Articles | Volume 13, issue 8
https://doi.org/10.5194/amt-13-4219-2020
https://doi.org/10.5194/amt-13-4219-2020
Research article
 | 
12 Aug 2020
Research article |  | 12 Aug 2020

Synergistic radar and radiometer retrievals of ice hydrometeors

Simon Pfreundschuh, Patrick Eriksson, Stefan A. Buehler, Manfred Brath, David Duncan, Richard Larsson, and Robin Ekelund

Related authors

GPROF V7 and beyond: assessment of current and potential future versions of the GPROF passive microwave precipitation retrievals against ground radar measurements over the continental US and the Pacific Ocean
Simon Pfreundschuh, Clément Guilloteau, Paula J. Brown, Christian D. Kummerow, and Patrick Eriksson
Atmos. Meas. Tech., 17, 515–538, https://doi.org/10.5194/amt-17-515-2024,https://doi.org/10.5194/amt-17-515-2024, 2024
Short summary
The Chalmers Cloud Ice Climatology: Retrieval implementation and validation
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1953,https://doi.org/10.5194/egusphere-2023-1953, 2023
Short summary
An improved near-real-time precipitation retrieval for Brazil
Simon Pfreundschuh, Ingrid Ingemarsson, Patrick Eriksson, Daniel A. Vila, and Alan J. P. Calheiros
Atmos. Meas. Tech., 15, 6907–6933, https://doi.org/10.5194/amt-15-6907-2022,https://doi.org/10.5194/amt-15-6907-2022, 2022
Short summary
Ice water path retrievals from Meteosat-9 using quantile regression neural networks
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022,https://doi.org/10.5194/amt-15-5701-2022, 2022
Short summary
GPROF-NN: a neural-network-based implementation of the Goddard Profiling Algorithm
Simon Pfreundschuh, Paula J. Brown, Christian D. Kummerow, Patrick Eriksson, and Teodor Norrestad​​​​​​​
Atmos. Meas. Tech., 15, 5033–5060, https://doi.org/10.5194/amt-15-5033-2022,https://doi.org/10.5194/amt-15-5033-2022, 2022
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024,https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024,https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024,https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024,https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024,https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary

Cited articles

Aires, F., Prigent, C., Buehler, S. A., Eriksson, P., Milz, M., and Crewell, S.: Towards more realistic hypotheses for the information content analysis of cloudy/precipitating situations – Application to a hyperspectral instrument in the microwave, Q. J. Roy. Meteor. Soc., 145, 1–14, https://doi.org/10.1002/qj.3315, 2019. a
Birman, C., Mahfouf, J.-F., Milz, M., Mendrok, J., Buehler, S. A., and Brath, M.: Information content on hydrometeors from millimeter and sub-millimeter wavelengths, Tellus, 69, 1271562, https://doi.org/10.1080/16000870.2016.1271562, 2017. a
Bony, S., Stevens, B., Frierson, D. M., Jakob, C., Kageyama, M., Pincus, R., Shepherd, T. G., Sherwood, S. C., Siebesma, A. P., Sobel, A. H., Watanabe, M., and Webb, M. J.: Clouds, circulation and climate sensitivity, Nat. Geosci., 8, 261, https://doi.org/10.1038/ngeo2398, 2015. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and Aerosols, book section 7, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 571–658, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Brath, M., Fox, S., Eriksson, P., Harlow, R. C., Burgdorf, M., and Buehler, S. A.: Retrieval of an ice water path over the ocean from ISMAR and MARSS millimeter and submillimeter brightness temperatures, Atmos. Meas. Tech., 11, 611–632, https://doi.org/10.5194/amt-11-611-2018, 2018. a
Download
Short summary
The next generation of European operational weather satellites will carry a novel microwave sensor, the Ice Cloud Imager (ICI), which will provide observations of clouds at microwave frequencies that were not available before. We investigate the potential benefits of combining observations from ICI with that of a radar. We find that such combined observations provide additional information on the properties of the cloud and help to reduce uncertainties in retrieved mass and number densities.