Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ...
This python code supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to...
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Online Access: | https://dx.doi.org/10.5281/zenodo.10013542 https://zenodo.org/doi/10.5281/zenodo.10013542 |
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ftdatacite:10.5281/zenodo.10013542 2023-12-03T10:30:02+01:00 Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... Geer, Alan 2023 https://dx.doi.org/10.5281/zenodo.10013542 https://zenodo.org/doi/10.5281/zenodo.10013542 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10013541 Apache License 2.0 http://www.apache.org/licenses/LICENSE-2.0 apache-2.0 SoftwareSourceCode article Software 2023 ftdatacite https://doi.org/10.5281/zenodo.1001354210.5281/zenodo.10013541 2023-11-03T11:04:49Z This python code supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of ... Software Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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This python code supports the draft manuscript "Simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation" which describes a way to infer the daily maps of the sea ice concentration and empirical properties of the sea ice (relating to its snow cover and its physical properties, such as air inclusions) along with the creation of a new empirical model for the sea ice surface emissivity. This is done using knowledge of the atmosphere state, skin temperature and ocean water emissivity from the European Centre for Medium-range Weather Forecasts (ECMWF) weather forecasting model and the observed radiances at microwave frequencies from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The inverse modelling and state estimation is achieved by combining empirical machine learning elements in a Bayesian-inspired network along with a number of physical components. The work also introduces the idea of an "empirical state", in this case describing the aspects of ... |
format |
Software |
author |
Geer, Alan |
spellingShingle |
Geer, Alan Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
author_facet |
Geer, Alan |
author_sort |
Geer, Alan |
title |
Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_short |
Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_full |
Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_fullStr |
Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_full_unstemmed |
Code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_sort |
code for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
publisher |
Zenodo |
publishDate |
2023 |
url |
https://dx.doi.org/10.5281/zenodo.10013542 https://zenodo.org/doi/10.5281/zenodo.10013542 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.5281/zenodo.10013541 |
op_rights |
Apache License 2.0 http://www.apache.org/licenses/LICENSE-2.0 apache-2.0 |
op_doi |
https://doi.org/10.5281/zenodo.1001354210.5281/zenodo.10013541 |
_version_ |
1784255665444749312 |