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
Related authors
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
Short summary
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
Short summary
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.
Elisa Carboni, Gareth E. Thomas, Richard Siddans, and Brian Kerridge
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-232, https://doi.org/10.5194/amt-2023-232, 2023
Preprint under review for AMT
Short summary
Short summary
We analyzed different satellite datasets of cloud properties with a new approach to quantify and interpret their interannual variability based on singular vector decomposition (SVD). The spatial pattern and its temporal evolution are strikingly similar for all the satellite datasets and follow the El Nino Southern Oscillation. The SVD approach reported here has potential for application to satellite data sets and to evaluate consistency between models and observations.
Robert G. Ryan, Eloise A. Marais, Eleanor Gershenson-Smith, Robbie Ramsay, Jan-Peter Muller, Jan-Lukas Tirpitz, and Udo Frieß
Atmos. Chem. Phys., 23, 7121–7139, https://doi.org/10.5194/acp-23-7121-2023, https://doi.org/10.5194/acp-23-7121-2023, 2023
Short summary
Short summary
We describe the first data retrieval from a newly installed instrument providing measurements of vertical profiles of air pollution over Central London during heatwaves in summer 2022. We use these observations with surface air quality network measurements to support interpretation that an exponential increase in biogenic emissions of isoprene during heatwaves provides the limiting ingredient for severe ozone pollution, leading to non-compliance with the national ozone air quality standard.
P. A. Strobl, C. Bielski, P. L. Guth, C. H. Grohmann, J.-P. Muller, C. López-Vázquez, D. B. Gesch, G. Amatulli, S. Riazanoff, and C. Carabajal
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 395–400, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021, https://doi.org/10.5194/isprs-archives-XLIII-B4-2021-395-2021, 2021
J.-P. Muller, Y. Tao, A. R. D. Putri, and S. J. Conway
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 667–671, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-667-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-667-2021, 2021
Nick Schutgens, Andrew M. Sayer, Andreas Heckel, Christina Hsu, Hiren Jethva, Gerrit de Leeuw, Peter J. T. Leonard, Robert C. Levy, Antti Lipponen, Alexei Lyapustin, Peter North, Thomas Popp, Caroline Poulsen, Virginia Sawyer, Larisa Sogacheva, Gareth Thomas, Omar Torres, Yujie Wang, Stefan Kinne, Michael Schulz, and Philip Stier
Atmos. Chem. Phys., 20, 12431–12457, https://doi.org/10.5194/acp-20-12431-2020, https://doi.org/10.5194/acp-20-12431-2020, 2020
Short summary
Short summary
We intercompare 14 different datasets of satellite observations of aerosol. Such measurements are challenging but also provide the best opportunity to globally observe an atmospheric component strongly related to air pollution and climate change. Our study shows that most datasets perform similarly well on a global scale but that locally errors can be quite different. We develop a technique to estimate satellite errors everywhere, even in the absence of surface reference data.
Caroline A. Poulsen, Gregory R. McGarragh, Gareth E. Thomas, Martin Stengel, Matthew W. Christensen, Adam C. Povey, Simon R. Proud, Elisa Carboni, Rainer Hollmann, and Roy G. Grainger
Earth Syst. Sci. Data, 12, 2121–2135, https://doi.org/10.5194/essd-12-2121-2020, https://doi.org/10.5194/essd-12-2121-2020, 2020
Short summary
Short summary
We have created a satellite cloud and radiation climatology from the ATSR-2 and AATSR on board ERS-2 and Envisat, respectively, which spans the period 1995–2012. The data set was created using a combination of optimal estimation and neural net techniques. The data set was created as part of the ESA Climate Change Initiative program. The data set has been compared with active CALIOP lidar measurements and compared with MAC-LWP AND CERES-EBAF measurements and is shown to have good performance.
Martin Stengel, Stefan Stapelberg, Oliver Sus, Stephan Finkensieper, Benjamin Würzler, Daniel Philipp, Rainer Hollmann, Caroline Poulsen, Matthew Christensen, and Gregory McGarragh
Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, https://doi.org/10.5194/essd-12-41-2020, 2020
Short summary
Short summary
The Cloud_cci AVHRR-PMv3 dataset contains global, cloud and radiative flux properties covering the period of 1982 to 2016. The properties were retrieved from AVHRR measurements recorded by afternoon satellites of the NOAA POES missions. Validation against CALIOP, BSRN and CERES demonstrates the high quality of the data. The Cloud_cci AVHRR-PMv3 dataset allows for a large variety of climate applications that build on cloud properties, radiative flux properties and/or the link between them.
Alba Lorente, K. Folkert Boersma, Piet Stammes, L. Gijsbert Tilstra, Andreas Richter, Huan Yu, Said Kharbouche, and Jan-Peter Muller
Atmos. Meas. Tech., 11, 4509–4529, https://doi.org/10.5194/amt-11-4509-2018, https://doi.org/10.5194/amt-11-4509-2018, 2018
Short summary
Short summary
Light reflected by Earth’s surface is different in each direction: it appears brighter or darker in certain viewing directions. Currently this effect is not accounted for in satellite retrievals; thus surface reflectance climatologies and cloud fractions show an east-west bias across orbits (GOME2,OMI). The effect for NO2 measurements in partly cloudy scenes is substantial. We recommend that this effect in UV/Vis sensors coherently accounted for, and will be especially beneficial for TROPOMI.
Gregory R. McGarragh, Caroline A. Poulsen, Gareth E. Thomas, Adam C. Povey, Oliver Sus, Stefan Stapelberg, Cornelia Schlundt, Simon Proud, Matthew W. Christensen, Martin Stengel, Rainer Hollmann, and Roy G. Grainger
Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, https://doi.org/10.5194/amt-11-3397-2018, 2018
Short summary
Short summary
Satellites are vital for measuring cloud properties necessary for climate prediction studies. We present a method to retrieve cloud properties from satellite based radiometric measurements. The methodology employed is known as optimal estimation and belongs in the class of statistical inversion methods based on Bayes' theorem. We show, through theoretical retrieval simulations, that the solution is stable and accurate to within 10–20% depending on cloud thickness.
Oliver Sus, Martin Stengel, Stefan Stapelberg, Gregory McGarragh, Caroline Poulsen, Adam C. Povey, Cornelia Schlundt, Gareth Thomas, Matthew Christensen, Simon Proud, Matthias Jerg, Roy Grainger, and Rainer Hollmann
Atmos. Meas. Tech., 11, 3373–3396, https://doi.org/10.5194/amt-11-3373-2018, https://doi.org/10.5194/amt-11-3373-2018, 2018
Short summary
Short summary
This paper presents a new cloud detection and classification framework, CC4CL. It applies a sophisticated optimal estimation method to derive cloud variables from satellite data of various polar-orbiting platforms and sensors (AVHRR, MODIS, AATSR). CC4CL provides explicit uncertainty quantification and long-term consistency for decadal timeseries at various spatial resolutions. We analysed 5 case studies to show that cloud height estimates are very realistic unless optically thin clouds overlap.
Edward Malina, Yukio Yoshida, Tsuneo Matsunaga, and Jan-Peter Muller
Atmos. Meas. Tech., 11, 1159–1179, https://doi.org/10.5194/amt-11-1159-2018, https://doi.org/10.5194/amt-11-1159-2018, 2018
Short summary
Short summary
We present an assessment of the predicted information content and retrieval errors for 13CH4 retrieval from the planned GOSAT-2 satellite, assuming a wide range of land surface conditions. Retrieval of this quantity may allow for estimation of methane source types (e.g. biological or non-biological) based on the δ13C metric. We conclude that GOSAT-2 can be used for this purpose (to an accuracy of 10 ‰) assuming sufficient spatial (regional) and temporal (at least monthly) averaging.
Martin Stengel, Stefan Stapelberg, Oliver Sus, Cornelia Schlundt, Caroline Poulsen, Gareth Thomas, Matthew Christensen, Cintia Carbajal Henken, Rene Preusker, Jürgen Fischer, Abhay Devasthale, Ulrika Willén, Karl-Göran Karlsson, Gregory R. McGarragh, Simon Proud, Adam C. Povey, Roy G. Grainger, Jan Fokke Meirink, Artem Feofilov, Ralf Bennartz, Jedrzej S. Bojanowski, and Rainer Hollmann
Earth Syst. Sci. Data, 9, 881–904, https://doi.org/10.5194/essd-9-881-2017, https://doi.org/10.5194/essd-9-881-2017, 2017
Short summary
Short summary
We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.
David Neubauer, Matthew W. Christensen, Caroline A. Poulsen, and Ulrike Lohmann
Atmos. Chem. Phys., 17, 13165–13185, https://doi.org/10.5194/acp-17-13165-2017, https://doi.org/10.5194/acp-17-13165-2017, 2017
Short summary
Short summary
When aerosol particles take up water their number may seem to be increased optically. However if aerosol particles are removed by precipitation (formation) their numbers will decrease. We applied methods to account for such effects in model and satellite data to analyse the change in cloud properties by changes in aerosol particle number. The agreement of model and satellite data improves when these effects are accounted for.
Matthew W. Christensen, David Neubauer, Caroline A. Poulsen, Gareth E. Thomas, Gregory R. McGarragh, Adam C. Povey, Simon R. Proud, and Roy G. Grainger
Atmos. Chem. Phys., 17, 13151–13164, https://doi.org/10.5194/acp-17-13151-2017, https://doi.org/10.5194/acp-17-13151-2017, 2017
Short summary
Short summary
The cloud-aerosol pairing algorithm (CAPA) is developed to quantify the impact of near-cloud aerosol retrievals on satellite-based aerosol–cloud statistical relationships. We find that previous satellite-based radiative forcing estimates of aerosol–cloud interactions represented in key climate reports are likely exaggerated by up to 50 % due to including retrieval artefacts in the aerosols located near clouds. It is demonstrated that this retrieval artefact can be corrected in current products.
Christopher J. Merchant, Frank Paul, Thomas Popp, Michael Ablain, Sophie Bontemps, Pierre Defourny, Rainer Hollmann, Thomas Lavergne, Alexandra Laeng, Gerrit de Leeuw, Jonathan Mittaz, Caroline Poulsen, Adam C. Povey, Max Reuter, Shubha Sathyendranath, Stein Sandven, Viktoria F. Sofieva, and Wolfgang Wagner
Earth Syst. Sci. Data, 9, 511–527, https://doi.org/10.5194/essd-9-511-2017, https://doi.org/10.5194/essd-9-511-2017, 2017
Short summary
Short summary
Climate data records (CDRs) contain data describing Earth's climate and should address uncertainty in the data to communicate what is known about climate variability or change and what range of doubt exists. This paper discusses good practice for including uncertainty information in CDRs for the essential climate variables (ECVs) derived from satellite data. Recommendations emerge from the shared experience of diverse ECV projects within the European Space Agency Climate Change Initiative.
S. Xiong and J.-P. Muller
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 585–591, https://doi.org/10.5194/isprs-archives-XLI-B7-585-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-585-2016, 2016
Jacqueline Campbell, Panagiotis Sidiropoulos, and Jan-Peter Muller
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B7, 71–75, https://doi.org/10.5194/isprs-archives-XLI-B7-71-2016, https://doi.org/10.5194/isprs-archives-XLI-B7-71-2016, 2016
Jan-Peter Muller, Yu Tao, Panagiotis Sidiropoulos, Klaus Gwinner, Konrad Willner, Lida Fanara, Marita Waehlisch, Stephan van Gasselt, Sebastian Walter, Ralf Steikert, Bjoern Schreiner, Anton Ivanov, Federico Cantini, Jessica Wardlaw, Jeremy Morley, James Sprinks, Michele Giordano, Stuart Marsh, Jungrack Kim, Robert Houghton, and Steven Bamford
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 453–458, https://doi.org/10.5194/isprs-archives-XLI-B4-453-2016, https://doi.org/10.5194/isprs-archives-XLI-B4-453-2016, 2016
Y. Tao and J.-P. Muller
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B4, 503–509, https://doi.org/10.5194/isprs-archives-XLI-B4-503-2016, https://doi.org/10.5194/isprs-archives-XLI-B4-503-2016, 2016
Elisa Carboni, Roy G. Grainger, Tamsin A. Mather, David M. Pyle, Gareth E. Thomas, Richard Siddans, Andrew J. A. Smith, Anu Dudhia, Mariliza E. Koukouli, and Dimitrios Balis
Atmos. Chem. Phys., 16, 4343–4367, https://doi.org/10.5194/acp-16-4343-2016, https://doi.org/10.5194/acp-16-4343-2016, 2016
Short summary
Short summary
The Infrared Atmospheric Sounding Interferometer (IASI) can be used to study volcanic emission of sulfur dioxide (SO2), returning both SO2 amount and altitude data. The series of analyzed eruptions (2008 to 2012) show that the biggest emitter of volcanic SO2 was Nabro, followed by Kasatochi and Grimsvotn. Our observations also show a tendency for volcanic SO2 to reach the level of the tropopause. This tendency was independent of the maximum amount of SO2 and of the volcanic explosive index.
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
Short summary
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
Short summary
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
Short summary
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
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Simulation and detection efficiency analysis for polar mesospheric clouds measurements using a spaceborne wide field of view ultraviolet imager
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
The algorithm of microphysical parameter profiles of aerosol and small cloud droplets based on the dual wavelength Lidar data
Information Content of Brightness Temperature Differences of Spaceborne Imagers with respect to Cloud Phase
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
ampycloud: an algorithm to characterize cloud layers above aerodromes using ceilometer measurements
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
The Chalmers Cloud Ice Climatology: Retrieval implementation and validation
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products
Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product
Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
Simulation and sensitivity analysis for cloud and precipitation measurements via spaceborne millimeter-wave radar
The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy
Optimizing cloud motion estimation on the edge with phase correlation and optical flow
A semi-Lagrangian method for detecting and tracking deep convective clouds in geostationary satellite observations
The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission
High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow
Evaluation of the spectral misalignment on the Earth Clouds, Aerosols and Radiation Explorer/multi-spectral imager cloud product
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Short summary
Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary
Short summary
This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-186, https://doi.org/10.5194/amt-2023-186, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Ultraviolet (UV) 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 research results demonstrate that the WFUI performs well in PMCs detection and has high detection efficiency. The relationship between IWC and detection efficiency follows an exponential function distribution.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
EGUsphere, https://doi.org/10.5194/egusphere-2024-192, https://doi.org/10.5194/egusphere-2024-192, 2024
Short summary
Short summary
This study proposed an inversion method of atmosphere 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. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmosphere particles and has good robustness.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
EGUsphere, https://doi.org/10.5194/egusphere-2024-540, https://doi.org/10.5194/egusphere-2024-540, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences (BTDs) of the SEVIRI imager) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud phase retrievals and to understand their potential and limitations.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
Short summary
Short summary
In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
Short summary
Short summary
The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
Short summary
Short summary
We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
Short summary
Short summary
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, and Pieter du Preez
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-254, https://doi.org/10.5194/amt-2023-254, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
ampycloud is new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer measurements. 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 was implemented as a Python package, that is made publicly available to the community under the 3-Clause BSD license.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1953, https://doi.org/10.5194/egusphere-2023-1953, 2023
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limited amount of observations of clouds that are currently available. Our work proposes to address this 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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
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...
