Articles | Volume 16, issue 7
https://doi.org/10.5194/amt-16-1803-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-1803-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Apr 2023
Research article |
| 05 Apr 2023
| 05 Apr 2023
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
Aviad Levis
Computer and Mathematical Sciences Department, California Institute of Technology, Pasadena, CA 91125, USA
Larry Di Girolamo
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
Vadim Holodovsky
Viterbi Faculty of Electrical and Computer Engineering, Technion –
Israel Institute of Technology, Haifa 3200003, Israel
Linda Forster
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Anthony B. Davis
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA 91109, USA
Yoav Y. Schechner
Viterbi Faculty of Electrical and Computer Engineering, Technion –
Israel Institute of Technology, Haifa 3200003, Israel
Related authors
Jesse Loveridge and Larry Di Girolamo
Atmos. Meas. Tech., 18, 3009–3033, https://doi.org/10.5194/amt-18-3009-2025, https://doi.org/10.5194/amt-18-3009-2025, 2025
Short summary
Short summary
Satellites can measure cloud geometry using stereoscopy. However, clouds are transparent and often have tenuous boundaries. We evaluate the effect of this on stereoscopy using numerical simulations. Stereoscopic techniques retrieve a cloud boundary that is ~100 m interior to the true boundary and is smoother, depending on the cloud shape and resolution of the instrument. This error is similar across views, demonstrating the strength of stereoscopy for detecting changes in cloud geometry.
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.
Dongwei Fu, Larry Di Girolamo, Robert M. Rauber, Greg M. McFarquhar, Stephen W. Nesbitt, Jesse Loveridge, Yulan Hong, Bastiaan van Diedenhoven, Brian Cairns, Mikhail D. Alexandrov, Paul Lawson, Sarah Woods, Simone Tanelli, Sebastian Schmidt, Chris Hostetler, and Amy Jo Scarino
Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, https://doi.org/10.5194/acp-22-8259-2022, 2022
Short summary
Short summary
Satellite-retrieved cloud microphysics are widely used in climate research because of their central role in water and energy cycles. Here, we provide the first detailed investigation of retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing method employed in climate research produces high biases of 6–8 µm (60 %–80 %) caused by 3-D radiative effects.
Linda Forster, Anna Weber, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2025-1369, https://doi.org/10.5194/egusphere-2025-1369, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
CrystalTrace is a Monte Carlo raytracing algorithm for simulating radiative transfer in ice clouds with different crystal shapes and orientations. Integrated into libradtran’s MYSTIC solver, it enables efficient, realistic simulations of ice alongside water clouds. By computing single-scattering properties online at the crystal scale, it eliminates the need for precomputed lookup tables, improving efficiency and angular resolution for satellite, airborne, and ground-based observation retrievals.
Jesse Loveridge and Larry Di Girolamo
Atmos. Meas. Tech., 18, 3009–3033, https://doi.org/10.5194/amt-18-3009-2025, https://doi.org/10.5194/amt-18-3009-2025, 2025
Short summary
Short summary
Satellites can measure cloud geometry using stereoscopy. However, clouds are transparent and often have tenuous boundaries. We evaluate the effect of this on stereoscopy using numerical simulations. Stereoscopic techniques retrieve a cloud boundary that is ~100 m interior to the true boundary and is smoother, depending on the cloud shape and resolution of the instrument. This error is similar across views, demonstrating the strength of stereoscopy for detecting changes in cloud geometry.
Puja Roy, Robert M. Rauber, and Larry Di Girolamo
Atmos. Chem. Phys., 24, 11653–11678, https://doi.org/10.5194/acp-24-11653-2024, https://doi.org/10.5194/acp-24-11653-2024, 2024
Short summary
Short summary
Cloud droplet temperature and lifetime impact cloud microphysical processes such as the activation of ice-nucleating particles. We investigate the thermal and radial evolution of supercooled cloud droplets and their surrounding environments with an aim to better understand observed enhanced ice formation at supercooled cloud edges. This analysis shows that the magnitude of droplet cooling during evaporation is greater than estimated from past studies, especially for drier environments.
Michie Vianca De Vera, Larry Di Girolamo, Guangyu Zhao, Robert M. Rauber, Stephen W. Nesbitt, and Greg M. McFarquhar
Atmos. Chem. Phys., 24, 5603–5623, https://doi.org/10.5194/acp-24-5603-2024, https://doi.org/10.5194/acp-24-5603-2024, 2024
Short summary
Short summary
Tropical oceanic low clouds remain a dominant source of uncertainty in cloud feedback in climate models due to their macrophysical properties (fraction, size, height, shape, distribution) being misrepresented. High-resolution satellite imagery over the Philippine oceans is used here to characterize cumulus macrophysical properties and their relationship to meteorological variables. Such information can act as a benchmark for cloud models and can improve low-cloud generation in climate models.
Genevieve Rose Lorenzo, Avelino F. Arellano, Maria Obiminda Cambaliza, Christopher Castro, Melliza Templonuevo Cruz, Larry Di Girolamo, Glenn Franco Gacal, Miguel Ricardo A. Hilario, Nofel Lagrosas, Hans Jarett Ong, James Bernard Simpas, Sherdon Niño Uy, and Armin Sorooshian
Atmos. Chem. Phys., 23, 10579–10608, https://doi.org/10.5194/acp-23-10579-2023, https://doi.org/10.5194/acp-23-10579-2023, 2023
Short summary
Short summary
Aerosol and weather interactions in Southeast Asia are complex and understudied. An emerging aerosol climatology was established in Metro Manila, the Philippines, from aerosol particle physicochemical properties and meteorology, revealing five sources. Even with local traffic, transported smoke from biomass burning, aged dust, and cloud processing, background marine particles dominate and correspond to lower aerosol optical depth in Metro Manila compared to other Southeast Asian megacities.
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.
Rose Marie Miller, Robert M. Rauber, Larry Di Girolamo, Matthew Rilloraza, Dongwei Fu, Greg M. McFarquhar, Stephen W. Nesbitt, Luke D. Ziemba, Sarah Woods, and Kenneth Lee Thornhill
Atmos. Chem. Phys., 23, 8959–8977, https://doi.org/10.5194/acp-23-8959-2023, https://doi.org/10.5194/acp-23-8959-2023, 2023
Short summary
Short summary
The influence of human-produced aerosols on clouds remains one of the uncertainties in radiative forcing of Earth’s climate. Measurements of aerosol chemistry from sources around the Philippines illustrate the linkage between aerosol chemical composition and cloud droplet characteristics. Differences in aerosol chemical composition in the marine layer from biomass burning, industrial, ship-produced, and marine aerosols are shown to impact cloud microphysical structure just above cloud base.
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.
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.
Paolo Dandini, Céline Cornet, Renaud Binet, Laetitia Fenouil, Vadim Holodovsky, Yoav Y. Schechner, Didier Ricard, and Daniel Rosenfeld
Atmos. Meas. Tech., 15, 6221–6242, https://doi.org/10.5194/amt-15-6221-2022, https://doi.org/10.5194/amt-15-6221-2022, 2022
Short summary
Short summary
3D cloud envelope and development velocity are retrieved from realistic simulations of multi-view
CLOUD (C3IEL) images. Cloud development velocity is derived by finding matching features
between acquisitions separated by 20 s. The tie points are then mapped from image to space via 3D
reconstruction of the cloud envelope obtained from 2 simultaneous images. The retrieved cloud
topography as well as the velocities are in good agreement with the estimates obtained from the
physical models.
Dongwei Fu, Larry Di Girolamo, Robert M. Rauber, Greg M. McFarquhar, Stephen W. Nesbitt, Jesse Loveridge, Yulan Hong, Bastiaan van Diedenhoven, Brian Cairns, Mikhail D. Alexandrov, Paul Lawson, Sarah Woods, Simone Tanelli, Sebastian Schmidt, Chris Hostetler, and Amy Jo Scarino
Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, https://doi.org/10.5194/acp-22-8259-2022, 2022
Short summary
Short summary
Satellite-retrieved cloud microphysics are widely used in climate research because of their central role in water and energy cycles. Here, we provide the first detailed investigation of retrieved cloud drop sizes from in situ and various satellite and airborne remote sensing techniques applied to real cumulus cloud fields. We conclude that the most widely used passive remote sensing method employed in climate research produces high biases of 6–8 µm (60 %–80 %) caused by 3-D radiative effects.
Birgit Heese, Athena Augusta Floutsi, Holger Baars, Dietrich Althausen, Julian Hofer, Alina Herzog, Silke Mewes, Martin Radenz, and Yoav Y. Schechner
Atmos. Chem. Phys., 22, 1633–1648, https://doi.org/10.5194/acp-22-1633-2022, https://doi.org/10.5194/acp-22-1633-2022, 2022
Short summary
Short summary
The aerosol distribution over Haifa, Israel, was measured for 2 years by a laser-based vertically resolved measurement technique called lidar. From these data, the aerosol types and their percentages of the observed aerosol mixtures were identified in terms of their size and shape. We found mostly desert dust from the surrounding deserts and sea salt from the close-by Mediterranean Sea. But aerosols from anthropogenic and industrial pollution from local and far away sources were also detected.
Yulan Hong and Larry Di Girolamo
Atmos. Chem. Phys., 20, 8267–8291, https://doi.org/10.5194/acp-20-8267-2020, https://doi.org/10.5194/acp-20-8267-2020, 2020
Short summary
Short summary
Cloud phase plays a crucial role in Earth radiation budget but is not well understood. Using A-Train satellite observations, this study provides climatological studies of cloud phase characteristics over Southeast Asia on multiple meteorological scales. Results show that ice, liquid, and ice over liquid clouds display distinct spatial heterogeneity and spectral radiance features. The intraseasonal and interannual behaviors of cloud phases are useful to track the MJO and ENSO.
Michel M. Verstraete, Linda A. Hunt, Hugo De Lemos, and Larry Di Girolamo
Earth Syst. Sci. Data, 12, 611–628, https://doi.org/10.5194/essd-12-611-2020, https://doi.org/10.5194/essd-12-611-2020, 2020
Short summary
Short summary
The radiometric camera-by-camera cloud mask product, available for each of the nine cameras of the MISR instrument, contains a variable number of missing values, especially wherever and whenever the instrument is switched from the Global to Local Mode of operation. This paper proposes a simple method for effectively replacing those missing values and demonstrates the performance of the process. MISR data and software tools are obtainable from public domain websites to explore this issue further.
Holger Baars, Albert Ansmann, Kevin Ohneiser, Moritz Haarig, Ronny Engelmann, Dietrich Althausen, Ingrid Hanssen, Michael Gausa, Aleksander Pietruczuk, Artur Szkop, Iwona S. Stachlewska, Dongxiang Wang, Jens Reichardt, Annett Skupin, Ina Mattis, Thomas Trickl, Hannes Vogelmann, Francisco Navas-Guzmán, Alexander Haefele, Karen Acheson, Albert A. Ruth, Boyan Tatarov, Detlef Müller, Qiaoyun Hu, Thierry Podvin, Philippe Goloub, Igor Veselovskii, Christophe Pietras, Martial Haeffelin, Patrick Fréville, Michaël Sicard, Adolfo Comerón, Alfonso Javier Fernández García, Francisco Molero Menéndez, Carmen Córdoba-Jabonero, Juan Luis Guerrero-Rascado, Lucas Alados-Arboledas, Daniele Bortoli, Maria João Costa, Davide Dionisi, Gian Luigi Liberti, Xuan Wang, Alessia Sannino, Nikolaos Papagiannopoulos, Antonella Boselli, Lucia Mona, Giuseppe D'Amico, Salvatore Romano, Maria Rita Perrone, Livio Belegante, Doina Nicolae, Ivan Grigorov, Anna Gialitaki, Vassilis Amiridis, Ourania Soupiona, Alexandros Papayannis, Rodanthi-Elisaveth Mamouri, Argyro Nisantzi, Birgit Heese, Julian Hofer, Yoav Y. Schechner, Ulla Wandinger, and Gelsomina Pappalardo
Atmos. Chem. Phys., 19, 15183–15198, https://doi.org/10.5194/acp-19-15183-2019, https://doi.org/10.5194/acp-19-15183-2019, 2019
Yuekui Yang, Kerry Meyer, Galina Wind, Yaping Zhou, Alexander Marshak, Steven Platnick, Qilong Min, Anthony B. Davis, Joanna Joiner, Alexander Vasilkov, David Duda, and Wenying Su
Atmos. Meas. Tech., 12, 2019–2031, https://doi.org/10.5194/amt-12-2019-2019, https://doi.org/10.5194/amt-12-2019-2019, 2019
Short summary
Short summary
The physical basis of the EPIC cloud product algorithms and an initial evaluation of their performance are presented. EPIC cloud products include cloud mask, effective height, and optical depth. Comparison with co-located retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the NASA Langley Atmospheric Sciences Data Center.
Frank Werner, Galina Wind, Zhibo Zhang, Steven Platnick, Larry Di Girolamo, Guangyu Zhao, Nandana Amarasinghe, and Kerry Meyer
Atmos. Meas. Tech., 9, 5869–5894, https://doi.org/10.5194/amt-9-5869-2016, https://doi.org/10.5194/amt-9-5869-2016, 2016
Short summary
Short summary
A research–level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. This yields reliable estimates of important cloud variables at a horizontal resolution of 30 m. Comparisons of the ASTER retrieval results with the operational cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) show a high agreement for 48 example cloud fields.
Guillaume Merlin, Jérôme Riedi, Laurent C. Labonnote, Céline Cornet, Anthony B. Davis, Phillipe Dubuisson, Marine Desmons, Nicolas Ferlay, and Frédéric Parol
Atmos. Meas. Tech., 9, 4977–4995, https://doi.org/10.5194/amt-9-4977-2016, https://doi.org/10.5194/amt-9-4977-2016, 2016
Short summary
Short summary
The vertical distribution of cloud cover has a significant impact on a large number of meteorological and climatic processes. Cloud top altitude (CTOP) and cloud geometrical thickness (CGT) are essential for understanding these processes. Previous studies established the possibility of retrieving those parameters from multi-angular oxygen A-band measurements. Here we perform a study and comparison of the performance of future instruments.
Feng Xu, Oleg Dubovik, Peng-Wang Zhai, David J. Diner, Olga V. Kalashnikova, Felix C. Seidel, Pavel Litvinov, Andrii Bovchaliuk, Michael J. Garay, Gerard van Harten, and Anthony B. Davis
Atmos. Meas. Tech., 9, 2877–2907, https://doi.org/10.5194/amt-9-2877-2016, https://doi.org/10.5194/amt-9-2877-2016, 2016
Short summary
Short summary
We developed an algorithm for aerosol and water-leaving radiance retrieval in a simultaneous way.
I. Astin and L. Di Girolamo
Atmos. Chem. Phys., 14, 9917–9922, https://doi.org/10.5194/acp-14-9917-2014, https://doi.org/10.5194/acp-14-9917-2014, 2014
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar
Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product
Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Algorithm for continual monitoring of fog based on geostationary satellite imagery
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)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Synergy of millimeter-wave radar and radiometer measurements for retrieving frozen hydrometeors in deep convective systems
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Augmenting the German weather radar network with vertically pointing cloud radars: implications of resolution and attenuation
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Vertical Wind and Drop Size Distribution Retrieval with the CloudCube G-band Doppler Radar
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Harmonized Cloud Datasets for OMI and TROPOMI Using the O2‐O2 477 nm Absorption Band
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Retrieving Vertical Profiles of Cloud Droplet Effective Radius using Multispectral Measurements from MODIS: Examples and Limitations
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Assessment of horizontally-oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Simulations of Spectral Polarimetric Variables measured in rain at W-band
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
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
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
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
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
Atmos. Meas. Tech., 18, 3495–3532, https://doi.org/10.5194/amt-18-3495-2025, https://doi.org/10.5194/amt-18-3495-2025, 2025
Short summary
Short summary
Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining which flight formed each contrail is a critical step to decreasing this impact. We introduce a dataset of synthetic contrail observations with known flight attributions that can be used to develop and assess geostationary-satellite-based contrail-to-flight attribution systems. We additionally introduce a new attribution algorithm and show that it outperforms previous methods.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025, https://doi.org/10.5194/amt-18-3287-2025, 2025
Short summary
Short summary
Accurate measurements of ice water content (IWC) and snowfall rate (SR) are challenging due to high spatial variability and limitations of our measurement techniques. Here, we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path (LWP) as a proxy for the occurrence of riming. LWP is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025, https://doi.org/10.5194/amt-18-3095-2025, 2025
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared, and the likelihood of them differing by 10 W m2 defines the closure.
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025, https://doi.org/10.5194/amt-18-2083-2025, 2025
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 based 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 observation experiments.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025, https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Short summary
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
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
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Short summary
This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
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 (ESM) 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.
Keiichi Ohara and Hirohiko Masunaga
EGUsphere, https://doi.org/10.5194/egusphere-2025-173, https://doi.org/10.5194/egusphere-2025-173, 2025
Short summary
Short summary
Ice particles (e.g., cloud ice, snow and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.
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.
Christian Heske, Florian Ewald, and Silke Groß
EGUsphere, https://doi.org/10.5194/egusphere-2025-691, https://doi.org/10.5194/egusphere-2025-691, 2025
Short summary
Short summary
This study proposes a new technique to augment polarimetric radar measurements of the national German operational radar network with vertically looking cloud radars. The method is tested in two different case studies by comparison to dedicated scanning radars revealing promising results. Future usage of the method is motivated by analyzing the coverage of the operational radar network finding advantageous locations with good radar coverage.
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.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
Short summary
Short summary
Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
EGUsphere, https://doi.org/10.5194/egusphere-2025-478, https://doi.org/10.5194/egusphere-2025-478, 2025
Short summary
Short summary
We introduce a new cloud retrieval algorithm using the O2-O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2-O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
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.
Andrew John Buggee and Peter Andrew Pilewskie
EGUsphere, https://doi.org/10.5194/egusphere-2025-546, https://doi.org/10.5194/egusphere-2025-546, 2025
Short summary
Short summary
A constrained optimal estimation technique was developed to utilize space-borne hyperspectral measurements of reflected solar radiation for retrieving a vertical profile of cloud droplet size, providing insight into the internal structure of a cloud. The improved accuracy and, to a lesser extent, the enhanced spectral sampling provided by next-generation space-borne spectrometers are essential for extracting vertically resolved droplet size information from moderately thick, warm clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
Short summary
Short summary
The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
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.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3841, https://doi.org/10.5194/egusphere-2024-3841, 2025
Short summary
Short summary
This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a year-long dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve the atmospheric model.
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.
Ioanna Tsikoudi, Alessandro Battaglia, Christine Unal, and Eleni Marinou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3164, https://doi.org/10.5194/egusphere-2024-3164, 2025
Short summary
Short summary
The study simulates spectral polarimetric variables for raindrops as observed by a cloud radar. Raindrops are modelled as oblate spheroids and backscattering properties are computed via the T-matrix method including noise, turbulence and spectral averaging effects. When comparing simulations to measurements, differences on the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to mm wavelengths.
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.
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.
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.
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.
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.
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.
Cited articles
Ahn, E., Huang, Y., Siems, S. T., and Manton, M. J.: A Comparison of Cloud
Microphysical Properties Derived From MODIS and CALIPSO With In Situ
Measurements Over the Wintertime Southern Ocean, J. Geophys. Res.-Atmos., 123, 11120–11140, https://doi.org/10.1029/2018JD028535, 2018.
Alexandrov, M. D., Emde, C., Van Diedenhoven, B., and Cairns, B.:
Application of Radon Transform to Multi-Angle Measurements Made by the
Research Scanning Polarimeter: A New Approach to Cloud Tomography. Part I:
Theory and Tests on Simulated Data, Frontiers in Remote Sensing, 2, 791130,
https://doi.org/10.3389/frsen.2021.791130, 2021.
Arridge, S. R. and Schotland, J. C.: Optical tomography: forward and inverse
problems, Inverse Problems, 25, 123010,
https://doi.org/10.1088/0266-5611/25/12/123010, 2009.
Bal, G.: Inverse transport theory and applications, Inverse Problems, 25,
053001, https://doi.org/10.1088/0266-5611/25/5/053001, 2009.
Bal, G. and Jollivet, A.: Stability estimates in stationary inverse
transport, Inverse Probl. Imag., 2, 427–454, https://doi.org/10.3934/ipi.2008.2.427, 2008.
Bellouin, N., Quaas, J., Gryspeerdt, E., Kinne, S., Stier, P.,
Watson-Parris, D., Boucher, O., Carslaw, K. S., Christensen, M., Daniau,
A.-L., Dufresne, J.-L., Feingold, G., Fiedler, S., Forster, P., Gettelman,
A., Haywood, J. M., Lohmann, U., Malavelle, F., Mauritsen, T., McCoy, D. T.,
Myhre, G., Mülmenstädt, J., Neubauer, D., Possner, A., Rugenstein,
M., Sato, Y., Schulz, M., Schwartz, S. E., Sourdeval, O., Storelvmo, T.,
Toll, V., Winker, D., and Stevens, B.: Bounding Global Aerosol Radiative
Forcing of Climate Change, Rev. Geophys., 58, e2019RG000660,
https://doi.org/10.1029/2019RG000660, 2020.
Bodas-Salcedo, A., Andrews, T., Karmalkar, A. V., and Ringer, M. A.: Cloud
liquid water path and radiative feedbacks over the Southern Ocean,
Geophys. Res. Lett., 43, 10938–10946, https://doi.org/10.1002/2016GL070770, 2016.
Box, M. A.: Radiative perturbation theory: a review, Environ. Modell. Softw., 17, 95–106, https://doi.org/10.1016/S1364-8152(01)00056-1, 2002.
Buras, R. and Mayer, B.: Efficient unbiased variance reduction techniques
for Monte Carlo simulations of radiative transfer in cloudy atmospheres: The
solution, J. Quant. Spectrosc. Ra., 112, 434–447, https://doi.org/10.1016/j.jqsrt.2010.10.005, 2011.
Byrd, R. H., Lu, P., Nocedal, J., and Zhu, C.: A Limited Memory Algorithm
for Bound Constrained Optimization, SIAM J. Sci. Comput., 16, 1190–1208,
https://doi.org/10.1137/0916069, 1995.
Cahalan, R. F., Oreopoulos, L., Marshak, A., Evans, K. F., Davis, A. B.,
Pincus, R., Yetzer, K. H., Mayer, B., Davies, R., Ackerman, T. P., Barker,
H. W., Clothiaux, E. E., Ellingson, R. G., Garay, M. J., Kassianov, E.,
Kinne, S., Macke, A., O'hirok, W., Partain, P. T., Prigarin, S. M., Rublev,
A. N., Stephens, G. L., Szczap, F., Takara, E. E., Várnai, T., Wen, G., and Zhuravleva, T. B.: THE I3RC: Bringing Together the Most Advanced
Radiative Transfer Tools for Cloudy Atmospheres, B. Am. Meteorol. Soc., 86, 1275–1294, https://doi.org/10.1175/BAMS-86-9-1275, 2005.
Chance, B., Cooper, C. E., Delpy, D. T., Reynolds, E. O. R., Arridge, S. R.,
and Schweiger, M.: Image reconstruction in optical tomography, Philos.
T. Roy. Soc. B, 352, 717–726, https://doi.org/10.1098/rstb.1997.0054, 1997.
Chazette, P., Totems, J., Baron, A., Flamant, C., and Bony, S.: Trade-wind clouds and aerosols characterized by airborne horizontal lidar measurements during the EUREC4A field campaign, Earth Syst. Sci. Data, 12, 2919–2936, https://doi.org/10.5194/essd-12-2919-2020, 2020.
Chen, K., Li, Q., and Wang, L.: Stability of stationary inverse transport
equation in diffusion scaling, Inverse Problems, 34, 025004,
https://doi.org/10.1088/1361-6420/aa990c, 2018.
Cornet, C. and Davies, R.: Use of MISR measurements to study the radiative
transfer of an isolated convective cloud: Implications for cloud optical
thickness retrieval, J. Geophys. Res.-Atmos., 113, D04202,
https://doi.org/10.1029/2007JD008921, 2008.
Culver, J. P., Ntziachristos, V., Holboke, M. J., and Yodh, A. G.:
Optimization of optode arrangements for diffuse optical tomography: A
singular-value analysis, Opt. Lett., 26, 701–703, https://doi.org/10.1364/OL.26.000701, 2001.
Czerninski, I. and Schechner, Y. Y.: Accelerating Inverse Rendering By Using
a GPU and Reuse of Light Paths, arXiv [preprint], https://doi.org/10.48550/arXiv.2110.00085, 30 September 2021.
Davies, R.: The Effect of Finite Geometry on the Three-Dimensional Transfer
of Solar Irradiance in Clouds, J. Atmos. Sci., 35, 1712–1725, https://doi.org/10.1175/1520-0469(1978)035<1712:TEOFGO>2.0.CO;2, 1978.
Davis, A., Marshak, A., Cahalan, R., and Wiscombe, W.: The Landsat Scale
Break in Stratocumulus as a Three-Dimensional Radiative Transfer Effect:
Implications for Cloud Remote Sensing, J. Atmos. Sci., 54, 241–260, https://doi.org/10.1175/1520-0469(1997)054<0241:TLSBIS>2.0.CO;2, 1997.
Davis, A. B. and Marshak, A.: Multiple Scattering in Clouds: Insights from
Three-Dimensional Diffusion/P1 Theory, Nucl. Sci. Eng., 137,
251–280, https://doi.org/10.13182/NSE01-A2190, 2001.
Davis, A. B. and Marshak, A.: Photon propagation in heterogeneous optical
media with spatial correlations: enhanced mean-free-paths and
wider-than-exponential free-path distributions, J. Quant. Spectrosc. Ra., 84, 3–34, https://doi.org/10.1016/S0022-4073(03)00114-6, 2004.
Davis, A. B., Forster, L., Diner, D. J., and Mayer, B.: Toward Cloud
Tomography from Space using MISR and MODIS: The Physics of Image Formation
for Opaque Convective Clouds, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14537, 27 July 2021.
Deutschmann, T., Beirle, S., Frieß, U., Grzegorski, M., Kern, C.,
Kritten, L., Platt, U., Prados-Román, C., Puḳīte, J., Wagner, T., Werner, B., and Pfeilsticker, K.: The Monte Carlo atmospheric radiative transfer model McArtim: Introduction and validation of Jacobians and 3D features, J. Quant. Spectrosc. Ra., 112, 1119–1137, https://doi.org/10.1016/j.jqsrt.2010.12.009, 2011.
Di Girolamo, L., Liang, L., and Platnick, S.: A global view of one-dimensional solar radiative transfer through oceanic water clouds, Geophys. Res. Lett., 37, L18809, https://doi.org/10.1029/2010GL044094, 2010.
Diner, D. J., Xu, F., Garay, M. J., Martonchik, J. V., Rheingans, B. E., Geier, S., Davis, A., Hancock, B. R., Jovanovic, V. M., Bull, M. A., Capraro, K., Chipman, R. A., and McClain, S. C.: The Airborne Multiangle SpectroPolarimetric Imager (AirMSPI): a new tool for aerosol and cloud remote sensing, Atmos. Meas. Tech., 6, 2007–2025, https://doi.org/10.5194/amt-6-2007-2013, 2013.
Doicu, A. and Efremenko, D. S.: Linearizations of the Spherical Harmonic
Discrete Ordinate Method (SHDOM), Atmosphere, 10, 292, https://doi.org/10.3390/atmos10060292, 2019.
Doicu, A., Mishchenko, M. I., and Trautmann, T.: Electromagnetic scattering
by discrete random media illuminated by a Gaussian beam II: Solution of the
radiative transfer equation, J. Quant. Spectrosc. Ra., 256, 107297,
https://doi.org/10.1016/j.jqsrt.2020.107297, 2020.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic
retrieval algorithms. I: Surrogate minimization method, J. Quant. Spectrosc. Ra., 277, 107954, https://doi.org/10.1016/j.jqsrt.2021.107954, 2022a.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic
retrieval algorithms. II: Adjoint method, J. Quant. Spectrosc. Ra., 108177,
https://doi.org/10.1016/j.jqsrt.2022.108177, 2022b.
Dubovik, O., Holben, B. N., Lapyonok, T., Sinyuk, A., Mishchenko, M. I.,
Yang, P., and Slutsker, I.: Non-spherical aerosol retrieval method employing
light scattering by spheroids, Geophys. Res. Lett., 29, 54-1–54-4,
https://doi.org/10.1029/2001GL014506, 2002.
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011.
Dwight, R. P. and Brezillon, J.: Effect of Approximations of the Discrete
Adjoint on Gradient-Based Optimization, AIAA J., 44, 3022–3031,
https://doi.org/10.2514/1.21744, 2006.
Dzambo, A. M., L'Ecuyer, T., Sinclair, K., van Diedenhoven, B., Gupta, S., McFarquhar, G., O'Brien, J. R., Cairns, B., Wasilewski, A. P., and Alexandrov, M.: Joint cloud water path and rainwater path retrievals from airborne ORACLES observations, Atmos. Chem. Phys., 21, 5513–5532, https://doi.org/10.5194/acp-21-5513-2021, 2021.
Efremenko, D. S., Loyola, D. G., Doicu, A., and Spurr, R. J. D.:
Multi-core-CPU and GPU-accelerated radiative transfer models based on the
discrete ordinate method, Comput. Phys. Commun., 185, 3079–3089,
https://doi.org/10.1016/j.cpc.2014.07.018, 2014.
Emde, C., Barlakas, V., Cornet, C., Evans, F., Korkin, S., Ota, Y.,
Labonnote, L. C., Lyapustin, A., Macke, A., Mayer, B., and Wendisch, M.:
IPRT polarized radiative transfer model intercomparison project – Phase A, J. Quant. Spectrosc. Ra., 164, 8–36, https://doi.org/10.1016/j.jqsrt.2015.05.007, 2015.
Emde, C., Buras-Schnell, R., Kylling, A., Mayer, B., Gasteiger, J., Hamann, U., Kylling, J., Richter, B., Pause, C., Dowling, T., and Bugliaro, L.: The libRadtran software package for radiative transfer calculations (version 2.0.1), Geosci. Model Dev., 9, 1647–1672, https://doi.org/10.5194/gmd-9-1647-2016, 2016.
Emde, C., Barlakas, V., Cornet, C., Evans, F., Wang, Z., Labonotte, L. C.,
Macke, A., Mayer, B., and Wendisch, M.: IPRT polarized radiative transfer
model intercomparison project – Three-dimensional test cases (phase B),
J. Quant. Spectrosc. Ra., 209, 19–44, https://doi.org/10.1016/j.jqsrt.2018.01.024, 2018.
Endo, S., Fridlind, A. M., Lin, W., Vogelmann, A. M., Toto, T., Ackerman, A.
S., McFarquhar, G. M., Jackson, R. C., Jonsson, H. H., and Liu, Y.: RACORO
continental boundary layer cloud investigations: 2. Large-eddy simulations
of cumulus clouds and evaluation with in situ and ground-based observations,
J. Geophys. Res.-Atmos., 120, 5993–6014, https://doi.org/10.1002/2014JD022525, 2015.
Eppstein, M. J., Fedele, F., Laible, J., Zhang, C., Godavarty, A., and
Sevick-Muraca, E. M.: A comparison of exact and approximate adjoint
sensitivities in fluorescence tomography, IEEE T. Med. Imaging, 22, 1215–1223, https://doi.org/10.1109/TMI.2003.818165, 2003.
Evans, K. F.: The Spherical Harmonics Discrete Ordinate Method for
Three-Dimensional Atmospheric Radiative Transfer, J. Atmos. Sci., 55, 429–446, https://doi.org/10.1175/1520-0469(1998)055<0429:TSHDOM>2.0.CO;2, 1998.
Evans, K. F.: Spherical Harmonics Discrete Ordinate Method, University of Colorado, Boulder [code], https://nit.coloradolinux.com/~evans/shdom/shdom.tar.gz, last access: 23 March 2023.
Ewald, F., Groß, S., Wirth, M., Delanoë, J., Fox, S., and Mayer, B.: Why we need radar, lidar, and solar radiance observations to constrain ice cloud microphysics, Atmos. Meas. Tech., 14, 5029–5047, https://doi.org/10.5194/amt-14-5029-2021, 2021.
Eytan, E., Khain, A., Pinsky, M., Altaratz, O., Shpund, J., and Koren, I.:
Shallow Cumulus Properties as Captured by Adiabatic Fraction in
High-Resolution LES Simulations, J. Atmos. Sci., 79, 409–428, https://doi.org/10.1175/JAS-D-21-0201.1, 2022.
Fielding, M. D., Chiu, J. C., Hogan, R. J., and Feingold, G.: A novel
ensemble method for retrieving properties of warm cloud in 3-D using
ground-based scanning radar and zenith radiances, J. Geophys. Res.-Atmos., 119, 10912–10930, https://doi.org/10.1002/2014JD021742, 2014.
Forster, L., Davis, A. B., Diner, D. J., and Mayer, B.: Toward Cloud
Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in
Opaque Convective Clouds, J. Atmos. Sci., 78, 155–166,
https://doi.org/10.1175/JAS-D-19-0262.1, 2020.
Fu, D., Di Girolamo, L., Liang, L., and Zhao, G.: Regional Biases in MODIS
Marine Liquid Water Cloud Drop Effective Radius Deduced Through Fusion With
MISR, J. Geophys. Res.-Atmos., 124, 13182–13196, https://doi.org/10.1029/2019JD031063, 2019.
Gao, M., Franz, B. A., Knobelspiesse, K., Zhai, P.-W., Martins, V., Burton, S., Cairns, B., Ferrare, R., Gales, J., Hasekamp, O., Hu, Y., Ibrahim, A., McBride, B., Puthukkudy, A., Werdell, P. J., and Xu, X.: Efficient multi-angle polarimetric inversion of aerosols and ocean color powered by a deep neural network forward model, Atmos. Meas. Tech., 14, 4083–4110, https://doi.org/10.5194/amt-14-4083-2021, 2021.
Garay, M. J., Davis, A. B., and Diner, D. J.: Tomographic reconstruction of
an aerosol plume using passive multiangle observations from the MISR
satellite instrument, Geophys. Res. Lett., 43, 12590–12596,
https://doi.org/10.1002/2016GL071479, 2016.
Gerber, H., Jensen, J. B., Davis, A. B., Marshak, A., and Wiscombe, W. J.:
Spectral Density of Cloud Liquid Water Content at High Frequencies, J. Atmos. Sci., 58, 497–503, https://doi.org/10.1175/1520-0469(2001)058<0497:SDOCLW>2.0.CO;2, 2001.
Girolamo, L. D.: Reciprocity principle applicable to reflected radiance measurements and the searchlight problem, Appl. Optics, 38, 3196–3198, https://doi.org/10.1364/AO.38.003196, 1999.
Girolamo, L. D.: Reciprocity principle for radiative transfer models that use periodic boundary conditions, J. Quant. Spectrosc. Ra., 73, 23–27, https://doi.org/10.1016/S0022-4073(01)00166-2, 2002.
Grosvenor, D. P., Sourdeval, O., Zuidema, P., Ackerman, A., Alexandrov, M.
D., Bennartz, R., Boers, R., Cairns, B., Chiu, J. C., Christensen, M.,
Deneke, H., Diamond, M., Feingold, G., Fridlind, A., Hünerbein, A.,
Knist, C., Kollias, P., Marshak, A., McCoy, D., Merk, D., Painemal, D.,
Rausch, J., Rosenfeld, D., Russchenberg, H., Seifert, P., Sinclair, K.,
Stier, P., van Diedenhoven, B., Wendisch, M., Werner, F., Wood, R., Zhang,
Z., and Quaas, J.: Remote Sensing of Droplet Number Concentration in Warm
Clouds: A Review of the Current State of Knowledge and Perspectives, Rev.
Geophys., 56, 409–453, https://doi.org/10.1029/2017RG000593, 2018.
Guillaume, A., Kahn, B. H., Yue, Q., Fetzer, E. J., Wong, S., Manipon, G.
J., Hua, H., and Wilson, B. D.: Horizontal and Vertical Scaling of Cloud
Geometry Inferred from CloudSat Data, J. Atmos. Sci., 75, 2187–2197, https://doi.org/10.1175/JAS-D-17-0111.1, 2018.
Hack, J. J., Caron, J. M., Danabasoglu, G., Oleson, K. W., Bitz, C., and
Truesdale, J. E.: CCSM–CAM3 Climate Simulation Sensitivity to Changes in
Horizontal Resolution, J. Climate, 19, 2267–2289, https://doi.org/10.1175/JCLI3764.1, 2006.
Hasekamp, O. P. and Landgraf, J.: Linearization of vector radiative transfer
with respect to aerosol properties and its use in satellite remote sensing,
J. Geophys. Res.-Atmos., 110, D04203, https://doi.org/10.1029/2004JD005260, 2005.
Hill, P. G., Hogan, R. J., Manners, J., and Petch, J. C.: Parametrizing the
horizontal inhomogeneity of ice water content using CloudSat data products,
Q. J. Roy. Meteor. Soc., 138, 1784–1793, https://doi.org/10.1002/qj.1893, 2012.
Holodovsky, V., Schechner, Y. Y., Levin, A., Levis, A., and Aides, A.:
In-situ multi-view multi-scattering stochastic tomography, in: 2016 IEEE
International Conference on Computational Photography (ICCP), Evanston, IL, 13–15 May 2016, IEEE, 1–12, https://doi.org/10.1109/ICCPHOT.2016.7492869, 2016.
Hoyer, S. and Hamman, J.: xarray: N-D labeled Arrays and Datasets in Python,
Journal of Open Research Software, 5, 10, https://doi.org/10.5334/jors.148, 2017.
Huang, D., Liu, Y., and Wiscombe, W.: Determination of cloud liquid water
distribution using 3D cloud tomography, J. Geophys. Res.-Atmos., 113, D13201, https://doi.org/10.1029/2007JD009133, 2008.
Huang, D., Gasiewski, A., and Wiscombe, W.: Tomographic retrieval of cloud liquid water fields from a single scanning microwave radiometer aboard a moving platform – Part 2: Observation system simulation experiments, Atmos. Chem. Phys., 10, 6699–6709, https://doi.org/10.5194/acp-10-6699-2010, 2010.
Jiang, W., Zhan, Y., Xi, S., Huang, D. D., and Lu, J.: Compressive
Sensing-Based 3-D Rain Field Tomographic Reconstruction Using Simulated
Satellite Signals, IEEE T. Geosci. Remote, 60, 1–13, https://doi.org/10.1109/TGRS.2021.3063617, 2022.
Jimenez, P. A., Hacker, J. P., Dudhia, J., Haupt, S. E., Ruiz-Arias, J. A.,
Gueymard, C. A., Thompson, G., Eidhammer, T., and Deng, A.: WRF-Solar:
Description and Clear-Sky Assessment of an Augmented NWP Model for Solar
Power Prediction, B. Am. Meteorol. Soc., 97, 1249–1264, https://doi.org/10.1175/BAMS-D-14-00279.1, 2016.
Jones, A. L. and Di Girolamo, L.: Design and Verification of a New
Monochromatic Thermal Emission Component for the I3RC Community Monte Carlo
Model, J. Atmos. Sci., 75, 885–906, https://doi.org/10.1175/JAS-D-17-0251.1, 2018.
Kahn, R. A., Li, W.-H., Moroney, C., Diner, D. J., Martonchik, J. V., and
Fishbein, E.: Aerosol source plume physical characteristics from space-based
multiangle imaging, J. Geophys. Res.-Atmos., 112, D11205, https://doi.org/10.1029/2006JD007647, 2007.
Kanewala, U. and Bieman, J. M.: Testing scientific software: A systematic
literature review, Inform. Software Tech., 56, 1219–1232,
https://doi.org/10.1016/j.infsof.2014.05.006, 2014.
Kato, S. and Marshak, A.: Solar zenith and viewing geometry-dependent errors
in satellite retrieved cloud optical thickness: Marine stratocumulus case,
J. Geophys. Res.-Atmos., 114, D01202, https://doi.org/10.1029/2008JD010579, 2009.
King, N. J. and Vaughan, G.: Using passive remote sensing to retrieve the
vertical variation of cloud droplet size in marine stratocumulus: An
assessment of information content and the potential for improved retrievals
from hyperspectral measurements, J. Geophys. Res.-Atmos., 117, D15206, https://doi.org/10.1029/2012JD017896, 2012.
Klose, A. D. and Hielscher, A. H.: Optical tomography using the
time-independent equation of radiative transfer – Part 2: inverse model,
J. Quant. Spectrosc. Ra., 72, 715–732, https://doi.org/10.1016/S0022-4073(01)00151-0, 2002.
Langmore, I., Davis, A. B., and Bal, G.: Multipixel Retrieval of Structural
and Optical Parameters in a 2-D Scene With a Path-Recycling Monte Carlo
Forward Model and a New Bayesian Inference Engine, IEEE T. Geosci. Remote, 51, 2903–2919, https://doi.org/10.1109/TGRS.2012.2217380, 2013.
Lebsock, M. and Su, H.: Application of active spaceborne remote sensing for
understanding biases between passive cloud water path retrievals, J.
Geophys. Res.-Atmos., 119, 8962–8979, https://doi.org/10.1002/2014JD021568, 2014.
Lee, B., Di Girolamo, L., Zhao, G., and Zhan, Y.: Three-Dimensional Cloud Volume Reconstruction from the Multi-angle Imaging SpectroRadiometer, Remote Sens.-Basel, 10, 1858, https://doi.org/10.3390/rs10111858, 2018.
Lee, E. K. H., Wardenier, J. P., Prinoth, B., Parmentier, V., Grimm, S. L.,
Baeyens, R., Carone, L., Christie, D., Deitrick, R., Kitzmann, D., Mayne,
N., Roman, M., and Thorsbro, B.: 3D Radiative Transfer for Exoplanet
Atmospheres. gCMCRT: A GPU-accelerated MCRT Code, Astrophys. J., 929, 180,
https://doi.org/10.3847/1538-4357/ac61d6, 2022.
Levis, A., Schechner, Y. Y., Aides, A., and Davis, A. B.: Airborne
Three-Dimensional Cloud Tomography, in: 2015 IEEE International Conference
on Computer Vision (ICCV), Santiago, Chile, 7–13 December 2015, IEEE, 3379–3387, https://doi.org/10.1109/ICCV.2015.386, 2015.
Levis, A., Schechner, Y. Y., and Davis, A. B.: Multiple-Scattering
Microphysics Tomography, in: 2017 IEEE Conference on Computer Vision and
Pattern Recognition (CVPR), Honolulu, HI, USA, 21–26 July 2017, IEEE, 5797–5806, https://doi.org/10.1109/CVPR.2017.614, 2017.
Levis, A., Schechner, Y. Y., Davis, A. B., and Loveridge, J.: Multi-View
Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size,
Remote Sens., 12, 2831, https://doi.org/10.3390/rs12172831, 2020.
Liemert, A. and Kienle, A.: Exact and efficient solution of the radiative
transport equation for the semi-infinite medium, Sci. Rep.-UK, 3, 2018,
https://doi.org/10.1038/srep02018, 2013.
Loeub, T., Levis, A., Holodovsky, V., and Schechner, Y. Y.: Monotonicity
Prior for Cloud Tomography, in: Computer Vision – ECCV 2020, edited by: Vedaldi, A., Bischof, H., Brox, T., and Frahm, J. M., Lecture Notes in Computer Science, Springer, Cham, 12363, 283–299, https://doi.org/10.1007/978-3-030-58523-5_17, 2020.
Loveridge, J., Levis, A., Aides, A., Forster, L., and Holodovsky, V.: Atmospheric Tomography with 3D Radiative Transfer, v4.1.2, Zenodo [code], https://doi.org/10.5281/zenodo.7062466, 2022.
Loveridge, J., Levis, A., Di Girolamo, L., Holodovsky, V., Forster, L., Davis, A. B., and Schechner, Y. Y.: Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization, Atmos. Meas. Tech., submitted, 2023a.
Loveridge, J., Levis, A., Holodovsky, V., and Forster, L.: Atmospheric Tomography with 3D Radiative Transfer, GitHub [code], https://github.com/CloudTomography/AT3D, last access: 28 March 2023b.
Marchand, R. and Ackerman, T.: Evaluation of radiometric measurements from
the NASA Multiangle Imaging Spectroradiometer (MISR): Two- and
three-dimensional radiative transfer modeling of an inhomogeneous
stratocumulus cloud deck, J. Geophys. Res.-Atmos., 109, D18208,
https://doi.org/10.1029/2004JD004710, 2004.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative smoothing
in fractal clouds, J. Geophys. Res.-Atmos., 100, 26247–26261, https://doi.org/10.1029/95JD02895, 1995.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Scale Invariance in
Liquid Water Distributions in Marine Stratocumulus. Part II: Multifractal
Properties and Intermittency Issues, J. Atmos. Sci., 54, 1423–1444, https://doi.org/10.1175/1520-0469(1997)054<1423:SIILWD>2.0.CO;2, 1997.
Marshak, A., Davis, A., Cahalan, R. F., and Wiscombe, W.: Nonlocal independent pixel approximation: direct and inverse problems, IEEE
T. Geosci. Remote, 36, 192–205, https://doi.org/10.1109/36.655329, 1998a.
Marshak, A., Davis, A., Wiscombe, W., and Cahalan, R.: Radiative effects of
sub-mean free path liquid water variability observed in stratiform clouds,
J. Geophys. Res.-Atmos., 103, 19557–19567, https://doi.org/10.1029/98JD01728, 1998b.
Marshak, A., Platnick, S., Várnai, T., Wen, G., and Cahalan, R. F.: Impact of three-dimensional radiative effects on satellite retrievals of cloud droplet sizes, J. Geophys. Res.-Atmos., 111, D09207, https://doi.org/10.1029/2005JD006686, 2006.
Martelli, F., Binzoni, T., Pifferi, A., Spinelli, L., Farina, A., and
Torricelli, A.: There's plenty of light at the bottom: statistics of photon
penetration depth in random media, Sci. Rep.-UK, 6, 27057,
https://doi.org/10.1038/srep27057, 2016.
Martin, W., Cairns, B., and Bal, G.: Adjoint methods for adjusting
three-dimensional atmosphere and surface properties to fit
multi-angle/multi-pixel polarimetric measurements, J. Quant. Spectrosc. Ra., 144, 68–85, https://doi.org/10.1016/j.jqsrt.2014.03.030, 2014.
Martin, W. G. K. and Hasekamp, O. P.: A demonstration of adjoint methods for
multi-dimensional remote sensing of the atmosphere and surface, J. Quant. Spectrosc. Ra., 204, 215–231, https://doi.org/10.1016/j.jqsrt.2017.09.031, 2018.
McBride, B. A., Martins, J. V., Barbosa, H. M. J., Birmingham, W., and Remer, L. A.: Spatial distribution of cloud droplet size properties from Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) measurements, Atmos. Meas. Tech., 13, 1777–1796, https://doi.org/10.5194/amt-13-1777-2020, 2020.
McFarlane, S. A., Mather, J. H., and Mlawer, E. J.: ARM's Progress on
Improving Atmospheric Broadband Radiative Fluxes and Heating Rates,
Meteor. Mon., 57, 20.1–20.24, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0046.1, 2016.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S.-I., van
Diedenhoven, B., and Xue, L.: Confronting the Challenge of Modeling Cloud
and Precipitation Microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
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, https://doi.org/10.1109/TGRS.2002.801160, 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, https://doi.org/10.1080/01431160601030975, 2007.
Nakajima, T. and Tanaka, M.: Algorithms for radiative intensity calculations
in moderately thick atmospheres using a truncation approximation, J. Quant. Spectrosc. Ra., 40, 51–69, https://doi.org/10.1016/0022-4073(88)90031-3, 1988.
Niu, H., Lin, Z.-J., Tian, F., Dhamne, S., and Liu, H.: Comprehensive
investigation of three-dimensional diffuse optical tomography with depth
compensation algorithm, J. Biomed. Opt., 15, 046005,
https://doi.org/10.1117/1.3462986, 2010.
Nocedal, J. and Wright, S.: Fundamentals of Unconstrained Optimization, in:
Numerical Optimization, Springer New York, New York, NY, 10–29,
https://doi.org/10.1007/978-0-387-40065-5_2, 2006.
Painemal, D., Spangenberg, D., Smith Jr., W. L., Minnis, P., Cairns, B., Moore, R. H., Crosbie, E., Robinson, C., Thornhill, K. L., Winstead, E. L., and Ziemba, L.: Evaluation of satellite retrievals of liquid clouds from the GOES-13 imager and MODIS over the midlatitude North Atlantic during the NAAMES campaign, Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, 2021.
Peterson, P.: F2PY: a tool for connecting Fortran and Python programs,
International Journal of Computational Science and Engineering, 4, 296–305,
https://doi.org/10.1504/IJCSE.2009.029165, 2009.
Pincus, R. and Evans, K. F.: Computational Cost and Accuracy in Calculating
Three-Dimensional Radiative Transfer: Results for New Implementations of
Monte Carlo and SHDOM, J. Atmos. Sci., 66, 3131–3146, https://doi.org/10.1175/2009JAS3137.1, 2009.
Ramon, D., Steinmetz, F., Jolivet, D., Compiègne, M., and Frouin, R.:
Modeling polarized radiative transfer in the ocean-atmosphere system with
the GPU-accelerated SMART-G Monte Carlo code, J. Quant. Spectrosc. Ra., 222–223, 89–107, https://doi.org/10.1016/j.jqsrt.2018.10.017, 2019.
Raschke, E., Ohmura, A., Rossow, W. B., Carlson, B. E., Zhang, Y.-C.,
Stubenrauch, C., Kottek, M., and Wild, M.: Cloud effects on the radiation
budget based on ISCCP data (1991 to 1995), Int. J. Climatol., 25, 1103–1125, https://doi.org/10.1002/joc.1157, 2005.
Ronen, R., Schechner, Y. Y., and Eytan, E.: 4D Cloud Scattering Tomography,
in: Proceedings of the IEEE/CVF International Conference on Computer Vision
(ICCV), Montreal, QC, Canada, 10–17 October 2021, IEEE, 5520–5529, https://doi.org/10.1109/ICCV48922.2021.00547, 2021.
Ronen, R., Holodovsky, V., and Schechner, Y. Y.: Variable Imaging Projection
Cloud Scattering Tomography, IEEE T. Pattern Anal., 1–12, https://doi.org/10.1109/TPAMI.2022.3195920, 2022.
Saito, M., Yang, P., Hu, Y., Liu, X., Loeb, N., Smith Jr., W. L., and Minnis,
P.: An Efficient Method for Microphysical Property Retrievals in Vertically
Inhomogeneous Marine Water Clouds Using MODIS-CloudSat Measurements, J. Geophys. Res.-Atmos., 124, 2174–2193, https://doi.org/10.1029/2018JD029659, 2019.
Saito, M., Yang, P., Ding, J., and Liu, X.: A Comprehensive Database of the
Optical Properties of Irregular Aerosol Particles for Radiative Transfer
Simulations, J. Atmos. Sci., 78, 2089–2111, https://doi.org/10.1175/JAS-D-20-0338.1, 2021.
Schilling, K., Schechner, Y. Y., and Koren, I.: CloudCT - Computed
Tomography Of Clouds By A Small Satellite Formation, in: 12th IAA symposium on Small Satellites for Earth Observation, Berlin Germany, 6–10 May 2019, https://www.cloudct.space/_files/ugd/aef793_e2334d0332014022aeb4a5891ff12e92.pdf (last access: 20 March 2023), 2019.
Seethala, C.: Evaluating the state-of-the-art of and errors in 1D satellite
cloud liquid water path retrievals with large eddy simulations and realistic
radiative transfer models, PhD thesis, University of Hamburg, Hamburg, https://doi.org/10.17617/2.1404650, 2012.
Sherwood, S. C., Bony, S., and Dufresne, J.-L.: Spread in model climate
sensitivity traced to atmospheric convective mixing, Nature, 505, 37–42,
https://doi.org/10.1038/nature12829, 2014.
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris, J.
R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and
Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple
Lines of Evidence, Rev. Geophys., 58, e2019RG000678, https://doi.org/10.1029/2019RG000678, 2020.
Shi, H.-J. M., Xie, Y., Byrd, R., and Nocedal, J.: A Noise-Tolerant
Quasi-Newton Algorithm for Unconstrained Optimization, arXiv [preprint], https://doi.org/10.48550/arXiv.2010.04352, 9 September 2021.
Sourdeval, O., Labonnote, L. C., Brogniez, G., Jourdan, O., Pelon, J., and Garnier, A.: A variational approach for retrieving ice cloud properties from infrared measurements: application in the context of two IIR validation campaigns, Atmos. Chem. Phys., 13, 8229–8244, https://doi.org/10.5194/acp-13-8229-2013, 2013.
Stephens, G. L. and Kummerow, C. D.: The Remote Sensing of Clouds and
Precipitation from Space: A Review, J. Atmos. Sci., 64, 3742–3765, https://doi.org/10.1175/2006JAS2375.1, 2007.
Tian, F., Niu, H., Khadka, S., Lin, Z.-J., and Liu, H.: Algorithmic depth
compensation improves quantification and noise suppression in functional
diffuse optical tomography, Biomed. Opt. Express, 1, 441–452,
https://doi.org/10.1364/BOE.1.000441, 2010.
van Harten, G., Diner, D. J., Daugherty, B. J. S., Rheingans, B. E., Bull,
M. A., Seidel, F. C., Chipman, R. A., Cairns, B., Wasilewski, A. P., and
Knobelspiesse, K. D.: Calibration and validation of Airborne Multiangle
SpectroPolarimetric Imager (AirMSPI) polarization measurements, Appl. Optics,
57, 4499–4513, https://doi.org/10.1364/AO.57.004499, 2018.
Van Weverberg, K., Morcrette, C. J., Petch, J., Klein, S. A., Ma, H.-Y.,
Zhang, C., Xie, S., Tang, Q., Gustafson Jr., W. I., Qian, Y., Berg, L. K.,
Liu, Y., Huang, M., Ahlgrimm, M., Forbes, R., Bazile, E., Roehrig, R., Cole,
J., Merryfield, W., Lee, W.-S., Cheruy, F., Mellul, L., Wang, Y.-C.,
Johnson, K., and Thieman, M. M.: CAUSES: Attribution of Surface Radiation
Biases in NWP and Climate Models near the U.S. Southern Great Plains,
J. Geophys. Res.-Atmos., 123, 3612–3644, https://doi.org/10.1002/2017JD027188, 2018.
Vial, J., Bony, S., Stevens, B., and Vogel, R.: Mechanisms and Model
Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review, in:
Shallow Clouds, Water Vapor, Circulation, and Climate Sensitivity, edited
by: Pincus, R., Winker, D., Bony, S., and Stevens, B., Springer
International Publishing, Cham, 159–181,
https://doi.org/10.1007/978-3-319-77273-8_8, 2018.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van
der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson,
A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ.,
Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman,
R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M.,
Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0: fundamental algorithms for scientific computing in Python, Nat. Methods, 17, 261–272, https://doi.org/10.1038/s41592-019-0686-2, 2020.
Wang, C., Platnick, S., Zhang, Z., Meyer, K., and Yang, P.: Retrieval of ice
cloud properties using an optimal estimation algorithm and MODIS infrared
observations: 1. Forward model, error analysis, and information content,
J. Geophys. Res.-Atmos., 121, 5809–5826, https://doi.org/10.1002/2015JD024526, 2016.
Wang, Y., Guo, M., Zhao, Y., and Jiang, J.: GPUs-RRTMG_LW: high-efficient and scalable computing for a longwave radiative transfer model on multiple GPUs, J. Supercomput., 77, 4698–4717, https://doi.org/10.1007/s11227-020-03451-3, 2021.
Wang, Z., Cui, S., Yang, J., Gao, H., Liu, C., and Zhang, Z.: A novel hybrid
scattering order-dependent variance reduction method for Monte Carlo
simulations of radiative transfer in cloudy atmosphere, J. Quant. Spectrosc. Ra., 189, 283–302, https://doi.org/10.1016/j.jqsrt.2016.12.002, 2017.
Wiscombe, W. J.: The Delta–M Method: Rapid Yet Accurate Radiative Flux
Calculations for Strongly Asymmetric Phase Functions, J. Atmos. Sci., 34, 1408–1422, https://doi.org/10.1175/1520-0469(1977)034<1408:TDMRYA>2.0.CO;2, 1977.
Xie, X. and Zhang, M.: Scale-aware parameterization of liquid cloud
inhomogeneity and its impact on simulated climate in CESM, J. Geophys. Res.-Atmos., 120, 8359–8371, https://doi.org/10.1002/2015JD023565, 2015.
Xu, F., Diner, D. J., Dubovik, O., and Schechner, Y.: A Correlated
Multi-Pixel Inversion Approach for Aerosol Remote Sensing, Remote Sens., 11, 746, https://doi.org/10.3390/rs11070746, 2019.
Yao, R., Intes, X., and Fang, Q.: Direct approach to compute Jacobians for
diffuse optical tomography using perturbation Monte Carlo-based photon
“replay”,” Biomed. Opt. Express, 9, 4588–4603,
https://doi.org/10.1364/BOE.9.004588, 2018.
Ye, J. C., Webb, K. J., Millane, R. P., and Downar, T. J.: Modified distorted Born iterative method with an approximate Fréchet derivative for optical diffusion tomography, J. Opt. Soc. Am. A, 16, 1814–1826, https://doi.org/10.1364/JOSAA.16.001814, 1999.
Zawada, D. J., Bourassa, A. E., and Degenstein, D. A.: Two-dimensional
analytic weighting functions for limb scattering, J. Quant. Spectrosc. Ra., 200, 125–136, https://doi.org/10.1016/j.jqsrt.2017.06.008, 2017.
Zawada, D. J., Rieger, L. A., Bourassa, A. E., and Degenstein, D. A.: Tomographic retrievals of ozone with the OMPS Limb Profiler: algorithm description and preliminary results, Atmos. Meas. Tech., 11, 2375–2393, https://doi.org/10.5194/amt-11-2375-2018, 2018.
Zhang, L. and Zhang, G.: Brief review on learning-based methods for optical
tomography, J. Innov. Opt. Heal. Sci., 12, 1930011, https://doi.org/10.1142/S1793545819300118, 2019.
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue,
H.: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing
of cloud droplet effective radius: Case studies based on large-eddy
simulations, J. Geophys. Res.-Atmos., 117, D19208,
https://doi.org/10.1029/2012JD017655, 2012.
Zhao, G. and Di Girolamo, L.: Statistics on the macrophysical properties of
trade wind cumuli over the tropical western Atlantic, J. Geophys. Res.-Atmos., 112, D10204, https://doi.org/10.1029/2006JD007371, 2007.
Zhao, H. and Zhong, Y.: Instability of an Inverse Problem for the Stationary
Radiative Transport Near the Diffusion Limit, SIAM J. Math. Anal., 51,
3750–3768, https://doi.org/10.1137/18M1222582, 2019.
Zhou, C., Zelinka, M. D., and Klein, S. A.: Impact of decadal cloud
variations on the Earth's energy budget, Nat. Geosci., 9, 871–874,
https://doi.org/10.1038/ngeo2828, 2016.
Zhu, C., Byrd, R. H., Lu, P., and Nocedal, J.: Algorithm 778: L-BFGS-B:
Fortran subroutines for large-scale bound-constrained optimization, ACM
T. Math. Software, 23, 550–560, https://doi.org/10.1145/279232.279236, 1997.
Zinner, T., Mayer, B., and Schröder, M.: Determination of
three-dimensional cloud structures from high-resolution radiance data,
J. Geophys. Res.-Atmos., 111, D08204, https://doi.org/10.1029/2005JD006062, 2006.
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.
We describe a new method for measuring the 3D spatial variations in water within clouds using...
