https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI/Head https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI http://www.nanopub.org/nschema#hasAssertion https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI/assertion https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI http://www.nanopub.org/nschema#hasProvenance https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI/provenance https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI http://www.nanopub.org/nschema#hasPublicationInfo https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI/pubinfo https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://www.nanopub.org/nschema#Nanopublication https://w3id.org/np/RApIBTEhpiRmzsog-fqMREy78C_7QWoz5unMsOYjxcOBI/assertion http://eurovoc.europa.eu/3949 http://schema.org/description http://eurovoc.europa.eu/3949 http://schema.org/name Applied sciences http://eurovoc.europa.eu/3949 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm http://eurovoc.europa.eu/3952 http://schema.org/description http://eurovoc.europa.eu/3952 http://schema.org/name Earth sciences http://eurovoc.europa.eu/3952 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm http://eurovoc.europa.eu/6384 http://schema.org/description http://eurovoc.europa.eu/6384 http://schema.org/name Climatology http://eurovoc.europa.eu/6384 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/DefinedTerm https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://purl.org/ontology/bibo/doi 10.1029/2021MS002717 https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/author mailto:jean.iaquinta@geo.uio.no https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/contentUrl https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/creator mailto:jean.iaquinta@geo.uio.no https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/dateCreated 2022-03-22 10:12:50.513184+00:00 https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/dateModified 2022-03-22 16:42:57.788902+00:00 https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/description Abstract Even to this date, most earth system models are coded in Fortran, especially those used at the largest compute scales. Our ocean model Veros takes a different approach: it is implemented using the high-level programming language Python. Besides numerous usability advantages, this allows us to leverage modern high-performance frameworks that emerged in tandem with the machine learning boom. By interfacing with the JAX library, Veros is able to run high-performance simulations on both central processing units (CPU) and graphical processing unit (GPU) through the same code base, with full support for distributed architectures. On CPU, Veros is able to match the performance of a Fortran reference, both on a single process and on hundreds of CPU cores. On GPU, we find that each device can replace dozens to hundreds of CPU cores, at a fraction of the energy consumption. We demonstrate the viability of using GPUs for earth system modeling by integrating a global 0.1° eddy-resolving setup in single precision, where we achieve 1.3 model years per day on a single compute instance with 16 GPUs, comparable to over 2,000 Fortran processes. https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/license https://choosealicense.com/no-permission/ https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/name Fast, Cheap, and Turbulent—Global Ocean Modeling With GPU Acceleration in Python https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://schema.org/sdDatePublished 2022-03-22 10:12:50.513184+00:00 https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://agupubs-onlinelibrary-wiley-com.ezproxy.uio.no/doi/10.1029/2021MS002717 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/author mailto:jean.iaquinta@geo.uio.no https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/contentUrl https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/creator mailto:jean.iaquinta@geo.uio.no https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/dateCreated 2022-03-22 10:16:52.428912+00:00 https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/dateModified 2022-03-22 16:43:13.035082+00:00 https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/description NVIDIA this week revealed plans to build the world’s most powerful AI supercomputer dedicated to predicting climate change. Named Earth-2, or E-2, the system would create a digital twin of Earth in Omniverse. The system would be the climate change counterpart to Cambridge-1, the world’s most powerful AI supercomputer for healthcare research. We unveiled Cambridge-1 earlier this year in the U.K. and it’s being used by a number of leading healthcare companies. https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/encodingFormat video/mp4 https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/license https://choosealicense.com/no-permission/ https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/name NVIDIA to Build Earth-2 Supercomputer to See Our Future https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://schema.org/sdDatePublished 2022-03-22 10:16:52.428912+00:00 https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://blogs.nvidia.com/wp-content/uploads/2021/11/e-2-earth-simulation.mp4 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/video https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/author mailto:jean.iaquinta@geo.uio.no https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/contentUrl https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/creator mailto:jean.iaquinta@geo.uio.no https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/dateCreated 2022-03-22 10:24:16.112470+00:00 https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/dateModified 2022-03-22 16:45:05.927843+00:00 https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/description Presentation outline • Refactoring the Model for Prediction Across Scales (Atm) for CPU&GPUs • EarthWorks: Toward a CPU&GPU portable Earth System Model • Handling the Big Data Problem • Machine Learning: The Silver Bullet? • Three Cs needed to pull this off https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/encodingFormat application/pdf https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/license https://choosealicense.com/no-permission/ https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/name EarthWorks: Towards an Earth System Model at Storm Resolving Resolutions https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://schema.org/sdDatePublished 2022-03-22 10:24:16.112470+00:00 https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://events.ecmwf.int/event/169/contributions/2744/attachments/1441/2603/HPC-WS_Loft.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/PresentationDigitalDocument https://gmd.copernicus.org/articles/14/2781/2021/ http://purl.org/ontology/bibo/doi 10.5194/gmd-14-2781-2021 https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/author mailto:jean.iaquinta@geo.uio.no https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/contentUrl https://gmd.copernicus.org/articles/14/2781/2021/ https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/creator mailto:jean.iaquinta@geo.uio.no https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/dateCreated 2022-03-22 10:20:21.944609+00:00 https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/dateModified 2022-03-22 16:45:22.129763+00:00 https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/description Abstract A high-resolution (1/20∘) global ocean general circulation model with graphics processing unit (GPU) code implementations is developed based on the LASG/IAP Climate System Ocean Model version 3 (LICOM3) under a heterogeneous-compute interface for portability (HIP) framework. The dynamic core and physics package of LICOM3 are both ported to the GPU, and three-dimensional parallelization (also partitioned in the vertical direction) is applied. The HIP version of LICOM3 (LICOM3-HIP) is 42 times faster than the same number of CPU cores when 384 AMD GPUs and CPU cores are used. LICOM3-HIP has excellent scalability; it can still obtain a speedup of more than 4 on 9216 GPUs compared to 384 GPUs. In this phase, we successfully performed a test of 1/20∘ LICOM3-HIP using 6550 nodes and 26 200 GPUs, and on a large scale, the model's speed was increased to approximately 2.72 simulated years per day (SYPD). By putting almost all the computation processes inside GPUs, the time cost of data transfer between CPUs and GPUs was reduced, resulting in high performance. Simultaneously, a 14-year spin-up integration following phase 2 of the Ocean Model Intercomparison Project (OMIP-2) protocol of surface forcing was performed, and preliminary results were evaluated. We found that the model results had little difference from the CPU version. Further comparison with observations and lower-resolution LICOM3 results suggests that the 1/20∘ LICOM3-HIP can reproduce the observations and produce many smaller-scale activities, such as submesoscale eddies and frontal-scale structures. https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/license https://choosealicense.com/no-permission/ https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/name The GPU version of LASG/IAP Climate System Ocean Model version 3 (LICOM3) under the heterogeneous-compute interface for portability (HIP) framework and its large-scale application https://gmd.copernicus.org/articles/14/2781/2021/ http://schema.org/sdDatePublished 2022-03-22 10:20:21.944609+00:00 https://gmd.copernicus.org/articles/14/2781/2021/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper https://gmd.copernicus.org/articles/14/2781/2021/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://gmd.copernicus.org/articles/14/2781/2021/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://purl.org/ontology/bibo/doi 10.1177/10943420211027539 https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/author mailto:jean.iaquinta@geo.uio.no https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/contentUrl https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/creator mailto:jean.iaquinta@geo.uio.no https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/dateCreated 2022-03-22 10:25:06.625346+00:00 https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/dateModified 2022-03-22 16:45:46.369940+00:00 https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/description Abstract Clouds represent a key uncertainty in future climate projection. While explicit cloud resolution remains beyond our computational grasp for global climate, we can incorporate important cloud effects through a computational middle ground called the Multi-scale Modeling Framework (MMF), also known as Super Parameterization. This algorithmic approach embeds high-resolution Cloud Resolving Models (CRMs) to represent moist convective processes within each grid column in a Global Climate Model (GCM). The MMF code requires no parallel data transfers and provides a self-contained target for acceleration. This study investigates the performance of the Energy Exascale Earth System Model-MMF (E3SM-MMF) code on the OLCF Summit supercomputer at an unprecedented scale of simulation. Hundreds of kernels in the roughly 10K lines of code in the E3SM-MMF CRM were ported to GPUs with OpenACC directives. A high-resolution benchmark using 4600 nodes on Summit demonstrates the computational capability of the GPU-enabled E3SM-MMF code in a full physics climate simulation. https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/license https://choosealicense.com/no-permission/ https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/name Unprecedented cloud resolution in a GPU-enabled full-physics atmospheric climate simulation on OLCF’s summit supercomputer https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://schema.org/sdDatePublished 2022-03-22 10:25:06.625346+00:00 https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roterms#Paper https://journals-sagepub-com.ezproxy.uio.no/doi/full/10.1177/10943420211027539 http://www.w3.org/1999/02/22-rdf-syntax-ns#type 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https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/#25ebd7f5-8cb2-46d9-9c07-ecc20f2c410f https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/contentSize 53208 https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/contentUrl https://api.rohub.org/api/ros/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/crate/download/ https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/contributor mailto:annefou@geo.uio.no https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/copyrightHolder https://ror.org/01xtthb56 https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/creator mailto:jean.iaquinta@geo.uio.no https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/dateCreated 2022-03-21 09:02:37.941263+00:00 https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/dateModified 2025-03-05 00:59:15.541821+00:00 https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/datePublished 2022-03-21 09:02:37.941263+00:00 https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://schema.org/description This Research Object is about the use of GPU (Graphics Processing Unit) accelerated computing to leverage the latest HPC (High-Performance Computer) architectures, and make it possible to execute Earth System Models (ESM) workflows (including pre-processing and post-processing as well as the climate simulations themselves) faster (for increased model throughput in terms of simulated years per computing days), at higher resolutions (for more fine-grained predictions, and in the most energy-efficient way. 👋 Everyone is welcome to contribute to this Research Object by adding relevant resources. 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Everyone is welcome to contribute to this Research Object by adding relevant resources. https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://w3id.org/ro/earth-science#Sentence GPU and its usage within the Earth System Modeling Community. https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://w3id.org/ro/earth-science#Sentence This Research Object is about the use of GPU (Graphics Processing Unit) accelerated computing to leverage the latest HPC (High-Performance Computer) architectures, and make it possible to execute Earth System Models (ESM) workflows (including pre-processing and post-processing as well as the climate simulations themselves) faster (for increased model throughput in terms of simulated years per computing days) at higher resolutions (for more fine-grained predictions, and in the most energy-efficient way. https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://www.opengis.net/ont/geosparql#hasGeometry https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/1ba1cc40-efa0-4be9-84eb-ee76845f7fde https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/ro#ResearchObject https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/roevo#LiveRO https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/Dataset https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://w3id.org/ro/earth-science#BibliographyResearchObject https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ http://www.w3.org/1999/02/22-rdf-syntax-ns#type https://w3id.org/ro/terms/earth-science#BibliographyResearchObject https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ https://w3id.org/contentdesc#Domain arithmetic https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ https://w3id.org/contentdesc#Domain computer processors https://w3id.org/ro-id/3668f58d-a6d5-4d73-8aa1-c2a89cb17807/ https://www.w3.org/ns/iana/link-relations/relation#cite-as Iaquinta, Jean, and Anne Foilloux. "GPU and its usage within the Earth System Modeling Community." ROHub. 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Michael Klemm, Chief Executive Officer, OpenMP Architecture Review Board, Prncipal Member of Technical Staff, HPC center of Excellence AMD. https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://schema.org/encodingFormat application/pdf https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://schema.org/license https://choosealicense.com/no-permission/ https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://schema.org/name Intro to GPU Programming with OpenMP API https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://schema.org/sdDatePublished 2022-03-23 08:44:27.624451+00:00 https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://purl.org/wf4ever/wf4ever#Resource https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/MediaObject https://www.openmp.org/wp-content/uploads/2021-10-20-Webinar-OpenMP-Offload-Programming-Introduction.pdf http://www.w3.org/1999/02/22-rdf-syntax-ns#type http://schema.org/PresentationDigitalDocument https://youtu.be/JnGPxZ9glVk http://schema.org/author https://orcid.org/0000-0002-1784-2920 https://youtu.be/JnGPxZ9glVk http://schema.org/contentUrl https://youtu.be/JnGPxZ9glVk https://youtu.be/JnGPxZ9glVk http://schema.org/creator https://orcid.org/0000-0002-1784-2920 https://youtu.be/JnGPxZ9glVk http://schema.org/dateCreated 2022-03-22 16:40:30.346743+00:00 https://youtu.be/JnGPxZ9glVk http://schema.org/dateModified 2022-03-22 16:45:59.025717+00:00 https://youtu.be/JnGPxZ9glVk http://schema.org/description Climate change is arguably the greatest threat facing humanity today. Accurately predicting climate change is critical to plan for its disastrous impacts well in advance and to adapt to sea level rise, ecosystem shifts, and food and water security needs. The Fourier Neural Operator (FNO) -- a novel AI model -- learns complex physical systems accurately and efficiently. Here we see the FNO emulate a high-resolution Earth dataset, ERA5, and predict the behavior of extreme weather events across the globe days in advance in just 0.25 seconds on NVIDIA GPUs. 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