Articles | Volume 9, issue 3
https://doi.org/10.5194/amt-9-909-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-9-909-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
07 Mar 2016
Research article |
| 07 Mar 2016
Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR
Imaging Group, Mullard Space Science Laboratory,
University College London, Holmbury St. Mary, Dorking, Surrey, RH5 6NT,
England, UK
now at: Earth and Environmental Dynamics Research Group,
Department of Geography, King's College London, The Strand, London, WC2R
2LS, UK
Caroline A. Poulsen
Rutherford Appleton Laboratory, Didcot, Oxfordshire,
UK
Gareth E. Thomas
Rutherford Appleton Laboratory, Didcot, Oxfordshire,
UK
Jan-Peter Muller
Imaging Group, Mullard Space Science Laboratory,
University College London, Holmbury St. Mary, Dorking, Surrey, RH5 6NT,
England, UK
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M. J. Wooster, G. Roberts, P. H. Freeborn, W. Xu, Y. Govaerts, R. Beeby, J. He, A. Lattanzio, D. Fisher, and R. Mullen
Atmos. Chem. Phys., 15, 13217–13239, https://doi.org/10.5194/acp-15-13217-2015, https://doi.org/10.5194/acp-15-13217-2015, 2015
Short summary
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Landscape fires strongly influence atmospheric chemistry, composition, and climate. Characterizing such fires at very high temporal resolution is best achieved using thermal observations of actively burning fires made from geostationary Earth Observation satellites. Here we detail the Fire Radiative Power (FRP) products generated by the Land Surface Analysis Satellite Applications Facility (LSA SAF) from data collected by the Meteosat geostationary satellites.
G. Roberts, M. J. Wooster, W. Xu, P. H. Freeborn, J.-J. Morcrette, L. Jones, A. Benedetti, H. Jiangping, D. Fisher, and J. W. Kaiser
Atmos. Chem. Phys., 15, 13241–13267, https://doi.org/10.5194/acp-15-13241-2015, https://doi.org/10.5194/acp-15-13241-2015, 2015
Short summary
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Characterising the dynamics of wildfires at high temporal resolution is best achieved using observations from geostationary satellite sensors. The SEVIRI Fire Radiative Power (FRP) products have been developed using such imagery at up to 15-minute temporal frequency. These data are used to estimate wildfire fuel consumption and to the characterise smoke emissions from the 2007 Peloponnese "mega fires" within an atmospheric transport model.
S. DeSouza-Machado, L. Strow, E. Maddy, O. Torres, G. Thomas, D. Grainger, and A. Robinson
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-8-443-2015, https://doi.org/10.5194/amtd-8-443-2015, 2015
Revised manuscript not accepted
Short summary
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The Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution
Imaging Spectroradiometer (MODIS) are instruments on the 1.30 pm polar
orbiting Aqua spacecraft. We describe a daytime estimation of dust and
volcanic ash layer heights, using a retrieval algorithm that uses the
information in the AIRS L1B thermal infrared data, constrained by the
MODIS L2 aerosol optical depths. CALIOP aerosol centroid heights are
used for dust height comparisons, as are AATSR volcanic plume heights.
T. Holzer-Popp, G. de Leeuw, J. Griesfeller, D. Martynenko, L. Klüser, S. Bevan, W. Davies, F. Ducos, J. L. Deuzé, R. G. Graigner, A. Heckel, W. von Hoyningen-Hüne, P. Kolmonen, P. Litvinov, P. North, C. A. Poulsen, D. Ramon, R. Siddans, L. Sogacheva, D. Tanre, G. E. Thomas, M. Vountas, J. Descloitres, J. Griesfeller, S. Kinne, M. Schulz, and S. Pinnock
Atmos. Meas. Tech., 6, 1919–1957, https://doi.org/10.5194/amt-6-1919-2013, https://doi.org/10.5194/amt-6-1919-2013, 2013
M. Krol, W. Peters, P. Hooghiemstra, M. George, C. Clerbaux, D. Hurtmans, D. McInerney, F. Sedano, P. Bergamaschi, M. El Hajj, J. W. Kaiser, D. Fisher, V. Yershov, and J.-P. Muller
Atmos. Chem. Phys., 13, 4737–4747, https://doi.org/10.5194/acp-13-4737-2013, https://doi.org/10.5194/acp-13-4737-2013, 2013
G. E. Thomas, N. Chalmers, B. Harris, R. G. Grainger, and E. J. Highwood
Atmos. Chem. Phys., 13, 393–410, https://doi.org/10.5194/acp-13-393-2013, https://doi.org/10.5194/acp-13-393-2013, 2013
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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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Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
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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.
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.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-109, https://doi.org/10.5194/amt-2023-109, 2023
Revised manuscript accepted for AMT
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals is always decreasing with the increasing distance from the cloud far edge. The decreasing is the direct consequence of the fact that the forward peak of particles phase functions is much larger than the receiver field of view.
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.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
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We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
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SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
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Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
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Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 6653–6668, https://doi.org/10.5194/amt-15-6653-2022, https://doi.org/10.5194/amt-15-6653-2022, 2022
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With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
Wenyu Wang, Zhenzhan Wang, Qiurui He, and Lanjie Zhang
Atmos. Meas. Tech., 15, 6489–6506, https://doi.org/10.5194/amt-15-6489-2022, https://doi.org/10.5194/amt-15-6489-2022, 2022
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This paper uses a neural network approach to retrieve the ice water path from FY-3B/MWHS polarimetric measurements, focusing on its unique 150 GHz quasi-polarized channels. The Level 2 product of CloudSat is used as the reference value for the neural network. The results show that the polarization information is helpful for the retrieval in scenes with thicker cloud ice, and the 150 GHz channels give a significant improvement compared to using only 183 GHz channels.
Miriam Latsch, Andreas Richter, Henk Eskes, Maarten Sneep, Ping Wang, Pepijn Veefkind, Ronny Lutz, Diego Loyola, Athina Argyrouli, Pieter Valks, Thomas Wagner, Holger Sihler, Michel van Roozendael, Nicolas Theys, Huan Yu, Richard Siddans, and John P. Burrows
Atmos. Meas. Tech., 15, 6257–6283, https://doi.org/10.5194/amt-15-6257-2022, https://doi.org/10.5194/amt-15-6257-2022, 2022
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The article investigates different S5P TROPOMI cloud retrieval algorithms for tropospheric trace gas retrievals. The cloud products show differences primarily over snow and ice and for scenes under sun glint. Some issues regarding across-track dependence are found for the cloud fractions as well as for the cloud heights.
Han Ding, Haoran Li, and Liping Liu
Atmos. Meas. Tech., 15, 6181–6200, https://doi.org/10.5194/amt-15-6181-2022, https://doi.org/10.5194/amt-15-6181-2022, 2022
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In this study, a framework for processing the Doppler spectra observations of a multi-mode pulse compression Ka–Ku cloud radar system is presented. We first proposed an approach to identify and remove the clutter signals in the Doppler spectrum. Then, we developed a new algorithm to remove the range sidelobe at the modes implementing the pulse compression technique. The radar observations from different modes were then merged using the shift-then-average method.
Andrew T. Prata, Roy G. Grainger, Isabelle A. Taylor, Adam C. Povey, Simon R. Proud, and Caroline A. Poulsen
Atmos. Meas. Tech., 15, 5985–6010, https://doi.org/10.5194/amt-15-5985-2022, https://doi.org/10.5194/amt-15-5985-2022, 2022
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Satellite observations are often used to track ash clouds and estimate their height, particle sizes and mass; however, satellite-based techniques are always associated with some uncertainty. We describe advances in a satellite-based technique that is used to estimate ash cloud properties for the June 2019 Raikoke (Russia) eruption. Our results are significant because ash warning centres increasingly require uncertainty information to correctly interpret,
aggregate and utilise the data.
Adrià Amell, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 15, 5701–5717, https://doi.org/10.5194/amt-15-5701-2022, https://doi.org/10.5194/amt-15-5701-2022, 2022
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Geostationary satellites continuously image a given location on Earth, a feature that satellites designed to characterize atmospheric ice lack. However, the relationship between geostationary images and atmospheric ice is complex. Machine learning is used here to leverage such images to characterize atmospheric ice throughout the day in a probabilistic manner. Using structural information from the image improves the characterization, and this approach compares favourably to traditional methods.
Alistair Bell, Pauline Martinet, Olivier Caumont, Frédéric Burnet, Julien Delanoë, Susana Jorquera, Yann Seity, and Vinciane Unger
Atmos. Meas. Tech., 15, 5415–5438, https://doi.org/10.5194/amt-15-5415-2022, https://doi.org/10.5194/amt-15-5415-2022, 2022
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Cloud radars and microwave radiometers offer the potential to improve fog forecasts when assimilated into a high-resolution model. As this process can be complex, a retrieval of model variables is sometimes made as a first step. In this work, results from a 1D-Var algorithm for the retrieval of temperature, humidity and cloud liquid water content are presented. The algorithm is applied first to a synthetic dataset and then to a dataset of real measurements from a recent field campaign.
Willi Schimmel, Heike Kalesse-Los, Maximilian Maahn, Teresa Vogl, Andreas Foth, Pablo Saavedra Garfias, and Patric Seifert
Atmos. Meas. Tech., 15, 5343–5366, https://doi.org/10.5194/amt-15-5343-2022, https://doi.org/10.5194/amt-15-5343-2022, 2022
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This study introduces the novel Doppler radar spectra-based machine learning approach VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn). VOODOO is a powerful probability-based extension to the existing Cloudnet hydrometeor target classification, enabling the detection of liquid-bearing cloud layers beyond complete lidar attenuation via user-defined p* threshold. VOODOO performs best for (multi-layer) stratiform and deep mixed-phase clouds with liquid water path > 100 g m−2.
Vikas Nataraja, Sebastian Schmidt, Hong Chen, Takanobu Yamaguchi, Jan Kazil, Graham Feingold, Kevin Wolf, and Hironobu Iwabuchi
Atmos. Meas. Tech., 15, 5181–5205, https://doi.org/10.5194/amt-15-5181-2022, https://doi.org/10.5194/amt-15-5181-2022, 2022
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A convolutional neural network (CNN) is introduced to retrieve cloud optical thickness (COT) from passive cloud imagery. The CNN, trained on large eddy simulations from the Sulu Sea, learns from spatial information at multiple scales to reduce cloud inhomogeneity effects. By considering the spatial context of a pixel, the CNN outperforms the traditional independent pixel approximation (IPA) across several cloud morphology metrics.
Cited articles
Allan, R. P.: Combining satellite data and models to estimate cloud radiative
effect at the surface and in the atmosphere, Meteorol. Appl., 18, 324–333, 2011.
Andrews, T., Gregory, J. M., Webb, M. J., and Taylor, K. E.: Forcing,
feedbacks and climate sensitivity in CMIP5 coupled atmosphere–ocean climate
models, Geophys. Res. Lett., 39, L09712, https://doi.org/10.1029/2012GL051607, 2012.
Ardanuy, P. E., Stowe, L. L., Gruber, A., and Weiss, M.: Shortwave, longwave,
and net cloud-radiative forcing as determined from Nimbus 7 observations, J.
Geophys. Res.-Atmos., 96, 18537–18549, 1991.
Baum, B. A. and Wielicki, B. A.: Cirrus cloud retrieval using infrared
sounding data – multilevel cloud errors, J. Appl. Meteorol., 33, 107–117, 1994.
Baran, A. J., Shcherbakov, V. N., Baker, B. A., Gayet, J. F., and
Lawson, R. P.: On the scattering phase function of nonsymmetric ice
crystals, Q. J. Roy. Meteor. Soc., 131, 2609–2616, 2005.
Cess, R. D.: Climate change: An appraisal of atmospheric feedback mechanisms employing zonal climatology, J. Atmos. Sci., 33, 1831–1843, 1976.
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Ghan, S. J., Kiehl,
J. T., Le Treut, H., Li, Z.-X., Liang, X.-Z., Mitchell, J. F. B., Morcrette,
J.-J., Randall, D. A., Riches, M. R., Roeckner, E., Schlese, U., Slingo, A.,
Taylor, K. E., Washington, W. M., Wetherald, R. T., and Yagai, I.:
Interpretation of cloud-climate feedback as produced by 14 atmospheric
general circulation models, Science, 245, 513–516,
https://doi.org/10.1126/science.245.4917.513, 1989.
Cess, R. D., Potter, G. L., Blanchet, J. P., Boer, G. J., Del Genio, A. D.,
Déqué, M., Dymnikov, V., Galin, V., Gates, W. L., Ghan, S. J., Kiehl, J. T.,
Lacis, A. A., Le Treut, H., Li, Z.-X., Liang, X.-Z., McAvaney, B. J.,
Meleshko, V. P., Mitchell, J. F. B., Morcrette, J.-J., Randall, D. A., Rikus, L.,
Roeckner, E., Royer, J. F., Schlese, U., Sheinin, D. A., Slingo, A., Sokolov, A. P.,
Taylor, K. E., Washington, W. M., Wetherald, R. T., Yagai, I., and Zhang, M.-H.: Intercomparison and interpretation of climate feedback processes in
19 atmospheric general circulation models, J. Geophys. Res.-Atmos., 95,
16601–16615, 1990.
Cess, R. D., Zhang, M. H., Ingram, W. J., Potter, G. L., Alekseev, V.,
Barker, H. W., Cohen-Solal, E., Colman, R. A., Dazlich, D. A., Del Genio, A.
D., Dix, M. R., Dymnikov, V., Esch, M., Fowler, L. D., Fraser, J. R., Galin, V.,
Gates, W. L., Hack, J. J., Kiehl, J. T., Le Treut, H., Lo, K. K.-W., McAvaney, B. J.,
Meleshko, V. P., Morcrette, J.-J., Randall, D. A., Roeckner, E., Royer, J.-F.,
Schlesinger, M. E., Sporyshev, P. V., Timbal, B., Volodin, E. M., Taylor, K. E.,
Wang, W., and Wetherald, R. T.: Cloud feedback in atmospheric general circulation models: an update, J. Geophys. Res.-Atmos., 101, 12791–12794, 1996.
Colman, R.: A comparison of climate feedbacks in general circulation
models, Clim. Dynam., 20, 865–873, 2003.
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain
elevation data 2010 (GMTED2010), US Geological Survey Open-File Report, 1073,
26, 2011.
Denis, M. A., Muller, J. P., and Mannstein, H.: ATSR-2 camera models
for the automated stereo photogrammetric retrieval of cloud-top
heights – initial assessments, Int. J. Remote Sens., 28, 1939–1955, 2007.
Fisher, D. and Muller, J. P.: Global warping coefficients for improving ATSR
co-registration, Remote Sensing Letters, 4, 151–160, 2013.
Fisher, D., Muller, J. P., and Yershov, V. N.: Automated stereo retrieval of
smoke plume injection heights and retrieval of smoke plume masks from AATSR
and their assessment with CALIPSO and MISR, IEEE T. Geosci. Remote, 52, 1249–1258,
https://doi.org/10.1109/TGRS.2013.2249073, 2014.
Garay, M. J., de Szoeke, S. P., and Moroney, C. M.: Comparison of marine
stratocumulus cloud top heights in the southeastern Pacific retrieved from
satellites with coincident ship-based observations, J. Geophys.
Res.-Atmos., 113, D18204, https://doi.org/10.1029/2008JD009975, 2008.
Ham, S.-H., Sohn, B.-J., Yang, P., and Baum, B.: Assessment of the quality of
MODIS Cloud products from radiance simulations, J. Appl. Meterol., 48,
1591–1612, 2009.
Heidinger, A. K., Pavolonis, M. J., Holz, R. E., Baum, B. A., and Berthier,
S.: Using CALIPSO to explore the sensitivity to cirrus height in the infrared
observations from NPOESS/VIIRS and GOES-R/ABI, J. Geophys.
Res.-Atmos., 115, D00H20, https://doi.org/10.1029/2009JD012152, 2010.
Hirschmuller, H. and Scharstein, D.: Evaluation of stereo
matching costs on images with radiometric differences, IEEE
T. Pattern Anal., 31, 1582–1599, 2009.
Hu, Y., Winker, D., Vaughan, M., Lin, B., Omar, A., Trepte, C., Flittner, D., Yang, P., Nasiri, S. L., Baum, B., and Holz, R.: CALIPSO/CALIOP cloud
phase discrimination algorithm, J. Atmos. Ocean. Tech., 26, 2293–2309,
2009.
Istomina, L. G., von Hoyningen-Huene, W., Kokhanovsky, A. A., and Burrows, J.
P.: The detection of cloud-free snow-covered areas using AATSR measurements,
Atmos. Meas. Tech., 3, 1005–1017, https://doi.org/10.5194/amt-3-1005-2010, 2010.
Jones, P. A.: Cloud-cover distributions and correlations, J. Appl. Meteorol., 31, 732–741, 1992.
Kahn, R. A., Chen, Y., Nelson, D. L., Leung, F. Y., Li, Q., Diner, D. J.,
and Logan, J. A.: Wildfire smoke injection heights: Two perspectives
from space, Geophys. Res. Lett., 35, L04809, https://doi.org/10.1029/2007GL032165, 2008.
Kiehl, J. T., Hack, J. J., and Briegleb, B. P.: The simulated Earth
radiation budget of the National Center for Atmospheric Research community
climate model CCM2 and comparisons with the Earth Radiation Budget
Experiment (ERBE), J. Geophys. Res.-Atmos., 99, 20815–20827, 1994.
Kiehl, J. T. and Trenberth, K. E.: Earth's annual global mean
energy budget, B. Am. Meteorol. Soc., 78, 197–208, 1997.
Llewellyn-Jones, D., Edwards, M., Mutlow, C., Birks, A., Barton, I., and
Tait, H.: AATSR: Global-change and surface-temperature measurements from
Envisat, Esa Bull-Eur. Space, 11–21, 2001.
Lorenz, D.: Stereoscopic imaging from polar orbit and synthetic stereo imaging, Adv. Space Res., 2, 133–142, 1985.
Marchand, R., Ackerman, T., Smyth, M., and Rossow, W. B.: A review of cloud
top height and optical depth histograms from MISR, ISCCP, and MODIS, J.
Geophys. Res.-Atmos., 115, D16206, https://doi.org/10.1029/2009JD013422, 2010.
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.
Menzel, W. P., Smith, W. L., and Stewart, T. R.: Improved cloud motion wind vector and altitude assignment using VAS, J. Clim. Appl. Meteorol., 22, 377–384, 1983.
Menzel, W. P., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C.,
Holz, R. E., Maddux, B., Baum, B. A., Strabala, K. I., and Gumley, L. E.:
MODIS global cloud-top pressure and amount estimation: algorithm description
and results, J. Appl. Meteorol. Clim., 47, 1175–1198, 2008.
Moroney, C., Davies, R., and Muller, J. P.: Operational retrieval of cloud-top heights using MISR data, IEEE T. Geosci. Remote, 40, 1532–1540, 2002.
Mueller, K., Moroney, C., Jovanovic, V., Garay, M. J., Muller, J. P., Di
Girolamo, L., and Davies, R.: MISR Level 2 cloud product algorithm
theoretical basis, JPL Tech. Doc. D-73327, Jet Propul. Lab., Calif. Inst. of
Technol., Pasadena., 2013.
Muller, J. P., Mandanayake, A., Moroney, C., Davies, R., Diner, D. J., and Paradise, S.: MISR stereoscopic image matchers: techniques and results, IEEE T. Geosci. Remote, 40, 1547–1559, 2002.
Muller, J. P., Denis, M. A., Dundas, R. D., Mitchell, K. L., Naud, C., and Mannstein, H.: Stereo cloud-top heights and cloud fraction retrieval from ATSR-2, Int. J. Remote Sens., 28, 1921–1938, 2007.
Mutlow, C. T., Murray, M. J., Bailey, P., and Birks, A.: ATSR-1/2 user guide
issue 1, ESA user Guide, 2391, 1999.
Nakajima, T., Tanaka, M., 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, 187–193, 1990.
Ohring, G., Wielicki, B., Spencer, R., Emery, B., and Datla, R.:
Satellite instrument calibration for measuring global climate change,
B. Am. Meteorol. Soc., 86, 1303, https://doi.org/10.1175/BAMS-86-9-1303, 2005.
Poulsen, C. A., Siddans, R., Thomas, G. E., Sayer, A. M., Grainger, R. G.,
Campmany, E., Dean, S. M., Arnold, C., and Watts, P. D.: Cloud retrievals
from satellite data using optimal estimation: evaluation and application to
ATSR, Atmos. Meas. Tech., 5, 1889–1910, https://doi.org/10.5194/amt-5-1889-2012, 2012.
Prata, A. J. and Turner, P. J.: Cloud-top height determination using ATSR data,
Remote Sens. Environ., 59, 1–13, 1997.
Ramanathan, V. L. R. D., Cess, R. D., Harrison, E. F., Minnis, P.,
Barkstrom, B. R., Ahmad, E., and Hartmann, D.: Cloud-radiative forcing and
climate: results from the Earth radiation budget experiment, Science, 243,
57–63, 1989.
Rodgers, C. D.: Inverse methods for atmospheric sounding: theory and
practice/series on atmospheric, Oceanic and Planetary Physics, Vol. 2, World
Scientific Publishing, Singapore, 2000.
Rossow, W. B. and Garder, L. C.: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP, J. Climate, 6, 2341–2369, 1993.
Rossow, W. B., Zhang, Y., and Wang, J.: A statistical model of cloud vertical
structure based on reconciling cloud layer amounts inferred from satellites
and radiosonde humidity profiles, J. Climate, 18, 3587–3605, 2005.
Sayer, A. M., Thomas, G. E., and Grainger, R. G.: A sea surface reflectance
model for (A)ATSR, and application to aerosol retrievals, Atmos. Meas. Tech., 3, 813–838, https://doi.org/10.5194/amt-3-813-2010, 2010.
Sayer, A. M., Poulsen, C. A., Arnold, C., Campmany, E., Dean, S., Ewen, G. B.
L., Grainger, R. G., Lawrence, B. N., Siddans, R., Thomas, G. E., and Watts,
P. D.: Global retrieval of ATSR cloud parameters and evaluation (GRAPE):
dataset assessment, Atmos. Chem. Phys., 11, 3913–3936,
https://doi.org/10.5194/acp-11-3913-2011, 2011.
Scharstein, D. and Szeliski, R.: A taxonomy and evaluation of dense two-frame
stereo correspondence algorithms, International journal of computer vision,
47, 7–42, 2002.
Seemann, S. W., Borbas, E. E., Knuteson, R. O., Stephenson, G. R., and
Huang, H.-L.: Development of a global infrared land surface emissivity
database for application to clear sky sounding retrievals from multi-spectral
satellite radiance measurements, J. Appl. Meteorol. Clim., 47, 108–123,
2008.
Seiz, G.: Ground-and satellite-based multi-view determination of 3-D cloud
geometry, Doctoral dissertation, PhD thesis, Institute of Geodesy and
Photogrammetry, IGP Mitteilungen, ETH Zurich, Switzerland, 2003.
Sherwood, S. C., Bony, S., and Dufresne, J. L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42,
2014.
Shin, D. and Pollard, J. K.: Cloud height determination from satellite stereo
images, in: Image Processing for Remote Sensing, IEE Colloquium on (4–1),
IET, https://doi.org/https://doi.org/ 10.1049/ic:19960158, 1996.
Simmons, A., Uppala, S., Dee, D., and Kobayashi, S.: ERA-Interim: New ECMWF
reanalysis products from 1989 onwards, ECMWF newsletter, 110, 25–35, 2007.
Smith, D., Mutlow, C., Delderfield, J., Watkins, B., and Mason, G.: ATSR
infrared radiometric calibration and in-orbit performance, Remote Sens.
Environ., 116, 4–16, 2012.
Soden, B. J. and Held, I. M.: An assessment of climate feedbacks in coupled
ocean–atmosphere models, J. Climate, 19, 3354–3360, 2006.
Soden, B. J., Held, I. M., Colman, R., Shell, K. M., Kiehl, J. T., and
Shields, C. A.: Quantifying climate feedbacks using radiative kernels, J.
Climate, 21, 3504–3520, 2008.
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89 GHz channels, J. Geophys. Res.-Oceans, 113, C02S03,
https://doi.org/10.1029/2005JC003384, 2008.
Stamnes, K., Tsay, S. C., Wiscombe, W., and Jayaweera, K.: Numerically stable
algorithm for discrete-ordinate-method radiative transfer in multiple
scattering and emitting layered media, Appl. Optics, 27, 2502–2509, 1988.
Stengel, M., Mieruch, S., Jerg, M., Karlsson, K.-G., Scheirer, R.,
Maddux, B., Meirink, J. F., Poulsen, C., Siddans, R., Walther, A., and
Hollmann, R.: The clouds climate change initiative: assessment of
state-of-the-art cloud property retrieval schemes applied to AVHRR heritage
measurements, Remote Sens. Environ., 162, 363–379,
https://doi.org/10.1016/j.rse.2013.10.035,
2013.
Stephens, G. L.: Cloud feedbacks in the climate system: a critical review, J.
Climate, 18, 237–273, 2005.
Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung,
J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.: Climate change 2013:
The Physical Science Basis, Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change,
Cambridge, UK, and New York, Cambridge University Press,
2013.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C.,
Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.:
Assessment of global cloud datasets from satellites: project and database
initiated by the GEWEX radiation panel, B. Am. Meteorol. Soc., 94,
1031–1049, 2013.
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, 2009.
Watts, P. D., Mutlow, C. T., Baran, A. J., and Zavody, A. M.: Study on cloud
properties derived from Meteosat Second Generation Observations, Eumetsat
Report, 2393–2403, 1998.
Watts, P. D., Bennartz, R., and Fell, F.: Retrieval of two-layer cloud
properties from multispectral observations using optimal estimation, J.
Geophys. Res.-Atmos., 116, D16203, https://doi.org/10.1029/2011JD015883, 2011.
Webb, M. J., Senior, C. A., Sexton, D. M. H., Ingram, W. J., Williams, K. D.,
Ringer, M. A., McAvaney, B. J., Colman, R., Soden, B. J., Gudgel, R.,
Knutson, T., Emori, S., Ogura, T., Tsushima, Y., Andronova, N., Li, B.,
Musat, I., Bony, S., and Taylor, K. E.: On the contribution of local feedback
mechanisms to the range of climate sensitivity in two GCM ensembles, Clim.
Dynam., 27, 17–38, 2006.
Wetherald, R. T. and Manabe, S.: Cloud feedback processes in a general
circulation model, J. Atmos. Sci., 45, 1397–1416, 1988.
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., Lee III, R. B., Louis
Smith, G., and Cooper, J. E.: Clouds and the Earth's radiant energy system
(CERES): an earth observing system experiment, B. Am. Meteorol. Soc., 77,
853–868, 1996.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z.,
Hunt, W. H., and Young, S. A.: Overview
of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos.
Ocean. Tech., 26, 2310–2323, 2009.
Zabih, R. and Woodfill, J.: Non-parametric local transforms for computing
visual correspondence, in: Computer Vision – ECCV'94, Springer Berlin,
Heidelberg, 151–158, 1994.
Zelinka, M. D., Klein, S. A., Taylor, K. E., Andrews, T., Webb, M. J.,
Gregory, J. M., and Forster, P. M.: Contributions of different cloud types to
feedbacks and rapid adjustments in CMIP5*, J. Climate, 26, 5007–5027, 2013.
Short summary
Observational data sets of cloud characteristics are of key importance in climate science for reducing uncertainty when predicting the future state of the Earth's climate. Here we present a composite method of observing cloud from the Advanced Along Track Scanning Radiometer, employing both radiometric and geometric approaches. Using this method we find improved accuracy for resolving the cloud top height for very-low and high clouds accompanied by small changes in the microphysical parameters.
Observational data sets of cloud characteristics are of key importance in climate science for...
