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7 results for “Specific Humidity”
Information content estimation output for specific humidity profiles
<p>Output of information content estimation based on optimal estimation theory <strong>[1]</strong>. Detailed descriptions of the information content estimation performed here can be found in Section 3.3 of <strong>[2]</strong>. The files have been created with the codes published on Github/Zenodo <strong>[3]</strong>.</p> <p>The cryptic file name suffixes _472, _481, _482, _483 and _484 represent different settings of the Neural Network retrieval to estimate the information content for different inputs:</p> <ul> <li>_472: TBs at all frequencies of the microwave radiometers HATPRO and MiRAC-P (instruments are described in <strong>[2]</strong>,<strong>[4]</strong>)</li> <li>_481: TBs only at K-band frequencies (22.24-31.4 GHz)</li> <li>_482: TBs at K- and V-band frequencies (22.24 - 58 GHz)</li> <li>_483: TBs at K- and G-band frequencies (22.24-31.4 GHz, 175.81-190.81 GHz)</li> <li>_484: TBs at K-, G-band and higher frequencies (22.24-31.4 GHz, 175.81-190.81 GHz, 243 GHz, 340 GHz)</li> </ul> <p>Detailed Neural Network settings can also be found in <strong>[2]</strong> and test_purpose.yaml in <strong>[3]</strong>.</p> <p>The file eval_info_content_idx.nc contains indices to consider a subset of the years 2001, 2006, 2011, 2015 of <strong>[5]</strong>, used as evaluation data in <strong>[2]</strong>, for the information content estimation. We did not use the full ERA5 evaluation data set because of computation time.</p> <p><strong>[1]:</strong> Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and practice, no. 2 in Series on atmospheric, oceanic and planetary physics, World Scientific, Singapore, repr edn., ISBN 978-981-02-2740-1, 2008.</p> <p><strong>[2]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p> <p><strong>[4]:</strong> Walbröl, A., Crewell, S., Engelmann, R., Orlandi, E., Griesche, H., Radenz, M., Hofer, J., Althausen, D., Maturilli, M., and Ebell, K.: Atmospheric temperature, water vapour and liquid water path from two microwave radiometers during MOSAiC, Scientific Data, 9, 534, https://doi.org/10.1038/s41597-022-01504-1, 2022.</p> <p><strong>[5]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p>
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations
<p>This data set contains predictions of the Neural Network retrievals described in <strong>[1]</strong>, where simulated microwave radiometer observations (brightness temperatures, TBs) from the evaluation data subset of <strong>[2]</strong> (years 2001, 2006, 2011, 2015) were used as input to the Neural Network. As described in Section 3.2 of <strong>[1]</strong>, we trained an ensemble of 20 Neural Networks for each retrieved atmospheric quantity and applied them to the ERA5 evaluation data set to estimate the robustness of the retrievals with respect to random perturbations. The following atmospheric quantities were retrieved: </p> <ul> <li>temperature profile (variable name 'temp_p', filename suffix 'temp_test_417'),</li> <li>boundary layer temperature profile (variable name 'temp_p', filename suffix 'temp_test_424'),</li> <li>specific humidity profile (variable name 'q_p', filename suffix 'q_test_472'),</li> <li>integrated water vapour (variable name 'iwv_p', filename suffix 'iwv_test_126')</li> </ul> <p>The cryptic 3-digit filename suffixes represent different settings of the Neural Network retrieval. More information can be found in <strong>[3]</strong>. Variables that do not have the "_p" suffix are ERA5 data and used as reference to estimate errors of the retrievals by comparing them with the predictions. The dimension 'n_s' represents the ERA5 data sample number while the dimension 'n_rand' designates the ensemble of Neural Networks.</p> <p>These files can be created when running run_NN_retrieval (contained in NN_retrieval.py, see <strong>[3]</strong>) with exec_type='20_runs' and eval_mode=True and test_id either "126", "417", "424" or "472". However, as this might take some hours, we provide them here.</p> <p> </p> <p><strong>[1]:</strong> Walbröl, A., Griesche, H. J., Mech, M., Crewell, S., and Ebell, K.: Combining low- and high-frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, Atmospheric Measurement Techniques, 17, 6223-6245, https://doi.org/10.5194/amt-17-6223-2024, 2024.</p> <p><strong>[2]:</strong> Walbröl, A., and Mech, M.: ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC (1.0.0). Zenodo. https://doi.org/10.5281/zenodo.10997365, 2024.</p> <p><strong>[3]: </strong>Walbröl, A.: Codes for: Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products (1.0.1). Zenodo. <a href="https://doi.org/10.5281/zenodo.11123136" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.11123136</a>, 2024.</p>
GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity
<p>This folder contains 5 prediction files ended with *_v2.h5. These files can be loaded using the provided code "prediction_data_loader.py". All variables are in their respective physical units.</p> <p>The *.tgz file contains the training and validation codes as well as sample training and validation datasets from METOP-B satellite. Please refer to the paper for details. All variables had been normalized in the training and validation datasets so no real physical meaning attached.</p> <p>Reference:</p> <p><a href="https://publications.copernicus.org/">Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals, Atmos. Meas. Tech., 18, 4025–4043, https://doi.org/10.5194/amt-18-4025-2025, 2025.</a></p> <p> </p> <p>POC: Jie.Gong@nasa.gov</p> <p>10/17/2024</p> <p> </p> <p>Update on 08/27/2025: The final paper has been published on AMT. Please see updated reference information above.</p> <p>--- THE END ---</p>
Specific Humidity at 10-m
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TROPESS Chemical Reanalysis Specific Humidity 6-Hourly 3-dimensional Product V1 (TRPSCRQ6H3D) at GES DISC
The TROPESS Chemical Reanalysis Specific Humidity 6-Hourly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 6-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
TROPESS Chemical Reanalysis Specific Humidity Monthly 3-dimensional Product V1 (TRPSCRQM3D) at GES DISC
The TROPESS Chemical Reanalysis Specific Humidity Monthly 3-dimensional Product contains vertical specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at monthly resolution, and a spatial resolution of 1.125 x 1.125 degrees at 27 pressure levels between 1000 and 60 hPa. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
TROPESS Chemical Reanalysis Surface Specific Humidity 2-Hourly 2-dimensional Product V1 (TRPSCRQ2H2D) at GES DISC
The TROPESS Chemical Reanalysis Surface Specific Humidity 2-Hourly 2-dimensional Product contains surface specific humidity values, a meteorological field. The data are part of the Tropospheric Chemical Reanalysis v2 (TCR-2) for the period 2005-2021. TCR-2 uses JPL's Multi-mOdel Multi-cOnstituent Chemical (MOMO-Chem) data assimilation framework that simultaneously optimizes both concentrations and emissions of multiple species from multiple satellite sensors.The data files are written in the netCDF version 4 file format, and each file contains a year of data at 2-hourly resolution, and a spatial resolution of 1.125 x 1.125 degrees. The principal investigator for the TCR-2 data is Miyazaki, Kazuyuki.
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