Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ...
Overview This dataset 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 (relati...
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Online Access: | https://dx.doi.org/10.5281/zenodo.10009497 https://zenodo.org/doi/10.5281/zenodo.10009497 |
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ftdatacite:10.5281/zenodo.10009497 2023-12-31T10:22:53+01:00 Data 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.10009497 https://zenodo.org/doi/10.5281/zenodo.10009497 unknown Zenodo https://dx.doi.org/10.5281/zenodo.10009498 https://dx.doi.org/10.5281/zenodo.10033377 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Dataset dataset 2023 ftdatacite https://doi.org/10.5281/zenodo.1000949710.5281/zenodo.1000949810.5281/zenodo.10033377 2023-12-01T10:27:35Z Overview This dataset 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 ... Dataset Sea ice DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
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DataCite Metadata Store (German National Library of Science and Technology) |
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Overview This dataset 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 ... |
format |
Dataset |
author |
Geer, Alan |
spellingShingle |
Geer, Alan Data 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 |
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_short |
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_full |
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_fullStr |
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_full_unstemmed |
Data for simultaneous inference of sea ice state and surface emissivity model using machine learning and data assimilation ... |
title_sort |
data 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.10009497 https://zenodo.org/doi/10.5281/zenodo.10009497 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.5281/zenodo.10009498 https://dx.doi.org/10.5281/zenodo.10033377 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_doi |
https://doi.org/10.5281/zenodo.1000949710.5281/zenodo.1000949810.5281/zenodo.10033377 |
_version_ |
1786834185321709568 |