MAchinE Learning for Scalable meTeoROlogy and cliMate

Deliverables

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

Author(s): Dueben, Peter
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.7108876

Stochastic downscaling of meteorological fields with deep neural networks

Author(s): Langguth, Michael; Gong, Bing; Ji, Yan; Mozaffari, Amirpasha; Schultz, Martin
Published in: Living Planet Symposium 2022, LPS2022, Bonn, Germany, 2022-05-23 - 2022-05-27, Issue 1, 2022
Publisher: Living Planet Symposium 2022

Clairvoyant prefetching for distributed machine learning I/O

Author(s): Nikoli Dryden; Roman Böhringer; Tal Ben-Nun; Torsten Hoefler
Published in: SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2021
Publisher: Association for Computing Machinery New York, NY, United States
DOI: 10.48550/arxiv.2101.08734

efficiently training large-scale neural networks with bidirectional pipelines

Author(s): Shigang Li; Torsten Hoefler
Published in: SC, 2022, ISBN 9781450384421
Publisher: Association for Computing Machinery New York, NY, United States
DOI: 10.1145/3458817.3476145

Near-optimal sparse allreduce for distributed deep learning

Author(s): Li, Shigang; Hoefler, Torsten
Published in: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming (PPoPP '22), 2022
Publisher: ACM
DOI: 10.1145/3503221.3508399

Neural Parameter Allocation Search

Author(s): Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
Published in: ICLR 2022, 2022
Publisher: ICLR
DOI: 10.48550/arxiv.2006.10598

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

Author(s): Dueben
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.7081282

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

Author(s): Cummins, Chris; Fisches, Zacharias V.; Ben-Nun, Tal; Hoefler, Torsten; O'Boyle, Michael F P; Leather, Hugh
Published in: 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, Issue 1, 2021
Publisher: 38th International Conference on Machine Learning

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

Author(s): 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
Published in: SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, Issue 1, 2023, ISBN 979-8-4007-0109-2
Publisher: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
DOI: 10.1145/3581784.3607067

Machine Learning for Weather and Predictions

Author(s): Dueben
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.7081333

Machine Learning for Weather and Climate Prediction

Author(s): Dueben, Peter
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.6792121

A Data-Centric Optimization Framework for Machine Learning

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

Machine Learning in Weather and Climate Modelling

Author(s): Dueben
Published in: 2021
Publisher: Zenodo
DOI: 10.5281/zenodo.7081199

Challenges and Limitations of Machine Learning for Atmospheric Sciences

Author(s): Dueben
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.7081632

Productive Performance Engineering for Weather and Climate Modeling with Python

Author(s): 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
Published in: SC'22, 2022
Publisher: SC'22

Spatial Mixture-of-Experts

Author(s): N. Dryden, T. Hoefler
Published in: NeurIPS'22, 2022
Publisher: Neural Information Processing Systems 35

Productive Performance Engineering for Weather and Climate Modeling with Python

Author(s): 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
Published in: SC22: International Conference for High Performance Computing, Networking, Storage and Analysis, Issue 41, 2022, ISSN 2331-8422
Publisher: ArXiv.org
DOI: 10.1109/sc41404.2022.00078

Machine Learning at ECMWF

Author(s): Peter Dueben
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.7100588

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

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

Author(s): Cummins, Chris; Fisches, Zacharias V.; Ben-Nun, Tal; Hoefler, Torsten; O'Boyle, Michael F P; Leather, Hugh
Published in: 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 ., Issue vol. 139, PMLR, pp. 2244-2253,, 2021, ISSN 2640-3498
Publisher: PMLR

Machine learning emulation of gravity wave drag in numerical weather forecasting

Author(s): Matthew Chantry; Sam Hatfield; Peter Dueben; Inna Polichtchouk; Tim Palmer
Published in: Journal of advances in modeling earth systems, Issue 3, 2021, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2021ms002477

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

Author(s): Lessig, Christian; Luise, Ilaria; Gong, Bing; Langguth, Michael; Stadtler, Scarlet; Schultz, Martin
Published in: arXiv, Issue 37, 2023, ISSN 2331-8422
Publisher: USA
DOI: 10.48550/arxiv.2308.13280

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

Author(s): Y. Ji; Y. Ji; B. Gong; M. Langguth; A. Mozaffari; X. Zhi
Published in: Geoscientific Model Development, Vol 16, Pp 2737-2752 (2023), Issue 1, 2023, ISSN 1991-959X
Publisher: Copernicus Gesellschaft mbH
DOI: 10.5194/gmd-16-2737-2023

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

Author(s): Besta, Maciej; Catarino, Afonso Claudino; Gianinazzi, Lukas; Blach, Nils; Nyczyk, Piotr; Niewiadomski, Hubert; Hoefler, Torsten
Published in: arXiv, Issue 34, 2023, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2311.18526

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

Author(s): Patrick Laloyaux1, Thorsten Kurth2, Peter Dominik Dueben1, and David Hall
Published in: Journal of Advances in Modeling Earth Systems, Issue 19422466, 2022, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2022ms003016

Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers

Author(s): Zied Ben Bouallègue; Jonathan A. Weyn; Mariana C. A. Clare; Jesper Dramsch; Peter Dueben; Matthew Chantry
Published in: Artificial Intelligence for the Earth Systems, Issue 34, 2023, ISSN 2769-7525
Publisher: 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

Author(s): Tom Kimpson; Margarita Choulga; Matthew Chantry; Gianpaolo Balsamo; Souhail Boussetta; Peter Dueben; Tim Palmer
Published in: eISSN: 1607-7938, Issue 34, 2023, ISSN 1027-5606
Publisher: European Geophysical Society
DOI: 10.5194/hess-27-4661-2023

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

Author(s): Ben-Bouallegue, Zied; Cooper, Fenwick; Chantry, Matthew; Düben, Peter; Bechtold, Peter; Sandu, Irina
Published in: Monthly Weather Review, Issue 1, 2023, ISSN 1520-0493
Publisher: Monthly Weather Review
DOI: 10.1175/mwr-d-22-0107.1

Machine Learning Emulation of 3D Cloud Radiative Effects

Author(s): David Meyer; Robin J. Hogan; Peter Dueben; Shannon Mason
Published in: Machine Learning Emulation of 3D Cloud Radiative Effects, Issue 2, 2022, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2021ms002550

Neural Graph Databases

Author(s): Besta, Maciej; Iff, Patrick; Scheidl, Florian; Osawa, Kazuki; Dryden, Nikoli; Podstawski, Michal; Chen, Tiancheng; Hoefler, Torsten
Published in: arXiv, Issue 1, 2022, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2209.09732

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

Author(s): Osawa, Kazuki; Li, Shigang; Hoefler, Torsten
Published in: arXiv, Issue 30, 2022, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2211.14133

Compressing multidimensional weather and climate data into neural networks

Author(s): Huang, Langwen; Hoefler, Torsten
Published in: arXiv, Issue 4, 2023, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2210.12538

Compressing atmospheric data into its real information content.

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

Machine Learning Emulation of Urban Land Surface Processes

Author(s): David Meyer1,2, Sue Grimmond1, Peter Dueben3, Robin Hogan1,3, and Maarten van Reeuwijk2
Published in: Journal of Advances in Modeling Earth Systems, Issue 19422466, 2021, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2021ms002744

Temperature forecasting by deep learning methods

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

Cached Operator Reordering: A Unified View for Fast GNN Training

Author(s): Bazinska, Julia; Ivanov, Andrei; Ben-Nun, Tal; Dryden, Nikoli; Besta, Maciej; Shen, Siyuan; Hoefler, Torsten
Published in: arXiv, Issue 35, 2023, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2308.12093

Spatial Mixture-of-Experts

Author(s): Dryden, Nikoli; Hoefler, Torsten
Published in: Advances in Neural Information Processing Systems 35, Issue 1, 2022, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2211.13491

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

Author(s): Frantar, Elias; Ashkboos, Saleh; Hoefler, Torsten; Alistarh, Dan
Published in: arXiv, Issue 1, 2022, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2210.17323

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

Author(s): Peter D. Dueben1, Martin G. Schultz2, Matthew Chantry1, David John Gagne II3, David Matthew Hall4, and Amy McGovern5
Published in: Artificial Intelligence for the Earth Systems, Issue 27697525, 2022, ISSN 2769-7525
Publisher: American Meteorological Society
DOI: 10.1175/aies-d-21-0002.1

STen: Productive and Efficient Sparsity in PyTorch

Author(s): Ivanov, Andrei; Dryden, Nikoli; Ben-Nun, Tal; Ashkboos, Saleh; Hoefler, Torsten
Published in: arXiv, Issue 40, 2023, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2304.07613

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

Author(s): Harris, Lucy; McRae, Andrew T. T.; Chantry, Matthew; Dueben, Peter D.; Palmer, Tim N.
Published in: Crossref, Issue 33, 2022, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2022ms003120

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

Author(s): 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
Published in: arXiv, Issue 32, 2023, ISSN 2331-8422
Publisher: arXiv
DOI: 10.48550/arxiv.2310.03742

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

Author(s): Sam Hatfield; Matthew Chantry; Peter Dueben; Philippe Lopez; Alan J. Geer; Tim Palmer
Published in: journal of advances in modeling earth systems, Issue 2, 2021, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2021ms002521

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

Author(s): Niraj Agarwal; Dmitri Kondrashov; Dmitri Kondrashov; Peter Dueben; E. A. Ryzhov; Pavel Berloff; Pavel Berloff
Published in: journal of advances in modeling earth systmes, Issue 3, 2021, ISSN 1942-2466
Publisher: American Geophysical Union
DOI: 10.1029/2021ms002537

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

Author(s): Cooper, Fenwick C.; McRae, Andrew T. T.; Chantry, Matthew; Antonio, Bobby; Palmer, Tim N.
Published in: Further analysis of cGAN: A system for Generative Deep Learning Post-processing of Precipitation, Issue 1, 2023, ISSN 1520-0493
Publisher: arXiv
DOI: 10.48550/arxiv.2309.15689

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

Author(s): Maike Sonnewald; Maike Sonnewald; Maike Sonnewald; Redouane Lguensat; Daniel C. Jones; Peter Dueben; Julien Brajard; Venkatramani Balaji; Venkatramani Balaji
Published in: Environmental Research Letters, Issue 3, 2021, ISSN 1748-9326
Publisher: Institute of Physics Publishing
DOI: 10.1088/1748-9326/ac0eb0

Machine Learning at ECMWF

Author(s): Dueben
Published in: 2022
Publisher: Zenodo
DOI: 10.5281/zenodo.7081735

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

Author(s): Ashkboos, Saleh and Huang, Langwen and Dryden, Nikoli and Ben-Nun, Tal and Dueben, Peter and Gianinazzi, Lukas and Kummer, Luca and Hoefler, Torsten
Published in: 2022
Publisher: arXiv
DOI: 10.48550/arxiv.2206.14786

Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization

Author(s): Pacchiardi, Lorenzo and Adewoyin, Rilwan and Dueben, Peter and Dutta, Ritabrata
Published in: 2021
Publisher: arXiv
DOI: 10.48550/arxiv.2112.08217

Searching for OpenAIRE data...

Deliverables

Publications

Share this page

Download