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
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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
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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
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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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Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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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
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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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.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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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.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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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 to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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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 SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-141, https://doi.org/10.5194/amt-2024-141, 2024
Revised manuscript accepted for AMT
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In satellite remote sensing, estimating cloud base height (CBH) is more challenging than estimating cloud top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI instrument on JAXA’s GCOM-C satellite to estimate CBH together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-894, https://doi.org/10.5194/egusphere-2024-894, 2024
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We present a technique to discriminate between the Cloudnet target classification of "Drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for the measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over two years at the BCO is presented.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-99, https://doi.org/10.5194/amt-2024-99, 2024
Revised manuscript accepted for AMT
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This study introduces the JAXA EarthCARE L2 cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation will be quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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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.
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.
Ho Yi Lydia Mak and Christine Unal
EGUsphere, https://doi.org/10.5194/egusphere-2024-1232, https://doi.org/10.5194/egusphere-2024-1232, 2024
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The dynamics of thunderclouds is studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
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.
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.
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.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
Short summary
Short summary
The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
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
Short summary
Short summary
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.
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...
