Articles | Volume 16, issue 13
https://doi.org/10.5194/amt-16-3363-2023
© Author(s) 2023. 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/amt-16-3363-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Jul 2023
Research article |
| 05 Jul 2023
| 05 Jul 2023
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Artem G. Feofilov
CORRESPONDING AUTHOR
LMD/IPSL, Sorbonne Université, ENS, PSL Research University, École polytechnique, Institut Polytechnique de Paris, CNRS, Paris, France
Hélène Chepfer
LMD/IPSL, Sorbonne Université, ENS, PSL Research University, École polytechnique, Institut Polytechnique de Paris, CNRS, Paris, France
Vincent Noël
Laboratoire d'Aérologie (Laero), Observatoire
Midi-Pyrénées, Université Toulouse 3, CNRS, IRD, Toulouse, France
Frederic Szczap
Laboratoire de Météorologie Physique (LaMP), UMR 6016, CNRS,
Aubière, France
Related authors
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2065, https://doi.org/10.5194/egusphere-2025-2065, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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.
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.
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.
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).
Artem G. Feofilov and Claudia J. Stubenrauch
Atmos. Chem. Phys., 19, 13957–13972, https://doi.org/10.5194/acp-19-13957-2019, https://doi.org/10.5194/acp-19-13957-2019, 2019
Short summary
Short summary
Clouds play an important role in the energy budget of the planet: optically thick clouds reflect the incoming solar radiation leading to cooling of the Earth, while thinner clouds act as
greenhouse filmspreventing escape of the Earth’s infrared radiation to space. Satellite observations provide a continuous survey of clouds over the whole globe. In this work, we use a combination of two space-borne sounders to retrieve and analyse the characteristics of diurnal variation of high-level clouds.
Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, and Rainer Hollmann
Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, https://doi.org/10.5194/essd-9-881-2017, 2017
Short summary
Short summary
We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.
Claudia J. Stubenrauch, Artem G. Feofilov, Sofia E. Protopapadaki, and Raymond Armante
Atmos. Chem. Phys., 17, 13625–13644, https://doi.org/10.5194/acp-17-13625-2017, https://doi.org/10.5194/acp-17-13625-2017, 2017
Short summary
Short summary
We present multi-year cloud climatologies from the advanced IR sounders AIRS and IASI. These data are particularly sensitive to cirrus. Cloud emissivity allows to distinguish between high opaque, thick cirrus and thin cirrus. By comparing tropical geographical change patterns of these cloud types with respect to changing tropical mean surface temperature, it is demonstrated that their response to climate change may be different, with potential consequences on the atmospheric circulation.
Peter A. Panka, Alexander A. Kutepov, Konstantinos S. Kalogerakis, Diego Janches, James M. Russell, Ladislav Rezac, Artem G. Feofilov, Martin G. Mlynczak, and Erdal Yiğit
Atmos. Chem. Phys., 17, 9751–9760, https://doi.org/10.5194/acp-17-9751-2017, https://doi.org/10.5194/acp-17-9751-2017, 2017
Short summary
Short summary
Recently, theoretical and laboratory studies have suggested an additional
nighttime channel of transfer of vibrational energy of OH molecules to CO2 in the
mesosphere and lower thermosphere (MLT). We show that new mechanism brings
modelled 4.3 μm emissions very close to the SABER/TIMED measurements. This
renders new opportunities for the application of the CO2 4.3 μm observations in
the study of the energetics and dynamics of the nighttime MLT.
Sofia E. Protopapadaki, Claudia J. Stubenrauch, and Artem G. Feofilov
Atmos. Chem. Phys., 17, 3845–3859, https://doi.org/10.5194/acp-17-3845-2017, https://doi.org/10.5194/acp-17-3845-2017, 2017
Short summary
Short summary
Upper tropospheric clouds cover about 30 % of the Earth and play a key role in the climate system by modulating the Earth's energy budget and heat transport. In this article, we study upper tropospheric cloud systems using cloud properties deduced from infrared sounders. Our analyses show that the size of the systems as well as the fraction of thin cirrus over the total anvil area increases with increasing convective depth.
Alexander A. Kutepov, Ladislav Rezac, and Artem G. Feofilov
Atmos. Meas. Tech., 10, 265–271, https://doi.org/10.5194/amt-10-265-2017, https://doi.org/10.5194/amt-10-265-2017, 2017
Short summary
Short summary
We show that the long-standing discrepancy between observed and calculated spectra of the PFS/MEx in the cores and wings of 4.3-micron region is explained by the non-thermal rotational distribution of molecules in the upper vibrational states of second hot (SH) CO2 bands above 90 km altitude. We
discuss the implications that accounting for this effect has for developing operational algorithms aimed at massive processing of PFS and other instrument limb observations.
A. G. Feofilov, C. J. Stubenrauch, and J. Delanoë
Atmos. Chem. Phys., 15, 12327–12344, https://doi.org/10.5194/acp-15-12327-2015, https://doi.org/10.5194/acp-15-12327-2015, 2015
Short summary
Short summary
We discuss the shape of ice water content (IWC) vertical profiles in high ice clouds and its effect on radiative properties of these clouds, both in short- and in long-wave bands (SW and LW). We suggest a set of primitive shapes (rectangular, isosceles trapezoid, lower and upper triangle) and propose a statistical parameterization using ice water path (IWP) as a single parameter. We estimate and explain simulated differences in LW/SW atmospheric radiances for suggested IWC shapes.
Zacharie Titus, Marine Bonazzola, Hélène Chepfer, Artem Feofilov, Marie-Laure Roussel, Benjamin Witschas, and Sophie Bastin
EGUsphere, https://doi.org/10.5194/egusphere-2025-2065, https://doi.org/10.5194/egusphere-2025-2065, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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.
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.
Mathilde Leroux and Vincent Noel
Atmos. Chem. Phys., 24, 6433–6454, https://doi.org/10.5194/acp-24-6433-2024, https://doi.org/10.5194/acp-24-6433-2024, 2024
Short summary
Short summary
This study investigates the long-term changes in the polar stratospheric cloud (PSC) season from 1980 to 2021 above Antarctica. We analyzed CALIOP observations from 2006 to 2020 to build a statistical temperature-based model. We applied our model to gridded reanalysis temperatures, leading to an integrated view of PSC occurrence that is free from sampling issues, allowing us to document the past evolution of the PSC season.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
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.
Matthias Tesche and Vincent Noel
Atmos. Meas. Tech., 15, 4225–4240, https://doi.org/10.5194/amt-15-4225-2022, https://doi.org/10.5194/amt-15-4225-2022, 2022
Short summary
Short summary
Mid-level and high clouds can be considered natural laboratories for studying cloud glaciation in the atmosphere. While they can be conveniently observed from ground with lidar, such measurements require a clear line of sight between the instrument and the target cloud. Here, observations of clouds with two spaceborne lidars are used to assess where ground-based lidar measurements of mid- and upper-level clouds are least affected by the light-attenuating effect of low-level clouds.
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.
Damien Héron, Stephanie Evan, Joris Pianezze, Thibaut Dauhut, Jerome Brioude, Karen Rosenlof, Vincent Noel, Soline Bielli, Christelle Barthe, and Jean-Pierre Cammas
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-870, https://doi.org/10.5194/acp-2020-870, 2020
Publication in ACP not foreseen
Short summary
Short summary
Upward transport within tropical cyclones of water vapor from the low troposphere into the colder upper troposphere/lower stratosphere can result in the moistening of this region. Balloon observations and model simulations of tropical cyclone Enawo in the less-observed Southwest Indian Ocean (the third most tropical cyclone active region on Earth) are used to show how convective overshoots within Enawo penetrate the tropopause directly, injecting water/ice into the stratosphere.
Thibaut Dauhut, Vincent Noel, and Iris-Amata Dion
Atmos. Chem. Phys., 20, 3921–3929, https://doi.org/10.5194/acp-20-3921-2020, https://doi.org/10.5194/acp-20-3921-2020, 2020
Short summary
Short summary
We document for the first time the diurnal cycle of the clouds in the tropical stratosphere, using the measurements from the lidar on board the International Space Station. The stratospheric clouds are concentrated over the convective centers. Their cloud fraction is minimal and limited to the vicinity of the tropopause during daytime. It presents two maxima: one in the early night and one shortly after midnight, when clouds also extend deeper in the stratosphere.
Artem G. Feofilov and Claudia J. Stubenrauch
Atmos. Chem. Phys., 19, 13957–13972, https://doi.org/10.5194/acp-19-13957-2019, https://doi.org/10.5194/acp-19-13957-2019, 2019
Short summary
Short summary
Clouds play an important role in the energy budget of the planet: optically thick clouds reflect the incoming solar radiation leading to cooling of the Earth, while thinner clouds act as
greenhouse filmspreventing escape of the Earth’s infrared radiation to space. Satellite observations provide a continuous survey of clouds over the whole globe. In this work, we use a combination of two space-borne sounders to retrieve and analyse the characteristics of diurnal variation of high-level clouds.
Thomas Fauchez, Steven Platnick, Tamás Várnai, Kerry Meyer, Céline Cornet, and Frédéric Szczap
Atmos. Chem. Phys., 18, 12105–12121, https://doi.org/10.5194/acp-18-12105-2018, https://doi.org/10.5194/acp-18-12105-2018, 2018
Short summary
Short summary
This paper presents the impact of cirrus cloud heterogeneities and 3-D effects on TOA solar reflectances from 50 m to 10 km spatial resolutions. We have shown that these effects are strongly dependent on spatial resolution as well as solar and viewing geometries and that it is difficult to find an optimal spatial resolution minimizing these various effects.
Vincent Noel, Hélène Chepfer, Marjolaine Chiriaco, and John Yorks
Atmos. Chem. Phys., 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, https://doi.org/10.5194/acp-18-9457-2018, 2018
Short summary
Short summary
From 3 years of observations from the CATS lidar on the International Space Station we document the daily cycle of the vertical distribution of clouds.
This is the first time this is documented over several continents and oceans using finely resolved measurements on a near-global scale from a single instrument.
We show that other instruments observing clouds from space, like CALIPSO, document extremes of the daily cycle over ocean and closer to the average over land.
Céline Cornet, Laurent C.-Labonnote, Fabien Waquet, Frédéric Szczap, Lucia Deaconu, Frédéric Parol, Claudine Vanbauce, François Thieuleux, and Jérôme Riédi
Atmos. Meas. Tech., 11, 3627–3643, https://doi.org/10.5194/amt-11-3627-2018, https://doi.org/10.5194/amt-11-3627-2018, 2018
Short summary
Short summary
Simulations of total and polarized cloud reflectance angular signatures such as the ones measured by the multi-angular and polarized radiometer POLDER3/PARASOL are used to evaluate cloud heterogeneity effects on cloud parameter retrievals. Effects on optical thickness, albedo of the cloudy scenes, effective radius and variance of the cloud droplet size distribution, cloud top pressure and aerosol above cloud are analyzed.
Thibault Vaillant de Guélis, Hélène Chepfer, Vincent Noel, Rodrigo Guzman, Philippe Dubuisson, David M. Winker, and Seiji Kato
Atmos. Meas. Tech., 10, 4659–4685, https://doi.org/10.5194/amt-10-4659-2017, https://doi.org/10.5194/amt-10-4659-2017, 2017
Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, and Rainer Hollmann
Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, https://doi.org/10.5194/essd-9-881-2017, 2017
Short summary
Short summary
We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.
Claudia J. Stubenrauch, Artem G. Feofilov, Sofia E. Protopapadaki, and Raymond Armante
Atmos. Chem. Phys., 17, 13625–13644, https://doi.org/10.5194/acp-17-13625-2017, https://doi.org/10.5194/acp-17-13625-2017, 2017
Short summary
Short summary
We present multi-year cloud climatologies from the advanced IR sounders AIRS and IASI. These data are particularly sensitive to cirrus. Cloud emissivity allows to distinguish between high opaque, thick cirrus and thin cirrus. By comparing tropical geographical change patterns of these cloud types with respect to changing tropical mean surface temperature, it is demonstrated that their response to climate change may be different, with potential consequences on the atmospheric circulation.
Guillaume Mioche, Olivier Jourdan, Julien Delanoë, Christophe Gourbeyre, Guy Febvre, Régis Dupuy, Marie Monier, Frédéric Szczap, Alfons Schwarzenboeck, and Jean-François Gayet
Atmos. Chem. Phys., 17, 12845–12869, https://doi.org/10.5194/acp-17-12845-2017, https://doi.org/10.5194/acp-17-12845-2017, 2017
Short summary
Short summary
This paper is a study about the mixed-phase clouds frequently occurring in the Arctic region. It is based on airborne measurements and highlights the microphysical properties of these particular clouds composed of liquid droplets at cloud top and ice crystals below precipitating down to the surface. This work may help to improve the representation of the mixed-phase clouds in numerical prediction models as well as the retrieval of their properties from remote sensing observations.
Peter A. Panka, Alexander A. Kutepov, Konstantinos S. Kalogerakis, Diego Janches, James M. Russell, Ladislav Rezac, Artem G. Feofilov, Martin G. Mlynczak, and Erdal Yiğit
Atmos. Chem. Phys., 17, 9751–9760, https://doi.org/10.5194/acp-17-9751-2017, https://doi.org/10.5194/acp-17-9751-2017, 2017
Short summary
Short summary
Recently, theoretical and laboratory studies have suggested an additional
nighttime channel of transfer of vibrational energy of OH molecules to CO2 in the
mesosphere and lower thermosphere (MLT). We show that new mechanism brings
modelled 4.3 μm emissions very close to the SABER/TIMED measurements. This
renders new opportunities for the application of the CO2 4.3 μm observations in
the study of the energetics and dynamics of the nighttime MLT.
Thomas Fauchez, Steven Platnick, Kerry Meyer, Céline Cornet, Frédéric Szczap, and Tamás Várnai
Atmos. Chem. Phys., 17, 8489–8508, https://doi.org/10.5194/acp-17-8489-2017, https://doi.org/10.5194/acp-17-8489-2017, 2017
Short summary
Short summary
This study presents impact of cirrus cloud horizontal heterogeneity on simulated thermal infrared brightness temperatures at the top of the atmosphere for spatial resolutions ranging from 50 m to 10 km. The cirrus is generated by the 3DCLOUD code and the radiative transfer by the 3DMCPOL code. Brightness temperatures are mostly impacted by the horizontal transport effect and plane-parallel bias at high and coarse spatial resolutions, respectively, with a minimum around 100 m–250 m.
Sofia E. Protopapadaki, Claudia J. Stubenrauch, and Artem G. Feofilov
Atmos. Chem. Phys., 17, 3845–3859, https://doi.org/10.5194/acp-17-3845-2017, https://doi.org/10.5194/acp-17-3845-2017, 2017
Short summary
Short summary
Upper tropospheric clouds cover about 30 % of the Earth and play a key role in the climate system by modulating the Earth's energy budget and heat transport. In this article, we study upper tropospheric cloud systems using cloud properties deduced from infrared sounders. Our analyses show that the size of the systems as well as the fraction of thin cirrus over the total anvil area increases with increasing convective depth.
Alexander A. Kutepov, Ladislav Rezac, and Artem G. Feofilov
Atmos. Meas. Tech., 10, 265–271, https://doi.org/10.5194/amt-10-265-2017, https://doi.org/10.5194/amt-10-265-2017, 2017
Short summary
Short summary
We show that the long-standing discrepancy between observed and calculated spectra of the PFS/MEx in the cores and wings of 4.3-micron region is explained by the non-thermal rotational distribution of molecules in the upper vibrational states of second hot (SH) CO2 bands above 90 km altitude. We
discuss the implications that accounting for this effect has for developing operational algorithms aimed at massive processing of PFS and other instrument limb observations.
A. G. Feofilov, C. J. Stubenrauch, and J. Delanoë
Atmos. Chem. Phys., 15, 12327–12344, https://doi.org/10.5194/acp-15-12327-2015, https://doi.org/10.5194/acp-15-12327-2015, 2015
Short summary
Short summary
We discuss the shape of ice water content (IWC) vertical profiles in high ice clouds and its effect on radiative properties of these clouds, both in short- and in long-wave bands (SW and LW). We suggest a set of primitive shapes (rectangular, isosceles trapezoid, lower and upper triangle) and propose a statistical parameterization using ice water path (IWP) as a single parameter. We estimate and explain simulated differences in LW/SW atmospheric radiances for suggested IWC shapes.
V. Noel, H. Chepfer, C. Hoareau, M. Reverdy, and G. Cesana
Atmos. Meas. Tech., 7, 1597–1603, https://doi.org/10.5194/amt-7-1597-2014, https://doi.org/10.5194/amt-7-1597-2014, 2014
C. Hoareau, P. Keckhut, V. Noel, H. Chepfer, and J.-L. Baray
Atmos. Chem. Phys., 13, 6951–6963, https://doi.org/10.5194/acp-13-6951-2013, https://doi.org/10.5194/acp-13-6951-2013, 2013
M. Reverdy, V. Noel, H. Chepfer, and B. Legras
Atmos. Chem. Phys., 12, 12081–12101, https://doi.org/10.5194/acp-12-12081-2012, https://doi.org/10.5194/acp-12-12081-2012, 2012
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
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Algorithm for continual monitoring of fog based on geostationary satellite imagery
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)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Synergy of millimeter-wave radar and radiometer measurements for retrieving frozen hydrometeors in deep convective systems
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Vertical Wind and Drop Size Distribution Retrieval with the CloudCube G-band Doppler Radar
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Harmonized Cloud Datasets for OMI and TROPOMI Using the O2‐O2 477 nm Absorption Band
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Assessment of horizontally-oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar
Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Riming-dependent Snowfall Rate and Ice Water Content Retrievals for W-band cloud radar
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
Radiative Closure Assessment of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACMB-DF Product
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
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
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
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
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests based on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observation experiments.
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
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
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
Short summary
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
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
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Short summary
This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Keiichi Ohara and Hirohiko Masunaga
EGUsphere, https://doi.org/10.5194/egusphere-2025-173, https://doi.org/10.5194/egusphere-2025-173, 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.
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
Short summary
This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
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
Short summary
The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
Short summary
Short summary
Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
EGUsphere, https://doi.org/10.5194/egusphere-2025-478, https://doi.org/10.5194/egusphere-2025-478, 2025
Short summary
Short summary
We introduce a new cloud retrieval algorithm using the O2-O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2-O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
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
Short summary
In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
Short summary
Short summary
The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
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
Short summary
We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3841, https://doi.org/10.5194/egusphere-2024-3841, 2025
Short summary
Short summary
This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a year-long dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve the atmospheric model.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
EGUsphere, https://doi.org/10.5194/egusphere-2024-3664, https://doi.org/10.5194/egusphere-2024-3664, 2025
Short summary
Short summary
Contrails, the linear clouds formed by aircraft, are have a substantial climate impact. Flight deviations to avoid forming contrails should decrease this impact. We introduce a method for matching contrails seen by satellites to the flights that made them. This can determine if avoidance was successful and improve contrail forecasts. We also introduce a synthetic contrail dataset to evaluate the accuracy of the matches. We show that our attributions are much more accurate than previous methods.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3916, https://doi.org/10.5194/egusphere-2024-3916, 2025
Short summary
Short summary
Accurate measurements of cloud water content IWC and snowfall rate SR are challenging due to high spatial variability and limitations of our measurement techniques. Here we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path as a proxy for the occurrence of riming, which is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Short summary
Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
Short summary
Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Short summary
This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1651, https://doi.org/10.5194/egusphere-2024-1651, 2024
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared and the likelihood of them differing by 10 W/m2 defines the closure.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
Short summary
Short summary
This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
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
Short summary
Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
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
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
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
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
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
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
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
Short summary
This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Short summary
The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary
Short summary
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Cited articles
Aerenson, T., Marchand, R., Chepfer, H., and Medeiros, B.: When Will MISR Detect
Rising High Clouds? J. Geophys. Res.-Atmos., 127, e2021JD035865,
https://doi.org/10.1029/2021JD035865, 2022.
Alkasem A., Szczap, F., Cornet, C., Shcherbakov, V., Gour, Y., Jourdan, O.,
Labonnote, L. C., and Mioche, G.: Effects of cirrus heterogeneity on lidar
CALIOP/CALIPSO data, JQSRT, 202, 38–49, https://doi.org/10.1016/j.jqsrt.2017.07.005,
2017.
Berry, E., Mace, G. G., and Gettelman, A. : Using A-Train Observations to
Evaluate Cloud Occurrence and Radiative Effects in the Community Atmosphere
Model during the Southeast Asia Summer Monsoon, J. Climate, 32,
4145–4165, https://doi.org/10.1175/JCLI-D-18-0693.1, 2019.
Beyerle, G., Gross, M. R., Haner, D. A., Kjome, N. T., McDermid, I. S.,
McGee, T. J., Rosen, J. M., Schäfer, H.-J., and Schrems, O.: A Lidar and
Backscatter Sonde Measurement Campaign at Table Mountain during
February-March 1997: Observations of Cirrus Clouds. J. Appl. Meteor., 40,
1275–1287, https://doi.org/10.1175/1520-0469(2001)058<1275:ALABSM>2.0.CO;2, 2001.
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H, J.-l. Dufresne, J.-L.,
Klein, S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John,
V. O.: COSP, Satellite simulation software for model assessment, B. Am.
Meteorol. Soc., 92, 1023–1043, https://doi.org/10.1175/2011BAMS2856.1, 2011.
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P., Brockmann, P., Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A., Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes, J., Devilliers, M., Ducharne, A., Dufresne, J. L., Dupont, E., Éthé, C., Fairhead, L., Falletti, L., Flavoni, S., Foujols, M. A., Gardoll, S., Gastineau, G., Ghattas, J., Grandpeix, J. Y., Guenet, B., Guez, L. E., Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A., Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur, G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G., Madeleine, J. B., Maignan, F., Marchand, M., Marti, O., Mellul, L., Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y., Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A., Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J., Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the IPSL-CM6A-LR
Climate Model, J. Adv. Model. Earth Syst., 12, e2019MS002010,
https://doi.org/10.1029/2019MS002010, 2020.
Cesana, G., Del Genio, A. D., and Chepfer, H.: The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), Earth Syst. Sci. Data, 11, 1745–1764, https://doi.org/10.5194/essd-11-1745-2019, 2019.
Chemke, R.: Future changes in the Hadley circulation: The role of ocean heat
transport, Geophys. Res. Lett., 48, e2020GL091372.
https://doi.org/10.1029/2020GL091372, 2021.
Cheng, Z., Liu, D., Yang, Y., Yang, L., and Huang, H.: Interferometric
filters for spectral discrimination in high-spectral-resolution lidar:
performance comparisons between Fabry–Perot interferometer and
field-widened Michelson interferometer, Appl. Opt., 52, 7838–7850, https://doi.org/10.1364/AO.52.007838, 2013.
Chepfer, H., Bony, S., Winker, D., Chiriaco, M., Dufresne, J.-L., and Sèze,
G.: Use of CALIPSO lidar observations to evaluate the cloudiness simulated
by a climate model, Geophys. Res. Let., 35, L15704,
https://doi.org/10.1029/2008GL034207, 2008.
Chepfer, H., Bony, S., Winker, D., Cesana, G., Dufresne, J.-L., Minnis, P.,
Stubenrauch, C. J., and Zeng, S.: The GCM Oriented Calipso Cloud Product
(CALIPSO-GOCCP), J. Geophys. Res., 115, D00H16, https://doi.org/10.1029/2009JD012251,
2010.
Chepfer, H., Cesana, G., Winker, D., Getzewich, B., and Vaughan, M.:
Comparison of two different cloud climatologies derived from CALIOP Level 1
observations: the CALIPSO-ST and the CALIPSO-GOCCP, J. Atmos. Ocean. Technol.,
30, 725–744 https://doi.org/10.1175/JTECH-D-12-00057.1, 2013.
Chepfer, H., Noël, V., Winker, D., and Chiriaco, M.: Where and when will
we observe cloud changes due to climate warming?, Geophys. Res. Lett., 41,
8387–8395, https://doi.org/10.1002/2014GL061792, 2014.
Chepfer, H., Noël, V., Chiriaco, M., Wielicki, B., Winker, D., Loeb, N.,
and Wood, R.: The potential of multi-decades space-born lidar to constrain
cloud feedbacks, J. Geophys. Res.-Atmos., 123, 5433–5454, https://doi.org/10.1002/2017JD027742, 2018.
Chepfer, H., Brogniez, H., and Noël, 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.
Chiriaco, M., Vautard, R., Chepfer, H., Haeffelin, M., Dudhia, J.,
Wanherdrick, Y., Morille, Y., and Protat, A.: The Ability of MM5 to Simulate
Ice Clouds: Systematic Comparison between Simulated and Measured Fluxes and
Lidar/Radar Profiles at the SIRTA Atmospheric Observatory, Mon. Weather
Rev., 134, 897–918, https://doi.org/10.1175/MWR3102.1, 2006.
Davis, S. M. and Rosenlof, K. H.: A multidiagnostic intercomparison of
tropical-width time series using reanalyses and satellite observations, J.
Climate, 25, 1061–1078, 2012.
do Carmo, J. P., Villele, de V., Wallace, K., Lefebvre, A., Ghose, K.,
Kanitz, T., Chassat, F., Corselle, B., Belhadj, T., and Bravetti, P.:
ATmospheric LIDar(ATLID): Pre-launch testing and calibration of the European
Space Agency instrument that will measure aerosols and thin clouds in the
atmosphere, Atmosphere, 12, 76, https://doi.org/10.3390/atmos12010076, 2021.
Donovan, D. P.: The Expected Impact of Multiple Scattering on ATLID Signals,
EPJ Conf., 119, 01006, https://doi.org/10.1051/epjconf/201611901006, 2016.
Durand, Y., Hélière, A., Bézy, J.-L., and Meynart, R.: The ESA
EarthCARE mission: Results of the ATLID instrument pre-developments,
Proc. SPIE, 6750, 675015, https://doi.org/10.1117/12.737932, 2007.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
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.
Feofilov, A. G., Stubenrauch, C. J., and Delanoë, J.: Ice water content vertical profiles of high-level clouds: classification and impact on radiative fluxes, Atmos. Chem. Phys., 15, 12327–12344, https://doi.org/10.5194/acp-15-12327-2015, 2015.
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.
Garnier, A., Pelon, J., Vaughan, M. A., Winker, D. M., Trepte, C. R., and Dubuisson, P.: Lidar multiple scattering factors inferred from CALIPSO lidar and IIR retrievals of semi-transparent cirrus cloud optical depths over oceans, Atmos. Meas. Tech., 8, 2759–2774, https://doi.org/10.5194/amt-8-2759-2015, 2015.
Guzman, R., Chepfer, H., Noël, V., Vaillant de Guelis, T., Kay, J. E.,
Raberanto, P., Cesana, G., Vaughan, M. A., and Winker, D. M.: Direct
atmosphere opacity observations from CALIPSO provide new constraints on
cloud-radiation interactions, J. Geophys. Res.-Atmos., 122, 1066–1085,
https://doi.org/10.1002/2016JD025946, 2017.
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G.
L.: A multipurpose radar simulation package: QuickBeam, B. Am. Meteorol.
Soc., 88, 1723–1727, https://doi.org/10.1175/BAMS-88-11-1723, 2007.
Héliere, A., Gelsthorpe, R., Le Hors, L., and Toulemont, Y.: ATLID, the Atmospheric Lidar on board the EarthCARE Satellite, Proc. SPIE 10564, International Conference on Space Optics – ICSO 2012, 105642D (20 November 2017), https://doi.org/10.1117/12.2309095, 2017.
Heymsfield, A., Krämer, M., Wood, N. B., Gettelman, A., Field, P. R., and Liu, G.: Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations, J. App. Meteorol. Climatol., 56, 189–215, https://doi.org/10.1175/JAMC-D-16-0230.1, 2017.
Heymsfield, A., Bansemer, A., Wood, N. B., Liu, G., Tanelli, S., Sy, O. O.,
Poellot, M., and Liu, C.: Toward Improving Ice Water Content and Snow-Rate
Retrievals from Radars. Part II: Results from Three Wavelength
Radar–Collocated In Situ Measurements and CloudSat–GPM–TRMM Radar Data,
J. Appl. Met. Clim., 57, 365–389, https://doi.org/10.1175/JAMC-D-17-0164.1, 2018.
Hogan, R. J. and Illingworth, A. J.: Parameterizing Ice Cloud Inhomogeneity
and the Overlap of Inhomogeneities Using Cloud Radar Data, J.
Atmos. Sci., 60, 756–767,
https://doi.org/10.1175/1520-0469(2003)060<0756:PICIAT>2.0.CO;2, 2003.
Hogan, R. J. and Kew, S.F.: A 3D stochastic cloud model for investigating
the radiative properties of inhomogeneous cirrus clouds, Q. J. Roy. Meteorol.
Soc., 131, 2585–2608, https://doi.org/10.1256/qj.04.144, 2005.
Hu, Y., Liu, Z., Winker, D., Vaughan, M., Noel, V., Bissonnette, L., Roy, G., and McGill, M.: Simple relation between lidar multiple scattering and depolarization for water clouds, Opt. Lett., 31, 1809–1811, 2006.
Hunt, W. H., Winker, D. M., Vaughan, M. A., Powell, K. A., Lucker, P. L., and
Weimer, C.: CALIPSO Lidar Description and Performance Assessment, J. Atmos.
Ocean. Technol., 26, 1214–1228, https://doi.org/10.1175/2009JTECHA1223.1, 2009.
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J.E., Kushner, P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb, W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for
Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, 2013.
Illingworth, A. J., Barker, H. W., Beljaars, A. et al.: 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.
Kärcher, B., Thornberry, T. D., Krämer, M., and Jensen, E. J.: On
the statistical distribution of total water in cirrus clouds, Geophys. Res.
Lett., 45, 9963–9971, https://doi.org/10.1029/2018GL079780, 2018.
Kingston, R. H.: Detection of Optical and Infrared Radiation, Springer Series
in Optical Sciences, v10, Springer, https://doi.org/10.1007/978-3-540-35948-7, 142 pp., 1978.
Kjellsson, J.: Weakening of the global atmospheric circulation with global
warming, Clim. Dynam., 45, 975–988,
https://doi.org/10.1007/s00382-014-2337-8, 2015.
Klein, S. A. and Jakob, C.: Validation and Sensitivities of Frontal Clouds
Simulated by the ECMWF Model, Mon. Weather Rev., 127, 2514–2531,
https://doi.org/10.1175/1520-0493(1999)127<2514:VASOFC>2.0.CO;2,
1999.
Konsta, D., Chepfer, H., and Dufresne, J.-L.: A process oriented
characterization of tropical oceanic clouds for climate model evaluation,
based on a statistical analysis of daytime A-Train observations, Clim. Dynam.,
39, 2091–2108, https://doi.org/10.1007/s00382012-1533-7, 2012.
Leahy, L. V., Wood, R., Charlson, R. J., Hostetler, C. A., Rogers, R. R.,
Vaughan, M. A., and Winker, D. M.: On the nature and extent of optically thin
marine low clouds, J. Geophys. Res.-Atmos., 117, D22201,
https://doi.org/10.1029/2012JD017929, 2022.
Liu, Z. and Sugimoto, N.: Simulation study for cloud detection with space
lidars using analog detection photomultiplier tubes, Appl. Opt., 41,
1750–1759, 2002.
Liu, Z., Hunt, W. H., Vaughan, M., Hostetler, C., McGill, M., Powell, K.,
Winker, D., and Hu, Y.: Estimating random errors due to shot noise in
backscatter lidar observations, Appl. Opt., 45, 4437–4447,
https://doi.org/10.1364/AO.45.004437, 2006.
Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A
comparison of simulated cloud radar output from the multiscale modeling
framework global climate model with CloudSat cloud radar observations, J.
Geophys. Res., 114, D00A20, https://doi.org/10.1029/2008JD009790, 2009.
McGill, M. J., Vaughan, M. A., Trepte, C. R., Hart, W. D., Hlavka, D. L.,
Winker, D. M., and Kuehn, R.: Airborne validation of spatial properties measured
by the CALIPSO lidar, J. Geophys. Res.-Atmos., 112, D20201,
https://doi.org/10.1029/2007JD008768, 2007.
McGill, M. J., Yorks, J. E., Scott, V. S., Kupchock, A. W., and Selmer, P.
A.: The Cloud-Aerosol Transport System (CATS): A technology demonstration on
the International Space Station, Proc. Spie., 9612, https://doi.org/10.1117/12.2190841,
2015.
Nam C., Bony, S., Dufresne, J. L., and Chepfer, H.: The 'too few, too bright'
tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, 21,
https://doi.org/10.1029/2012GL053421, 2012.
Noel, V.: Monthly global grids of lidar-consistent opaque cloud fractions and altitude of attenuation 2008–2034 according to predictions from CESM2 and IPSL-CM6 GCMs, Zenodo [data set], https://doi.org/10.5281/zenodo.8059374, 2023.
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.
Perpina, M., Noel, V., Chepfer, H., Guzman, R., and Feofilov, A. G.: Link
between opaque cloud properties and atmospheric dynamics in observations and
simulations of current climate in the Tropics, and impact on future
predictions. J. Geophys. Res.-Atmos., 126, e2020JD033899.
https://doi.org/10.1029/2020JD033899, 2021.
Platt, C. M. R.: Lidar and radiometric observations of cirrus clouds, J.
Atmos. Sci., 30, 1191–1204, 1973.
Rasch, P. J., Xie, S., Ma, P.-L., Lin, W., Wang, H., Tang, Q., Burrows, S. M., Caldwell, P., Zhang, K., Easter, R. C., Cameron-Smith, P., Singh, B., Wan, H., Golaz, J.-C., Harrop, B. E., Roesler, E., Bacmeister, J., Larson, V. E., Evans, K. J., Qian, Y., Taylor, M., Leung, L. R., Zhang, Y., Brent, L., Branstetter, M., Hannay, C., Mahajan, S., Mametjanov, A., Neale, R., Richter, J. H., Yoon, J.-H., Zender, C. S., Bader, D., Flanner, M., Foucar, J. G., Jacob, R., Keen, N., Klein, S. A., Liu, X., Salinger, A. G., Shrivastava, M., and Yang, Y.: An Overview of the Atmospheric Component of the
Energy Exascale Earth System Model, J. Adv. Model. Earth Syst., 11, 2377–2411,
https://doi.org/10.1029/2019MS001629, 2019.
Reitebuch, O., Lemmerz, C., Lux, O., Marksteiner, U., Rahm, S., Weiler, F., Witschas, B., Meringer, M., Schmidt, K., Huber, D., Nikolaus, I., Geiss, A., Vaughan, M., Dabas, A., Flament, T., Stieglitz, H., Isaksen, L., Rennie, M., de Kloe, J., Marseille, G.-J., Stoffelen, A., Wernham, D., Kanitz, T., Straume, A.-G., Fehr, T., von Bismarck, J., Floberghagen, R., and Parrinello, T.: Initial Assessment of the Performance of
the First Wind Lidar in Space on Aeolus, EPJ Web Conf., 237, 01010,
https://doi.org/10.1051/epjconf/202023701010, 2020.
Reverdy M., Chepfer, H., Donovan, D., Noël, V., Cesana, G., Hoareau, C.,
Chiriaco, M., and Bastin, S.: An EarthCARE/ATLID simulator to evaluate cloud
description in climate models, J. Geophys. Res.-Atmos., 120,
11090–11113, https://doi.org/10.1002/2015JD023919, 2015.
Richardson, M. T., Roy, R. J., and Lebsock, M. D.: Satellites Suggest Rising Tropical High Cloud Altitude: 2002–2021, Geophys. Res. Lett., 49, e2022GL098160, https://doi.org/10.1029/2022GL098160, 2022.
Rogers, R. R., Vaughan, M. A., Hostetler, C. A., Burton, S. P., Ferrare, R. A., Young, S. A., Hair, J. W., Obland, M. D., Harper, D. B., Cook, A. L., and Winker, D. M.: Looking through the haze: evaluating the CALIPSO level 2 aerosol optical depth using airborne high spectral resolution lidar data, Atmos. Meas. Tech., 7, 4317–4340, https://doi.org/10.5194/amt-7-4317-2014, 2014.
Sassen, K. and Comstock, J. M.: A midlatitude cirrus cloud climatology from
the facility for atmospheric remote sensing. Part III: Radiative properties,
J. Atmos. Sci., 58, 2113–2127, https://doi.org/10.1175/1520-0469(2001)058<2113:Amcccf>2.0.Co;2, 2001.
Schwalm, C. R., Glendon, S., and Duffy, P. B.: RCP85 tracks cumulative CO2
emissions, P. Natl. Acad. Sci. USA, 117, 19656–19657,
https://doi.org/10.1073/pnas.2007117117, 2020.
Shcherbakov, V., Szczap, F., Alkasem, A., Mioche, G., and Cornet, C.: Empirical model of multiple-scattering effect on single-wavelength lidar data of aerosols and clouds, Atmos. Meas. Tech., 15, 1729–1754, https://doi.org/10.5194/amt-15-1729-2022, 2022.
Stevens, B., H-Moeng, C., Ackerman A. S., Bretherton, C. S., Chlond, A., de
Roode, S., Edwards, J., Golaz, J. C., Jiang, H., Khairoutdinov, M.,
Kirkpatrick, M. P., Lewellen, D. C., Lock, A. L., Müller, F., Stevens,
D. E., Whelan, E., and Zhu, P.: Evaluation of large-eddy simulations via
observations of nocturnal marine stratocumulus, Mon. Weather Rev., 133,
1443–1462, 2005.
Straume, A. G., Rennie, M., Isaksen, L., de Kloe, J., Marseille, G.-J., Stoffelen, A., Flament, T., Stieglitz, H., Dabas, A., Huber, D., Reitebuch, O., Lemmerz, C., Lux, O., Marksteiner, U., Weiler, F., Witschas, B., Meringer, M., Schmidt, K., Nikolaus, I., Geiss, A., Flamant, P., Kanitz, T., Wernham, D., von Bismarck, J., Bley, S., Fehr, T., Floberghagen, R., and Parrinello, T.: ESA's Space-Based Doppler Wind Lidar
Mission Aeolus – First Wind and Aerosol Product Assessment Results, EPJ Web
Conf. 237, 01007, https://doi.org/10.1051/epjconf/202023701007, 2020.
Su, H., Jiang, J. H., Zhai, C., Shen, T. J., Neelin, J. D., Stephens, G. L.,
and Yung, Y. L: Weakening and strengthening structures in the Hadley
Circulation change under global warming and implications for cloud response
and climate sensitivity: Circulation, Clouds, Climate Sensitivity, J. Geophys. Res.-Atmos., 119, 5787–5805.
https://doi.org/10.1002/2014JD021642, 2014.
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018.
Szczap, F., Gour, Y., Fauchez, T., Cornet, C., Faure, T., Jourdan, O., Penide, G., and Dubuisson, P.: A flexible three-dimensional stratocumulus, cumulus and cirrus cloud generator (3DCLOUD) based on drastically simplified atmospheric equations and the Fourier transform framework, Geosci. Model Dev., 7, 1779–1801, https://doi.org/10.5194/gmd-7-1779-2014, 2014.
Takahashi, H., Lebsock, M. D., Richardson, M., Marchand, R., and Kay, J. E.:
When Will Spaceborne Cloud Radar Detect Upward Shifts in Cloud Heights?, J.
Geophys. Res.-Atmos., 124, 2018JD030242, https://doi.org/10.1029/2018JD030242,
2019.
Vaillant de Guélis, T., Chepfer, H., Noel, V., Guzman, R., Dubuisson, P., Winker, D. M., and Kato, S.: The link between outgoing longwave radiation and the altitude at which a spaceborne lidar beam is fully attenuated, Atmos. Meas. Tech., 10, 4659–4685, https://doi.org/10.5194/amt-10-4659-2017, 2017.
Vaillant de Guélis T., H. Chepfer, Noël, V., Guzman, R., Bonazzola,
M., and Winker, D. M.: Space lidar observations constrain longwave cloud
feedback, Nature Sci. Rep., 8, 16570, https://doi.org/10.1038/s41598-018-34943-1, 2018.
Voudouri, K. A., Giannakaki, E., Komppula, M., and Balis, D.: Variability in cirrus cloud properties using a PollyXT Raman lidar over high and tropical latitudes, Atmos. Chem. Phys., 20, 4427–4444, https://doi.org/10.5194/acp-20-4427-2020, 2020.
Waliser, D., Gleckler, P. J., Ferraro, R., Taylor, K. E., Ames, S., Biard, J., Bosilovich, M. G., Brown, O., Chepfer, H., Cinquini, L., Durack, P. J., Eyring, V., Mathieu, P.-P., Lee, T., Pinnock, S., Potter, G. L., Rixen, M., Saunders, R., Schulz, J., Thépaut, J.-N., and Tuma, M.: Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6, Geosci. Model Dev., 13, 2945–2958, https://doi.org/10.5194/gmd-13-2945-2020, 2020.
Winker, D. M., Pelon, J., Coakley Jr., J. A., Ackerman, S. A., Charlson, R.
J., Colarco, P. R., Flamant, P., Fu, Q., Hoff, R. M., Kittaka, C., Kubar, T.
L., Le Treut, H., Mccormick, M. P., Mégie, G., Poole, L., Powell, K.,
Trepte, C., Vaughan, M. A., and Wielicki, B. A.: The CALIPSO Mission,
A Global 3D View of Aerosols and Clouds, B. Am. Meteor. Soc., 91,
1211–1230, https://doi.org/10.1175/2010BAMS3009.1, 2010.
Winker, D., Chepfer, H., Noël, V., and Cai, X.: Observational
constraints on cloud feedbacks: The role of active satellite sensors,
Surv. Geophys., 38, 1483–1508,
https://doi.org/10.1007/s10712-017-9452-0, 2017.
Young, S. A., Vaughan, M. A., Garnier, A., Tackett, J. L., Lambeth, J. D., and Powell, K. A.: Extinction and optical depth retrievals for CALIPSO's Version 4 data release, Atmos. Meas. Tech., 11, 5701–5727, https://doi.org/10.5194/amt-11-5701-2018, 2018.
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and
Partitioning Cloud Feedbacks Using Cloud Property Histograms. Part II:
Attribution to Changes in Cloud Amount, Altitude, and Optical Depth, J.
Climate, 25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1,
2012.
Zelinka, M. D., Zhou, C., and Klein, S. A.: Insights from a refined
decomposition of cloud feedbacks, Geophys. Res. Lett., 43, 9259–9269,
https://doi.org/10.1002/2016GL069917, 2016.
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M., Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate sensitivity in CMIP6 models,
Geophys. Res. Lett., 47, e2019GL085782,
https://doi.org/10.1029/2019GL085782, 2020.
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.
The response of clouds to human-induced climate warming remains the largest source of...
Special issue