Articles | Volume 8, issue 8
https://doi.org/10.5194/amt-8-3419-2015
© Author(s) 2015. 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-8-3419-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
24 Aug 2015
Research article |
| 24 Aug 2015
Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution
C. K. Carbajal Henken
CORRESPONDING AUTHOR
Institute for Space Sciences, Freie Universität Berlin (FUB), Berlin, Germany
L. Doppler
Deutscher Wetterdienst, Meteorologisches Observatorium Lindenberg, Richard Assmann Observatorium (DWD, MOL-RAO), Lindenberg, Germany
R. Lindstrot
EUMETSAT, Eumetsat-Allee 1, Darmstadt, Germany
R. Preusker
Institute for Space Sciences, Freie Universität Berlin (FUB), Berlin, Germany
J. Fischer
Institute for Space Sciences, Freie Universität Berlin (FUB), Berlin, Germany
Related authors
Gaël Kermarrec, Xavier Calbet, Zhiguo Deng, and Cintia Carbajal Henken
Atmos. Chem. Phys., 25, 3567–3581, https://doi.org/10.5194/acp-25-3567-2025, https://doi.org/10.5194/acp-25-3567-2025, 2025
Short summary
Short summary
Atmospheric delays affect global navigation satellite system (GNSS) signals. This study analyses the wet delay, a variable component caused by atmospheric water vapor, using a novel filtering method to examine small-scale turbulent variations. Case studies at five global stations revealed daily and seasonal turbulence patterns. This research will improve water vapour and cloud models, enhance nowcasting, and refine stochastic modelling for GNSS and very long baseline interferometry.
Jan El Kassar, Cintia Carbajal Henken, Xavier Calbet, Pilar Rípodas, Rene Preusker, and Jürgen Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3605, https://doi.org/10.5194/egusphere-2024-3605, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Water vapour is a key ingredient in virtually all meteorological processes. We present an algorithm which uses observations of the Flexible Combined Imager (FCI) to estimate the total column of water vapour over sun-lit, clear-sky pixels. FCI is a satellite instrument and every 10 minutes it takes an image which covers Europe, Africa and the Atlantic with a resolution of 1 km. Such high resolution water vapour fields will provide valuable information for weather forecasters and researchers.
Xavier Calbet, Cintia Carbajal Henken, Sergio DeSouza-Machado, Bomin Sun, and Tony Reale
Atmos. Meas. Tech., 15, 7105–7118, https://doi.org/10.5194/amt-15-7105-2022, https://doi.org/10.5194/amt-15-7105-2022, 2022
Short summary
Short summary
Water vapor concentration in the atmosphere at small scales (< 6 km) is considered. The measurements show Gaussian random field behavior following Kolmogorov's theory of turbulence two-thirds law. These properties can be useful when estimating the water vapor variability within a given observed satellite scene or when different water vapor measurements have to be merged consistently.
Montserrat Costa-Surós, Odran Sourdeval, Claudia Acquistapace, Holger Baars, Cintia Carbajal Henken, Christa Genz, Jonas Hesemann, Cristofer Jimenez, Marcel König, Jan Kretzschmar, Nils Madenach, Catrin I. Meyer, Roland Schrödner, Patric Seifert, Fabian Senf, Matthias Brueck, Guido Cioni, Jan Frederik Engels, Kerstin Fieg, Ksenia Gorges, Rieke Heinze, Pavan Kumar Siligam, Ulrike Burkhardt, Susanne Crewell, Corinna Hoose, Axel Seifert, Ina Tegen, and Johannes Quaas
Atmos. Chem. Phys., 20, 5657–5678, https://doi.org/10.5194/acp-20-5657-2020, https://doi.org/10.5194/acp-20-5657-2020, 2020
Short summary
Short summary
The impact of anthropogenic aerosols on clouds is a key uncertainty in climate change. This study analyses large-domain simulations with a new high-resolution model to investigate the differences in clouds between 1985 and 2013 comparing multiple observational datasets. The differences in aerosol and in cloud droplet concentrations are clearly detectable. For other quantities, the detection and attribution proved difficult, despite a substantial impact on the Earth's energy budget.
Nils Madenach, Cintia Carbajal Henken, René Preusker, Odran Sourdeval, and Jürgen Fischer
Atmos. Chem. Phys., 19, 13535–13546, https://doi.org/10.5194/acp-19-13535-2019, https://doi.org/10.5194/acp-19-13535-2019, 2019
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.
A. Hollstein, J. Fischer, C. Carbajal Henken, and R. Preusker
Atmos. Meas. Tech., 8, 1757–1771, https://doi.org/10.5194/amt-8-1757-2015, https://doi.org/10.5194/amt-8-1757-2015, 2015
Short summary
Short summary
Cloud detection is one of the key components for the exploitation of Earth observation images. We discuss the use of probabilistic algorithms for MERIS and AATSR on-board the ENVISAT satellite. As a new approach, we used an automated search to find the best combination of channels for the algorithm, which led to a number of unusual combinations that have not been used in the past. We show how very small samples of manually classified cloud truth images can be used to set up efficient algorithms.
C. K. Carbajal Henken, R. Lindstrot, R. Preusker, and J. Fischer
Atmos. Meas. Tech., 7, 3873–3890, https://doi.org/10.5194/amt-7-3873-2014, https://doi.org/10.5194/amt-7-3873-2014, 2014
Short summary
Short summary
Presented here is the FAME-C (Freie Universität Berlin AATSR and MERIS cloud) algorithm, which uses satellite measurements in the visible, near-infrared and infrared part of the spectrum to retrieve cloud macrophysical properties, such as cloud amount and two independent cloud top heights, and cloud optical and microphysical properties, such as cloud top thermodynamic phase, cloud optical thickness and effective radius, which describes the particle size distribution.
Gaël Kermarrec, Xavier Calbet, Zhiguo Deng, and Cintia Carbajal Henken
Atmos. Chem. Phys., 25, 3567–3581, https://doi.org/10.5194/acp-25-3567-2025, https://doi.org/10.5194/acp-25-3567-2025, 2025
Short summary
Short summary
Atmospheric delays affect global navigation satellite system (GNSS) signals. This study analyses the wet delay, a variable component caused by atmospheric water vapor, using a novel filtering method to examine small-scale turbulent variations. Case studies at five global stations revealed daily and seasonal turbulence patterns. This research will improve water vapour and cloud models, enhance nowcasting, and refine stochastic modelling for GNSS and very long baseline interferometry.
Roberto Román, Daniel González-Fernández, Juan Carlos Antuña-Sánchez, Celia Herrero del Barrio, Sara Herrero-Anta, África Barreto, Victoria E. Cachorro, Lionel Doppler, Ramiro González, Christoph Ritter, David Mateos, Natalia Kouremeti, Gustavo Copes, Abel Calle, María José Granados-Muñoz, Carlos Toledano, and Ángel M. de Frutos
EGUsphere, https://doi.org/10.5194/egusphere-2025-667, https://doi.org/10.5194/egusphere-2025-667, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
This paper presents a novel technique to extract starlight signals from all-sky images and retrieve aerosol optical depth (AOD). It is validated against lunar photometry, showing a strong correlation between both data series. This innovative approach will expand nocturnal AOD measurements to more locations, as all-sky cameras are a simpler and more cost-effective alternative to stellar and lunar photometers.
Jan El Kassar, Cintia Carbajal Henken, Xavier Calbet, Pilar Rípodas, Rene Preusker, and Jürgen Fischer
EGUsphere, https://doi.org/10.5194/egusphere-2024-3605, https://doi.org/10.5194/egusphere-2024-3605, 2024
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Water vapour is a key ingredient in virtually all meteorological processes. We present an algorithm which uses observations of the Flexible Combined Imager (FCI) to estimate the total column of water vapour over sun-lit, clear-sky pixels. FCI is a satellite instrument and every 10 minutes it takes an image which covers Europe, Africa and the Atlantic with a resolution of 1 km. Such high resolution water vapour fields will provide valuable information for weather forecasters and researchers.
P. Russo, A. Laguarda, G. Abal, and L. Doppler
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 565–572, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-565-2023, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-565-2023, 2023
Xavier Calbet, Cintia Carbajal Henken, Sergio DeSouza-Machado, Bomin Sun, and Tony Reale
Atmos. Meas. Tech., 15, 7105–7118, https://doi.org/10.5194/amt-15-7105-2022, https://doi.org/10.5194/amt-15-7105-2022, 2022
Short summary
Short summary
Water vapor concentration in the atmosphere at small scales (< 6 km) is considered. The measurements show Gaussian random field behavior following Kolmogorov's theory of turbulence two-thirds law. These properties can be useful when estimating the water vapor variability within a given observed satellite scene or when different water vapor measurements have to be merged consistently.
Panagiotis G. Kosmopoulos, Stelios Kazadzis, Alois W. Schmalwieser, Panagiotis I. Raptis, Kyriakoula Papachristopoulou, Ilias Fountoulakis, Akriti Masoom, Alkiviadis F. Bais, Julia Bilbao, Mario Blumthaler, Axel Kreuter, Anna Maria Siani, Kostas Eleftheratos, Chrysanthi Topaloglou, Julian Gröbner, Bjørn Johnsen, Tove M. Svendby, Jose Manuel Vilaplana, Lionel Doppler, Ann R. Webb, Marina Khazova, Hugo De Backer, Anu Heikkilä, Kaisa Lakkala, Janusz Jaroslawski, Charikleia Meleti, Henri Diémoz, Gregor Hülsen, Barbara Klotz, John Rimmer, and Charalampos Kontoes
Atmos. Meas. Tech., 14, 5657–5699, https://doi.org/10.5194/amt-14-5657-2021, https://doi.org/10.5194/amt-14-5657-2021, 2021
Short summary
Short summary
Large-scale retrievals of the ultraviolet index (UVI) in real time by exploiting the modern Earth observation data and techniques are capable of forming operational early warning systems that raise awareness among citizens of the health implications of high UVI doses. In this direction a novel UVI operating system, the so-called UVIOS, was introduced for massive outputs, while its performance was tested against ground-based measurements revealing a dependence on the input quality and resolution.
Montserrat Costa-Surós, Odran Sourdeval, Claudia Acquistapace, Holger Baars, Cintia Carbajal Henken, Christa Genz, Jonas Hesemann, Cristofer Jimenez, Marcel König, Jan Kretzschmar, Nils Madenach, Catrin I. Meyer, Roland Schrödner, Patric Seifert, Fabian Senf, Matthias Brueck, Guido Cioni, Jan Frederik Engels, Kerstin Fieg, Ksenia Gorges, Rieke Heinze, Pavan Kumar Siligam, Ulrike Burkhardt, Susanne Crewell, Corinna Hoose, Axel Seifert, Ina Tegen, and Johannes Quaas
Atmos. Chem. Phys., 20, 5657–5678, https://doi.org/10.5194/acp-20-5657-2020, https://doi.org/10.5194/acp-20-5657-2020, 2020
Short summary
Short summary
The impact of anthropogenic aerosols on clouds is a key uncertainty in climate change. This study analyses large-domain simulations with a new high-resolution model to investigate the differences in clouds between 1985 and 2013 comparing multiple observational datasets. The differences in aerosol and in cloud droplet concentrations are clearly detectable. For other quantities, the detection and attribution proved difficult, despite a substantial impact on the Earth's energy budget.
Nils Madenach, Cintia Carbajal Henken, René Preusker, Odran Sourdeval, and Jürgen Fischer
Atmos. Chem. Phys., 19, 13535–13546, https://doi.org/10.5194/acp-19-13535-2019, https://doi.org/10.5194/acp-19-13535-2019, 2019
Panagiotis-Ioannis Raptis, Stelios Kazadzis, Julian Gröbner, Natalia Kouremeti, Lionel Doppler, Ralf Becker, and Constantinos Helmis
Atmos. Meas. Tech., 11, 1143–1157, https://doi.org/10.5194/amt-11-1143-2018, https://doi.org/10.5194/amt-11-1143-2018, 2018
Short summary
Short summary
The purpose of this work is to retrieve integrated water vapour using spectral measurements from Precision Solar Spectroradiometer (PSR). Two different approaches were developed one using single-channel direct sun irradiance measurements, and the second one integrating at a certain spectral region. The results of the spectral approach are closer to the retrievals of non-photometric techniques (GPS, microwave radiometer and radiosondes), suggesting this method provide more accurate IWV product.
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.
Ilias Fountoulakis, Alberto Redondas, Kaisa Lakkala, Alberto Berjon, Alkiviadis F. Bais, Lionel Doppler, Uwe Feister, Anu Heikkila, Tomi Karppinen, Juha M. Karhu, Tapani Koskela, Katerina Garane, Konstantinos Fragkos, and Volodya Savastiouk
Atmos. Meas. Tech., 10, 4491–4505, https://doi.org/10.5194/amt-10-4491-2017, https://doi.org/10.5194/amt-10-4491-2017, 2017
Short summary
Short summary
Results of the temperature characterization of the global UV spectral measurements of eight different Brewer spectrophotometers operating in Greece, Finland, Germany and Spain are presented. Different temperature characterization methods are evaluated and an improved methodology for the correction of the measurements for the effects of temperature is presented.
Claudia Di Biagio, Paola Formenti, Lionel Doppler, Cécile Gaimoz, Noel Grand, Gerard Ancellet, Jean-Luc Attié, Silvia Bucci, Philippe Dubuisson, Federico Fierli, Marc Mallet, and François Ravetta
Atmos. Chem. Phys., 16, 10591–10607, https://doi.org/10.5194/acp-16-10591-2016, https://doi.org/10.5194/acp-16-10591-2016, 2016
Short summary
Short summary
Pollution aerosols strongly influence the composition of the Western Mediterranean, but at present little is known on their optical properties. Here, we report observations of pollution aerosols measured during the TRAQA airborne campaign in summer 2012. Data from this study indicate a large variability of the absorption for pollution particles. This variability strongly influences their direct radiative effect, with possible consequences on the hydrological cycle in this part of the basin.
C. Di Biagio, L. Doppler, C. Gaimoz, N. Grand, G. Ancellet, J.-C. Raut, M. Beekmann, A. Borbon, K. Sartelet, J.-L. Attié, F. Ravetta, and P. Formenti
Atmos. Chem. Phys., 15, 9611–9630, https://doi.org/10.5194/acp-15-9611-2015, https://doi.org/10.5194/acp-15-9611-2015, 2015
Short summary
Short summary
Observations from this study indicate that continental pollution largely affects the atmospheric composition and structure of the western Mediterranean basin. Pollution plumes reach 3000-4000 m in altitude and present a very complex and highly stratified structure, characterized by fresh and aged layers both in the boundary layer and in the free troposphere. Also we report the observations of high levels of ultrafine particles over the basin, possibly linked to new particle formation events.
A. Hollstein, J. Fischer, C. Carbajal Henken, and R. Preusker
Atmos. Meas. Tech., 8, 1757–1771, https://doi.org/10.5194/amt-8-1757-2015, https://doi.org/10.5194/amt-8-1757-2015, 2015
Short summary
Short summary
Cloud detection is one of the key components for the exploitation of Earth observation images. We discuss the use of probabilistic algorithms for MERIS and AATSR on-board the ENVISAT satellite. As a new approach, we used an automated search to find the best combination of channels for the algorithm, which led to a number of unusual combinations that have not been used in the past. We show how very small samples of manually classified cloud truth images can be used to set up efficient algorithms.
H. Diedrich, R. Preusker, R. Lindstrot, and J. Fischer
Atmos. Meas. Tech., 8, 823–836, https://doi.org/10.5194/amt-8-823-2015, https://doi.org/10.5194/amt-8-823-2015, 2015
J. Slobodda, A. Hünerbein, R. Lindstrot, R. Preusker, K. Ebell, and J. Fischer
Atmos. Meas. Tech., 8, 567–578, https://doi.org/10.5194/amt-8-567-2015, https://doi.org/10.5194/amt-8-567-2015, 2015
Short summary
Short summary
In this paper the representativeness of ground-based cloud observatories and their comparability to satellite data and weather prediction models is examined. It is performed by analysing correlation of time series of SEVIRI pixels. The representativeness strongly depends on the used channels and ranges between 1km and over 20km.
C. K. Carbajal Henken, R. Lindstrot, R. Preusker, and J. Fischer
Atmos. Meas. Tech., 7, 3873–3890, https://doi.org/10.5194/amt-7-3873-2014, https://doi.org/10.5194/amt-7-3873-2014, 2014
Short summary
Short summary
Presented here is the FAME-C (Freie Universität Berlin AATSR and MERIS cloud) algorithm, which uses satellite measurements in the visible, near-infrared and infrared part of the spectrum to retrieve cloud macrophysical properties, such as cloud amount and two independent cloud top heights, and cloud optical and microphysical properties, such as cloud top thermodynamic phase, cloud optical thickness and effective radius, which describes the particle size distribution.
R. Lindstrot, M. Stengel, M. Schröder, J. Fischer, R. Preusker, N. Schneider, T. Steenbergen, and B. R. Bojkov
Earth Syst. Sci. Data, 6, 221–233, https://doi.org/10.5194/essd-6-221-2014, https://doi.org/10.5194/essd-6-221-2014, 2014
A. Hollstein and R. Lindstrot
Atmos. Meas. Tech., 7, 599–607, https://doi.org/10.5194/amt-7-599-2014, https://doi.org/10.5194/amt-7-599-2014, 2014
C. A. Randles, S. Kinne, G. Myhre, M. Schulz, P. Stier, J. Fischer, L. Doppler, E. Highwood, C. Ryder, B. Harris, J. Huttunen, Y. Ma, R. T. Pinker, B. Mayer, D. Neubauer, R. Hitzenberger, L. Oreopoulos, D. Lee, G. Pitari, G. Di Genova, J. Quaas, F. G. Rose, S. Kato, S. T. Rumbold, I. Vardavas, N. Hatzianastassiou, C. Matsoukas, H. Yu, F. Zhang, H. Zhang, and P. Lu
Atmos. Chem. Phys., 13, 2347–2379, https://doi.org/10.5194/acp-13-2347-2013, https://doi.org/10.5194/acp-13-2347-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Wet-Radome Attenuation in ARM Cloud Radars and Its Utilization in Radar Calibration Using Disdrometer Measurements
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
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
Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
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
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
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Short summary
This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Short summary
The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Short summary
In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Short summary
We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Short summary
Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
Short summary
Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Yuli Liu and Ian S. Adams
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-188, https://doi.org/10.5194/amt-2024-188, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This paper presents our latest development in tomographic cloud reconstruction algorithms that use multi-angle TB observations to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomographic technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the tomography technique effectively increases detection sensitivity for small ice cloud particles.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Short summary
This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-143, https://doi.org/10.5194/amt-2024-143, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (EMS) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2615, https://doi.org/10.5194/egusphere-2024-2615, 2024
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation (WRA) logarithmic behavior as a function of rainfall rates in light and moderate rain conditions. This WRA technique is applied to the measurements during the ARM TRACER campaign and reports Ze offsets that compare favorably with results from other traditional calibration methods.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
Short summary
Short summary
This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1936, https://doi.org/10.5194/egusphere-2024-1936, 2024
Short summary
Short summary
Retrievals of CO2 column-averaged dry air mole fractions from space can be done with spaceborne spectrometers such as OCO-2. Clouds in the vicinity of a footprint lead to spectral perturbations that bias those retrievals well beyond the required accuracy for global assessments of CO2 sources and sinks. This paper presents two physics-based mitigation techniques for these biases based on accompanying imagery, which can be used operationally.
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.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
EGUsphere, https://doi.org/10.5194/egusphere-2023-2885, https://doi.org/10.5194/egusphere-2023-2885, 2024
Short summary
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth’s surface. In the context of photovoltaic power production, FLS is particularly important, as FLS, impact large regions simultaneously, making regional power grid balancing hard. This study introduces a new machine leanring based algorithm developed for the MSG geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 H day cycle.
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.
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
Short summary
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests 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
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.
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.
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.
Cited articles
Baum, B. A., Yang, P., Heymsfield, A. J., Platnick, S., King, M. D., Hu, Y., and Bedka, S. T.: Bulk scattering properties for the remote sensing of ice clouds. Part II: Narrowband models, J. Appl. Meteorol., 44, 1896–1911, 2005.
Bennartz, R. and Fischer, J.: A modified k-distribution approach applied to narrow band water vapour and oxygen absorption estimates in the near infrared, J. Quant. Spectrosc. Ra., 66, 539–553, 2000.
Carbajal Henken, C. K., Lindstrot, R., Preusker, R., and Fischer, J.: FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations, Atmos. Meas. Tech., 7, 3873–3890, https://doi.org/10.5194/amt-7-3873-2014, 2014.
Clothiaux, E. E., Ackerman, T. P., Mace, G. G., Moran, K. P., Marchand, R. T., Miller, M. A., and Martner, B. E.: Objective determination of cloud heights and radar reflectivities using a combination of active remote sensors at the ARM CART sites, J. Appl. Meteorol., 39, 645–665, 2000.
Desmons, M., Ferlay, N., Parol, F., Mcharek, L., and Vanbauce, C.: Improved information about the vertical location and extent of monolayer clouds from POLDER3 measurements in the oxygen A-band, Atmos. Meas. Tech., 6, 2221–2238, https://doi.org/10.5194/amt-6-2221-2013, 2013.
Doppler, L., Carbajal-Henken, C., Pelon, J., Ravetta, F., and Fischer, J.: Extension of radiative transfer code MOMO, matrix-operator model to the thermal infrared–Clear air validation by comparison to RTTOV and application to CALIPSO-IIR, J. Quant. Spectrosc. Ra., 144, 49–67, 2014a.
Doppler, L., Preusker, R., Bennartz, R., and Fischer, J.: k-bin and k-IR: k-distribution methods without correlation approximation for non-fixed instrument response function and extension to the thermal infrared Applications to satellite remote sensing, J. Quant. Spectrosc. Ra., 133, 382–395, 2014b.
ESA: BEAM Earth Observation Toolbox and Development Platform, available at: http://www.brockmann-consult.de/cms/web/beam, last access: May 2014.
Fell, F. and Fischer, J.: Numerical simulation of the light field in the atmosphere–ocean system using the matrix-operator method, J. Quant. Spectrosc. Ra., 69, 351–388, 2001.
Ferlay, N., Thieuleux, F., Cornet, C., Davis, A. B., Dubuisson, P., Ducos, F., Parol, F., Riédi, J., and Vanbauce, C.: Toward new inferences about cloud structures from multidirectional measurements in the oxygen A band: Middle-of-cloud pressure and cloud geometrical thickness from POLDER-3/PARASOL, J. Appl. Meteorol. Clim., 49, 2492–2507, 2010.
Fischer, J. and Grassl, H.: Detection of cloud-top height from backscattered radiances within the oxygen A band. Part 1: Theoretical study, J. Appl. Meteorol., 30, 1245–1259, 1991.
Fomferra, N. and Brockmann, C.: Beam-the ENVISAT MERIS and AATSR toolbox, in: MERIS (A) ATSR Workshop 2005, vol. 597, p. 13, 2005.
Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P. N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J. F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., and Wind, G.: Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms, Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, 2014.
Henken, C. C., Lindstrot, R., Filipitsch, F., Walther, A., Preusker, R., and Fischer, J.: FAME-C: Retrieval of cloud top pressure with vertically inhomogeneous cloud profiles, in: AIP Conference Proceedings, vol. 1531, p. 412, 2013.
Hollstein, A. and Fischer, J.: Radiative transfer solutions for coupled atmosphere ocean systems using the matrix operator technique, J. Quant. Spectrosc. Ra., 113, 536–548, 2012.
Hollstein, A., Fischer, J., Carbajal Henken, C., and Preusker, R.: Bayesian cloud detection for MERIS, AATSR, and their combination, Atmos. Meas. Tech., 8, 1757–1771, https://doi.org/10.5194/amt-8-1757-2015, 2015.
Hollmann, R., Merchant, C., Saunders, R., Downy, C., Buchwitz, M., Cazenave, A., Chuvieco, E., Defourny, P., De Leeuw, G., Forsberg, R., Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., van Roozendael, M., and Wagner, W.: The ESA climate change initiative: Satellite data records for essential climate variables, B. Am. Meteorol. Soc., 94, 1541–1552, 2013.
Koelemeijer, R., Stammes, P., Hovenier, J., and De Haan, J.: Global distributions of effective cloud fraction and cloud top pressure derived from oxygen A band spectra measured by the Global Ozone Monitoring Experiment: comparison to ISCCP data, J. Geophys. Res.-Atmos., 107, AAC 5-1–AAC 5-9, 2002.
Kokhanovsky, A. A. and Rozanov, V. V.: Cloud bottom altitude determination from a satellite, IEEE Geosci. Remote S., 2, 280, 2005.
Korpela, A., Dybbroe, A., and Thoss, A.: Retrieving Cloud Top Temperature and Height in Semi-transparent and fractional cloudiness using AVHRR, Reports Meteorologi 100, SMHI, Norrköping, Sweden. NWCSAF Visiting Scientist Report, 35 pp., 2001.
Li, Y., Thompson, D. W., Stephens, G. L., and Bony, S.: A global survey of the instantaneous linkages between cloud vertical structure and large-scale climate, J. Geophys. Res.-Atmos., 119, 3770–3792, 2014.
Lindstrot, R., Preusker, R., and Fischer, J.: Empirical Correction of Stray Light within the MERIS Oxygen A-Band Channel, J. Atmos. Ocean. Tech., 27, 1185–1194, 2010a.
Lindstrot, R., Preusker, R., and Fischer, J.: Remote Sensing of Multilayer Cloud-Top Pressure Using Combined Measurements of MERIS and AATSR on board Envisat, J. Appl. Meteorol. Clim., 49, 1191–1204, 2010b.
Luo, Y., Zhang, R., and Wang, H.: Comparing occurrences and vertical structures of hydrometeors between eastern China and the Indian monsoon region using CloudSat/CALIPSO data, J. Climate, 22, 1052–1064, 2009.
Mace, G. G., Marchand, R., Zhang, Q., and Stephens, G.: Global hydrometeor occurrence as observed by CloudSat: Initial observations from summer 2006, Geophys. Res. Lett., 34, L09808, https://doi.org/10.1029/2006GL029017, 2007.
McClatchey, R. A., Fenn, R., Selby, J. A., Volz, F., and Garing, J.: Optical properties of the atmosphere, Rep. AFCRL-72-0497, Air Force Cambridge Research Lab., Bedford, Mass., 85 pp., 1972.
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.
METOffice: RTTOV v9, available at: http://research.metoffice.gov.uk/research/interproj/nwpsaf/rtm/rtm_rttov9.html, last access: May 2014.
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.
Naud, C. M., Muller, J.-P., Clothiaux, E. E., Baum, B. A., and Menzel, W. P.: Intercomparison of multiple years of MODIS, MISR and radar cloud-top heights, Ann. Geophys., 23, 2415–2424, https://doi.org/10.5194/angeo-23-2415-2005, 2005.
Naud, C. M., Del Genio, A. D., Bauer, M., and Kovari, W.: Cloud vertical distribution across warm and cold fronts in CloudSat-CALIPSO data and a general circulation model, J. Climate, 23, 3397–3415, 2010.
Pavolonis, M. J. and Heidinger, A. K.: Daytime cloud overlap detection from AVHRR and VIIRS, J. Appl. Meteorol., 43, 762–778, 2004.
Polonsky, I., Labonnote, L., and Cooper, S.: Level 2 cloud optical depth product process description and interface control document, CloudSat Project, NASA Earth System Science Pathfinder Mission, Institute for Research in the Atmosphere, Colorado State University 21 pp., 2008.
Preusker, R. and Lindstrot, R.: Remote Sensing of Cloud-Top Pressure Using Moderately Resolved Measurements within the Oxygen A Band-A Sensitivity Study, J. Appl. Meteorol. Clim., 48, 1562–1574, 2009.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287, 1999.
Rothman, L. S., Gordon, I. E., Barbe, A., Benner, D. C., Bernath, P. F., Birk, M., Boudon, V., Brown, L. R., Campargue, A., Champion, J.-P., Chance, K., Coudert, L. H., Dana, V., Devi, V. M., Fally, S., Flaud, J.-M., Gamache, R. R., Goldman, A., Jacquemart, D., Kleiner, I., Lacome, N., Lafferty, W. J., Mandin, J.-Y., Massie, S. T., Mikhailenko, S. N., Miller, C. E., Moazzen-Ahmadi, N., Naumenko, O. V., Nikitin, A. V., Orphal, J., Perevalov, V. I., Perrin, A., Predoi-Cross, A., Rinsland, C. P., Rotger, M.,Šimečková, M., Smith, M. A. H., Sung, K., Tashkun, S. A., Tennyson, J., Toth, R. A., Vandaele, A. C., and Vander Auwera, J.: The HITRAN 2008 molecular spectroscopic database, J. Quant. Spectrosc. Ra., 110, 533–572, 2009.
Rozanov, V. V. and Kokhanovsky, A. A.: Semianalytical cloud retrieval algorithm as applied to the cloud top altitude and the cloud geometrical thickness determination from top-of-atmosphere reflectance measurements in the oxygen A band, J. Geophys. Res.-Atmos., 109, D05202, https://doi.org/10.1029/2003JD004104, 2004.
Saiedy, F., Hilleary, D., and Morgan, W.: Cloud-top altitude measurements from satellites, Appl. Optics, 4, 495–500, 1965.
Saiedy, F., Jacobowitz, H., and Wark, D.: On cloud-top determination from Gemini-5, J. Atmos. Sci., 24, 63–69, 1967.
Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements, J. Geophys. Res.-Atmos., 113, D00A12, https://doi.org/10.1029/2008JD009972, 2008.
Saunders, R., Matricardi, M., and Geer, A.: RTTOV-9 Users Guide, NWP SAF Rep. NWPSAF-MO-UD-016, User guide, Met Office, 2010.
Sherwood, S. C., Chae, J.-H., Minnis, P., and McGill, M.: Underestimation of deep convective cloud tops by thermal imagery, Geophys. Res. Lett., 31, L11102, https://doi.org/10.1029/2004GL019699, 2004.
Smith, D.: Empirical Nonlinearity Correction for 12um Channel, Tech. rep., RAL Space AATSR Technical note, Doc No: PO-TN-RAL-AT-0562, Issue: 1.1, 2014.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and the CloudSat Science Team: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, 2002.
Vanbauce, C., Buriez, J.-C., Parol, F., Bonnel, B., Seze, G., and Couvert, P.: Apparent pressure derived from ADEOS-POLDER observations in the oxygen A-band over ocean, Geophys. Res. Lett., 25, 3159–3162, 1998.
Vanbauce, C., Cadet, B., and Marchand, R. T.: Comparison of POLDER apparent and corrected oxygen pressure to ARM/MMCR cloud boundary pressures, Geophys. Res. Lett., 30, 1212, , https://doi.org/10.1029/2002GL016449, 2003.
Wang, J. and Rossow, W. B.: Effects of cloud vertical structure on atmospheric circulation in the GISS GCM, J. Climate, 11, 3010–3029, 1998.
Wang, P., Stammes, P., van der A, R., Pinardi, G., and van Roozendael, M.: FRESCO+: an improved O2A-band cloud retrieval algorithm for tropospheric trace gas retrievals, Atmos. Chem. Phys., 8, 6565–6576, https://doi.org/10.5194/acp-8-6565-2008, 2008.
Weisz, E., Li, J., Menzel, W. P., Heidinger, A. K., Kahn, B. H., and Liu, C.-Y.: Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals, Geophys. Res. Lett., 34, L17811, https://doi.org/10.1029/2007GL030676, 2007.
Winker, D. M., Pelon, J. R., and McCormick, M. P.: The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds, in: Third International Asia-Pacific Environmental Remote Sensing Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 1–11, International Society for Optics and Photonics, 2003.
Wiscombe, W. J.: Improved Mie scattering algorithms, Appl. Optics, 19, 1505–1509, 1980.
Wu, D., Ackerman, S., Davies, R., Diner, D., Garay, M., Kahn, B., Maddux, B., Moroney, C., Stephens, G., Veefkind, J., and Vaughan, M. A.: Vertical distributions and relationships of cloud occurrence frequency as observed by MISR, AIRS, MODIS, OMI, CALIPSO, and CloudSat, Geophys. Res. Lett., 36, L09821, https://doi.org/10.1029/2009GL037464, 2009.
Yamamoto, G. and Wark, D.: Discussion of the letter by RA Hanel,"Determination of cloud altitude from a satellite", J. Geophys. Res., 66, 3596–3596, 1961.
Yin, J., Wang, D., Zhai, G., and Wang, Z.: Observational characteristics of cloud vertical profiles over the continent of East Asia from the CloudSat data, Acta Meteorol. Sin., 27, 26–39, 2013.
Yuan, J., Houze Jr., R. A., and Heymsfield, A. J.: Vertical structures of anvil clouds of tropical mesoscale convective systems observed by CloudSat, J. Atmos. Sci., 68, 1653–1674, 2011.
Short summary
This work presents a study on the sensitivity of two independent satellite cloud height retrievals to cloud vertical distribution. The difference in sensitivity of an oxygen-A absorption band and a thermal infrared based cloud height retrieval, the former being more sensitive to cloud vertical distribution, is exploited by relating the cloud height differences to cloud vertical extent. This could potentially provide additional information on cloud vertical distribution on a global scale.
This work presents a study on the sensitivity of two independent satellite cloud height...
