Articles | Volume 14, issue 8
https://doi.org/10.5194/amt-14-5369-2021
https://doi.org/10.5194/amt-14-5369-2021
Research article
 | Highlight paper
 | 
06 Aug 2021
Research article | Highlight paper |  | 06 Aug 2021

Physical characteristics of frozen hydrometeors inferred with parameter estimation

Alan J. Geer

Related authors

Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model
Rohit Mangla, Mary Borderies, Philippe Chambon, Alan Geer, and James Hocking
Atmos. Meas. Tech., 18, 2751–2779, https://doi.org/10.5194/amt-18-2751-2025,https://doi.org/10.5194/amt-18-2751-2025, 2025
Short summary
Bulk hydrometeor optical properties for microwave and sub-millimetre radiative transfer in RTTOV-SCATT v13.0
Alan J. Geer, Peter Bauer, Katrin Lonitz, Vasileios Barlakas, Patrick Eriksson, Jana Mendrok, Amy Doherty, James Hocking, and Philippe Chambon
Geosci. Model Dev., 14, 7497–7526, https://doi.org/10.5194/gmd-14-7497-2021,https://doi.org/10.5194/gmd-14-7497-2021, 2021
Short summary
Introducing hydrometeor orientation into all-sky microwave and submillimeter assimilation
Vasileios Barlakas, Alan J. Geer, and Patrick Eriksson
Atmos. Meas. Tech., 14, 3427–3447, https://doi.org/10.5194/amt-14-3427-2021,https://doi.org/10.5194/amt-14-3427-2021, 2021
Short summary
All-sky assimilation of infrared radiances sensitive to mid- and upper-tropospheric moisture and cloud
Alan J. Geer, Stefano Migliorini, and Marco Matricardi
Atmos. Meas. Tech., 12, 4903–4929, https://doi.org/10.5194/amt-12-4903-2019,https://doi.org/10.5194/amt-12-4903-2019, 2019
Short summary
Correlated observation error models for assimilating all-sky infrared radiances
Alan J. Geer
Atmos. Meas. Tech., 12, 3629–3657, https://doi.org/10.5194/amt-12-3629-2019,https://doi.org/10.5194/amt-12-3629-2019, 2019
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025,https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025,https://doi.org/10.5194/amt-18-2083-2025, 2025
Short summary
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025,https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025,https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025,https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary

Cited articles

Aires, F., Prigent, C., Bernardo, F., Jiménez, C., Saunders, R., and Brunel, P.: A Tool to Estimate Land-Surface Emissivities at Microwave frequencies (TELSEM) for use in numerical weather prediction, Q. J. Roy. Meteorol. Soc., 137, 690–699, https://doi.org/10.1002/qj.803, 2011. a
Allen, J. T., Tippett, M. K., Kaheil, Y., Sobel, A. H., Lepore, C., Nong, S., and Muehlbauer, A.: An extreme value model for US hail size, Mon. Weath. Rev., 145, 4501–4519, https://doi.org/10.1175/MWR-D-17-0119.1, 2017. a
Auligné, T., McNally, A. P., and Dee, D. P.: Adaptive bias correction for satellite data in a numerical weather prediction system, Q. J. Roy. Meteorol. Soc., 133, 631–642, https://doi.org/10.1002/qj.56, 2007. a
Bailey, M. P. and Hallett, J.: A comprehensive habit diagram for atmospheric ice crystals: Confirmation from the laboratory, AIRS II, and other field studies, J. Atmos. Sci., 66, 2888–2899, https://doi.org/10.1175/2009JAS2883.1, 2009. a
Baordo, F. and Geer, A. J.: Assimilation of SSMIS humidity-sounding channels in all-sky conditions over land using a dynamic emissivity retrieval, Q. J. Roy. Meteorol. Soc., 142, 2854–2866, https://doi.org/10.1002/qj.2873, 2016. a
Download
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.
Share