Convolutional neural network training dataset and results of AWI-ICENet1 retracker
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| Natura: | Dataset Open Access |
| Lingua: | en |
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PANGAEA
2024
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| _version_ | 1867170122223845376 |
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| author | Helm, Veit |
| author_facet | Helm, Veit |
| collection | Datos científicos de ciencias marinas y ambientales |
| contents | This data set include the simulated and corresponding reference data in binary format used for the training of the AWI-ICENet1 retracker algorithm, which is a convolutional neural network (CNN). The simulation is carried out at 1000 randomly selected locations spread over the Antarctic ice sheet. At each location a reference waveform is simulated based on the local topography. This waveform is modulated using 95 different attenuation rates ranging from 1 to 20 dB (step width 0.2 dB). Finally 45 noisy waveforms are generated from each modulated waveform. Therefore, at each location 95*40=3800 waveforms are generated. The simulated data consist of a total of 3.8 Mio waveforms. The AWI-ICENet1 retracker is applied to the full CryoSat-2 time series. Monthly elevation change is estimated for Greenland and Antarctica and compared to estimates derived from ICESat-2. Here, we provide raster data sets of the elevation change, rates of elevation change and additional parameters such as correlation with backscatter and leading edge width as netcdf files. The CryoSat-2 elevation change products of AWI-ICENet1 are estimated for the time periods: 2011-2022, 2019-2022 for Greenland and Antarctica and 2011-2014 for Greenland only and are provided as monthly gridded georeferenced netcdf files. As input to the AWI processing we used the reprocessed Baseline_E Level_1B waveform product provided by ESA. Additional we provide the ICESat-2 elevation change products for 2019-2022 for Greenland and Antarctica as monthly gridded georeferenced netcdf files which are based on the same processing strategy. As input to our processing we used the ATL06.006 ICESat-2 data product provided by NASA. |
| format | Dataset Open Access |
| id | pangaea_https___doi_org_10_1594_PANGAEA_964596 |
| institution | PANGAEA |
| language | en |
| publishDate | 2024 |
| publisher | PANGAEA |
| record_format | pangaea |
| spellingShingle | Convolutional neural network training dataset and results of AWI-ICENet1 retracker Helm, Veit AI-CORE; altimetry; Antarctica; Artificial Intelligence for Cold Regions; Binary Object; Binary Object (File Size); CNN_AWI-ICENet1_CY_LRM_ANT; CNN_AWI-ICENet1_CY_LRM_GRE; CNN_AWI-ICENet1_training; DATE/TIME; elevation change; File content; Greenland; Location; netCDF file; netCDF file (File Size); Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; simulated Cryosat-2 waveforms; SPP1158 This data set include the simulated and corresponding reference data in binary format used for the training of the AWI-ICENet1 retracker algorithm, which is a convolutional neural network (CNN). The simulation is carried out at 1000 randomly selected locations spread over the Antarctic ice sheet. At each location a reference waveform is simulated based on the local topography. This waveform is modulated using 95 different attenuation rates ranging from 1 to 20 dB (step width 0.2 dB). Finally 45 noisy waveforms are generated from each modulated waveform. Therefore, at each location 95*40=3800 waveforms are generated. The simulated data consist of a total of 3.8 Mio waveforms. The AWI-ICENet1 retracker is applied to the full CryoSat-2 time series. Monthly elevation change is estimated for Greenland and Antarctica and compared to estimates derived from ICESat-2. Here, we provide raster data sets of the elevation change, rates of elevation change and additional parameters such as correlation with backscatter and leading edge width as netcdf files. The CryoSat-2 elevation change products of AWI-ICENet1 are estimated for the time periods: 2011-2022, 2019-2022 for Greenland and Antarctica and 2011-2014 for Greenland only and are provided as monthly gridded georeferenced netcdf files. As input to the AWI processing we used the reprocessed Baseline_E Level_1B waveform product provided by ESA. Additional we provide the ICESat-2 elevation change products for 2019-2022 for Greenland and Antarctica as monthly gridded georeferenced netcdf files which are based on the same processing strategy. As input to our processing we used the ATL06.006 ICESat-2 data product provided by NASA. |
| title | Convolutional neural network training dataset and results of AWI-ICENet1 retracker |
| topic | AI-CORE; altimetry; Antarctica; Artificial Intelligence for Cold Regions; Binary Object; Binary Object (File Size); CNN_AWI-ICENet1_CY_LRM_ANT; CNN_AWI-ICENet1_CY_LRM_GRE; CNN_AWI-ICENet1_training; DATE/TIME; elevation change; File content; Greenland; Location; netCDF file; netCDF file (File Size); Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas; simulated Cryosat-2 waveforms; SPP1158 |
| url | https://doi.org/10.1594/PANGAEA.964596 |