E3SM/EAM Simulation Data Outputs
Simulation data provided from the Energy Exascale Earth System Model (E3SM) simulation code in association to a paper to be published, Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. E3SM is a fully coupled Earth model system that combines inde...
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ftzenodo:oai:zenodo.org:5715462 2023-05-15T18:18:15+02:00 E3SM/EAM Simulation Data Outputs Banesh, Divya Panda, Nishant Biswas, Ayan Van Roekel, Luke Oyen, Diane Urban, Nathan Grosskopf, Michael Wolfe, Jonathan Lawrence, Earl 2021-11-19 https://zenodo.org/record/5715462 https://doi.org/10.5281/zenodo.5715462 unknown doi:10.5281/zenodo.5715461 https://zenodo.org/record/5715462 https://doi.org/10.5281/zenodo.5715462 oai:zenodo.org:5715462 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/other dataset 2021 ftzenodo https://doi.org/10.5281/zenodo.571546210.5281/zenodo.5715461 2023-03-10T22:01:04Z Simulation data provided from the Energy Exascale Earth System Model (E3SM) simulation code in association to a paper to be published, Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. E3SM is a fully coupled Earth model system that combines independent components for atmosphere, ocean, land surface, sea ice and land ice. It includes a driver and flux coupler to exchange data across components, resulting in an integrated modeling system. Through the simulations are fully coupled, we focus our analyses on the atmospheric component, the E3SM Atmosphere Model (EAM) to generate the data. EAM data is discretized using a variable resolution spectral element method, allowing for a regionally refined mesh that can be tessellated at higher resolutions in particular regions of interest. EAM consists of the High-Order Methods Modeling Environment Spectral Element dynamical core and the EAM physics and chemistry parameterizations. Dataset Sea ice Zenodo |
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Open Polar |
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ftzenodo |
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unknown |
description |
Simulation data provided from the Energy Exascale Earth System Model (E3SM) simulation code in association to a paper to be published, Fast Gaussian Process Estimation for Large-Scale In Situ Inference using Convolutional Neural Networks. E3SM is a fully coupled Earth model system that combines independent components for atmosphere, ocean, land surface, sea ice and land ice. It includes a driver and flux coupler to exchange data across components, resulting in an integrated modeling system. Through the simulations are fully coupled, we focus our analyses on the atmospheric component, the E3SM Atmosphere Model (EAM) to generate the data. EAM data is discretized using a variable resolution spectral element method, allowing for a regionally refined mesh that can be tessellated at higher resolutions in particular regions of interest. EAM consists of the High-Order Methods Modeling Environment Spectral Element dynamical core and the EAM physics and chemistry parameterizations. |
format |
Dataset |
author |
Banesh, Divya Panda, Nishant Biswas, Ayan Van Roekel, Luke Oyen, Diane Urban, Nathan Grosskopf, Michael Wolfe, Jonathan Lawrence, Earl |
spellingShingle |
Banesh, Divya Panda, Nishant Biswas, Ayan Van Roekel, Luke Oyen, Diane Urban, Nathan Grosskopf, Michael Wolfe, Jonathan Lawrence, Earl E3SM/EAM Simulation Data Outputs |
author_facet |
Banesh, Divya Panda, Nishant Biswas, Ayan Van Roekel, Luke Oyen, Diane Urban, Nathan Grosskopf, Michael Wolfe, Jonathan Lawrence, Earl |
author_sort |
Banesh, Divya |
title |
E3SM/EAM Simulation Data Outputs |
title_short |
E3SM/EAM Simulation Data Outputs |
title_full |
E3SM/EAM Simulation Data Outputs |
title_fullStr |
E3SM/EAM Simulation Data Outputs |
title_full_unstemmed |
E3SM/EAM Simulation Data Outputs |
title_sort |
e3sm/eam simulation data outputs |
publishDate |
2021 |
url |
https://zenodo.org/record/5715462 https://doi.org/10.5281/zenodo.5715462 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
doi:10.5281/zenodo.5715461 https://zenodo.org/record/5715462 https://doi.org/10.5281/zenodo.5715462 oai:zenodo.org:5715462 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.571546210.5281/zenodo.5715461 |
_version_ |
1766194774347350016 |