Articles | Volume 26, issue 20
https://doi.org/10.5194/acp-26-15017-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-15017-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Relationships between deep convective clouds, cirrus clouds, and the tropical easterly jet over the Indian region observed with Aeolus spaceborne Doppler wind lidar
Zacharie Titus
CORRESPONDING AUTHOR
Laboratoire de Météorologie Dynamique/IPSL, Sorbonne Université, Institut Polytechnique de Paris, CNRS, Palaiseau, France
Marine Bonazzola
Laboratoire de Météorologie Dynamique/IPSL, Sorbonne Université, Institut Polytechnique de Paris, CNRS, Palaiseau, France
Hélène Chepfer
Laboratoire de Météorologie Dynamique/IPSL, Sorbonne Université, Institut Polytechnique de Paris, CNRS, Palaiseau, France
Artem Feofilov
Laboratoire de Météorologie Dynamique/IPSL, Sorbonne Université, Institut Polytechnique de Paris, CNRS, Palaiseau, France
Marie-Laure Roussel
Laboratoire de Météorologie Dynamique/IPSL, Sorbonne Université, Institut Polytechnique de Paris, CNRS, Palaiseau, France
Related authors
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem G. Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
Atmos. Chem. Phys., 26, 443–475, https://doi.org/10.5194/acp-26-443-2026, https://doi.org/10.5194/acp-26-443-2026, 2026
Short summary
Short summary
Aeolus spaceborne Doppler Wind Lidar observes perfectly co-located vertical profiles of clouds and vertical profiles of horizontal wind that can be used to study cloud-wind interactions. At regional scale, we show that over the Indian Ocean, high cloud fractions increase when the Tropical Easterly Jet is active. At a smaller scale, we observe for the first time from space differences in the wind profiles within the cloud and its surrounding clear sky, that can be imputed to convective motions.
Marie-Laure Roussel, Hélène Chepfer, Zacharie Titus, and Marine Bonazzola
Atmos. Chem. Phys., 26, 117–134, https://doi.org/10.5194/acp-26-117-2026, https://doi.org/10.5194/acp-26-117-2026, 2026
Short summary
Short summary
Clouds are crucial for understanding and predicting climate, yet their processes remain uncertain in global models. We reproduced detailed observations from a lidar and a wind-lidar using an advanced instrument simulator to evaluate how well the model represents clouds. A dedicated module was developed for the wind-lidar. Both instruments reveal comparable cloud biases, confirming its value for improving model evaluation and enabling consistent multi-decade assessments with more recent sensors.
Zacharie Titus, Amélie Cuynet, Elodie Salmon, and Catherine Ottlé
The Cryosphere, 19, 2105–2114, https://doi.org/10.5194/tc-19-2105-2025, https://doi.org/10.5194/tc-19-2105-2025, 2025
Short summary
Short summary
The representation of lake ice dynamics is key to model water–atmosphere energy and mass transfers in cold environments. The use of albedo satellite products to constrain the modeling of ice coverage appears to be very suitable and valuable. In this work, we show how the modeling of lake albedo and ice phenology in the land surface model ORCHIDEE was improved by accounting for fractional ice cover calibrated against lake surface albedo data.
Artem G. Feofilov, Arezki Haddouche, Hélène Chepfer, Sergey Khaykin, Florian Lapouge, Patricia Delville, Marc-Antoine Drouin, Martial Haeffelin, Jean-Charles Dupont, Cheikh Dione, and Christophe Pietras
EGUsphere, https://doi.org/10.5194/egusphere-2026-6375, https://doi.org/10.5194/egusphere-2026-6375, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Do a satellite and a ground-based laser see a cloud the same way? Yes, but only under specific conditions. Seen from above and from below, light is weakened differently, so thick clouds look different in the backscatter even with perfect instruments. Using simulations and real measurements, we show which clouds can be compared. As the instruments are rarely at the same spot, we pair observations by the similarity of their vertical shapes, not by distance. Our rules help check satellite data.
Océane Soares, Sergey Khaykin, Sophie Godin-Beekmann, Artem Feofilov, Tetsu Sakai, Yoshita Jin, Isamu Morino, Ben Liley, and Richard Querel
EGUsphere, https://doi.org/10.5194/egusphere-2026-5639, https://doi.org/10.5194/egusphere-2026-5639, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Volcanic eruptions and extreme wildfires can send particles high into the atmosphere, where they can remain for months or even years. Using nearly two decades of satellite observations together with measurements from the ground, we compare major events and show that their impacts can last for very different lengths of time. Our results help explain what controls how long these particles remain in the atmosphere.
Jean Lac, Hélène Chepfer, Matthew D. Shupe, and Hannes Griesche
Atmos. Chem. Phys., 26, 4189–4213, https://doi.org/10.5194/acp-26-4189-2026, https://doi.org/10.5194/acp-26-4189-2026, 2026
Short summary
Short summary
Satellite observations show that Arctic spring experiences a rapid increase in liquid-containing clouds over sea ice. Our study shows that this transition is mostly driven by warmer temperatures in early spring than in late spring, favoring more liquid clouds formation, rather than a limited moisture source in early spring. It suggests that, in the future, this transition is likely to occur earlier in spring considering the rapid Arctic warming.
Sergey Khaykin, Michaël Sicard, Thierry Leblanc, Tetsu Sakai, Nickolay Balugin, Gwenaël Berthet, Stëphane Chevrier, Fernando Chouza, Artem Feofilov, Dominique Gantois, Sophie Godin-Beekmann, Arezki Haddouche, Yoshitaka Jin, Isamu Morino, Nicolas Kadygrov, Thomas Lecas, Ben Liley, Richard Querel, Ghasssan Taha, and Vladimir Yushkov
Atmos. Chem. Phys., 26, 607–622, https://doi.org/10.5194/acp-26-607-2026, https://doi.org/10.5194/acp-26-607-2026, 2026
Short summary
Short summary
In April 2024, the Ruang volcano in Indonesia sent large amounts of gas and particles high into the atmosphere, which then spread worldwide. Using the new European EarthCARE satellite and its advanced laser instrument ATLID (Atmospheric LIDar), together with ground and balloon observations, we tracked how these particles doubled levels in the tropics and spread into both hemispheres. The study shows ATLID’s power to reveal how eruptions can affect climate, clouds, and ozone for more than a year.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem G. Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
Atmos. Chem. Phys., 26, 443–475, https://doi.org/10.5194/acp-26-443-2026, https://doi.org/10.5194/acp-26-443-2026, 2026
Short summary
Short summary
Aeolus spaceborne Doppler Wind Lidar observes perfectly co-located vertical profiles of clouds and vertical profiles of horizontal wind that can be used to study cloud-wind interactions. At regional scale, we show that over the Indian Ocean, high cloud fractions increase when the Tropical Easterly Jet is active. At a smaller scale, we observe for the first time from space differences in the wind profiles within the cloud and its surrounding clear sky, that can be imputed to convective motions.
Marie-Laure Roussel, Hélène Chepfer, Zacharie Titus, and Marine Bonazzola
Atmos. Chem. Phys., 26, 117–134, https://doi.org/10.5194/acp-26-117-2026, https://doi.org/10.5194/acp-26-117-2026, 2026
Short summary
Short summary
Clouds are crucial for understanding and predicting climate, yet their processes remain uncertain in global models. We reproduced detailed observations from a lidar and a wind-lidar using an advanced instrument simulator to evaluate how well the model represents clouds. A dedicated module was developed for the wind-lidar. Both instruments reveal comparable cloud biases, confirming its value for improving model evaluation and enabling consistent multi-decade assessments with more recent sensors.
Zacharie Titus, Amélie Cuynet, Elodie Salmon, and Catherine Ottlé
The Cryosphere, 19, 2105–2114, https://doi.org/10.5194/tc-19-2105-2025, https://doi.org/10.5194/tc-19-2105-2025, 2025
Short summary
Short summary
The representation of lake ice dynamics is key to model water–atmosphere energy and mass transfers in cold environments. The use of albedo satellite products to constrain the modeling of ice coverage appears to be very suitable and valuable. In this work, we show how the modeling of lake albedo and ice phenology in the land surface model ORCHIDEE was improved by accounting for fractional ice cover calibrated against lake surface albedo data.
Alexander Kutepov and Artem Feofilov
Geosci. Model Dev., 17, 5331–5347, https://doi.org/10.5194/gmd-17-5331-2024, https://doi.org/10.5194/gmd-17-5331-2024, 2024
Short summary
Short summary
Infrared CO2 cooling of the middle and upper atmosphere is increasing. We developed a new routine for very fast and accurate calculations of this cooling in general circulation models. The new algorithm accounts for non-local thermodynamic equilibrium and is about 1000 times faster than the standard matrix algorithms. It is based on advanced techniques for non-equilibrium emission calculations in stellar atmospheres, which so far have not been used in Earth’s and planetary atmospheres.
Valentin Wiener, Marie-Laure Roussel, Christophe Genthon, Étienne Vignon, Jacopo Grazioli, and Alexis Berne
Earth Syst. Sci. Data, 16, 821–836, https://doi.org/10.5194/essd-16-821-2024, https://doi.org/10.5194/essd-16-821-2024, 2024
Short summary
Short summary
This paper presents 7 years of data from a precipitation radar deployed at the Dumont d'Urville station in East Antarctica. The main characteristics of the dataset are outlined in a short statistical study. Interannual and seasonal variability are also investigated. Then, we extensively describe the processing method to retrieve snowfall profiles from the radar data. Lastly, a brief comparison is made with two climate models as an application example of the dataset.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
Short summary
Short summary
The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Marine Bonazzola, Hélène Chepfer, Po-Lun Ma, Johannes Quaas, David M. Winker, Artem Feofilov, and Nick Schutgens
Geosci. Model Dev., 16, 1359–1377, https://doi.org/10.5194/gmd-16-1359-2023, https://doi.org/10.5194/gmd-16-1359-2023, 2023
Short summary
Short summary
Aerosol has a large impact on climate. Using a lidar aerosol simulator ensures consistent comparisons between modeled and observed aerosol. We present a lidar aerosol simulator that applies a cloud masking and an aerosol detection threshold. We estimate the lidar signals that would be observed at 532 nm by the Cloud-Aerosol Lidar with Orthogonal Polarization overflying the atmosphere predicted by a climate model. Our comparison at the seasonal timescale shows a discrepancy in the Southern Ocean.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
Short summary
Short summary
We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
Short summary
Short summary
Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
Short summary
Short summary
By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Cited articles
Ackerman, T. P., Liou, K.-N., Valero, F. P., 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
Aeolus DISC: The Earth Explorer Mission Aeolus for atmospheric wind observations – Final Report from the Aeolus Data Innovation and Science Cluster DISC of Phase E, Aeolus DISC reports, https://doi.org/10.57676/93c3-n766, 2024. a
Ali, S., Mehta, S. K., Ananthavel, A., and Reddy, T. V. R.: Temporal and vertical distributions of the occurrence of cirrus clouds over a coastal station in the Indian monsoon region, Atmos. Chem. Phys., 22, 8321–8342, https://doi.org/10.5194/acp-22-8321-2022, 2022. a
Betts, A.: Non-precipitating cumulus convection and its parameterization, Q. J. R. Meteorol. Soc., 99, 178–196, https://doi.org/10.1002/qj.49709941915, 1973. a
Chen, T.-C.: On the kinetic energy budget of the summer-mean flow at 200 mb in the tropics, Tellus, 34, 55–62, https://doi.org/10.1111/j.2153-3490.1982.tb01792.x, 1982. a, b
Chen, T.-C. and van Loon, H.: Interannual variation of the tropical easterly jet, Mon. Weather Rev., 115, 1739–1759, https://doi.org/10.1175/1520-0493(1987)115<1739:IVOTTE>2.0.CO;2, 1987. a
Chepfer, H., Brogniez, H., and Noel, V.: Diurnal variations of cloud and relative humidity profiles across the tropics, Sci. Rep., 9, 16045, https://doi.org/10.1038/s41598-019-52437-6, 2019. a, b, c, d
Corcos, M., Hertzog, A., Plougonven, R., and Podglajen, A.: A simple model to assess the impact of gravity waves on ice-crystal populations in the tropical tropopause layer, Atmos. Chem. Phys., 23, 6923–6939, https://doi.org/10.5194/acp-23-6923-2023, 2023. a, b
Das, S. K., Chiang, C.-W., and Nee, J.-B.: Influence of tropical easterly jet on upper tropical cirrus: an observational study from CALIPSO, Aura-MLS, and NCEP/NCAR data, J. Geophys. Res.-Atmos., 116, https://doi.org/10.1029/2011JD015923, 2011. a, b, c, d
Diongue, A., Lafore, J.-P., Redelsperger, J.-L., and Roca, R.: Numerical study of a Sahelian synoptic weather system: Initiation and mature stages of convection and its interactions with the large-scale dynamics, Q. J. R. Meteorol. Soc., 128, 1899–1927, https://doi.org/10.1256/003590002320603467, 2002. a, b, c, d
Donovan, D. P., van Zadelhoff, G.-J., and Wang, P.: The EarthCARE lidar cloud and aerosol profile processor (A-PRO): the A-AER, A-EBD, A-TC, and A-ICE products, Atmos. Meas. Tech., 17, 5301–5340, https://doi.org/10.5194/amt-17-5301-2024, 2024. a
Feofilov, A. G. and Stubenrauch, C. J.: Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders, Atmos. Chem. Phys., 19, 13957–13972, https://doi.org/10.5194/acp-19-13957-2019, 2019. a, b
Feofilov, A. G., Chepfer, H., Noël, V., Guzman, R., Gindre, C., Ma, P.-L., and Chiriaco, M.: Comparison of scattering ratio profiles retrieved from ALADIN/Aeolus and CALIOP/CALIPSO observations and preliminary estimates of cloud fraction profiles, Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, 2022. a
Fu, Q., Smith, M., and Yang, Q.: The impact of cloud radiative effects on the tropical tropopause layer temperatures, Atmosphere, 9, 377–390, https://doi.org/10.3390/atmos9100377, 2018. a, b
Gasparini, B., Rasch, P. J., Hartmann, D. L., Wall, C. J., and Dütsch, M.: A Lagrangian perspective on tropical anvil cloud lifecycle in present and future climate, J. Geophys. Res.-Atmos., 126, https://doi.org/10.1029/2020JD033487, 2021. a
Hartmann, D. L. and Berry, S. E.: The balanced radiative effect of tropical anvil clouds, J. Geophys. Res.-Atmos., 122, 5003–5020, https://doi.org/10.1002/2017JD026460, 2017. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J. N.: The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a
Höjgård-Olsen, E., Chepfer, H., and Brogniez, H.: Satellite observed sensitivity of tropical clouds and moisture to sea surface temperature on various time and space scales: 1. Focus on high level cloud situations over ocean, J. Geophys. Res.-Atmos., 127, https://doi.org/10.1029/2021JD035438, 2022. a
Iqbal, W., Syed, F., Sajjad, H., Nikulin, G., Kjellström, E., and Hannachi, A.: Mean climate and representation of jet streams in the CORDEX South Asia simulations by the regional climate model RCA4, Theor. Appl. Climatol., 129, 1–19, https://doi.org/10.1007/s00704-016-1755-4, 2017. a, b
Jensen, E., Ueyama, R., Pfister, L., and Atlas, R.: The impacts of gravity waves and wind shear on the lifecycle of cirrus clouds in the tropical tropopause layer, J. Geophys. Res.-Atmos., 130, https://doi.org/10.1029/2024JD042308, 2025. a
Koteswaram, P.: The easterly jet stream in the tropics, Tellus, 10, 43–57, https://doi.org/10.3402/tellusa.v10i1.9220, 1958. a
Kripalani, R., Ha, K.-J., Ho, C.-H., Oh, J.-H., Preethi, B., Mujumdar, M., and Prabhu, A.: Erratic Asian summer monsoon 2020: COVID-19 lockdown initiatives possible cause for these episodes?, Clim. Dynam., 59, 1339–1352, https://doi.org/10.1007/s00382-021-06042-x, 2022. a
Krisch, I., Hindley, N. P., Reitebuch, O., and Wright, C. J.: On the derivation of zonal and meridional wind components from Aeolus horizontal line-of-sight wind, Atmos. Meas. Tech., 15, 3465–3479, https://doi.org/10.5194/amt-15-3465-2022, 2022. a, b
Krishnamurthy, V. and Shukla, J.: Intraseasonal and interannual variability of rainfall over India, J. Climate, 13, 4366–4377, https://doi.org/10.1175/1520-0442(2000)013<0001:IAIVOR>2.0.CO;2, 2000. a
Liu, S., Huang, S., Tan, Y., Wen, Z., Chen, X., and Guo, Y.: Characteristics and mechanisms of the interannual variability of the Northwest–Southeast shift of the tropical easterly Jet’s core in July, J. Climate, 37, 3219–3235, https://doi.org/10.1175/JCLI-D-23-0291.1, 2024. a, b, c, d
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, https://doi.org/10.1175/3222.1, 2004. a, b
Mapes, B. E. and Houze Jr., R. A.: Cloud clusters and superclusters over the oceanic warm pool, Mon. Weather Rev., 121, 1398–1416, https://doi.org/10.1175/1520-0493(1993)121<1398:CCASOT>2.0.CO;2, 1993. a
Massie, S., Gettelman, A., Randel, W., and Baumgardner, D.: Distribution of tropical cirrus in relation to convection, J. Geophys. Res.-Atmos., 107, AAC-19-1–AAC-19-16, https://doi.org/10.1029/2001JD001293, 2002. a
McKim, B., Bony, S., and Dufresne, J.-L.: Weak anvil cloud area feedback suggested by physical and observational constraints, Nat. Geosci., 17, 392–397, https://doi.org/10.1038/s41561-024-01414-4, 2024. a
Mehta, S. K.: Observational evidence of the relationship between the tropical tropopause and tropical easterly jet streams over the Indian monsoon region, Atmos. Sci. Lett., 25, https://doi.org/10.1002/asl.1230, 2024. a, b
Mohan, T. and Rao, T.: Differences in the mean wind and its diurnal variation between wet and dry spells of the monsoon over southeast India, J. Geophys. Res.-Atmos., 121, 6993–7006, https://doi.org/10.1002/2015JD024704, 2016. a, b
Nicholson, S. E.: On the factors modulating the intensity of the tropical rainbelt over West Africa, Int. J. Climatol., 29, 673–689, https://doi.org/10.1002/joc.1702, 2009. a
Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station, Atmos. Chem. Phys., 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, 2018. a
Pattanaik, D. and Satyan, V.: Fluctuations of tropical easterly jet during contrasting monsoons over India: A GCM study, Meteorol. Atmos. Phys., 75, 51–60, https://doi.org/10.1007/s007030070015, 2000. a
Ramanathan, V., Barkstrom, B. R., and Harrison, E. F.: Climate and the Earth's radiation budget, Phys. Today, 42, 22–32, 1989. a
Reitebuch, O.: The spaceborne wind lidar mission ADM-Aeolus, in: Atmospheric physics: Background–methods–trends, 815–827, Springer, https://doi.org/10.1007/978-3-642-30183-4_49, 2012. a
Rennie, M. P., Isaksen, L., Weiler, F., de Kloe, J., Kanitz, T., and Reitebuch, O.: The impact of Aeolus wind retrievals on ECMWF global weather forecasts, Q. J. R. Meteorol. Soc., 147, 3555–3586, 2021. a
Roja Raman, M., Jagannadha Rao, V., Venkat Ratnam, M., Rajeevan, M., Rao, S., Narayana Rao, D., and Prabhakara Rao, N.: Characteristics of the Tropical Easterly Jet: Long-term trends and their features during active and break monsoon phases, J. Geophys. Res.-Atmos., 114, D19105, https://doi.org/10.1029/2009JD012065, 2009. a
Sassen, K., Wang, Z., and Liu, D.: Cirrus clouds and deep convection in the tropics: Insights from CALIPSO and CloudSat, J. Geophys. Res.-Atmos., 114, D00H06, https://doi.org/10.1029/2009JD011916, 2009. a
Sathiyamoorthy, V., Pal, P., and Joshi, P.: Intraseasonal variability of the tropical easterly jet, Meteorol. Atmos. Phys., 96, 305–316, 2007. a
Sokol, A. B., Wall, C. J., and Hartmann, D. L.: Greater climate sensitivity implied by anvil cloud thinning, Nat. Geosci., 17, 398–403, 2024. a
Stoffelen, A., Pailleux, J., Källén, E., Vaughan, J. M., Isaksen, L., Flamant, P., Wergen, W., Andersson, E., Schyberg, H., Culoma, A., Meynart, R., Endemann, M., and Ingmann, P.: The atmospheric dynamics mission for global wind field measurement, Bull. Am. Meteorol. Soc., 86, 73–88, https://doi.org/10.1175/BAMS-86-1-73, 2005. a
Stubenrauch, C. J., Bonazzola, M., Protopapadaki, S. E., and Musat, I.: New cloud system metrics to assess bulk ice cloud schemes in a GCM, J. Adv. Model. Earth Sy., 11, 3212–3234, https://doi.org/10.1029/2019MS001642, 2019. a
Titus, Z.: ALADIN/Aeolus – Wind-cloud interactions dataset, Aeris [data set], https://doi.org/10.25326/746, 2024. a
Ye, J., Guo, Y., Wen, Z., Zhao, P., and Huang, S.: Longitudinal oscillation mode of the tropical easterly jet in June: role of precipitation anomalies in Asian monsoon region, Clim. Dynam., 60, 1543–1558, https://doi.org/10.1007/s00382-022-06391-1, 2023. a, b
Zuidema, P.: Convective clouds over the Bay of Bengal, Mon. Weather Rev., 131, 780–798, https://doi.org/10.1175/1520-0493(2003)131<0780:ccotbo>2.0.co;2, 2003. a
Short summary
In this paper, we use satellite observations to study the relationships between profiles of clouds and profiles of wind from June to October 2020 around India. We show that the wind can be modified by 3 m s-1 because of the presence of deep convective clouds that spread out between 14 and 17 km of altitude. We also found that cirrus clouds between 14 and 17 km are more frequent over the Arabian Sea because of fast westward winds.
In this paper, we use satellite observations to study the relationships between profiles of...
Altmetrics
Final-revised paper
Preprint