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https://pypi.org/project/NREL-sup3r/\n\n.. |PythonV| image:: https://badge.fury.io/py/NREL-sup3r.svg\n    :target: https://badge.fury.io/py/NREL-sup3r\n\n.. |Codecov| image:: https://codecov.io/gh/nrel/sup3r/branch/main/graph/badge.svg\n    :target: https://codecov.io/gh/nrel/sup3r\n\n.. |Zenodo| image:: https://zenodo.org/badge/422324608.svg\n    :target: https://zenodo.org/badge/latestdoi/422324608\n\n\nDeveloped by NREL, Sup3r is open-source software that transforms coarse,\nlow-resolution data into actionable and accessible hyper-local data at\nunprecedented speed and scale.\n\nSup3r (pronounced “super”) is open-source software paired with publicly\navailable datasets that leverages generative adversarial networks to\nefficiently downscale global climate model data, improving resolution across\nspace and time while preserving physical accuracy.\n\nBy dramatically cutting computational time and cost, Sup3r delivers the data\nneeded for efficient, highly detailed energy system modeling and analysis.\n\nGetting Started\n===============\n\nHere are some options to get started with sup3r:\n\n#. Learn `how to install sup3r \u003chttps://nrel.github.io/sup3r/#installing-sup3r\u003e`_.\n#. Learn `how sup3r works \u003chttps://nrel.github.io/sup3r/#how-it-works\u003e`_.\n#. Learn about our `current applications of sup3r \u003chttps://nrel.github.io/sup3r/#applications-of-sup3r\u003e`_.\n#. Learn about the methods and validation of sup3r from `our publications \u003chttps://nrel.github.io/sup3r/index.html#publications\u003e`_.\n#. To access output datasets, see `our data records \u003chttps://nrel.github.io/sup3r/#data-records\u003e`_ from previous applications.\n#. To get started running sup3r software, check out our `test suite \u003chttps://github.com/NREL/sup3r/tree/main/tests\u003e`_ that uses the software on small pieces of test data.\n#. To get started loading in data for training or inference, start with the `data handler object \u003chttps://nrel.github.io/sup3r/_autosummary/sup3r.preprocessing.data_handlers.base.DataHandler.html#sup3r.preprocessing.data_handlers.base.DataHandler\u003e`_ that is our basic data structure that opens NREL .h5 and other .nc data files.\n#. To get started with model training, check out our `training tests \u003chttps://github.com/NREL/sup3r/blob/main/tests/training/test_train_gan.py\u003e`_ that initialize and train a basic GAN on small test data.\n#. To get started with model inference, check out our `forward pass tests \u003chttps://github.com/NREL/sup3r/blob/main/tests/forward_pass/test_forward_pass.py\u003e`_ that run a simple inference on small test data. Alternatively, see the examples linked below.\n#. To see previous examples of sup3r code, configs, pretrained models, and data, see our `published examples \u003chttps://github.com/NREL/sup3r/tree/main/examples\u003e`_.\n#. To setup full runs on an HPC environment, check out the sup3r command line interface `(CLI) \u003chttps://nrel.github.io/sup3r/_cli/sup3r.html#sup3r\u003e`_.\n\n\nHow it Works\n============\nSup3r uses a generative machine learning approach to produce synthetic\nhigh-resolution spatiotemporal energy resource data from coarse, low-resolution\ninputs. The process is described step-by-step below.\n\n.. top-graphic-start\n\n.. raw:: html\n\n    \u003cp align=\"center\"\u003e\n        \u003cimg width=\"750\" src=\"docs/source/_static/Sup3rCC_Top_Graphic_v2.jpg\" /\u003e\n        \u003cbr\u003e\n    \u003c/p\u003e\n\n.. top-graphic-end\n\nStep 1: Learns High-Resolution Physics From Historical Data (Training Phase)\n----------------------------------------------------------------------------\n\nDuring what’s called the “training phase,” Sup3r learns how large-scale weather\npatterns relate to local, detailed conditions using high-resolution historical\nweather data. Its advanced model design allows it to learn from both simulated\ndata and real-world weather measurements. This helps build neural networks that\ncapture both scientific understanding and real data, making them useful for a\nvariety of applications.\n\nAt the heart of this process is a generative adversarial network (GAN), which\nis like a game between two competing players:\n\n- The Generator: Learns to create realistic high-resolution data from coarse\n  climate inputs\n- The Discriminator: Learns to tell the difference between real data (e.g.,\n  observed high-resolution weather) and the Generator’s output.\n\nUltimately, the model’s success is determined by the Generator’s ability to\n“fool” the Discriminator by producing data that is indistinguishable from\nreal-world data. By optimizing for both adherence to the climate inputs and\nphysical realism, Sup3r not only minimizes quantitative bias but also ensures\nthe physics of the high-resolution data are realistic. This is especially\nimportant for downstream applications such as power system operational\nmodeling, where fine-scale spatial structure and high-frequency temporal\ndynamics matter as much as statistical accuracy.\n\n.. training-flow-start\n\n.. raw:: html\n\n    \u003cp align=\"center\"\u003e\n        \u003cimg width=\"600\" src=\"docs/source/_static/Sup3r_training_flow_chart.jpg\" /\u003e\n        \u003cbr\u003e\n    \u003c/p\u003e\n\n.. training-flow-end\n\nStep 2: Collects Coarse Climate Data and Scenarios\n--------------------------------------------------\n\nThe Sup3r software can use input data from global climate models that have very\nlow resolutions (up to 100 kilometers wide per grid cell with 1 average data\npoint per day). This publicly available input data is useful for understanding\ngeneral trends but is too coarse to help with local decision making, like where\nto place a wind farm or how a certain neighborhood might suffer from extreme\nheat. One of Sup3r’s key strengths lies in its flexibility, allowing users to\nchoose from countless publicly available climate datasets. This feature\nempowers users to easily explore various potential futures and make informed\ndecisions based on a range of possibilities.\n\nStep 3: Generates Realistic, High-Resolution Climate Data (Inference Phase)\n---------------------------------------------------------------------------\nDuring what’s called the “inference phase,” Sup3r uses generative models that\nwere trained in Step 1 and the low-resolution climate data gathered in Step #2\nto produce hyper-local data at a 0.5- to 4-kilometer resolution with a 5- to\n60-minute frequency (depending on the application) for all requested\nmeteorological variables. Sup3r has been proven to generate output that\nreproduces the large-scale dynamics in the data from Step 2 while capturing\nrealistic physics at the finest scales.\n\n.. inference-flow-start\n\n.. raw:: html\n\n    \u003cp align=\"center\"\u003e\n        \u003cimg width=\"600\" src=\"docs/source/_static/Sup3r_inference_flow_chart.jpg\" /\u003e\n        \u003cbr\u003e\n    \u003c/p\u003e\n\n.. inference-flow-end\n\nApplications of Sup3r\n=====================\n\nSup3rCC\n-------\nSup3rCC is an application of the Sup3r software that downscales global climate\nmodel outputs to 4-km spatial and hourly temporal resolution. It provides\nhigh-resolution data on temperature, humidity, wind, and solar irradiance,\nsupporting analysis of energy system resilience under future climate\nconditions. Notably, Sup3rCC does not represent real historical weather events,\nunlike Sup3rWind or Sup3rUHI (described below).\n\nTo learn more about Sup3rCC, check out the publication list below or the\n`Sup3rCC example \u003chttps://github.com/NREL/sup3r/tree/main/examples/sup3rcc\u003e`_.\n\nSup3rWind\n---------\nSup3rWind uses the Sup3r software to produce high-resolution historical wind\nresource data by downscaling global reanalysis datasets—which combine\nhistorical weather observations with modern forecasting models—to 2-km spatial\nand 5-minute temporal resolution. It improves the representation of extreme\nwind events and preserves important spatiotemporal patterns for use in energy\nsystem planning and operations. Sup3rWind data is used by wind energy\ndevelopers worldwide.\n\nTo learn more about Sup3rWind, check out the publication list below or the\n`Sup3rWind example\n\u003chttps://github.com/NREL/sup3r/tree/main/examples/sup3rwind\u003e`_.\n\nSup3rUHI\n--------\nSup3rUHI applies the Sup3r software to urban environments, combining satellite\nobservations and ground measurements to generate hyper-local temperature and\nhumidity time series. It supports both historical analysis and future scenario\nmodeling, enabling precise, data-driven planning for high-risk heat events.\n\nTo learn more about Sup3rUHI, check out the publication list below or the\n`Sup3rUHI repo \u003chttps://github.com/NREL/sup3ruhi\u003e`_.\n\n\nInstalling sup3r\n================\n\nNOTE: The installation instructions below assume that you have python installed\non your machine and are using either `conda \u003chttps://docs.conda.io/en/latest/index.html\u003e`__\nor `pixi \u003chttps://pixi.sh/latest/\u003e`_ as your package/environment manager.\n\nOption 1: Install from PIP (recommended for analysts):\n------------------------------------------------------\n\n1. Create a new environment: ``conda create --name sup3r python=3.11``\n\n2. Activate environment: ``conda activate sup3r``\n\n3. Install sup3r: ``pip install NREL-sup3r``\n\n4. Run this if you want to train models on GPUs: ``pip install tensorflow[and-cuda]``\n\n   4.1 For OSX use instead: ``python -m pip install tensorflow-metal``\n\nOption 2: Clone repo (recommended for developers)\n-------------------------------------------------\n\n1. Run ``git clone git@github.com:NREL/sup3r.git``\n2. ``cd sup3r``.\n3. Make sure the branch is correct (install from main!)\n4. If you are using conda, create and activate a new environment:\n   ``conda create --name sup3r python=3.11`` and ``conda activate sup3r``\n\n   4.1 Install ``sup3r`` and its dependencies by running: ``pip install .`` (or ``pip install -e .`` for editable install)\n\n   4.2 Run this if you want to train models on GPUs: ``pip install tensorflow[and-cuda]``\n\n5. Alternatively, run ``pixi install``\n6. *Optional*: Set up the pre-commit hooks with ``pip install pre-commit`` or ``pixi add pre-commit`` and ``pre-commit install``\n\nRecommended Citation\n====================\n\nUpdate with current version and DOI:\n\nBrandon Benton, Grant Buster, Guilherme Pimenta Castelao, Malik Hassanaly,\nPavlo Pinchuk, Slater Podgorny, Andrew Glaws, and Ryan King. Super Resolution\nfor Renewable Resource Data (sup3r). https://github.com/NREL/sup3r (version\nv0.2.3), 2025. https://doi.org/10.5281/zenodo.15586596\n\nPublications\n============\n\nEstimating the impacts of increasing temperatures and the efficacy of climate\nadaptation strategies in urban microclimates with deep learning, *Urban Climate*\n(2025) https://doi.org/10.1016/j.uclim.2025.102603\n\nSecond-Generation Downscaled Earth System Model Data using Generative Machine\nLearning, *Data in Brief* (2025) https://doi.org/10.1016/j.dib.2025.111774\n\nSuper-Resolution for Renewable Energy Resource Data with Wind from Reanalysis\nData and Application to Ukraine, *Energies* (2025) https://doi.org/10.3390/en18143769\n\nHigh-Resolution Meteorology With Climate Change Impacts From Global Climate\nModel Data Using Generative Machine Learning, *Nature Energy* (2024)\nhttps://doi.org/10.1038/s41560-024-01507-9\n\nAdversarial Super-Resolution of Climatological Wind and Solar Data,\n*Proceedings of the National Academy of Sciences* (2020)\nhttps://doi.org/10.1073/pnas.1918964117\n\nData Records\n============\nSuper-Resolution for Renewable Energy Resource Data with Climate Change Impacts\n(Sup3rCC). [Data set]. Open Energy Data Initiative (OEDI). National Renewable\nEnergy Laboratory (NREL). https://doi.org/10.25984/1970814\n\nSuper-Resolution for Renewable Energy Resource Data with Wind from Reanalysis\n(Sup3rWind). [Data set]. Open Energy Data Initiative (OEDI). National Renewable\nEnergy Laboratory. https://data.openei.org/submissions/8455\n\nSuper-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI).\n[Data set]. Open Energy Data Initiative (OEDI). National Renewable Energy Lab\n(NREL). https://data.openei.org/submissions/6220\n\nAcknowledgments\n===============\n\nThis work was authored by the National Renewable Energy Laboratory, operated\nfor the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308.\nThis research was supported by the Grid Modernization Initiative of the U.S.\nDepartment of Energy (DOE) as part of its Grid Modernization Laboratory\nConsortium, a strategic partnership between DOE and the national laboratories\nto bring together leading experts, technologies, and resources to collaborate\non the goal of modernizing the nation’s grid. Funding provided by the the DOE\nOffice of Energy Efficiency and Renewable Energy (EERE), the DOE Office of\nElectricity (OE), DOE Grid Deployment Office (GDO), the DOE Office of Fossil\nEnergy and Carbon Management (FECM), and the DOE Office of Cybersecurity,\nEnergy Security, and Emergency Response (CESER), the DOE Advanced Scientific\nComputing Research (ASCR) program, the DOE Solar Energy Technologies Office\n(SETO), the DOE Wind Energy Technologies Office (WETO), the United States\nAgency for International Development (USAID), and the Laboratory Directed\nResearch and Development (LDRD) program at the National Renewable Energy\nLaboratory. The research was performed using computational resources sponsored\nby the Department of Energy's Office of Energy Efficiency and Renewable Energy\nand located at the National Renewable Energy Laboratory. The views expressed in\nthe article do not necessarily represent the views of the DOE or the U.S.\nGovernment. The U.S. Government retains and the publisher, by accepting the\narticle for publication, acknowledges that the U.S. Government retains a\nnonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce\nthe published form of this work, or allow others to do so, for U.S. Government\npurposes.\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.5281/zenodo.15586596","https://doi.org/10.1016/j.uclim.2025.102603","https://doi.org/10.1016/j.dib.2025.111774","https://doi.org/10.3390/en18143769","https://doi.org/10.1038/s41560-024-01507-9","https://doi.org/10.1073/pnas.1918964117","https://doi.org/10.25984/1970814"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/20039","html_url":"https://ost.ecosyste.ms/projects/20039"}