Articles | Volume 16, issue 11
https://doi.org/10.5194/amt-16-2865-2023
https://doi.org/10.5194/amt-16-2865-2023
Research article
 | 
09 Jun 2023
Research article |  | 09 Jun 2023

Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product

Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon

Related authors

Advantages of G-band radar in multi-frequency liquid-phase microphysical retrievals
Benjamin M. Courtier, Alessandro Battaglia, and Kamil Mroz
Atmos. Meas. Tech., 17, 6875–6888, https://doi.org/10.5194/amt-17-6875-2024,https://doi.org/10.5194/amt-17-6875-2024, 2024
Short summary
Enhancing consistency of microphysical properties of precipitation across the melting layer in dual-frequency precipitation radar data
Kamil Mroz, Alessandro Battaglia, and Ann M. Fridlind
Atmos. Meas. Tech., 17, 1577–1597, https://doi.org/10.5194/amt-17-1577-2024,https://doi.org/10.5194/amt-17-1577-2024, 2024
Short summary
In-orbit cross-calibration of millimeter conically scanning spaceborne radars
Alessandro Battaglia, Filippo Emilio Scarsi, Kamil Mroz, and Anthony Illingworth
Atmos. Meas. Tech., 16, 3283–3297, https://doi.org/10.5194/amt-16-3283-2023,https://doi.org/10.5194/amt-16-3283-2023, 2023
Short summary
Triple-frequency radar retrieval of microphysical properties of snow
Kamil Mroz, Alessandro Battaglia, Cuong Nguyen, Andrew Heymsfield, Alain Protat, and Mengistu Wolde
Atmos. Meas. Tech., 14, 7243–7254, https://doi.org/10.5194/amt-14-7243-2021,https://doi.org/10.5194/amt-14-7243-2021, 2021
Short summary
Linking rain into ice microphysics across the melting layer in stratiform rain: a closure study
Kamil Mróz, Alessandro Battaglia, Stefan Kneifel, Leonie von Terzi, Markus Karrer, and Davide Ori
Atmos. Meas. Tech., 14, 511–529, https://doi.org/10.5194/amt-14-511-2021,https://doi.org/10.5194/amt-14-511-2021, 2021
Short summary

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025,https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025,https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025,https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025,https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Satellite-based detection of deep convective clouds: the sensitivity of infrared methods, and implications for cloud climatology
Andrzej Zbigniew Kotarba and Izabela Wojciechowska
EGUsphere, https://doi.org/10.5194/egusphere-2024-3693,https://doi.org/10.5194/egusphere-2024-3693, 2025
Short summary

Cited articles

Baedi, R., de Wit, J., Russchenberg, H., Erkelens, J., and Poiares Baptista, J.: Estimating effective radius and liquid water content from radar and lidar based on the CLARE98 data-set, Phys. Chem. Earth Pt. B, 25, 1057–1062, https://doi.org/10.1016/S1464-1909(00)00152-0, 2000. a
Battaglia, A. and Kollias, P.: Using ice clouds for mitigating the EarthCARE Doppler radar mispointing, IEEE T. Geosci. Remote, 53, 2079–2085, https://doi.org/10.1109/TGRS.2014.2353219, 2014. a
Battaglia, A. and Panegrossi, G.: What Can We Learn from the CloudSat Radiometric Mode Observations of Snowfall over the Ice-Free Ocean?, Remote Sens.-Basel, 12, 3285, https://doi.org/10.3390/rs12203285, 2020. a
Battaglia, A., Tanelli, S., Kobayashi, S., Zrnic, D., Hogan, R. J., and Simmer, C.: Multiple-scattering in radar systems: A review, J. Quant. Spectrosc. Ra., 111, 917–947, https://doi.org/10.1016/j.jqsrt.2009.11.024, 2010. a
Battaglia, A., Mroz, K., Lang, T., Tridon, F., Tanelli, S., Tian, L., and Heymsfield, G. M.: Using a multiwavelength suite of microwave instruments to investigate the microphysical structure of deep convective cores, J. Geophys. Res.-Atmos., 121, 9356–9381, https://doi.org/10.1002/2016JD025269, 2016. a
Download
Short summary
We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Share