Articles | Volume 26, issue 21
https://doi.org/10.5194/acp-26-15593-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/acp-26-15593-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A preliminary analysis of tropical anvil-cloud dynamics in EarthCARE
Institute for Space-Earth Environmental Research, Nagoya University, Nagoya, Japan
Related authors
Keiichi Ohara and Hirohiko Masunaga
Atmos. Meas. Tech., 19, 4099–4119, https://doi.org/10.5194/amt-19-4099-2026, https://doi.org/10.5194/amt-19-4099-2026, 2026
Short summary
Short summary
The properties of cloud ice, snow, and graupel remain difficult to estimate accurately. This study synergistically uses two airborne radars and millimeter-wave radiometer observations to estimate vertical distribution of ice mass, size, number density, fall speed, and vertical air motion. The proposed algorithm is proven to attain reasonable performance in comparison with in-situ measurements of cloud properties, demonstrating improved accuracy in deep cloud layers owing to multi-sensor synergy.
Keiichi Ohara and Hirohiko Masunaga
Atmos. Meas. Tech., 18, 4791–4807, https://doi.org/10.5194/amt-18-4791-2025, https://doi.org/10.5194/amt-18-4791-2025, 2025
Short summary
Short summary
Ice particles (e.g., cloud ice, snow, and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.
Keiichi Ohara and Hirohiko Masunaga
Atmos. Meas. Tech., 19, 4099–4119, https://doi.org/10.5194/amt-19-4099-2026, https://doi.org/10.5194/amt-19-4099-2026, 2026
Short summary
Short summary
The properties of cloud ice, snow, and graupel remain difficult to estimate accurately. This study synergistically uses two airborne radars and millimeter-wave radiometer observations to estimate vertical distribution of ice mass, size, number density, fall speed, and vertical air motion. The proposed algorithm is proven to attain reasonable performance in comparison with in-situ measurements of cloud properties, demonstrating improved accuracy in deep cloud layers owing to multi-sensor synergy.
Keiichi Ohara and Hirohiko Masunaga
Atmos. Meas. Tech., 18, 4791–4807, https://doi.org/10.5194/amt-18-4791-2025, https://doi.org/10.5194/amt-18-4791-2025, 2025
Short summary
Short summary
Ice particles (e.g., cloud ice, snow, and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.
Cited articles
Ackerman, T. P., Liou, K.-N., Valero, F. P. J., and Pfister, L.: Heating Rates in Tropical Anvils, J. Atmos. Sci., 45, 1606–1623, https://doi.org/10.1175/1520-0469(1988)045<1606:HRITA>2.0.CO;2, 1988. a
Aoki, S., Kubota, T., Horie, H., and Ohno, Y.: Characteristics of multiple-trip echoes observed by EarthCARE Cloud Profiling Radar, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2026-3256, 2026a. a, b
Aoki, S., Kubota, T., and Turk, F. J.: Exploring vertical motions in convective and stratiform precipitation using spaceborne radar observations: insights from EarthCARE and GPM coincidence dataset, Atmos. Meas. Tech., 19, 79–100, https://doi.org/10.5194/amt-19-79-2026, 2026b. a
Atlas, R. L., Bretherton, C. S., Sokol, A. B., Blossey, P. N., and Khairoutdinov, M. F.: Tropical Cirrus Are Highly Sensitive to Ice Microphysics Within a Nudged Global Storm-Resolving Model, Geophys. Res. Lett., 51, e2023GL105868, https://doi.org/10.1029/2023GL105868, 2024. a, b
Battaglia, A.: Impact of second-trip echoes for space-borne high-pulse-repetition-frequency nadir-looking W-band cloud radars, Atmos. Meas. Tech., 14, 7809–7820, https://doi.org/10.5194/amt-14-7809-2021, 2021. a
Battaglia, A. and Simmer, C.: How Does Multiple Scattering Affect the Spaceborne W-Band Radar Measurements at Ranges Close to and Crossing the Sea-Surface Range?, IEEE T. Geosci. Remote, 46, 1644–1651, https://doi.org/10.1109/TGRS.2008.916085, 2008. a
Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K. A., Voigt, A., and Medeiros, B.: Thermodynamic control of anvil cloud amount, P. Natl. Acad. Sci. USA, 113, 8927–8932, https://doi.org/10.1073/pnas.1601472113, 2016. a
Deng, M. and Mace, G. G.: Cirrus Microphysical Properties and Air Motion Statistics Using Cloud Radar Doppler Moments. Part I: Algorithm Description, J. Appl. Meteorol. Clim., 45, 1690–1709, https://doi.org/10.1175/JAM2433.1, 2006. a
Eisinger, M., Marnas, F., Wallace, K., Kubota, T., Tomiyama, N., Ohno, Y., Tanaka, T., Tomita, E., Wehr, T., and Bernaerts, D.: The EarthCARE mission: science data processing chain overview, Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, 2024. a
Fueglistaler, S., Dessler, A. E., Dunkerton, T. J., Folkins, I., Fu, Q., and Mote, P. W.: Tropical tropopause layer, Rev. Geophys., 47, RG1004, https://doi.org/10.1029/2008RG000267, 2009. a
Galfione, A., Battaglia, A., Puigdomènech Treserras, B., and Kollias, P.: First insights into deep convection by the Doppler velocity measurements of the EarthCARE Cloud Profiling Radar, Atmos. Meas. Tech., 18, 6747–6763, https://doi.org/10.5194/amt-18-6747-2025, 2025. a, b
Garrett, T. J., Navarro, B. C., Twohy, C. H., Jensen, E. J., Baumgardner, D. G., Bui, P. T., Gerber, H., Herman, R. L., Heymsfield, A. J., Lawson, P., Minnis, P., Nguyen, L., Poellot, M., Pope, S. K., Valero, F. P. J., and Weinstock, E. M.: Evolution of a Florida Cirrus Anvil, J. Atmos. Sci., 62, 2352–2372, https://doi.org/10.1175/JAS3495.1, 2005. a
Gasparini, B., Blossey, P. N., Hartmann, D. L., Lin, G., and Fan, J.: What Drives the Life Cycle of Tropical Anvil Clouds?, J. Adv. Model. Earth Sy., 11, 2586–2605, https://doi.org/10.1029/2019MS001736, 2019. a, b, c, d
Gasparini, B., Sokol, A. B., Wall, C. J., Hartmann, D. L., and Blossey, P. N.: Diurnal Differences in Tropical Maritime Anvil Cloud Evolution, J. Climate, 35, 1655–1677, https://doi.org/10.1175/JCLI-D-21-0211.1, 2022. a
Gasparini, B., Sullivan, S. C., Sokol, A. B., Kärcher, B., Jensen, E., and Hartmann, D. L.: Opinion: Tropical cirrus – from micro-scale processes to climate-scale impacts, Atmos. Chem. Phys., 23, 15413–15444, https://doi.org/10.5194/acp-23-15413-2023, 2023. a
Hagihara, Y., Ohno, Y., Horie, H., Roh, W., Satoh, M., and Kubota, T.: Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation, Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, 2023. a, b
Hartmann, D. L. and Larson, K.: An important constraint on tropical cloud-climate feedback, Geophys. Res. Lett., 29, https://doi.org/10.1029/2002GL015835, 2002. a
Hartmann, D. L., Gasparini, B., Berry, S. E., and Blossey, P. N.: The Life Cycle and Net Radiative Effect of Tropical Anvil Clouds, J. Adv. Model. Earth Sy., 10, 3012–3029, https://doi.org/10.1029/2018MS001484, 2018. a
Heymsfield, G. M., Tian, L., Heymsfield, A. J., Li, L., and Guimond, S.: Characteristics of Deep Tropical and Subtropical Convection from Nadir-Viewing High-Altitude Airborne Doppler Radar, J. Atmos. Sci., 67, 285–308, 2010. a
Horner, G. and Gryspeerdt, E.: The evolution of deep convective systems and their associated cirrus outflows, Atmos. Chem. Phys., 23, 14239–14253, https://doi.org/10.5194/acp-23-14239-2023, 2023. a, b
Hotta, H., Suzuki, K., Kikuchi, M., Aoki, S., and Kubota, T.: Characteristics of tropical clouds with strong updrafts revealed by Doppler-velocity measurements from EarthCARE/CPR, Atmos. Chem. Phys., 26, 11605–11626, https://doi.org/10.5194/acp-26-11605-2026, 2026. a
Igel, M. R., Drager, A. J., and van den Heever, S. C.: A CloudSat cloud object partitioning technique and assessment and integration of deep convective anvil sensitivities to sea surface temperature, J. Geophys. Res., 119, 10515–10535, https://doi.org/10.1002/2014JD021717, 2014. a
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H., Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P., Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota, T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki, R., Sato, K., Satoh, M., Shephard, M. W., Velázzquez-Blázquez, A., Wandinger, U., Wehr, T., and van Zadelhoff, G.-J.: The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015. a, b
Imura, Y., Aoki, S., Kubota, T., Nakatsuka, H., Ohno, Y., and Okamoto, H.: Observation modes of EarthCARE/CPR with different Doppler measurement accuracy: evaluation of their applicability, Atmos. Meas. Tech., 19, 5617–5631, https://doi.org/10.5194/amt-19-5617-2026, 2026. a, b
Ito, M. and Masunaga, H.: Process-Level Assessment of the Iris Effect Over Tropical Oceans, Geophys. Res. Lett., 49, e2022GL097997, https://doi.org/10.1029/2022GL097997, 2022. a
JAXA: EarthCARE/CPR L2A CPR one-sensor Cloud Products, JAXA [data set], https://doi.org/10.57746/EO.01jdvd2gqq34e6yz9p8kfe68x5, 2026a. a
JAXA: EarthCARE L2B CPR-ATLID Synergy Cloud Products, JAXA [data set], https://doi.org/10.57746/EO.01jkwjeg2y0bqndnsvjvaaskaw, 2026b. a
Jensen, E. J., Toon, O. B., Pfister, L., and Selkirk, H. B.: Dehydration of the upper troposphere and lower stratosphere by subvisible cirrus clouds near the tropical tropopause, Geophys. Res. Lett., 23, 825–828, https://doi.org/10.1029/96GL00722, 1996. a, b
Kärcher, B. and Spichtinger, P.: Cloud-controlling Factors of Cirrus, in: Clouds in the Perturbed Climate System: Their Relationship to Energy Balance, Atmospheric Dynamics, and Precipitation, edited by: Heintzenberg, J. and Charlson, R. J., The MIT Press, https://doi.org/10.7551/mitpress/9780262012874.003.0011, 2009. a
Kingsmill, D. E. and Houze Jr., R. A.: Kinematic characteristics of air flowing into and out of precipitating convection over the west Pacific warm pool: An airborne Doppler radar survey, Q. J. Roy. Meteor. Soc., 125, 1165–1207, https://doi.org/10.1002/qj.1999.49712555605, 1999. a
Kollias, P., Puidgomènech Treserras, B., Battaglia, A., Borque, P. C., and Tatarevic, A.: Processing reflectivity and Doppler velocity from EarthCARE's cloud-profiling radar: the C-FMR, C-CD and C-APC products, Atmos. Meas. Tech., 16, 1901–1914, https://doi.org/10.5194/amt-16-1901-2023, 2023. a
Kubota, T., Kikuchi, M., Muto, M., Hashimoto, M., Imura, Y., Maruyama, K., Hoffmann, A., Hummel, T., Koopman, R., Malina, E., Rusli, S., Tzallas, V., von Bismarck, J., and Frommknecht, B.: Summary of the EarthCARE science and validation workshop 2025 -Early achievements and future perspectives for the Earth Cloud Aerosol and Radiation Explorer (EarthCARE) satellite mission-, Journal of the European Meteorological Society, 4, 100037, https://doi.org/10.1016/j.jemets.2026.100037, 2026. a
Lilly, D. K.: Cirrus Outflow Dynamics, J. Atmos. Sci., 45, 1594–1605, https://doi.org/10.1175/1520-0469(1988)045<1594:COD>2.0.CO;2, 1988. a
Luo, Z. and Rossow, W. B.: Characterizing tropical cirrus life cycle, evolution, and interaction with upper-tropospheric water vapor using Lagrangian trajectory analysis of satellite observations, J. Climate, 17, 4541–4563, 2004. a
Mace, G. G., Heymsfield, A. J., and Poellot, M. R.: On retrieving the microphysical properties of cirrus clouds using the moments of the millimeter-wavelength Doppler spectrum, J. Geophys. Res., 107, AAC 22-1–AAC 22-26, https://doi.org/10.1029/2001JD001308, 2002. a
Massie, S., Gettelman, A., Randel, W., and Baumgardner, D.: Distribution of tropical cirrus in relation to convection, J. Geophys. Res., 107, AAC 19-1–AAC 19-16, https://doi.org/10.1029/2001JD001293, 2002. a
Masunaga, H.: The Edge Intensification of Eastern Pacific ITCZ Convection, J. Climate, 36, 3469–3480, https://doi.org/10.1175/JCLI-D-22-0382.1, 2023. a
Masunaga, H. and Bony, S.: Radiative invigoration of tropical convection by preceding cirrus clouds, J. Atmos. Sci., 75, 1327–1342, https://doi.org/10.1002/2016MS000855, 2018. a
Moncrieff, M. W.: Organized Convective Systems: Archetypal Dynamical Models, Mass and Momentum Flux Theory, and Parametrization, Q. J. Roy. Meteor. Soc., 118, 819–850, https://doi.org/10.1002/qj.49711850703, 1992. a
Muhlbauer, A., Kalesse, H., and Kollias, P.: Vertical velocities and turbulence in midlatitude anvil cirrus: A comparison between in situ aircraft measurements and ground-based Doppler cloud radar retrievals, Geophys. Res. Lett., 41, 7814–7821, https://doi.org/10.1002/2014GL062279, 2014. a
Nakajima, T., Kikuchi, M., Ohno, Y., Horie, H., Okamoto, T., Nishizawa, T., Nakajima, T. Y., Suzuki, K., Satoh, M., and Kubota, T.: EarthCARE JAXA Level 2 Algorithm Theoretical Basis Document (L2 ATBD), JAXA, NDX-110018, https://www.eorc.jaxa.jp/EARTHCARE/document/reference/dev/EarthCARE_L2_ATBD.pdf (last access: 1 November 2026), 2026. a, b
Ohno, T. and Satoh, M.: Roles of Cloud Microphysics on Cloud Responses to Sea Surface Temperatures in Radiative-Convective Equilibrium Experiments Using a High-Resolution Global Nonhydrostatic Model, J. Adv. Model. Earth Sy., 10, 1970–1989, https://doi.org/10.1029/2018MS001386, 2018. a, b
Ohno, T., Noda, A. T., Seiki, T., and Satoh, M.: Importance of Pressure Changes in High Cloud Area Feedback Due to Global Warming, Geophys. Res. Lett., 48, e2021GL093646, https://doi.org/10.1029/2021GL093646, 2021. a
Okamoto, H., Iwasaki, S., Yasui, M., Horie, H., Kuroiwa, H., and Kumagai, H.: An algorithm for retrieval of cloud microphysics using 95-GHz cloud radar and lidar, J. Geophys. Res., 108, https://doi.org/10.1029/2001JD001225, 2003. a
Orr, B. W. and Kropfli, R. A.: A Method for Estimating Particle Fall Velocities from Vertically Pointing Doppler Radar, J. Atmos. Ocean. Tech., 16, 29–37, https://doi.org/10.1175/1520-0426(1999)016<0029:AMFEPF>2.0.CO;2, 1999. a
Poujol, B. and Bony, S.: Measuring Clear-Air Vertical Motions From Space, AGU Adv., 5, e2024AV001267, https://doi.org/10.1029/2024AV001267, 2024. a, b
Protat, A. and Williams, C. R.: The Accuracy of Radar Estimates of Ice Terminal Fall Speed from Vertically Pointing Doppler Radar Measurements, J. Appl. Meteorol. Clim., 50, 2120–2138, https://doi.org/10.1175/JAMC-D-10-05031.1, 2011. a
Protopapadaki, S. E., Stubenrauch, C. J., and Feofilov, A. G.: Upper tropospheric cloud systems derived from IR sounders: properties of cirrus anvils in the tropics, Atmos. Chem. Phys., 17, 3845–3859, https://doi.org/10.5194/acp-17-3845-2017, 2017. a
Puigdomènech Treserras, B., Kollias, P., Battaglia, A., Tanelli, S., and Nakatsuka, H.: EarthCARE's cloud profiling radar antenna pointing correction using surface Doppler measurements, Atmos. Meas. Tech., 18, 5607–5618, https://doi.org/10.5194/amt-18-5607-2025, 2025. a
Roh, W., Satoh, M., Matsugishi, S., Aoki, S., Kubota, T., and Okamoto, H.: Vertical motions in clouds from EarthCare satellite and a global storm-resolving modeling, Sci. Rep., 16, 2622, https://doi.org/10.1038/s41598-025-32256-8, 2025. a
Sato, K. and Okamoto, H.: Refinement of global ice microphysics using spaceborne active sensors, J. Geophys. Res., 116, https://doi.org/10.1029/2011JD015885, 2011. a
Sato, K., Okamoto, H., Yamamoto, M. K., Fukao, S., Kumagai, H., Ohno, Y., Horie, H., and Abo, M.: 95-GHz Doppler radar and lidar synergy for simultaneous ice microphysics and in-cloud vertical air motion retrieval, J. Geophys. Res., 114, D03203, https://doi.org/10.1029/2008JD010222, 2009. a, b, c
Sato, K., Okamoto, H., Nishizawa, T., Jin, Y., Nakajima, T. Y., Wang, M., Satoh, M., Roh, W., Ishimoto, H., and Kudo, R.: JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products, Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, 2025. a, b, c
Seeley, J. T., Jeevanjee, N., Langhans, W., and Romps, D. M.: Formation of Tropical Anvil Clouds by Slow Evaporation, Geophys. Res. Lett., 46, 492–501, https://doi.org/10.1029/2018GL080747, 2019. a
Seiki, T., Horie, H., Hagihara, Y., Aoki, S., and Noda, A. T.: Global identification of dominant ice-particle growth in cirrus clouds using EarthCARE satellite observations, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4819, 2025. a
Sherwood, S. C.: On moistening of the tropical troposphere by cirrus clouds, J. Geophys. Res., 104, 11949–11960, https://doi.org/10.1029/1999JD900162, 1999. a
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System, B. Am. Meteorol. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Stubenrauch, C. J., Kinne, S., Mandorli, G., Rossow, W. B., Winker, D. M., Ackerman, S. A., Chepfer, H., Di Girolamo, L., Garnier, A., Heidinger, A., Karlsson, K.-G., Meyer, K., Minnis, P., Platnick, S., Stengel, M., Sun-Mack, S., Veglio, P., Walther, A., Cai, X., Young, A. H., and Zhao, G.: Lessons Learned from the Updated GEWEX Cloud Assessment Database, Surv. Geophys., 45, 1999–2048, https://doi.org/10.1007/s10712-024-09824-0, 2024. a
Takahashi, H., Luo, Z. J., Stephens, G., and Mulholland, J. P.: Revisiting the Land-Ocean Contrasts in Deep Convective Cloud Intensity Using Global Satellite Observations, Geophys. Res. Lett., 50, e2022GL102089, https://doi.org/10.1029/2022GL102089, 2023. a
Wall, C. J., Norris, J. R., Gasparini, B., Smith, W. L., Thieman, M. M., and Sourdeval, O.: Observational Evidence that Radiative Heating Modifies the Life Cycle of Tropical Anvil Clouds, J. Climate, 33, 8621–8640, https://doi.org/10.1175/JCLI-D-20-0204.1, 2020. a, b
Wehr, T., Kubota, T., Tzeremes, G., Wallace, K., Nakatsuka, H., Ohno, Y., Koopman, R., Rusli, S., Kikuchi, M., Eisinger, M., Tanaka, T., Taga, M., Deghaye, P., Tomita, E., and Bernaerts, D.: The EarthCARE mission – science and system overview, Atmos. Meas. Tech., 16, 3581–3608, https://doi.org/10.5194/amt-16-3581-2023, 2023. a
Short summary
EarthCARE is the first space mission with the capability to measure vertical motion inside clouds. This work assesses the utility of EarthCARE observations for studying the vertical motion of air in tropical anvil clouds. The findings are encouraging in that EarthCARE not only provides robust estimates of vertical air motion but has the potential to quantify the horizontal motion as well. This will be useful for better understanding how anvil clouds are formed and sustained over time.
EarthCARE is the first space mission with the capability to measure vertical motion inside...
Altmetrics
Final-revised paper
Preprint