Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5369-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5369-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
06 Aug 2021
Research article | Highlight paper |
| 06 Aug 2021
| 06 Aug 2021
Physical characteristics of frozen hydrometeors inferred with parameter estimation
ECMWF, Shinfield Park, Reading, RG2 9AX, UK
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Total article views: 5,527 (including HTML, PDF, and XML)
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Cited
20 citations as recorded by crossref.
- Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation V. Barlakas et al. https://doi.org/10.5194/amt-14-3427-2021
- Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals I. Kaur et al. https://doi.org/10.3390/rs14071594
- Implementation of a Discrete Dipole Approximation Scattering Database Into Community Radiative Transfer Model I. Moradi et al. https://doi.org/10.1029/2022JD036957
- Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model R. Mangla et al. https://doi.org/10.5194/amt-18-2751-2025
- Assessing the impact of passive microwave cloudy and precipitating observations onto the three‐dimensional ensemble‐variational kilometer‐scale AROME numerical weather prediction system E. Chardon–Legrand et al. https://doi.org/10.1002/qj.70103
- Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework E. Villeneuve et al. https://doi.org/10.5194/amt-17-3567-2024
- Optimizing 1D-Var near-surface wind speed from SSMIS with RapidScat S. Samuel et al. https://doi.org/10.1016/j.rse.2026.115560
- A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors M. Barreyat et al. https://doi.org/10.1002/asl.1142
- Marine Cirrus Properties from Satellite Remote Sensing over the South China Sea: Spatiotemporal Variations and a Case Study H. Weng et al. https://doi.org/10.1007/s00376-025-4305-0
- Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems S. Pfreundschuh et al. https://doi.org/10.5194/amt-15-677-2022
- A Nonspherical Cloud Scattering Database Using Aggregates of Roughened Bullet Rosettes Model for the Advanced Radiative Transfer Modeling System (ARMS) Z. Huang et al. https://doi.org/10.1007/s00376-024-4117-7
- Assessing forecast benefits of future constellations of microwave sounders on small satellites using an ensemble of data assimilations K. Lean et al. https://doi.org/10.1002/qj.4939
- Effects of Inhomogeneous Ice Particle Habit Distribution on Passive Microwave Radiative Transfer Simulations J. Kim et al. https://doi.org/10.1109/TGRS.2024.3349484
- Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts T. Thodsan et al. https://doi.org/10.3390/atmos13060956
- The future of Earth system prediction: Advances in model-data fusion A. Gettelman et al. https://doi.org/10.1126/sciadv.abn3488
- Advancements and continued challenges in observations and global modelling of atmospheric ice mass P. Eriksson et al. https://doi.org/10.5194/acp-26-2741-2026
- Assimilating FY3D‐MWRI 23.8 GHz observations in the CMA‐GFS 4DVAR system based on a pseudo All‐Sky data assimilation method H. Xie et al. https://doi.org/10.1002/qj.4544
- Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0 A. Geer et al. https://doi.org/10.5194/gmd-14-7497-2021
- Optimal Ice Particle Models of Different Cloud Types for Radiative Transfer Simulation at 183 GHz Frequency Band Z. Li et al. https://doi.org/10.3390/rs18010168
- A mixing scheme of ice particle models for global ice cloud measurements B. Wu et al. https://doi.org/10.1016/j.rse.2024.114356
20 citations as recorded by crossref.
- Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation V. Barlakas et al. https://doi.org/10.5194/amt-14-3427-2021
- Fast Radiative Transfer Approximating Ice Hydrometeor Orientation and Its Implication on IWP Retrievals I. Kaur et al. https://doi.org/10.3390/rs14071594
- Implementation of a Discrete Dipole Approximation Scattering Database Into Community Radiative Transfer Model I. Moradi et al. https://doi.org/10.1029/2022JD036957
- Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model R. Mangla et al. https://doi.org/10.5194/amt-18-2751-2025
- Assessing the impact of passive microwave cloudy and precipitating observations onto the three‐dimensional ensemble‐variational kilometer‐scale AROME numerical weather prediction system E. Chardon–Legrand et al. https://doi.org/10.1002/qj.70103
- Synergistic approach of frozen hydrometeor retrievals: considerations on radiative transfer and model uncertainties in a simulated framework E. Villeneuve et al. https://doi.org/10.5194/amt-17-3567-2024
- Optimizing 1D-Var near-surface wind speed from SSMIS with RapidScat S. Samuel et al. https://doi.org/10.1016/j.rse.2026.115560
- A 1D Bayesian inversion of microwave radiances using several radiative properties of solid hydrometeors M. Barreyat et al. https://doi.org/10.1002/asl.1142
- Marine Cirrus Properties from Satellite Remote Sensing over the South China Sea: Spatiotemporal Variations and a Case Study H. Weng et al. https://doi.org/10.1007/s00376-025-4305-0
- Synergistic radar and sub-millimeter radiometer retrievals of ice hydrometeors in mid-latitude frontal cloud systems S. Pfreundschuh et al. https://doi.org/10.5194/amt-15-677-2022
- A Nonspherical Cloud Scattering Database Using Aggregates of Roughened Bullet Rosettes Model for the Advanced Radiative Transfer Modeling System (ARMS) Z. Huang et al. https://doi.org/10.1007/s00376-024-4117-7
- Assessing forecast benefits of future constellations of microwave sounders on small satellites using an ensemble of data assimilations K. Lean et al. https://doi.org/10.1002/qj.4939
- Effects of Inhomogeneous Ice Particle Habit Distribution on Passive Microwave Radiative Transfer Simulations J. Kim et al. https://doi.org/10.1109/TGRS.2024.3349484
- Satellite Radiance Data Assimilation Using the WRF-3DVAR System for Tropical Storm Dianmu (2021) Forecasts T. Thodsan et al. https://doi.org/10.3390/atmos13060956
- The future of Earth system prediction: Advances in model-data fusion A. Gettelman et al. https://doi.org/10.1126/sciadv.abn3488
- Advancements and continued challenges in observations and global modelling of atmospheric ice mass P. Eriksson et al. https://doi.org/10.5194/acp-26-2741-2026
- Assimilating FY3D‐MWRI 23.8 GHz observations in the CMA‐GFS 4DVAR system based on a pseudo All‐Sky data assimilation method H. Xie et al. https://doi.org/10.1002/qj.4544
- Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0 A. Geer et al. https://doi.org/10.5194/gmd-14-7497-2021
- Optimal Ice Particle Models of Different Cloud Types for Radiative Transfer Simulation at 183 GHz Frequency Band Z. Li et al. https://doi.org/10.3390/rs18010168
- A mixing scheme of ice particle models for global ice cloud measurements B. Wu et al. https://doi.org/10.1016/j.rse.2024.114356
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
Satellite observations sensitive to cloud and precipitation help improve the quality of weather forecasts. However, they are sensitive to things that models do not forecast, such as the shapes and sizes of snow and ice particles. These details can be estimated from the observations themselves and then incorporated in the satellite simulators used in weather forecasting. This approach, known as parameter estimation, will be increasingly useful to build models of poorly known physical processes.
Satellite observations sensitive to cloud and precipitation help improve the quality of weather...
