MAchinE Learning for Scalable meTeoROlogy and cliMate

Livrables

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

Publications

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

Auteurs: Dueben, Peter
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.7108876

Stochastic downscaling of meteorological fields with deep neural networks

Auteurs: Langguth, Michael; Gong, Bing; Ji, Yan; Mozaffari, Amirpasha; Schultz, Martin
Publié dans: Living Planet Symposium 2022, LPS2022, Bonn, Germany, 2022-05-23 - 2022-05-27, Numéro 1, 2022
Éditeur: Living Planet Symposium 2022

Clairvoyant prefetching for distributed machine learning I/O

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

efficiently training large-scale neural networks with bidirectional pipelines

Auteurs: Shigang Li; Torsten Hoefler
Publié dans: SC, 2022, ISBN 9781450384421
Éditeur: Association for Computing Machinery New York, NY, United States
DOI: 10.1145/3458817.3476145

Near-optimal sparse allreduce for distributed deep learning

Auteurs: Li, Shigang; Hoefler, Torsten
Publié dans: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '22), 2022
Éditeur: ACM
DOI: 10.1145/3503221.3508399

Neural Parameter Allocation Search

Auteurs: Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
Publié dans: ICLR 2022, 2022
Éditeur: ICLR
DOI: 10.48550/arxiv.2006.10598

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

Auteurs: Dueben
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.7081282

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

Auteurs: Cummins, Chris; Fisches, Zacharias V.; Ben-Nun, Tal; Hoefler, Torsten; O'Boyle, Michael F P; Leather, Hugh
Publié dans: 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, Numéro 1, 2021
Éditeur: 38th International Conference on Machine Learning

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

Auteurs: 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
Publié dans: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Numéro 1, 2023, ISBN 979-8-4007-0109-2
Éditeur: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
DOI: 10.1145/3581784.3607067

Machine Learning for Weather and Predictions

Auteurs: Dueben
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.7081333

Machine Learning for Weather and Climate Prediction

Auteurs: Dueben, Peter
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.6792121

A Data-Centric Optimization Framework for Machine Learning

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

Machine Learning in Weather and Climate Modelling

Auteurs: Dueben
Publié dans: 2021
Éditeur: Zenodo
DOI: 10.5281/zenodo.7081199

Challenges and Limitations of Machine Learning for Atmospheric Sciences

Auteurs: Dueben
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.7081632

Productive Performance Engineering for Weather and Climate Modeling with Python

Auteurs: 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
Publié dans: SC'22, 2022
Éditeur: SC'22

Spatial Mixture-of-Experts

Auteurs: N. Dryden, T. Hoefler
Publié dans: NeurIPS'22, 2022
Éditeur: Neural Information Processing Systems 35

Productive Performance Engineering for Weather and Climate Modeling with Python

Auteurs: 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
Publié dans: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, Numéro 41, 2022, ISSN 2331-8422
Éditeur: ArXiv.org
DOI: 10.1109/sc41404.2022.00078

Machine Learning at ECMWF

Auteurs: Peter Dueben
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.7100588

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

Auteurs: Maciej Besta; Robert Gerstenberger; Marc Fischer; Michal Podstawski; Nils Blach; Berke Egeli; Georgy Mitenkov; Wojciech Chlapek; Marek Michalewicz; Hubert Niewiadomski; Juergen Mueller; Torsten Hoefler
Publié dans: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Numéro 1, 2023, ISBN 979-8-4007-0109-2
Éditeur: 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

Auteurs: Cummins, Chris; Fisches, Zacharias V.; Ben-Nun, Tal; Hoefler, Torsten; O'Boyle, Michael F P; Leather, Hugh
Publié dans: 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 ., Numéro vol. 139, PMLR, pp. 2244-2253,, 2021, ISSN 2640-3498
Éditeur: PMLR

Machine learning emulation of gravity wave drag in numerical weather forecasting

Auteurs: Matthew Chantry; Sam Hatfield; Peter Dueben; Inna Polichtchouk; Tim Palmer
Publié dans: Journal of advances in modeling earth systems, Numéro 3, 2021, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2021ms002477

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

Auteurs: Lessig, Christian; Luise, Ilaria; Gong, Bing; Langguth, Michael; Stadtler, Scarlet; Schultz, Martin
Publié dans: arXiv, Numéro 37, 2023, ISSN 2331-8422
Éditeur: USA
DOI: 10.48550/arxiv.2308.13280

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

Auteurs: Y. Ji; Y. Ji; B. Gong; M. Langguth; A. Mozaffari; X. Zhi
Publié dans: Geoscientific Model Development, Vol 16, Pp 2737-2752 (2023), Numéro 1, 2023, ISSN 1991-959X
Éditeur: Copernicus Gesellschaft mbH
DOI: 10.5194/gmd-16-2737-2023

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

Auteurs: Besta, Maciej; Catarino, Afonso Claudino; Gianinazzi, Lukas; Blach, Nils; Nyczyk, Piotr; Niewiadomski, Hubert; Hoefler, Torsten
Publié dans: arXiv, Numéro 34, 2023, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2311.18526

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

Auteurs: Patrick Laloyaux1, Thorsten Kurth2, Peter Dominik Dueben1, and David Hall
Publié dans: Journal of Advances in Modeling Earth Systems, Numéro 19422466, 2022, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2022ms003016

Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers

Auteurs: Zied Ben Bouallègue; Jonathan A. Weyn; Mariana C. A. Clare; Jesper Dramsch; Peter Dueben; Matthew Chantry
Publié dans: Artificial Intelligence for the Earth Systems, Numéro 34, 2023, ISSN 2769-7525
Éditeur: 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

Auteurs: Tom Kimpson; Margarita Choulga; Matthew Chantry; Gianpaolo Balsamo; Souhail Boussetta; Peter Dueben; Tim Palmer
Publié dans: eISSN: 1607-7938, Numéro 34, 2023, ISSN 1027-5606
Éditeur: European Geophysical Society
DOI: 10.5194/hess-27-4661-2023

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

Auteurs: Ben-Bouallegue, Zied; Cooper, Fenwick; Chantry, Matthew; Düben, Peter; Bechtold, Peter; Sandu, Irina
Publié dans: Monthly Weather Review, Numéro 1, 2023, ISSN 1520-0493
Éditeur: Monthly Weather Review
DOI: 10.1175/mwr-d-22-0107.1

Machine Learning Emulation of 3D Cloud Radiative Effects

Auteurs: David Meyer; Robin J. Hogan; Peter Dueben; Shannon Mason
Publié dans: Machine Learning Emulation of 3D Cloud Radiative Effects, Numéro 2, 2022, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2021ms002550

Neural Graph Databases

Auteurs: Besta, Maciej; Iff, Patrick; Scheidl, Florian; Osawa, Kazuki; Dryden, Nikoli; Podstawski, Michal; Chen, Tiancheng; Hoefler, Torsten
Publié dans: arXiv, Numéro 1, 2022, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2209.09732

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

Auteurs: Osawa, Kazuki; Li, Shigang; Hoefler, Torsten
Publié dans: arXiv, Numéro 30, 2022, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2211.14133

Compressing multidimensional weather and climate data into neural networks

Auteurs: Huang, Langwen; Hoefler, Torsten
Publié dans: arXiv, Numéro 4, 2023, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2210.12538

Compressing atmospheric data into its real information content.

Auteurs: Milan Klöwer, Miha Razinger, Juan J. Dominguez, Peter D. Düben & Tim N. Palmer
Publié dans: Nature Computational Science, Numéro 26628457, 2021, Page(s) Nat Comput Sci 1, 713–724 (2021), ISSN 2662-8457
Éditeur: Nature Computational Science
DOI: 10.1038/s43588-021-00156-2

Machine Learning Emulation of Urban Land Surface Processes

Auteurs: David Meyer1,2, Sue Grimmond1, Peter Dueben3, Robin Hogan1,3, and Maarten van Reeuwijk2
Publié dans: Journal of Advances in Modeling Earth Systems, Numéro 19422466, 2021, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2021ms002744

Temperature forecasting by deep learning methods

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

Cached Operator Reordering: A Unified View for Fast GNN Training

Auteurs: Bazinska, Julia; Ivanov, Andrei; Ben-Nun, Tal; Dryden, Nikoli; Besta, Maciej; Shen, Siyuan; Hoefler, Torsten
Publié dans: arXiv, Numéro 35, 2023, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2308.12093

Spatial Mixture-of-Experts

Auteurs: Dryden, Nikoli; Hoefler, Torsten
Publié dans: Advances in Neural Information Processing Systems 35, Numéro 1, 2022, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2211.13491

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

Auteurs: Frantar, Elias; Ashkboos, Saleh; Hoefler, Torsten; Alistarh, Dan
Publié dans: arXiv, Numéro 1, 2022, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2210.17323

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

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

STen: Productive and Efficient Sparsity in PyTorch

Auteurs: Ivanov, Andrei; Dryden, Nikoli; Ben-Nun, Tal; Ashkboos, Saleh; Hoefler, Torsten
Publié dans: arXiv, Numéro 40, 2023, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2304.07613

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

Auteurs: Harris, Lucy; McRae, Andrew T. T.; Chantry, Matthew; Dueben, Peter D.; Palmer, Tim N.
Publié dans: Crossref, Numéro 33, 2022, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2022ms003120

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

Auteurs: 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
Publié dans: arXiv, Numéro 32, 2023, ISSN 2331-8422
Éditeur: arXiv
DOI: 10.48550/arxiv.2310.03742

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

Auteurs: Sam Hatfield; Matthew Chantry; Peter Dueben; Philippe Lopez; Alan J. Geer; Tim Palmer
Publié dans: journal of advances in modeling earth systems, Numéro 2, 2021, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2021ms002521

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

Auteurs: Niraj Agarwal; Dmitri Kondrashov; Dmitri Kondrashov; Peter Dueben; E. A. Ryzhov; Pavel Berloff; Pavel Berloff
Publié dans: journal of advances in modeling earth systmes, Numéro 3, 2021, ISSN 1942-2466
Éditeur: American Geophysical Union
DOI: 10.1029/2021ms002537

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

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

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

Auteurs: Maike Sonnewald; Maike Sonnewald; Maike Sonnewald; Redouane Lguensat; Daniel C. Jones; Peter Dueben; Julien Brajard; Venkatramani Balaji; Venkatramani Balaji
Publié dans: Environmental Research Letters, Numéro 3, 2021, ISSN 1748-9326
Éditeur: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ac0eb0

Machine Learning at ECMWF

Auteurs: Dueben
Publié dans: 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.7081735

Machine learning for weather and climate predictions

Auteurs: Dueben, Peter
Publié dans: Numéro 1, 2021
Éditeur: Zenodo
DOI: 10.5281/zenodo.5152016

How to reduce numerical precision in weather and climate simulations

Auteurs: Dueben, Peter
Publié dans: Numéro 4, 2021
Éditeur: Zenodo
DOI: 10.5281/zenodo.5151995

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

Auteurs: Ashkboos, Saleh and Huang, Langwen and Dryden, Nikoli and Ben-Nun, Tal and Dueben, Peter and Gianinazzi, Lukas and Kummer, Luca and Hoefler, Torsten
Publié dans: 2022
Éditeur: arXiv
DOI: 10.48550/arxiv.2206.14786

Machine learning, high-performance computing and numerical weather prediction

Auteurs: Dueben, Peter D.
Publié dans: Numéro 33, 2021
Éditeur: Zenodo
DOI: 10.5281/zenodo.5533706

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

Auteurs: Dueben, Peter D.
Publié dans: Numéro 5, 2021
Éditeur: Zenodo
DOI: 10.5281/zenodo.5533639

MAELSTROM: First benchmarks at ISC22

Auteurs: Nassyr, Stepan
Publié dans: Numéro 1, 2022
Éditeur: JSC Accelerating Devices Lab
DOI: 10.34732/xdvblg-tufftf

Machine learning for weather and climate prediction

Auteurs: Dueben, Peter Dominik
Publié dans: Numéro 5, 2023
Éditeur: Zenodo
DOI: 10.5281/zenodo.8025367

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization

Auteurs: Pacchiardi, Lorenzo and Adewoyin, Rilwan and Dueben, Peter and Dutta, Ritabrata
Publié dans: 2021
Éditeur: arXiv
DOI: 10.48550/arxiv.2112.08217

The next decade of machine learning at ECMWF

Auteurs: Dueben, Peter
Publié dans: Numéro 4, 2021
Éditeur: Zenodo
DOI: 10.5281/zenodo.5152043

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

Auteurs: Dueben, Peter Dominik
Publié dans: Numéro 5, 2023
Éditeur: Zenodo
DOI: 10.5281/zenodo.8025410

Machine learning and predictability of weather and climate

Auteurs: Dueben, Peter Dominik
Publié dans: Numéro 1, 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.8025284

Machine Learning for Weather and Climate Modelling

Auteurs: Dueben, Peter Dominik
Publié dans: Numéro 32, 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.8025265

Downscaling and global high-resolution modelling

Auteurs: Dueben, Peter Dominik
Publié dans: Numéro 5, 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.8025314

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

Auteurs: Dueben, Peter Dominik
Publié dans: Numéro 30, 2022
Éditeur: Zenodo
DOI: 10.5281/zenodo.8025270

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