Training and test data for retrievals based on MiRAC-P observations during MOSAiC
The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been dev...
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Online Access: | https://doi.org/10.5281/zenodo.5741748 |
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ftzenodo:oai:zenodo.org:5741748 2024-09-09T19:19:22+00:00 Training and test data for retrievals based on MiRAC-P observations during MOSAiC Orlandi, Emiliano Walbröl, Andreas Orlandi, Emiliano Walbröl, Andreas 2021-11-30 https://doi.org/10.5281/zenodo.5741748 unknown Zenodo https://zenodo.org/communities/crc172-ac3 https://doi.org/10.5281/zenodo.5741747 https://doi.org/10.5281/zenodo.5741748 oai:zenodo.org:5741748 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode MiRAC-P LHUMPRO Microwave Radiometer Remote Sensing Arctic MOSAiC Retrieval Water Vapor IWV info:eu-repo/semantics/other 2021 ftzenodo https://doi.org/10.5281/zenodo.574174810.5281/zenodo.5741747 2024-07-26T07:14:26Z The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign. More information can be found in the following publication to which this dataset belongs to: (work in progress) Other/Unknown Material Arctic Zenodo Arctic |
institution |
Open Polar |
collection |
Zenodo |
op_collection_id |
ftzenodo |
language |
unknown |
topic |
MiRAC-P LHUMPRO Microwave Radiometer Remote Sensing Arctic MOSAiC Retrieval Water Vapor IWV |
spellingShingle |
MiRAC-P LHUMPRO Microwave Radiometer Remote Sensing Arctic MOSAiC Retrieval Water Vapor IWV Orlandi, Emiliano Walbröl, Andreas Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
topic_facet |
MiRAC-P LHUMPRO Microwave Radiometer Remote Sensing Arctic MOSAiC Retrieval Water Vapor IWV |
description |
The dataset consists of one netCDF file that contains the entire training and test data for the retrieval of integrated water vapour (prw) from brightness temperatures (tb) measured by the MiRAC-P (microwave radiometer for Arctic clouds, aka. LHUMPRO-243-340). A neural network retrieval has been developed to derive the prw. The trained retrieval is applied on the MiRAC-P observations gathered onboard the research vessel Polarstern during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. For the data to be specialized on Arctic conditions they are based on ERA-Interim reanalysis. An IDL-based radiative transfer model has been used to simulate brightness temperatures. The elevation angle (ele) is always 90° because the MiRAC-P performed zenith scans only throughout the MOSAiC campaign. More information can be found in the following publication to which this dataset belongs to: (work in progress) |
author2 |
Orlandi, Emiliano Walbröl, Andreas |
format |
Other/Unknown Material |
author |
Orlandi, Emiliano Walbröl, Andreas |
author_facet |
Orlandi, Emiliano Walbröl, Andreas |
author_sort |
Orlandi, Emiliano |
title |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_short |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_full |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_fullStr |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_full_unstemmed |
Training and test data for retrievals based on MiRAC-P observations during MOSAiC |
title_sort |
training and test data for retrievals based on mirac-p observations during mosaic |
publisher |
Zenodo |
publishDate |
2021 |
url |
https://doi.org/10.5281/zenodo.5741748 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://zenodo.org/communities/crc172-ac3 https://doi.org/10.5281/zenodo.5741747 https://doi.org/10.5281/zenodo.5741748 oai:zenodo.org:5741748 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.574174810.5281/zenodo.5741747 |
_version_ |
1809759474085265408 |