simplistic ML solutions for benchmark data sets in D1.2 on existing hardware solutions

Report on hardware performance benchmarking for simplistic ML solutions for benchmark data sets in D12 on existing hardware solutions

Report on a survey of MAELSTROM applications and ML tools and architectures
Plan for Dissemination and Communication
Initial list of hardware related requirements for ML solutions in W&C

Initial list of hardware related requirements for ML solutions in WC

Report on software performance benchmarking for ML solutions from deliverable D1.3

Report on software performance benchmarking for ML solutions from deliverable D13

Plan for Gender Balance
Report on the application of ML solutions within the W&C workflow
Report on solution design and architecture blueprint
Roadmap analysis of technologies relevant for ML solutions in W&C

Roadmap analysis of technologies relevant for ML solutions in WC

Report on improved data processing tools, and the weather data loading pipeline designed for large-scale deep learning training for the benchmark datasets from Deliverable D1.1

Data Management Plan

First version of datasets and cost functions to develop ML solutions for A1-A6

Web Portal including Software Collaboration Platform

Publikacje

Almost 5 years of deep learning in Earth system modelling – where do we come from and where do we go

Autorzy: Dueben, Peter
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.7108876

Stochastic downscaling of meteorological fields with deep neural networks

Autorzy: Langguth, Michael; Gong, Bing; Ji, Yan; Mozaffari, Amirpasha; Schultz, Martin
Opublikowane w: Living Planet Symposium 2022, LPS2022, Bonn, Germany, 2022-05-23 - 2022-05-27, Numer 1, 2022
Wydawca: Living Planet Symposium 2022

Clairvoyant prefetching for distributed machine learning I/O

Autorzy: Nikoli Dryden; Roman Böhringer; Tal Ben-Nun; Torsten Hoefler
Opublikowane w: SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2021
Wydawca: Association for Computing Machinery New York, NY, United States
DOI: 10.48550/arxiv.2101.08734

efficiently training large-scale neural networks with bidirectional pipelines

Autorzy: Shigang Li; Torsten Hoefler
Opublikowane w: SC, 2022, ISBN 9781450384421
Wydawca: Association for Computing Machinery New York, NY, United States
DOI: 10.1145/3458817.3476145

Near-optimal sparse allreduce for distributed deep learning

Autorzy: Li, Shigang; Hoefler, Torsten
Opublikowane w: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '22), 2022
Wydawca: ACM
DOI: 10.1145/3503221.3508399

Neural Parameter Allocation Search

Autorzy: Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
Opublikowane w: ICLR 2022, 2022
Wydawca: ICLR
DOI: 10.48550/arxiv.2006.10598

Overview of State of the Art Use of ML/AI for Earth System Science

Autorzy: Dueben
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.7081282

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Autorzy: Cummins, Chris; Fisches, Zacharias V.; Ben-Nun, Tal; Hoefler, Torsten; O'Boyle, Michael F P; Leather, Hugh
Opublikowane w: Cummins , C , Fisches , Z V , Ben-Nun , T , Hoefler , T , O'Boyle , M F P & Leather , H 2021 , PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations . in Proceedings of the 38th International Conference on Machine Learning . Proceedings of Machine Learning Research , vol. 139 , pp. 2244-2253 , Thirty-eighth International Conference on Machine Learning , 1, Numer 1, 2021
Wydawca: 38th International Conference on Machine Learning

High-Performance and Programmable Attentional Graph Neural Networks with Global Tensor Formulations

Autorzy: Maciej Besta; Pawel Renc; Robert Gerstenberger; Paolo Sylos Labini; Alexandros Ziogas; Tiancheng Chen; Lukas Gianinazzi; Florian Scheidl; Kalman Szenes; Armon Carigiet; Patrick Iff; Grzegorz Kwasniewski; Raghavendra Kanakagiri; Chio Ge; Sammy Jaeger; Jarosław Wąs; Flavio Vella; Torsten Hoefler
Opublikowane w: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Numer 1, 2023, ISBN 979-8-4007-0109-2
Wydawca: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
DOI: 10.1145/3581784.3607067

Machine Learning for Weather and Predictions

Autorzy: Dueben
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.7081333

Machine Learning for Weather and Climate Prediction

Autorzy: Dueben, Peter
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.6792121

A Data-Centric Optimization Framework for Machine Learning

Autorzy: Oliver Rausch; Tal Ben-Nun; Nikoli Dryden; Andrei Ivanov; Shigang Li; Torsten Hoefler
Opublikowane w: ICS '22: Proceedings of the 36th ACM International Conference on Supercomputing, 2022, ISBN 9781450392815
Wydawca: Association for Computing Machinery New York, NY, United States
DOI: 10.48550/arxiv.2110.10802

Machine Learning in Weather and Climate Modelling

Autorzy: Dueben
Opublikowane w: 2021
Wydawca: Zenodo
DOI: 10.5281/zenodo.7081199

Challenges and Limitations of Machine Learning for Atmospheric Sciences

Autorzy: Dueben
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.7081632

Productive Performance Engineering for Weather and Climate Modeling with Python

Autorzy: T. Ben-Nun, L. Groner, F. Deconinck, T. Wicky, E. Davis, J. Dahm, O. Elbert, R. George, J. McGibbon, L. Trümper, E. Wu, O. Fuhrer, T. Schulthess, T. Hoefler
Opublikowane w: SC'22, 2022
Wydawca: SC'22

Spatial Mixture-of-Experts

Autorzy: N. Dryden, T. Hoefler
Opublikowane w: NeurIPS'22, 2022
Wydawca: Neural Information Processing Systems 35

Productive Performance Engineering for Weather and Climate Modeling with Python

Autorzy: Ben-Nun, Tal; Groner, Linus; Deconinck, Florian; Wicky, Tobias; Davis, Eddie; Dahm, Johann; Elbert, Oliver D.; George, Rhea; McGibbon, Jeremy; Trümper, Lukas; Wu, Elynn; Fuhrer, Oliver; Schulthess, Thomas; Hoefler, Torsten
Opublikowane w: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, Numer 41, 2022, ISSN 2331-8422
Wydawca: ArXiv.org
DOI: 10.1109/sc41404.2022.00078

Machine Learning at ECMWF

Autorzy: Peter Dueben
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.7100588

The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores

Autorzy: Maciej Besta; Robert Gerstenberger; Marc Fischer; Michal Podstawski; Nils Blach; Berke Egeli; Georgy Mitenkov; Wojciech Chlapek; Marek Michalewicz; Hubert Niewiadomski; Juergen Mueller; Torsten Hoefler
Opublikowane w: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Numer 1, 2023, ISBN 979-8-4007-0109-2
Wydawca: Proceedings of the International Conference for High Performance Computing
DOI: 10.1145/3581784.3607068

PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations

Autorzy: Cummins, Chris; Fisches, Zacharias V.; Ben-Nun, Tal; Hoefler, Torsten; O'Boyle, Michael F P; Leather, Hugh
Opublikowane w: Cummins , C , Fisches , Z V , Ben-Nun , T , Hoefler , T , O'Boyle , M F P & Leather , H 2021 , PROGRAML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations . in Proceedings of the 38th International Conference on Machine Learning . Proceedings of Machine Learning Research , vol. 139 , pp. 2244-2253 , International Conference on Machine Learning 2021 , 1/07/21 ., Numer vol. 139, PMLR, pp. 2244-2253,, 2021, ISSN 2640-3498
Wydawca: PMLR

Machine learning emulation of gravity wave drag in numerical weather forecasting

Autorzy: Matthew Chantry; Sam Hatfield; Peter Dueben; Inna Polichtchouk; Tim Palmer
Opublikowane w: Journal of advances in modeling earth systems, Numer 3, 2021, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2021ms002477

AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning

Autorzy: Lessig, Christian; Luise, Ilaria; Gong, Bing; Langguth, Michael; Stadtler, Scarlet; Schultz, Martin
Opublikowane w: arXiv, Numer 37, 2023, ISSN 2331-8422
Wydawca: USA
DOI: 10.48550/arxiv.2308.13280

CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting

Autorzy: Y. Ji; Y. Ji; B. Gong; M. Langguth; A. Mozaffari; X. Zhi
Opublikowane w: Geoscientific Model Development, Vol 16, Pp 2737-2752 (2023), Numer 1, 2023, ISSN 1991-959X
Wydawca: Copernicus Gesellschaft mbH
DOI: 10.5194/gmd-16-2737-2023

HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers

Autorzy: Besta, Maciej; Catarino, Afonso Claudino; Gianinazzi, Lukas; Blach, Nils; Nyczyk, Piotr; Niewiadomski, Hubert; Hoefler, Torsten
Opublikowane w: arXiv, Numer 34, 2023, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2311.18526

Deep Learning to Estimate Model Biases in an Operational NWP Assimilation System

Autorzy: Patrick Laloyaux1, Thorsten Kurth2, Peter Dominik Dueben1, and David Hall
Opublikowane w: Journal of Advances in Modeling Earth Systems, Numer 19422466, 2022, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2022ms003016

Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers

Autorzy: Zied Ben Bouallègue; Jonathan A. Weyn; Mariana C. A. Clare; Jesper Dramsch; Peter Dueben; Matthew Chantry
Opublikowane w: Artificial Intelligence for the Earth Systems, Numer 34, 2023, ISSN 2769-7525
Wydawca: Artificial Intelligence for the Earth Systems
DOI: 10.1175/aies-d-23-0027.1

Deep learning for quality control of surface physiographic fields using satellite Earth observations

Autorzy: Tom Kimpson; Margarita Choulga; Matthew Chantry; Gianpaolo Balsamo; Souhail Boussetta; Peter Dueben; Tim Palmer
Opublikowane w: eISSN: 1607-7938, Numer 34, 2023, ISSN 1027-5606
Wydawca: European Geophysical Society
DOI: 10.5194/hess-27-4661-2023

Statistical Modeling of 2-m Temperature and 10-m Wind Speed Forecast Errors

Autorzy: Ben-Bouallegue, Zied; Cooper, Fenwick; Chantry, Matthew; Düben, Peter; Bechtold, Peter; Sandu, Irina
Opublikowane w: Monthly Weather Review, Numer 1, 2023, ISSN 1520-0493
Wydawca: Monthly Weather Review
DOI: 10.1175/mwr-d-22-0107.1

Machine Learning Emulation of 3D Cloud Radiative Effects

Autorzy: David Meyer; Robin J. Hogan; Peter Dueben; Shannon Mason
Opublikowane w: Machine Learning Emulation of 3D Cloud Radiative Effects, Numer 2, 2022, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2021ms002550

Neural Graph Databases

Autorzy: Besta, Maciej; Iff, Patrick; Scheidl, Florian; Osawa, Kazuki; Dryden, Nikoli; Podstawski, Michal; Chen, Tiancheng; Hoefler, Torsten
Opublikowane w: arXiv, Numer 1, 2022, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2209.09732

PipeFisher: Efficient Training of Large Language Models Using Pipelining and Fisher Information Matrices

Autorzy: Osawa, Kazuki; Li, Shigang; Hoefler, Torsten
Opublikowane w: arXiv, Numer 30, 2022, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2211.14133

Compressing multidimensional weather and climate data into neural networks

Autorzy: Huang, Langwen; Hoefler, Torsten
Opublikowane w: arXiv, Numer 4, 2023, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2210.12538

Compressing atmospheric data into its real information content.

Autorzy: Milan Klöwer, Miha Razinger, Juan J. Dominguez, Peter D. Düben & Tim N. Palmer
Opublikowane w: Nature Computational Science, Numer 26628457, 2021, Strona(/y) Nat Comput Sci 1, 713–724 (2021), ISSN 2662-8457
Wydawca: Nature Computational Science
DOI: 10.1038/s43588-021-00156-2

Machine Learning Emulation of Urban Land Surface Processes

Autorzy: David Meyer1,2, Sue Grimmond1, Peter Dueben3, Robin Hogan1,3, and Maarten van Reeuwijk2
Opublikowane w: Journal of Advances in Modeling Earth Systems, Numer 19422466, 2021, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2021ms002744

Temperature forecasting by deep learning methods

Autorzy: Gong, Bing; Langguth, Michael; Ji, Yan; Mozaffari, Amirpasha; Stadtler, Scarlet; Mache, Karim; Schultz, Martin G.
Opublikowane w: Geoscientific Model Development, Vol 15, Pp 8931-8956 (2022), Numer 1, 2022, ISSN 1991-959X
Wydawca: Copernicus Gesellschaft mbH
DOI: 10.5194/gmd-2021-430

Cached Operator Reordering: A Unified View for Fast GNN Training

Autorzy: Bazinska, Julia; Ivanov, Andrei; Ben-Nun, Tal; Dryden, Nikoli; Besta, Maciej; Shen, Siyuan; Hoefler, Torsten
Opublikowane w: arXiv, Numer 35, 2023, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2308.12093

Spatial Mixture-of-Experts

Autorzy: Dryden, Nikoli; Hoefler, Torsten
Opublikowane w: Advances in Neural Information Processing Systems 35, Numer 1, 2022, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2211.13491

GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Autorzy: Frantar, Elias; Ashkboos, Saleh; Hoefler, Torsten; Alistarh, Dan
Opublikowane w: arXiv, Numer 1, 2022, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2210.17323

Challenges and Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook

Autorzy: Peter D. Dueben1, Martin G. Schultz2, Matthew Chantry1, David John Gagne II3, David Matthew Hall4, and Amy McGovern5
Opublikowane w: Artificial Intelligence for the Earth Systems, Numer 27697525, 2022, ISSN 2769-7525
Wydawca: American Meteorological Society
DOI: 10.1175/aies-d-21-0002.1

STen: Productive and Efficient Sparsity in PyTorch

Autorzy: Ivanov, Andrei; Dryden, Nikoli; Ben-Nun, Tal; Ashkboos, Saleh; Hoefler, Torsten
Opublikowane w: arXiv, Numer 40, 2023, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2304.07613

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

Autorzy: Harris, Lucy; McRae, Andrew T. T.; Chantry, Matthew; Dueben, Peter D.; Palmer, Tim N.
Opublikowane w: Crossref, Numer 33, 2022, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2022ms003120

A High-Performance Design, Implementation, Deployment, and Evaluation of The Slim Fly Network

Autorzy: Blach, Nils; Besta, Maciej; De Sensi, Daniele; Domke, Jens; Harake, Hussein; Li, Shigang; Iff, Patrick; Konieczny, Marek; Lakhotia, Kartik; Kubicek, Ales; Ferrari, Marcel; Petrini, Fabrizio; Hoefler, Torsten
Opublikowane w: arXiv, Numer 32, 2023, ISSN 2331-8422
Wydawca: arXiv
DOI: 10.48550/arxiv.2310.03742

Building Tangent-Linear and Adjoint Models for Data Assimilation With Neural Networks

Autorzy: Sam Hatfield; Matthew Chantry; Peter Dueben; Philippe Lopez; Alan J. Geer; Tim Palmer
Opublikowane w: journal of advances in modeling earth systems, Numer 2, 2021, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2021ms002521

A comparison of data-driven approaches to build low-dimensional ocean models

Autorzy: Niraj Agarwal; Dmitri Kondrashov; Dmitri Kondrashov; Peter Dueben; E. A. Ryzhov; Pavel Berloff; Pavel Berloff
Opublikowane w: journal of advances in modeling earth systmes, Numer 3, 2021, ISSN 1942-2466
Wydawca: American Geophysical Union
DOI: 10.1029/2021ms002537

Further analysis of cGAN: A system for Generative Deep Learning Post-processing of Precipitation

Autorzy: Cooper, Fenwick C.; McRae, Andrew T. T.; Chantry, Matthew; Antonio, Bobby; Palmer, Tim N.
Opublikowane w: Further analysis of cGAN: A system for Generative Deep Learning Post-processing of Precipitation, Numer 1, 2023, ISSN 1520-0493
Wydawca: arXiv
DOI: 10.48550/arxiv.2309.15689

Bridging observations, theory and numerical simulation of the ocean using machine learning

Autorzy: Maike Sonnewald; Maike Sonnewald; Maike Sonnewald; Redouane Lguensat; Daniel C. Jones; Peter Dueben; Julien Brajard; Venkatramani Balaji; Venkatramani Balaji
Opublikowane w: Environmental Research Letters, Numer 3, 2021, ISSN 1748-9326
Wydawca: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ac0eb0

Machine Learning at ECMWF

Autorzy: Dueben
Opublikowane w: 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.7081735

Machine learning for weather and climate predictions

Autorzy: Dueben, Peter
Opublikowane w: Numer 1, 2021
Wydawca: Zenodo
DOI: 10.5281/zenodo.5152016

How to reduce numerical precision in weather and climate simulations

Autorzy: Dueben, Peter
Opublikowane w: Numer 4, 2021
Wydawca: Zenodo
DOI: 10.5281/zenodo.5151995

ENS-10: A Dataset For Post-Processing EnsembleWeather Forecasts

Autorzy: Ashkboos, Saleh and Huang, Langwen and Dryden, Nikoli and Ben-Nun, Tal and Dueben, Peter and Gianinazzi, Lukas and Kummer, Luca and Hoefler, Torsten
Opublikowane w: 2022
Wydawca: arXiv
DOI: 10.48550/arxiv.2206.14786

Machine learning, high-performance computing and numerical weather prediction

Autorzy: Dueben, Peter D.
Opublikowane w: Numer 33, 2021
Wydawca: Zenodo
DOI: 10.5281/zenodo.5533706

Challenges when preparing machine learning tools for use in operational weather predictions

Autorzy: Dueben, Peter D.
Opublikowane w: Numer 5, 2021
Wydawca: Zenodo
DOI: 10.5281/zenodo.5533639

MAELSTROM: First benchmarks at ISC22

Autorzy: Nassyr, Stepan
Opublikowane w: Numer 1, 2022
Wydawca: JSC Accelerating Devices Lab
DOI: 10.34732/xdvblg-tufftf

Machine learning for weather and climate prediction

Autorzy: Dueben, Peter Dominik
Opublikowane w: Numer 5, 2023
Wydawca: Zenodo
DOI: 10.5281/zenodo.8025367

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization

Autorzy: Pacchiardi, Lorenzo and Adewoyin, Rilwan and Dueben, Peter and Dutta, Ritabrata
Opublikowane w: 2021
Wydawca: arXiv
DOI: 10.48550/arxiv.2112.08217

The next decade of machine learning at ECMWF

Autorzy: Dueben, Peter
Opublikowane w: Numer 4, 2021
Wydawca: Zenodo
DOI: 10.5281/zenodo.5152043

Km-scale weather models, machine-learned weather models, and km-scale machine-learned weather models

Autorzy: Dueben, Peter Dominik
Opublikowane w: Numer 5, 2023
Wydawca: Zenodo
DOI: 10.5281/zenodo.8025410

Machine learning and predictability of weather and climate

Autorzy: Dueben, Peter Dominik
Opublikowane w: Numer 1, 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.8025284

Machine Learning for Weather and Climate Modelling

Autorzy: Dueben, Peter Dominik
Opublikowane w: Numer 32, 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.8025265

Downscaling and global high-resolution modelling

Autorzy: Dueben, Peter Dominik
Opublikowane w: Numer 5, 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.8025314

Machine Learning at the European Centre for Medium-Range Weather Forecasts -- Some notes on the progress 2018-2022

Autorzy: Dueben, Peter Dominik
Opublikowane w: Numer 30, 2022
Wydawca: Zenodo
DOI: 10.5281/zenodo.8025270

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