Empowering weather and climate forecast
| # | Year | Publication | Download |
|---|---|---|---|
| 1 | 2021 | Chantry, M. et al.: "Machine Learning Emulation of Gravity Wave Drag in Numerical Weather Forecasting". Journal of Advances in Modeling Earth Systems, 13, e2021MS002477. https://doi.org/10.1029/2021MS002477 | Download PDF |
| 2 | 2021 | Sonnewald, et al.: "Bridging observations, theory and numerical simulation of the ocean using machine learning". Environ. Res. Lett. 16 073008 | Download PDF |
| 3 | 2021 | Hatfield, et al.: "Building Tangent-Linear and Adjoint Models for Data Assimilation With Neural Networks". Journal of Advances in Modeling Earth Systems, 13, e2021MS002521 | Download PDF |
| 4 | 2021 | Agarwal et al.: "A Comparison of Data-Driven Approaches to Build Low-Dimensional Ocean Models". Journal of Advances in Modeling Earth Systems, 13, e2021MS002537 | Download PDF |
| 5 | 2021 | Kloewer et al.: "Compressing atmospheric data into its real information content". Nat Comput Sci 1, 713–724 (2021). https://doi.org/10.1038/s43588-021-00156-2 | Download PDF |
| 6 | 2022 | Rausch, Ben-Nun et al.: "A Data-centric Optimization Framework for Machine Learning". Proceedings of the 36th ACM International Conference on Supercomputing | Download PDF Download PDF |
| 7 | 2022 | Gong B, Langguth M, Ji Y, Mozaffari A, Stadtler S, Mache K, Schultz MG.: "Temperature forecasting by deep learning methods". EGU Geoscientific Model Development | Download PDF |
| 8 | 2022 | Dueben et al.: "Benchmark Datasets for Machine Learning in the Atmospheric Sciences: Definition, Status, and Outlook". Artificial Intelligence for the Earth Systems, 1(3), e210002. Retrieved Sep 1, 2022 | Download PDF |
| 9 | 2022 | Ashkboos et al.: "ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast". Advances in Neural Information Processing Systems | Download PDF |
| 10 | 2022 | David Meyer et al.: "Machine learning emulation of urban land surface processes". Journal of Advances in Modeling Earth Systems, 14, e2021MS002744 | Download PDF |
| 11 | 2022 | David Meyer et al.: "Machine Learning Emulation of 3D Cloud Radiative Effects". Journal of Advances in Modeling Earth Systems, 14, e2021MS002550 | Download PDF |
| 12 | 2022 | Patrick Laloyaux et al.: "Deep learning to estimate model biases in an operational NWP assimilation system". Journal of Advances in Modeling Earth Systems, 14, e2022MS003016 | Download PDF |
| 13 | 2022 | Lorenzo Pacchiardi et al.: "Probabilistic Forecasting with Generative Networks via Scoring Rule Minimization". Journal of Machine Learning Research | Download PDF |
| 14 | 2021 | Nikoli Dryden et al.: "Clairvoyant Prefetching for Distributed Machine Learning I/O". Supercomputing | Download PDF |
| 15 | 2021 | Shigang Li and Torsten Hoefler: "Chimera: Efficiently Training Large-Scale Neural Networks with Bidirectional Pipelines". Supercomputing | Download PDF |
| 16 | 2022 | Shigang Li and Torsten Hoefler: "Near-Optimal Sparse Allreduce for Distributed Deep Learning". PPoPP | Download PDF |
| 17 | 2021 | Chris Cummins et al.: "ProGraML: A Graph-based Program Representation for Data Flow Analysis and Compiler Optimizations". International Conference on Learning Representations (ICLR) | Download PDF |
| 18 | 2022 | Bryan Plummer et al.: "Neural Parameter Allocation Search". International Conference on Learning Representations (ICLR) | Download PDF |
| 19 | 2022 | Tal Ben-Nun et al. : "Productive Performance Engineering for Weather and Climate Modeling with Python". Supercomputing | Download PDF |
| 20 | 2022 | Saleh Ashkboos et al.: "A Dataset For Post-Processing Ensemble Weather Forecast". Proceedings of the Neural Information Processing (NeurIPS) Systems Track on Datasets and Benchmarks | Download PDF |
| 21 | 2023 | Karthick Panner Selvam, Mats Brorsson: "Performance Analysis and Benchmarking of a Temperature Downscaling Deep Learning Model". 31st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing | Download PDF |
| 22 | 2023 | Ji, Y., Gong, B., Langguth, M., Mozaffari, A., and Zhi, X.: "CLGAN: a generative adversarial network (GAN)-based video prediction model for precipitation nowcasting". Geoscientific Model Development | Download PDF |
| 23 | 2022 | Langwen Huang, Torsten Hoefler: "Compressing multidimensional weather and climate data into neural networks". 10.48550/arXiv.2210.12538 | Download PDF |
| 24 | 2023 | Maciej Besta, Robert Gerstenberger, Marc Fischer, Michal Podstawski, Nils Blach, Berke Egeli, Georgy Mitenkov, Wojciech Chlapek, Marek Michalewicz, Hubert Niewiadomski, Juergen Mueller, Torsten Hoefler: "The Graph Database Interface: Scaling Online Transactional and Analytical Graph Workloads to Hundreds of Thousands of Cores". SC '23: International Conference for High Performance Computing, Networking, Storage and Analysis | Download PDF |
| 25 | 2023 | 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: "High-Performance and Programmable Attentional Graph Neural Networks with Global Tensor Formulations". SC '23: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis | Download PDF |
| 26 | 2022 | András Strausz; Flavio Vella; Salvatore Di Girolamo; Maciej Besta; Torsten Hoefler: "Asynchronous Distributed-Memory Triangle Counting and LCC with RMA Caching". 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS) | |
| 27 | 2023 | Maciej Besta, Afonso Claudino Catarino, Lukas Gianinazzi, Nils Blach, Piotr Nyczyk, Hubert Niewiadomski, Torsten Hoefler: "HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers". Proceedings of Learning on Graphs (LOG) | Download PDF |
| 28 | 2023 | Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, Torsten Hoefler: "Cached Operator Reordering: A Unified View for Fast GNN Training". 10.48550/arXiv.2308.12093 | Download PDF |
| 29 | 2024 | Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Onur Mutlu, Torsten Hoefler: "Topologies of Reasoning: Demystifying Chains, Trees, and Graphs of Thoughts". 10.48550/arXiv.2401.14295 | Download PDF |
| 30 | Langwen Huang, Lukas Gianinazzi, Yuejiang Yu, Peter D. Dueben, Torsten Hoefler: "DiffDA: a diffusion model for weather-scale data assimilation". arXiv:2401.05932 | Download PDF |
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| 31 | 2022 | András Strausz, Flavio Vella, Salvatore Di Girolamo, Maciej Besta, Torsten Hoefler: "Asynchronous Distributed-Memory Triangle Counting and LCC with RMA Caching". 2022 IEEE International Parallel and Distributed Processing Symposium (IPDPS) | |
| 32 | 2022 | Nikoli Dryden, Torsten Hoefler: "Spatial Mixture-of-Experts". arXiv:2211.13491 | Download PDF |
| 33 | 2023 | Christian Lessig, Ilaria Luise, Bing Gong, Michael Langguth, Scarlet Stadtler, Martin Schultz: "AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning". arXiv:2308.13280 | Download PDF |
| 34 | 2024 | Zied Ben Bouallegue et al.: "The rise of data-driven weather forecasting: A first statistical assessment of machine learning-based weather forecasts in an operational-like context". Bulletin of the American Meteorological Society | Download PDF |
| 35 | 2024 | Zied Ben Bouallegue et al.: "Improving Medium-Range Ensemble Weather Forecasts with Hierarchical Ensemble Transformers". Artificial Intelligence for the Earth Systems | Download PDF |
| 36 | 2023 | Tom Kimpson et al.: "Deep learning for quality control of surface physiographic fields using satellite Earth observations". Hydrology and Earth System Sciences | Download PDF |
| 37 | 2023 | Kochkov et al.: "Neural General Circulation Models for Weather and Climate". https://arxiv.org/abs/2311.07222 | Download PDF |
| 38 | 2024 | Rasp et al.: "WeatherBench 2: A benchmark for the next generation of data-driven global weather models". https://arxiv.org/abs/2308.15560 | Download PDF |
| 39 | 2023 | Bauer et al.: "Deep learning and a changing economy in weather and climate prediction". Nature Reviews Earth & Environment | Download PDF |
| 40 | 2023 | Zied Ben Bouallegue et al.: "Statistical Modeling of 2-m Temperature and 10-m Wind Speed Forecast Errors". Monthly Weather Review | Download PDF |
| 41 | 2022 | Lucy Harris et al.: "A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts". Journal of Advances in Modelling Earth Systems | Download PDF |