Articles | Volume 12, issue 3
https://doi.org/10.5194/amt-12-1717-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-12-1717-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Mar 2019
Research article |
| 18 Mar 2019
| 18 Mar 2019
Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Joint Institute for Regional Earth System
Science and Engineering, University of California Los Angeles, Los Angeles,
CA 90095, USA
Jussi Leinonen
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Heather Q. Cronk
Cooperative Institute for Research in the
Atmosphere, Colorado State University, Fort Collins, CO 80521, USA
James McDuffie
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Matthew D. Lebsock
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Graeme L. Stephens
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91125, USA
Department of Meteorology, University of Reading, Reading, RG6 6BB,
UK
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Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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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.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
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This study demonstrates the potential for 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.
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
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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.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett
EGUsphere, https://doi.org/10.5194/egusphere-2024-961, https://doi.org/10.5194/egusphere-2024-961, 2024
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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
EGUsphere, https://doi.org/10.5194/egusphere-2024-829, https://doi.org/10.5194/egusphere-2024-829, 2024
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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.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-28, https://doi.org/10.5194/amt-2024-28, 2024
Revised manuscript accepted for AMT
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This manuscript describes a new treatment of the spatial mis-registration 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.
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
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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.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests 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 observations experiments.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
EGUsphere, https://doi.org/10.5194/egusphere-2024-540, https://doi.org/10.5194/egusphere-2024-540, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences (BTDs) of the SEVIRI imager) 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.
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
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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
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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
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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.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
EGUsphere, https://doi.org/10.5194/egusphere-2024-327, https://doi.org/10.5194/egusphere-2024-327, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest, 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.
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
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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.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
EGUsphere, https://doi.org/10.5194/egusphere-2023-1085, https://doi.org/10.5194/egusphere-2023-1085, 2023
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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.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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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.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
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The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
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Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Cited articles
Bennartz, R.: Global assessment of marine boundary layer cloud droplet number
concentration from satellite, J. Geophys. Res., 112, D02201,
https://doi.org/10.1029/2006JD007547, 2007.
Betts, A. K.: Mixing Line Analysis of Clouds and Cloudy Boundary Layers, J.
Atmos. Sci., 42, 2751–2763, https://doi.org/10.1175/1520-0469(1985)042<2751:MLAOCA>2.0.CO;2, 1985.
Boers, R. and Mitchell, R. M.: Absorption feedback in stratocumulus clouds
Influence on cloud top albedo, Tellus A,
46, 229–241, https://doi.org/10.3402/tellusa.v46i3.15476, 1994.
Boesch, H., Brown, L., Castano, R., Christi, M., Connor, B., Crisp, D.,
Eldering, A., Fisher, B., Frankenberg, C., Gunson, M., Granat, R., McDuffie,
J., Miller, C., Natraj, V., O'Brien, D., O'Dell, C., Osterman, G., Oyafuso,
F., Payne, V., Polonsky, I., Smyth, M., Spurr, R., Thompson, D., and Toon, G.:
Orbiting Carbon Observatory (OCO)-2 Level 2 Full Physics Algorithm
Theoretical Basis Document, Pasadena, CA, available at:
https://docserver.gesdisc.eosdis.nasa.gov/public/project/OCO/OCO2_L2_ATBD.V8.pdf (last access: 10 March 2019), 2017.
Bony, S. and Dufresne, J.-L.: Marine boundary layer clouds at the heart of
tropical cloud feedback uncertainties in climate models, Geophys. Res. Lett.,
32, L20806, https://doi.org/10.1029/2005GL023851, 2005.
Brenguier, J., Pawlowska, H., Schüller, L., Preusker, R., Fischer, J., and
Fouquart, Y.: Radiative Properties of Boundary Layer Clouds: Droplet
Effective Radius versus Number Concentration, J. Atmos. Sci., 57,
803–821, https://doi.org/10.1175/1520-0469(2000)057<0803:RPOBLC>2.0.CO;2, 2000.
Chambers, L. H., Wielicki, B. A., and Loeb, N. G.: Shortwave Flux from
Satellite-Measured Radiance: A Theoretical Study over Marine Boundary Layer
Clouds, J. Appl. Meteorol., 40, 2144–2161,
https://doi.org/10.1175/1520-0450(2001)040<2144:SFFSMR>2.0.CO;2,
2001.
Cox, C. and Munk, W.: Statistics of the sea surface derived from sun glitter,
J. Mar. Res., 13, 198–227, 1954.
Crisp, D.: Measuring atmospheric carbon dioxide from space with the Orbiting
Carbon Observatory-2 (OCO-2), Proc. SPIE 9607, Earth Observing Systems XX,
960702 https://doi.org/10.1117/12.2187291, 2015.
Crisp, D., Atlas, R., Breon, F.-M., Brown, L., Burrows, J., Ciais, P.,
Connor, B., Doney, S., Fung, I., Jacob, D., Miller, C., O'Brien, D., Pawson, S., Randerson, J., Rayner, P., Salawitch, R., Sander, S.,
Sen, B., Stephens, G., Tans, P., Toon, G., Wennberg, P., Wofsy, S.,
Yung, Y., Kuang, Z., Chudasama, B., Sprague, G., Weiss, B., Pollock, R.,
Kenyon, D., and Schroll, S.: The Orbiting Carbon Observatory (OCO) mission,
Adv. Space Res., 34, 700–709, https://doi.org/10.1016/j.asr.2003.08.062, 2004.
Crisp, D., Pollock, H. R., Rosenberg, R., Chapsky, L., Lee, R. A. M.,
Oyafuso, F. A., Frankenberg, C., O'Dell, C. W., Bruegge, C. J., Doran, G. B.,
Eldering, A., Fisher, B. M., Fu, D., Gunson, M. R., Mandrake, L., Osterman,
G. B., Schwandner, F. M., Sun, K., Taylor, T. E., Wennberg, P. O., and Wunch,
D.: The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2)
instrument and its radiometrically calibrated products, Atmos. Meas. Tech.,
10, 59–81, https://doi.org/10.5194/amt-10-59-2017, 2017.
Davis, A. B., Merlin, G., Cornet, C., Labonnote, L. C., Riédi, J.,
Ferlay, N., Dubuisson, P., Min, Q., Yang, Y., and Marshak, A.: Cloud
information content in EPIC/DSCOVR's oxygen A- and B-band channels: An
optimal estimation approach, J. Quant. Spectrosc. Ra., 216, 6–16,
https://doi.org/10.1016/j.jqsrt.2018.05.007, 2018.
Dong, X., Ackerman, T. P., Clothiaux, E. E., Pilewskie, P., and Han, Y.:
Microphysical and radiative properties of boundary layer stratiform clouds
deduced from ground-based measurements, J. Geophys. Res.-Atmos., 102,
23829–23843, https://doi.org/10.1029/97JD02119, 1997.
Dong, X., Mace, G. G., Minnis, P., Smith, W. L., Poellot, M., Marchand, R.
T., and Rapp, A. D.: Comparison of Stratus Cloud Properties Deduced from
Surface, GOES, and Aircraft Data during the March 2000 ARM Cloud IOP, J.
Atmos. Sci., 59, 3265–3284,
https://doi.org/10.1175/1520-0469(2002)059<3265:COSCPD>2.0.CO;2, 2002.
Drouin, B. J., Benner, D. C., Brown, L. R., Cich, M. J., Crawford, T. J.,
Devi, V. M., Guillaume, A., Hodges, J. T., Mlawer, E. J., Robichaud, D. J.,
Oyafuso, F., Payne, V. H., Sung, K., Wishnow, E. H., and Yu, S.:
Multispectrum analysis of the oxygen A-band, J. Quant. Spectrosc. Ra., 186,
118–138, https://doi.org/10.1016/j.jqsrt.2016.03.037, 2016.
Eldering, A., O'Dell, C. W., Wennberg, P. O., Crisp, D., Gunson, M. R.,
Viatte, C., Avis, C., Braverman, A., Castano, R., Chang, A., Chapsky, L.,
Cheng, C., Connor, B., Dang, L., Doran, G., Fisher, B., Frankenberg, C., Fu,
D., Granat, R., Hobbs, J., Lee, R. A. M., Mandrake, L., McDuffie, J., Miller,
C. E., Myers, V., Natraj, V., O'Brien, D., Osterman, G. B., Oyafuso, F.,
Payne, V. H., Pollock, H. R., Polonsky, I., Roehl, C. M., Rosenberg, R.,
Schwandner, F., Smyth, M., Tang, V., Taylor, T. E., To, C., Wunch, D., and
Yoshimizu, J.: The Orbiting Carbon Observatory-2: first 18 months of science
data products, Atmos. Meas. Tech., 10, 549–563,
https://doi.org/10.5194/amt-10-549-2017, 2017.
Fischer, J. and Grassl, H.: Detection of Cloud-Top Height from Backscattered
Radiances within the Oxygen A Band. Part 1: Theoretical Study, J. Appl.
Meteorol., 30, 1245–1259,
https://doi.org/10.1175/1520-0450(1991)030<1245:DOCTHF>2.0.CO;2, 1991.
Grosvenor, D. P. and Wood, R.: The effect of solar zenith angle on MODIS
cloud optical and microphysical retrievals within marine liquid water clouds,
Atmos. Chem. Phys., 14, 7291–7321, https://doi.org/10.5194/acp-14-7291-2014,
2014.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M.
D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M.,
Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A.,
Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D.,
Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K.,
Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang,
Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm
Clouds: A Review of the Current State of Knowledge and Perspectives, Rev.
Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Hanel, R. A.: Determination of cloud altitude from a satellite, J. Geophys.
Res., 66, 1300–1300, https://doi.org/10.1029/JZ066i004p01300, 1961.
Heidinger, A. K. and Stephens, G. L.: Molecular Line Absorption in a
Scattering Atmosphere. Part II: Application to Remote Sensing in the
O2 A band, J. Atmos. Sci., 57, 1615–1634,
https://doi.org/10.1175/1520-0469(2000)057<1615:MLAIAS>2.0.CO;2, 2000.
Jones, A., Roberts, D. L., and Slingo, A.: A climate model study of indirect
radiative forcing by anthropogenic sulphate aerosols, Nature, 370, 450–453,
https://doi.org/10.1038/370450a0, 1994.
Latham, J., Rasch, P., Chen, C.-C., Kettles, L., Gadian, A., Gettelman, A.,
Morrison, H., Bower, K., and Choularton, T.: Global temperature stabilization
via controlled albedo enhancement of low-level maritime clouds, Philos. T. Roy. Soc. A, 366, 3969–3987,
https://doi.org/10.1098/rsta.2008.0137, 2008.
L'Ecuyer, T. S. and Jiang, J. H.: Touring the atmosphere aboard the A-Train,
Phys. Today, 63, 36–41, https://doi.org/10.1063/1.3463626, 2010.
Li, S. and Min, Q.: Diagnosis of multilayer clouds using photon path length
distributions, J. Geophys. Res., 115, D20202, https://doi.org/10.1029/2009JD013774,
2010.
Liang, L., Di Girolamo, L., and Sun, W.: Bias in MODIS cloud drop effective
radius for oceanic water clouds as deduced from optical thickness variability
across scattering angles, J. Geophys. Res.-Atmos., 120, 7661–7681,
https://doi.org/10.1002/2015JD023256, 2015.
Lohmann, U. and Feichter, J.: Global indirect aerosol effects: a review,
Atmos. Chem. Phys., 5, 715–737, https://doi.org/10.5194/acp-5-715-2005,
2005.
Mariage, V., Pelon, J., Blouzon, F., Victori, S., Geyskens, N., Amarouche,
N., Drezen, C., Guillot, A., Calzas, M., Garracio, M., Wegmuller, N.,
Sennéchael, N., and Provost, C.: IAOOS microlidar-on-buoy development and
first atmospheric observations obtained during 2014 and 2015 arctic drifts,
Opt. Express, 25, 73–84,
https://doi.org/10.1364/OE.25.000A73, 2017.
Martin, G. M., Johnson, D. W., and Spice, A.: The Measurement and
Parameterization of Effective Radius of Droplets in Warm Stratocumulus
Clouds, J. Atmos. Sci., 51, 1823–1842,
https://doi.org/10.1175/1520-0469(1994)051<1823:TMAPOE>2.0.CO;2, 1994.
Meinig, C., Lawrence-Slavas, N., Jenkins, R., and Tabisola, H. M.: The use of
Saildrones to examine spring conditions in the Bering Sea: Vehicle
specification and mission performance, in: OCEANS 2015 – MTS/IEEE
Washington, pp. 1–6, IEEE,
2015.
Merlin, G., Riedi, J., Labonnote, L. C., Cornet, C., Davis, A. B., Dubuisson,
P., Desmons, M., Ferlay, N., and Parol, F.: Cloud information content
analysis of multi-angular measurements in the oxygen A-band: application to
3MI and MSPI, Atmos. Meas. Tech., 9, 4977–4995,
https://doi.org/10.5194/amt-9-4977-2016, 2016.
Min, Q. and Harrison, L. C.: Retrieval of Atmospheric Optical Depth Profiles
from Downward-Looking High-Resolution O2 A-Band Measurements:
Optically Thin Conditions, J. Atmos. Sci., 61, 2469–2477,
https://doi.org/10.1175/1520-0469(2004)061<2469:ROAODP>2.0.CO;2, 2004.
Min, Q.-L., Harrison, L. C., Kiedron, P., Berndt, J., and Joseph, E.: A
high-resolution oxygen A-band and water vapor band spectrometer, J. Geophys.
Res., 109, D02202, https://doi.org/10.1029/2003JD003540, 2004.
Moore, N., Crowell, S., Rayner, P., Kumer, J., O'Dell, C., O'Brien, D.,
Utembe, S., Polonsky, I., Schimel, D., and Lemen, J.: The Potential of the
Geostationary Carbon Cycle Observatory (GeoCarb) to Provide Multi-scale
Constraints on the Carbon Cycle in the Americas, Front. Environ. Sci., 6, 109, https://doi.org/10.3389/fenvs.2018.00109, 2018.
Munro, R., Lang, R., Klaes, D., Poli, G., Retscher, C., Lindstrot, R.,
Huckle, R., Lacan, A., Grzegorski, M., Holdak, A., Kokhanovsky, A.,
Livschitz, J., and Eisinger, M.: The GOME-2 instrument on the Metop series of
satellites: instrument design, calibration, and level 1 data processing – an
overview, Atmos. Meas. Tech., 9, 1279–1301,
https://doi.org/10.5194/amt-9-1279-2016, 2016.
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and
Effective Particle Radius of Clouds from Reflected Solar Radiation
Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893,
https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990.
Nakajima, T. Y., Suzuki, K., and Stephens, G. L.: Droplet Growth in Warm
Water Clouds Observed by the A-Train. Part I: Sensitivity Analysis of the
MODIS-Derived Cloud Droplet Sizes, J. Atmos. Sci., 67, 1884–1896,
https://doi.org/10.1175/2009JAS3280.1, 2010.
Natraj, V. and Spurr, R. J. D.: A fast linearized pseudo-spherical two orders
of scattering model to account for polarization in vertically inhomogeneous
scattering–absorbing media, J. Quant. Spectrosc. Ra., 107, 263–293,
https://doi.org/10.1016/j.jqsrt.2007.02.011, 2007.
O'Brien, D. M. and Mitchell, R. M.: Error Estimates for Retrieval of
Cloud-Top Pressure Using Absorption in the A Band of Oxygen, J. Appl.
Meteorol., 31, 1179–1192,
https://doi.org/10.1175/1520-0450(1992)031<1179:EEFROC>2.0.CO;2, 1992.
Omar, A. H., Winker, D. M., Vaughan, M. A., Hu, Y., Trepte, C. R., Ferrare,
R. A., Lee, K.-P., Hostetler, C. A., Kittaka, C., Rogers, R. R., Kuehn, R.
E., and Liu, Z.: The CALIPSO Automated Aerosol Classification and Lidar Ratio
Selection Algorithm, J. Atmos. Ocean. Tech., 26, 1994–2014,
https://doi.org/10.1175/2009JTECHA1231.1, 2009.
Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and
optical thickness retrievals over the Southeast Pacific with VOCALS-REx in
situ measurements, J. Geophys. Res.-Atmos., 116, D24206, https://doi.org/10.1029/2011JD016155, 2011.
Painemal, D., Chiu, J.-Y. C., Minnis, P., Yost, C., Zhou, X., Cadeddu, M.,
Eloranta, E., Lewis, E. R., Ferrare, R., and Kollias, P.: Aerosol and cloud
microphysics covariability in the northeast Pacific boundary layer estimated
with ship-based and satellite remote sensing observations, J. Geophys.
Res.-Atmos., 122, 2403–2418, https://doi.org/10.1002/2016JD025771, 2017.
Pawlowska, H. and Brenguier, J.-L.: Microphysical properties of stratocumulus
clouds during ACE-2, Tellus B, 52, 868–887,
https://doi.org/10.1034/j.1600-0889.2000.00076.x, 2000.
Platnick, S.: Vertical photon transport in cloud remote sensing problems, J.
Geophys. Res.-Atmos., 105, 22919–22935, https://doi.org/10.1029/2000JD900333, 2000.
Platnick, S., Ackerman, S., King, M., et al.: MODIS atmosphere L2 cloud product (06_L2),
https://doi.org/10.5067/MODIS/MYD06_L2.006, 2015.
Raes, F., Bates, T., McGovern, F., and Van Liedekerke, M.: The 2nd Aerosol
Characterization Experiment (ACE-2): general overview and main results,
Tellus B, 52, 111–125, https://doi.org/10.1034/j.1600-0889.2000.00124.x, 2000.
Richardson, M. and Stephens, G. L.: Information content of OCO-2 oxygen
A-band channels for retrieving marine liquid cloud properties, Atmos. Meas.
Tech., 11, 1515–1528, https://doi.org/10.5194/amt-11-1515-2018, 2018.
Richardson, M., McDuffie, J., Stephens, G. L., Cronk, H. Q., and Taylor, T.
E.: The OCO-2 oxygen A-band response to liquid marine cloud properties from
CALIPSO and MODIS, J. Geophys. Res.-Atmos., 122, 8255–8275, https://doi.org/10.1002/2017JD026561, 2017.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding Theory and Practice,
World Scientific, Singapore, 2000.
Rossow, W. B. and Schiffer, R. A.: ISCCP Cloud Data Products, B. Am.
Meteorol. Soc., 72, 2–20, https://doi.org/10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2,
1991.
Schuessler, O., Loyola Rodriguez, D. G., Doicu, A., and Spurr, R.:
Information Content in the Oxygen A-Band for the Retrieval of Macrophysical
Cloud Parameters, IEEE T. Geosci. Remote, 52,
3246–3255, https://doi.org/10.1109/TGRS.2013.2271986, 2014.
Spurr, R. J. D.: VLIDORT: A linearized pseudo-spherical vector discrete
ordinate radiative transfer code for forward model and retrieval studies in
multilayer multiple scattering media, J. Quant. Spectrosc. Ra.,
102, 316–342, https://doi.org/10.1016/j.jqsrt.2006.05.005, 2006.
Stephens, G. and Heidinger, A.: Molecular Line Absorption in a Scattering
Atmosphere. Part I: Theory, J. Atmos. Sci., 57, 1599–1614,
https://doi.org/10.1175/1520-0469(2000)057<1599:MLAIAS>2.0.CO;2,
2000.
Szczodrak, M., Austin, P. H., and Krummel, P. B.: Variability of Optical Depth
and Effective Radius in Marine Stratocumulus Clouds, J. Atmos. Sci., 58,
2912–2926, https://doi.org/10.1175/1520-0469(2001)058<2912:VOODAE>2.0.CO;2, 2001.
Taylor, T. E., O'Dell, C. W., Frankenberg, C., Partain, P. T., Cronk, H. Q.,
Savtchenko, A., Nelson, R. R., Rosenthal, E. J., Chang, A. Y., Fisher, B.,
Osterman, G. B., Pollock, R. H., Crisp, D., Eldering, A., and Gunson, M. R.:
Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms: validation
against collocated MODIS and CALIOP data, Atmos. Meas. Tech., 9, 973–989,
https://doi.org/10.5194/amt-9-973-2016, 2016.
Várnai, T. and Marshak, A.: Observations of Three-Dimensional Radiative
Effects that Influence MODIS Cloud Optical Thickness Retrievals, J. Atmos.
Sci., 59, 1607–1618, https://doi.org/10.1175/1520-0469(2002)059<1607:OOTDRE>2.0.CO;2, 2002.
Vaughan, M. A., Powell, K. A., Winker, D. M., Hostetler, C. A., Kuehn, R. E.,
Hunt, W. H., Getzewich, B. J., Young, S. A., Liu, Z., and McGill, M. J.: Fully
Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar
Measurements, J. Atmos. Ocean. Tech., 26, 2034–2050,
https://doi.org/10.1175/2009JTECHA1228.1, 2009.
Wood, R.: Drizzle in Stratiform Boundary Layer Clouds. Part I: Vertical and
Horizontal Structure, J. Atmos. Sci., 62, 3011–3033,
https://doi.org/10.1175/JAS3529.1, 2005.
Wood, R.: Stratocumulus Clouds, Mon. Weather Rev., 140,
2373–2423, https://doi.org/10.1175/MWR-D-11-00121.1, 2012.
Wood, R. and Bretherton, C. S.: On the Relationship between Stratiform Low
Cloud Cover and Lower-Tropospheric Stability, J. Climate, 19, 6425–6432,
https://doi.org/10.1175/JCLI3988.1, 2006.
Wood, R., Mechoso, C. R., Bretherton, C. S., Weller, R. A., Huebert, B.,
Straneo, F., Albrecht, B. A., Coe, H., Allen, G., Vaughan, G., Daum, P.,
Fairall, C., Chand, D., Gallardo Klenner, L., Garreaud, R., Grados, C.,
Covert, D. S., Bates, T. S., Krejci, R., Russell, L. M., de Szoeke, S.,
Brewer, A., Yuter, S. E., Springston, S. R., Chaigneau, A., Toniazzo, T.,
Minnis, P., Palikonda, R., Abel, S. J., Brown, W. O. J., Williams, S.,
Fochesatto, J., Brioude, J., and Bower, K. N.: The VAMOS
Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx): goals,
platforms, and field operations, Atmos. Chem. Phys., 11, 627–654,
https://doi.org/10.5194/acp-11-627-2011, 2011.
Yamamoto, G. and Wark, D. Q.: Discussion of the letter by Hanel, R. A.,
“Determination of cloud altitude from a satellite”, J. Geophys. Res., 66,
3596–3596, https://doi.org/10.1029/JZ066i010p03596, 1961.
Yang, Y., Marshak, A., Mao, J., Lyapustin, A., and Herman, J.: A method of
retrieving cloud top height and cloud geometrical thickness with oxygen A and
B bands for the Deep Space Climate Observatory (DSCOVR) mission: Radiative
transfer simulations, J. Quant. Spectrosc. Ra., 122, 141–149,
https://doi.org/10.1016/j.jqsrt.2012.09.017, 2013.
Zeng, S., Riedi, J., Trepte, C. R., Winker, D. M., and Hu, Y.-X.: Study of
global cloud droplet number concentration with A-Train satellites, Atmos.
Chem. Phys., 14, 7125–7134, https://doi.org/10.5194/acp-14-7125-2014, 2014.
Zuidema, P., Leon, D., Pazmany, A., and Cadeddu, M.: Aircraft millimeter-wave
passive sensing of cloud liquid water and water vapor during VOCALS-REx,
Atmos. Chem. Phys., 12, 355–369, https://doi.org/10.5194/acp-12-355-2012,
2012.
Zuidema, P., Redemann, J., Haywood, J., Wood, R., Piketh, S., Hipondoka, M.,
and Formenti, P.: Smoke and Clouds above the Southeast Atlantic: Upcoming
Field Campaigns Probe Absorbing Aerosol's Impact on Climate, B. Am. Meteorol.
Soc., 97, 1131–1135, https://doi.org/10.1175/BAMS-D-15-00082.1, 2016.
Short summary
We retrieve cloud properties, including geometric thickness, by combining hyperspectral Orbiting Carbon Observatory-2 (OCO-2) A-band measurements with CALIPSO lidar. This uses cloudy scene data that are not used in OCO-2's main mission, which is aimed at clear-sky atmospheric CO2 abundance. This is the first retrieval using such hyperspectral information and promises to provide a unique constraint on the properties of low liquid clouds over the ocean.
We retrieve cloud properties, including geometric thickness, by combining hyperspectral Orbiting...
