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...
| Main Authors: | , |
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| Format: | Dataset |
| Language: | unknown |
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Zenodo
2021
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| Online Access: | https://dx.doi.org/10.5281/zenodo.5741748 https://zenodo.org/record/5741748 |
| Summary: | 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) |
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