Articles | Volume 18, issue 13
https://doi.org/10.5194/amt-18-3009-2025
© Author(s) 2025. 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-18-3009-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Jul 2025
Research article |
| 10 Jul 2025
| 10 Jul 2025
Errors in stereoscopic retrievals of cloud top height for single-layer clouds
Jesse Loveridge
CORRESPONDING AUTHOR
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
now at: Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO 80523, USA
Larry Di Girolamo
Department of Climate, Meteorology & Atmospheric Sciences, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA
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Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
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
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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.
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
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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
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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
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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
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
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
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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.
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Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
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
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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.
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
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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.
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
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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.
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: Validation and Intercomparisons
Assessment and application of melting-layer simulations for spaceborne radars within the RTTOV-SCATT v13.1 model
A method to retrieve mixed-phase cloud vertical structure from airborne lidar
Attribution of riming and aggregation processes by application of the vertical distribution of particle shape (VDPS) and spectral retrieval techniques to cloud radar observations
Evaluating parallax and shadow correction methods for global horizontal irradiance retrievals from Meteosat SEVIRI
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Evaluating spectral cloud effective radius retrievals from the Enhanced MODIS Airborne Simulator (eMAS) during ORACLES
Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images
Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights
Exploring the characteristics of Fengyun-4A Advanced Geostationary Radiation Imager (AGRI) visible reflectance using the China Meteorological Administration Mesoscale (CMA-MESO) forecasts and its implications for data assimilation
Validating global horizontal irradiance retrievals from Meteosat SEVIRI at increased spatial resolution against a dense network of ground-based observations
Effectiveness of Cirrus Detection with MODIS Cloud Mask data
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This study provides a detailed description of the radar simulator available within version 13 of the RTTOV (Radiative Transfer for the TIROS Operational Vertical Sounder) software. It is applied to the Météo-France global numerical weather prediction model, with the objective of simulating Dual-frequency Precipitation Radar reflectivity observations. Additionally, the simulation of the bright band is addressed and then successfully applied to model forecasts for the purpose of classifying NWP (numerical weather prediction) model columns between stratiform and convective categories.
Ewan Crosbie, Johnathan W. Hair, Amin R. Nehrir, Richard A. Ferrare, Chris Hostetler, Taylor Shingler, David Harper, Marta Fenn, James Collins, Rory Barton-Grimley, Brian Collister, K. Lee Thornhill, Christiane Voigt, Simon Kirschler, and Armin Sorooshian
Atmos. Meas. Tech., 18, 2639–2658, https://doi.org/10.5194/amt-18-2639-2025, https://doi.org/10.5194/amt-18-2639-2025, 2025
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A method was developed to extract information from airborne lidar observations about the distribution of ice and liquid water within clouds. The method specifically targets signatures of horizontal and vertical gradients in ice and water that appear in the polarization of the lidar signals. The method was tested against direct measurements of the cloud properties collected by a second aircraft.
Audrey Teisseire, Anne-Claire Billault-Roux, Teresa Vogl, and Patric Seifert
Atmos. Meas. Tech., 18, 1499–1517, https://doi.org/10.5194/amt-18-1499-2025, https://doi.org/10.5194/amt-18-1499-2025, 2025
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This study demonstrates the ability of a new method delivering the vertical distribution of particle shape to highlight riming and aggregation processes, identifying graupel and aggregates, respectively, as isometric particles. The distinction between these processes can be achieved using lidar or spectral techniques, as demonstrated in the case studies. The capability of the new method to identify rimed particles and aggregates without differentiating them can simplify statistical work.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-4139, https://doi.org/10.5194/egusphere-2024-4139, 2025
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Oliver G. A. Driver, Marc E. J. Stettler, and Edward Gryspeerdt
Atmos. Meas. Tech., 18, 1115–1134, https://doi.org/10.5194/amt-18-1115-2025, https://doi.org/10.5194/amt-18-1115-2025, 2025
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Contrails (clouds caused by planes) play a large role in the climate warming caused by aviation. Satellites are a good tool to validate modelled impact estimates. Many contrails are either too narrow or too disperse to detect. This work shows that only around half of contrails are observable but that the most climatically important are easier to detect. It supports the use of satellites for contrail observation but highlights the need for observability considerations for specific applications.
Kerry Meyer, Steven Platnick, G. Thomas Arnold, Nandana Amarasinghe, Daniel Miller, Jennifer Small-Griswold, Mikael Witte, Brian Cairns, Siddhant Gupta, Greg McFarquhar, and Joseph O'Brien
Atmos. Meas. Tech., 18, 981–1011, https://doi.org/10.5194/amt-18-981-2025, https://doi.org/10.5194/amt-18-981-2025, 2025
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Satellite remote sensing retrievals of cloud droplet size are used to understand clouds and their interactions with aerosols and radiation but require many simplifying assumptions. Evaluation of these retrievals is typically done by comparing against direct measurements of droplets from airborne cloud probes. This paper details an evaluation of proxy airborne remote sensing droplet size retrievals against several cloud probes and explores the impact of key assumptions on retrieval agreement.
Yongbo Zhou, Tianrui Cao, and Lijian Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2025-242, https://doi.org/10.5194/egusphere-2025-242, 2025
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Different Cloud Optical Parameterizations (COPs) in RTTOV are evaluated by the comparing the observed and synthetic satellite images. The optimal COP for liquid water cloud is parameterized in terms of effective diameter (Deff) in combination with the COP for ice cloud developed by the Space Science and Engineering Center (SSEC), with the Deff for ice cloud parameterized in terms of ice water content and temperature. The findings will benefit the remote sensing and data assimilation community.
Lea Volkmer, Tobias Kölling, Tobias Zinner, and Bernhard Mayer
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The importance of the consideration of cloud motion for the stereographic determination of cloud top height from aircraft observations is demonstrated using measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). A method for cloud motion correction using model winds from the European Centre for Medium-Range Weather Forecasts is presented and validated using both real measurements and realistic radiative transfer simulations.
Yongbo Zhou, Yubao Liu, Wei Han, Yuefei Zeng, Haofei Sun, Peilong Yu, and Lijian Zhu
Atmos. Meas. Tech., 17, 6659–6675, https://doi.org/10.5194/amt-17-6659-2024, https://doi.org/10.5194/amt-17-6659-2024, 2024
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The study explored differences between the visible reflectance provided by the Fengyun-4A satellite and its equivalent derived from the China Meteorological Administration Mesoscale model using a forward operator. The observation-minus-simulation biases were able to monitor the performance of the satellite visible instrument. The biases were corrected based on a first-order approximation method, which promotes the data assimilation of satellite visible reflectance in real-world cases.
Job I. Wiltink, Hartwig Deneke, Yves-Marie Saint-Drenan, Chiel C. van Heerwaarden, and Jan Fokke Meirink
Atmos. Meas. Tech., 17, 6003–6024, https://doi.org/10.5194/amt-17-6003-2024, https://doi.org/10.5194/amt-17-6003-2024, 2024
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Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) global horizontal irradiance (GHI) retrievals are validated at standard and increased spatial resolution against a network of 99 pyranometers. GHI accuracy is strongly dependent on the cloud regime. Days with variable cloud conditions show significant accuracy improvements when retrieved at higher resolution. We highlight the benefits of dense network observations and a cloud-regime-resolved approach in validating GHI retrievals.
Żaneta Nguyen Huu, Andrzej Z. Kotarba, and Agnieszka Wypych
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-163, https://doi.org/10.5194/amt-2024-163, 2024
Revised manuscript accepted for AMT
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Clouds affect Earth's energy balance, with high-altitude cirrus clouds contributing to atmospheric warming. While active satellite sensors are the most accurate for detecting cirrus clouds, they are not ideal for long-term studies. This study compares MODIS and CALIOP data, testing six MODIS methods and two ISCCP tests. The ATC test was the most effective, achieving 72.98 % accuracy during daytime and 59.50 % at night, making it suitable for creating a high-level cloud mask.
Ethel Villeneuve, Philippe Chambon, and Nadia Fourrié
Atmos. Meas. Tech., 17, 3567–3582, https://doi.org/10.5194/amt-17-3567-2024, https://doi.org/10.5194/amt-17-3567-2024, 2024
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In cloudy situations, infrared and microwave observations are complementary, with infrared being sensitive to cloud tops and microwave sensitive to precipitation. However, infrared satellite observations are underused. This study aims to quantify if the inconsistencies in the modelling of clouds prevent the use of cloudy infrared observations in the process of weather forecasting. It shows that the synergistic use of infrared and microwave observations is beneficial, despite inconsistencies.
Woosub Roh, Masaki Satoh, Yuichiro Hagihara, Hiroaki Horie, Yuichi Ohno, and Takuji Kubota
Atmos. Meas. Tech., 17, 3455–3466, https://doi.org/10.5194/amt-17-3455-2024, https://doi.org/10.5194/amt-17-3455-2024, 2024
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The advantage of the use of Doppler velocity in the categorization of the hydrometeors is that Doppler velocities suffer less impact from the attenuation of rain and wet attenuation on an antenna. The ground Cloud Profiling Radar observation of the radar reflectivity for the precipitation case is limited because of wet attenuation on an antenna. We found the main contribution to Doppler velocities is the terminal velocity of hydrometeors by analysis of simulation results.
Santo Fedele Colosimo, Nathaniel Brockway, Vijay Natraj, Robert Spurr, Klaus Pfeilsticker, Lisa Scalone, Max Spolaor, Sarah Woods, and Jochen Stutz
Atmos. Meas. Tech., 17, 2367–2385, https://doi.org/10.5194/amt-17-2367-2024, https://doi.org/10.5194/amt-17-2367-2024, 2024
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Cirrus clouds are poorly understood components of the climate system, in part due to the challenge of observing thin, sub-visible ice clouds. We address this issue with a new observational approach that uses the remote sensing of near-infrared ice water absorption features from a high-altitude aircraft. We describe the underlying principle of this approach and present a new procedure to retrieve ice concentration in cirrus clouds. Our retrievals compare well with in situ observations.
Shuai Li, Hua Zhang, Yonghang Chen, Zhili Wang, Xiangyu Li, Yuan Li, and Yuanyuan Xue
Atmos. Meas. Tech., 17, 2011–2024, https://doi.org/10.5194/amt-17-2011-2024, https://doi.org/10.5194/amt-17-2011-2024, 2024
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In this paper, Xinjiang was the test area, and nine evaluation indexes of FY-2F/CTA, including precision rate, false rate, missing rate, consistency rate, strong rate, weak rate, bias, AE, and RMSE, were calculated and analyzed under complex underlying surface (subsurface types, temperature and altitude conditions) and different weather conditions (dust effects and different cloud cover levels). The precision, consistency, and error indexes of FY-2F/CTA were tested and evaluated.
Lea Volkmer, Veronika Pörtge, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 17, 1703–1719, https://doi.org/10.5194/amt-17-1703-2024, https://doi.org/10.5194/amt-17-1703-2024, 2024
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Three-dimensional radiative transfer simulations are used to evaluate the performance of retrieval algorithms in the derivation of cloud geometry (cloud top heights) and cloud droplet size distributions from two-dimensional polarized radiance measurements of the airborne spectrometer of the Munich Aerosol Cloud Scanner. The cloud droplet size distributions are derived for the effective radius and variance. The simulations are based on cloud data from highly resolved large-eddy simulations.
Chaojun Shi, Leile Han, Ke Zhang, Hongyin Xiang, Xingkuan Li, Zibo Su, and Xian Zheng
Atmos. Meas. Tech., 17, 979–997, https://doi.org/10.5194/amt-17-979-2024, https://doi.org/10.5194/amt-17-979-2024, 2024
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This article mainly studies the problem of ground cloud classification and significantly improves the accuracy of ground cloud classification by applying an improved deep-learning method. The research results show that the method proposed in this article has a significant impact on the classification results of ground cloud images. These conclusions have important implications for providing new insights and future research directions in the field of ground cloud classification.
Shannon L. Mason, Howard W. Barker, Jason N. S. Cole, Nicole Docter, David P. Donovan, Robin J. Hogan, Anja Hünerbein, Pavlos Kollias, Bernat Puigdomènech Treserras, Zhipeng Qu, Ulla Wandinger, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, https://doi.org/10.5194/amt-17-875-2024, 2024
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When the EarthCARE mission enters its operational phase, many retrieval data products will be available, which will overlap both in terms of the measurements they use and the geophysical quantities they report. In this pre-launch study, we use simulated EarthCARE scenes to compare the coverage and performance of many data products from the European Space Agency production model, with the intention of better understanding the relation between products and providing a compact guide to users.
Bo-Ram Kim, Gyuyeon Kim, Minjeong Cho, Yong-Sang Choi, and Jhoon Kim
Atmos. Meas. Tech., 17, 453–470, https://doi.org/10.5194/amt-17-453-2024, https://doi.org/10.5194/amt-17-453-2024, 2024
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This study introduces the GEMS cloud algorithm and validates its results using data from GEMS and other environmental satellites. The GEMS algorithm is able to detect the lowest cloud heights among the four satellites, and its effective cloud fraction and cloud centroid pressure are well reflected in the retrieval results. The study highlights the algorithm's usefulness in correcting errors in trace gases caused by clouds in the East Asian region.
Kenan Wu, Tianwen Wei, Jinlong Yuan, Haiyun Xia, Xin Huang, Gaopeng Lu, Yunpeng Zhang, Feifan Liu, Baoyou Zhu, and Weidong Ding
Atmos. Meas. Tech., 16, 5811–5825, https://doi.org/10.5194/amt-16-5811-2023, https://doi.org/10.5194/amt-16-5811-2023, 2023
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A compact all-fiber coherent Doppler wind lidar (CDWL) working at the 1.5 µm wavelength is applied to probe the dynamics and microphysics structure of thunderstorms. It was found that thunderclouds below the 0 ℃ isotherm have significant spectrum broadening and an increase in skewness, and that lightning affects the microphysics structure of the thundercloud. It is proven that the precise spectrum of CDWL is a promising indicator for studying the charge structure of thunderstorms.
Imke Schirmacher, Pavlos Kollias, Katia Lamer, Mario Mech, Lukas Pfitzenmaier, Manfred Wendisch, and Susanne Crewell
Atmos. Meas. Tech., 16, 4081–4100, https://doi.org/10.5194/amt-16-4081-2023, https://doi.org/10.5194/amt-16-4081-2023, 2023
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CloudSat’s relatively coarse spatial resolution, low sensitivity, and blind zone limit its assessment of Arctic low-level clouds, which affect the surface energy balance. We compare cloud fractions from CloudSat and finely resolved airborne radar observations to determine CloudSat’s limitations. Cloudsat overestimates cloud fractions above its blind zone, especially during cold-air outbreaks over open water, and misses a cloud fraction of 32 % and half of the precipitation inside its blind zone.
Richard M. Schulte, Matthew D. Lebsock, and John M. Haynes
Atmos. Meas. Tech., 16, 3531–3546, https://doi.org/10.5194/amt-16-3531-2023, https://doi.org/10.5194/amt-16-3531-2023, 2023
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In order to constrain climate models and better understand how clouds might change in future climates, accurate satellite estimates of cloud liquid water content are important. The satellite currently best suited to this purpose, CloudSat, is not sensitive enough to detect some non-raining low clouds. In this study we show that information from two other satellite instruments, MODIS and CALIOP, can be combined to provide cloud water estimates for many of the clouds that are missed by CloudSat.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
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Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Hong Chen, K. Sebastian Schmidt, Steven T. Massie, Vikas Nataraja, Matthew S. Norgren, Jake J. Gristey, Graham Feingold, Robert E. Holz, and Hironobu Iwabuchi
Atmos. Meas. Tech., 16, 1971–2000, https://doi.org/10.5194/amt-16-1971-2023, https://doi.org/10.5194/amt-16-1971-2023, 2023
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We introduce the Education and Research 3D Radiative Transfer Toolbox (EaR3T) and propose a radiance self-consistency approach for quantifying and mitigating 3D bias in legacy airborne and spaceborne imagery retrievals due to spatially inhomogeneous clouds and surfaces.
Kumar Abhijeet, Thota Narayana Rao, Nidamanuri Rama Rao, and Kasimahanthi Amar Jyothi
Atmos. Meas. Tech., 16, 871–888, https://doi.org/10.5194/amt-16-871-2023, https://doi.org/10.5194/amt-16-871-2023, 2023
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The present study focuses on retrieving and validating raindrop size distribution (DSD) relations for monsoonal rainfall, which are required for retrieving DSDs with polarimetric radar measurements. The seasonal variation in DSD is quite large and significant, and as a result the coefficients also vary considerably between the seasons and from those existing elsewhere. Among the existing DSD methods, the N-gamma method performs better than the other methods.
Gianluca Di Natale, David D. Turner, Giovanni Bianchini, Massimo Del Guasta, Luca Palchetti, Alessandro Bracci, Luca Baldini, Tiziano Maestri, William Cossich, Michele Martinazzo, and Luca Facheris
Atmos. Meas. Tech., 15, 7235–7258, https://doi.org/10.5194/amt-15-7235-2022, https://doi.org/10.5194/amt-15-7235-2022, 2022
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In this paper, we describe a new approach to test the consistency of the precipitating ice cloud optical and microphysical properties in Antarctica, Dome C, retrieved from hyperspectral measurements in the far-infrared, with the reflectivity detected by a co-located micro rain radar operating at 24 GHz. The retrieved ice crystal sizes were found in accordance with the direct measurements of an optical imager, also installed at Dome C, which can collect the falling ice particles.
Sergey Y. Matrosov, Alexei Korolev, Mengistu Wolde, and Cuong Nguyen
Atmos. Meas. Tech., 15, 6373–6386, https://doi.org/10.5194/amt-15-6373-2022, https://doi.org/10.5194/amt-15-6373-2022, 2022
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A remote sensing method to retrieve sizes of particles in ice clouds and precipitation from radar measurements at two wavelengths is described. This method is based on relating the particle size information to the ratio of radar signals at these two wavelengths. It is demonstrated that this ratio is informative about different characteristic particle sizes. Knowing atmospheric ice particle sizes is important for many applications such as precipitation estimation and climate modeling.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 15, 4307–4322, https://doi.org/10.5194/amt-15-4307-2022, https://doi.org/10.5194/amt-15-4307-2022, 2022
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Space profiling lidars offer a unique insight into cloud properties in Earth’s atmosphere, and are considered the most reliable source of cloud information. However, lidar-based cloud climatologies are infrequently sampled: every 7 to 91 d, and only along the ground track. This study evaluated how accurate are the cloud data from existing (CALIPSO, ICESat-2, Aeolus) and planned (EarthCARE) space lidars, when compared to a cloud climatology obtained with observations taken every day.
Edward Gryspeerdt, Daniel T. McCoy, Ewan Crosbie, Richard H. Moore, Graeme J. Nott, David Painemal, Jennifer Small-Griswold, Armin Sorooshian, and Luke Ziemba
Atmos. Meas. Tech., 15, 3875–3892, https://doi.org/10.5194/amt-15-3875-2022, https://doi.org/10.5194/amt-15-3875-2022, 2022
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Droplet number concentration is a key property of clouds, influencing a variety of cloud processes. It is also used for estimating the cloud response to aerosols. The satellite retrieval depends on a number of assumptions – different sampling strategies are used to select cases where these assumptions are most likely to hold. Here we investigate the impact of these strategies on the agreement with in situ data, the droplet number climatology and estimates of the indirect radiative forcing.
Henning Dorff, Heike Konow, and Felix Ament
Atmos. Meas. Tech., 15, 3641–3661, https://doi.org/10.5194/amt-15-3641-2022, https://doi.org/10.5194/amt-15-3641-2022, 2022
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This study elaborates how aircraft-based horizontal geometries of trade wind cumuli differ whether a one-dimensional profiling radar or a two-dimensional imager is used. Cloud size distributions are examined in terms of sensitivity to sample size, resolution, and instrument field of view. While the radar cannot reproduce the double power law distribution due to coarse resolution and restriction to vertical transects, the imager also reveals the elliptic cloud structure enhancing with wind speed.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
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The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Pradeep Khatri, Tadahiro Hayasaka, Hitoshi Irie, Husi Letu, Takashi Y. Nakajima, Hiroshi Ishimoto, and Tamio Takamura
Atmos. Meas. Tech., 15, 1967–1982, https://doi.org/10.5194/amt-15-1967-2022, https://doi.org/10.5194/amt-15-1967-2022, 2022
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Cloud properties observed by the Second-generation Global Imager (SGLI) onboard the Global Change Observation Mission – Climate (GCOM-C) satellite are evaluated using surface observation data. The study finds that SGLI-observed cloud properties are qualitative enough, although water cloud properties are suggested to be more qualitative, and both water and ice cloud properties can reproduce surface irradiance quite satisfactorily. Thus, SGLI cloud products are very useful for different studies.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, Rodrigo Guzman, Cyprien Gindre, Po-Lun Ma, and Marjolaine Chiriaco
Atmos. Meas. Tech., 15, 1055–1074, https://doi.org/10.5194/amt-15-1055-2022, https://doi.org/10.5194/amt-15-1055-2022, 2022
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Space-borne lidars have been providing invaluable information of atmospheric optical properties since 2006, and new lidar missions are on the way to ensure continuous observations. In this work, we compare the clouds estimated from space-borne ALADIN and CALIOP lidar observations. The analysis of collocated data shows that the agreement between the retrieved clouds is good up to 3 km height. Above that, ALADIN detects 40 % less clouds than CALIOP, except for polar stratospheric clouds (PSCs).
Gregor Köcher, Tobias Zinner, Christoph Knote, Eleni Tetoni, Florian Ewald, and Martin Hagen
Atmos. Meas. Tech., 15, 1033–1054, https://doi.org/10.5194/amt-15-1033-2022, https://doi.org/10.5194/amt-15-1033-2022, 2022
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We present a setup for systematic characterization of differences between numerical weather models and radar observations for convective weather situations. Radar observations providing dual-wavelength and polarimetric variables to infer information about hydrometeor shapes and sizes are compared against simulations using microphysics schemes of varying complexity. Differences are found in ice and liquid phase, pointing towards issues of some schemes in reproducing particle size distributions.
Simon Pfreundschuh, Stuart Fox, Patrick Eriksson, David Duncan, Stefan A. Buehler, Manfred Brath, Richard Cotton, and Florian Ewald
Atmos. Meas. Tech., 15, 677–699, https://doi.org/10.5194/amt-15-677-2022, https://doi.org/10.5194/amt-15-677-2022, 2022
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We test a novel method to remotely measure ice particles in clouds. This is important because such measurements are required to improve climate and weather models. The method combines a radar with newly developed sensors measuring microwave radiation at very short wavelengths. We use observations made from aircraft flying above the cloud and compare them to real measurements from inside the cloud. This works well given that one can model the ice particles in the cloud sufficiently well.
David Painemal, Douglas Spangenberg, William L. Smith Jr., Patrick Minnis, Brian Cairns, Richard H. Moore, Ewan Crosbie, Claire Robinson, Kenneth L. Thornhill, Edward L. Winstead, and Luke Ziemba
Atmos. Meas. Tech., 14, 6633–6646, https://doi.org/10.5194/amt-14-6633-2021, https://doi.org/10.5194/amt-14-6633-2021, 2021
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Cloud properties derived from satellite sensors are critical for the global monitoring of climate. This study evaluates satellite-based cloud properties over the North Atlantic using airborne data collected during NAAMES. Satellite observations of droplet size and cloud optical depth tend to compare well with NAAMES data. The analysis indicates that the satellite pixel resolution and the specific viewing geometry need to be taken into account in research applications.
Charles H. White, Andrew K. Heidinger, and Steven A. Ackerman
Atmos. Meas. Tech., 14, 3371–3394, https://doi.org/10.5194/amt-14-3371-2021, https://doi.org/10.5194/amt-14-3371-2021, 2021
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Automated detection of clouds in satellite imagery is an important practice that is useful for predicting and understanding both weather and climate. Cloud detection is often difficult at night and over cold surfaces. In this paper, we discuss how a complex statistical model (a neural network) can more accurately detect clouds compared to currently used approaches. Overall, our results suggest that our approach could result in more reliable assessments of global cloud cover.
Hong Chen, Sebastian Schmidt, Michael D. King, Galina Wind, Anthony Bucholtz, Elizabeth A. Reid, Michal Segal-Rozenhaimer, William L. Smith, Patrick C. Taylor, Seiji Kato, and Peter Pilewskie
Atmos. Meas. Tech., 14, 2673–2697, https://doi.org/10.5194/amt-14-2673-2021, https://doi.org/10.5194/amt-14-2673-2021, 2021
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In this paper, we accessed the shortwave irradiance derived from MODIS cloud optical properties by using aircraft measurements. We developed a data aggregation technique to parameterize spectral surface albedo by snow fraction in the Arctic. We found that undetected clouds have the most significant impact on the imagery-derived irradiance. This study suggests that passive imagery cloud detection could be improved through a multi-pixel approach that would make it more dependable in the Arctic.
Steven Compernolle, Athina Argyrouli, Ronny Lutz, Maarten Sneep, Jean-Christopher Lambert, Ann Mari Fjæraa, Daan Hubert, Arno Keppens, Diego Loyola, Ewan O'Connor, Fabian Romahn, Piet Stammes, Tijl Verhoelst, and Ping Wang
Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, https://doi.org/10.5194/amt-14-2451-2021, 2021
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The high-resolution satellite Sentinel-5p TROPOMI observes several atmospheric gases. To account for cloud interference with the observations, S5P cloud data products (CLOUD OCRA/ROCINN_CAL, OCRA/ROCINN_CRB, and FRESCO) provide vital input: cloud fraction, cloud height, and cloud optical thickness. Here, S5P cloud parameters are validated by comparing with other satellite sensors (VIIRS, MODIS, and OMI) and with ground-based CloudNet data. The agreement depends on product type and cloud height.
Jędrzej S. Bojanowski and Jan P. Musiał
Atmos. Meas. Tech., 13, 6771–6788, https://doi.org/10.5194/amt-13-6771-2020, https://doi.org/10.5194/amt-13-6771-2020, 2020
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Satellites such as NOAA's Advanced Very High Resolution Radiometer can uniquely observe changes in cloud cover but are affected by orbital drift that results in shifted image acquisition times, which in turn lead to spurious trends in cloud cover detected during climatological analyses. Providing a detailed quantification of these trends, we show that climate data records must be analysed with caution, as for some periods and regions they do not comply with the requirements for climate data.
Andrzej Z. Kotarba
Atmos. Meas. Tech., 13, 4995–5012, https://doi.org/10.5194/amt-13-4995-2020, https://doi.org/10.5194/amt-13-4995-2020, 2020
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This paper evaluates the operational approach for producing global (Level 3) cloud amount based on MODIS cloud masks (Level 2). Using CALIPSO we calculate the actual cloud fractions for each cloud mask category, which are 21.5 %, 27.7 %, 66.6 %, and 94.7 % instead of assumed 0 %, 0 %, 100 %, and 100 %. Consequently we find the operational procedure unreliable, especially on a regional/local scale. A method of how to correct and calibrate MODIS global data using CALIPSO detections is suggested.
Benjamin Marchant, Steven Platnick, Kerry Meyer, and Galina Wind
Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, https://doi.org/10.5194/amt-13-3263-2020, 2020
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Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the Earth's atmosphere and are quite difficult to detect from space. The detection of multilayer clouds is important to better understand how they interact with the light and their impact on the climate. So, for the instrument MODIS an algorithm has been developed to detect those clouds, and this paper presents an evaluation of this algorithm by comparing it with
other instruments.
Alexis Hunzinger, Joseph C. Hardin, Nitin Bharadwaj, Adam Varble, and Alyssa Matthews
Atmos. Meas. Tech., 13, 3147–3166, https://doi.org/10.5194/amt-13-3147-2020, https://doi.org/10.5194/amt-13-3147-2020, 2020
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The calibration of weather radars is one of the most dominant sources of errors hindering their use. This work takes a technique for tracking the changes in radar calibration using the radar clutter from the ground and extends it to higher-frequency research radars. It demonstrates that after modifications the technique is successful but that special care needs to be taken in its application at high frequencies. The technique is verified using data from multiple DOE ARM field campaigns.
Dieter R. Poelman and Wolfgang Schulz
Atmos. Meas. Tech., 13, 2965–2977, https://doi.org/10.5194/amt-13-2965-2020, https://doi.org/10.5194/amt-13-2965-2020, 2020
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The objective of this work is to quantify the similarities and contrasts between the lightning observations from the Lightning Imaging Sensor (LIS) on the International Space Station (ISS) and the ground-based European Cooperation for Lightning Detection (EUCLID) network. This work is timely, given that the Meteosat Third Generation (MTG), which has a lightning imager (LI) on board, is going to be launched in 2 years.
Manfred Brath, Robin Ekelund, Patrick Eriksson, Oliver Lemke, and Stefan A. Buehler
Atmos. Meas. Tech., 13, 2309–2333, https://doi.org/10.5194/amt-13-2309-2020, https://doi.org/10.5194/amt-13-2309-2020, 2020
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Microwave dual-polarization observations consistently show that larger atmospheric ice particles tend to have a preferred orientation. We provide a publicly available database of microwave and submillimeter wave scattering properties of oriented ice particles based on discrete dipole approximation scattering calculations. Detailed radiative transfer simulations, recreating observed polarization patterns, are additionally presented in this study.
Erin A. Riley, Jessica M. Kleiss, Laura D. Riihimaki, Charles N. Long, Larry K. Berg, and Evgueni Kassianov
Atmos. Meas. Tech., 13, 2099–2117, https://doi.org/10.5194/amt-13-2099-2020, https://doi.org/10.5194/amt-13-2099-2020, 2020
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Discrepancies in hourly shallow cumuli cover estimates can be substantial. Instrument detection differences contribute to long-term bias in shallow cumuli cover estimates, whereas narrow field-of-view configurations impact measurement uncertainty as averaging time decreases. A new tool is introduced to visually assess both impacts on sub-hourly cloud cover estimates. Accurate shallow cumuli cover estimation is needed for model–observation comparisons and studying cloud-surface interactions.
Robin Ekelund, Patrick Eriksson, and Simon Pfreundschuh
Atmos. Meas. Tech., 13, 501–520, https://doi.org/10.5194/amt-13-501-2020, https://doi.org/10.5194/amt-13-501-2020, 2020
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Atmospheric ice particles (e.g. snow and ice crystals) are an important part of weather, climate, and the hydrological cycle. This study investigates whether combined satellite measurements by radar and radiometers at microwave wavelengths can be used to find the most likely shape of such ice particles. The method was limited when using only currently operating sensors (CloudSat radar and the GPM Microwave Imager) but shows promise if the upcoming Ice Cloud Imager is also considered.
Juan Huo, Daren Lu, Shu Duan, Yongheng Bi, and Bo Liu
Atmos. Meas. Tech., 13, 1–11, https://doi.org/10.5194/amt-13-1-2020, https://doi.org/10.5194/amt-13-1-2020, 2020
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Cloud top height (CTH) is one of the important cloud parameters providing information about the vertical structure of cloud water content. To better understand the accuracy of CTH derived from passive satellite data, 2 years of ground-based Ka-band radar measurements are compared with CTH inferred from Terra/Aqua MODIS and Himawari AHI. It is found that MODIS and AHI underestimate CTH relative to radar by −1.10 km. Both MODIS and AHI CTH retrieval accuracy depend strongly on cloud depth.
Vladimir S. Kostsov, Anke Kniffka, Martin Stengel, and Dmitry V. Ionov
Atmos. Meas. Tech., 12, 5927–5946, https://doi.org/10.5194/amt-12-5927-2019, https://doi.org/10.5194/amt-12-5927-2019, 2019
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Cloud liquid water path (LWP) is one of the target atmospheric parameters retrieved remotely from ground-based and space-borne platforms. The LWP data delivered by the satellite instruments SEVIRI and AVHRR together with the data provided by the ground-based radiometer RPG-HATPRO near St. Petersburg, Russia, have been compared. Our study revealed considerable differences between LWP data from SEVIRI and AVHRR in winter over ice-covered relatively small water bodies in this region.
Jonathan K. P. Shonk, Jui-Yuan Christine Chiu, Alexander Marshak, David M. Giles, Chiung-Huei Huang, Gerald G. Mace, Sally Benson, Ilya Slutsker, and Brent N. Holben
Atmos. Meas. Tech., 12, 5087–5099, https://doi.org/10.5194/amt-12-5087-2019, https://doi.org/10.5194/amt-12-5087-2019, 2019
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Retrievals of cloud optical depth made using AERONET radiometers in “cloud mode” rely on the assumption that all cloud is liquid. The presence of ice cloud therefore introduces errors in the retrieved optical depth, which can be over 25 in optically thick ice clouds. However, such clouds are not frequent and the long-term mean optical depth error is about 3 for a sample of real clouds. A correction equation could improve the retrieval further, although this would require extra instrumentation.
Cited articles
Aerenson, T., Marchand, R., Chepfer, H., and Medeiros, B.: When Will MISR Detect Rising High Clouds?, J. Geophys. Res.-Atmos., 127, e2021JD035865, https://doi.org/10.1029/2021JD035865, 2022.
Albrecht, B., Ghate, V., Mohrmann, J., Wood, R., Zuidema, P., Bretherton, C., Schwartz, C., Eloranta, E., Glienke, S., Donaher, S., Sarkar, M., McGibbon, J., Nugent, A. D., Shaw, R. A., Fugal, J., Minnis, P., Paliknoda, R., Lussier, L., Jensen, J., Vivekanandan, J., Ellis, S., Tsai, P., Rilling, R., Haggerty, J., Campos, T., Stell, M., Reeves, M., Beaton, S., Allison, J., Stossmeister, G., Hall, S., and Schmidt, S.: Cloud System Evolution in the Trades (CSET): Following the Evolution of Boundary Layer Cloud Systems with the NSF–NCAR GV, B. Am. Meteorol. Soc., 100, 93–121, https://doi.org/10.1175/BAMS-D-17-0180.1, 2019.
Anderson, G. P., Clough, S. A., Kneizys, F. X., Chetwynd, J. H., and Shettle, E. P.: AFGL atmospheric constituent profiles (0.120km), Air Force Geophys. Lab., Environ. Res. Papers, Tech. Rep. AFGL-TR-86-0110, 1986.
Barnes, W. L., Xiong, X., Guenther, B. W., and Salomonson, V.: Development, characterization, and performance of the EOS MODIS sensors, in: Earth Observing Systems VIII, International Society for Optics and Photonics, 337–345, https://doi.org/10.1117/12.504818, 2003.
Battaglia, A., Kollias, P., Dhillon, R., Lamer, K., Khairoutdinov, M., and Watters, D.: Mind the gap – Part 2: Improving quantitative estimates of cloud and rain water path in oceanic warm rain using spaceborne radars, Atmos. Meas. Tech., 13, 4865–4883, https://doi.org/10.5194/amt-13-4865-2020, 2020.
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E., Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS Cloud-Top Property Refinements for Collection 6, J. Appl. Meteorol. Clim., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012.
Beals, M. A., Fugal, J. P., Shaw, R. A., Lu, J., Spuler, S. M., and Stith, J. L.: Holographic measurements of inhomogeneous cloud mixing at the centimeter scale, Science, 350, 87–90, https://doi.org/10.1126/science.aab0751, 2015.
Beekmans, C., Schneider, J., Läbe, T., Lennefer, M., Stachniss, C., and Simmer, C.: Cloud photogrammetry with dense stereo for fisheye cameras, Atmos. Chem. Phys., 16, 14231–14248, https://doi.org/10.5194/acp-16-14231-2016, 2016.
Beyer, R. A., Alexandrov, O., and McMichael, S.: The Ames Stereo Pipeline: NASA's Open Source Software for Deriving and Processing Terrain Data, Earth Space Sci., 5, 537–548, https://doi.org/10.1029/2018EA000409, 2018.
Bleyer, M. and Breiteneder, C.: Stereo Matching–State-of-the-Art and Research Challenges, in: Advanced Topics in Computer Vision, edited by: Farinella, G. M., Battiato, S., and Cipolla, R., Springer, London, 143–179, https://doi.org/10.1007/978-1-4471-5520-1_6, 2013.
Boers, R., Spinhirne, J. D., and Hart, W. D.: Lidar Observations of the Fine-Scale Variability of Marine Stratocumulus Clouds, J. Appl. Meteorol. Clim., 27, 797–810, https://doi.org/10.1175/1520-0450(1988)027<0797:LOOTFS>2.0.CO;2, 1988.
Boutle, I. A., Abel, S. J., Hill, P. G., and Morcrette, C. J.: Spatial variability of liquid cloud and rain: observations and microphysical effects, Q. J. Roy. Meteor. Soc., 140, 583–594, https://doi.org/10.1002/qj.2140, 2014.
Braun, S., Stephens, G. L., Berndt, E., Blanchard, Y., Blanchet, J.-P., Carmichael, G., da Silva, A., Ferrare, R., Ivanco, M., Kacenelenbogen, M., Kirschbaum, D., Libois, Q., Mace, G., Omar, A., Petersen, W., Redemann, J., Seidel, F., van den Heever, S., Waliser, D., and Winker, D.: Aerosol, Cloud, Convection, and Precipitation (ACCP) Science & Applications, https://aos.gsfc.nasa.gov/docs/ACCP_Science_Narrative-(Mar2022).pdf (last accessed: 7 July 2025), 2022.
Burns, D., Kollias, P., Tatarevic, A., Battaglia, A., and Tanelli, S.: The performance of the EarthCARE Cloud Profiling Radar in marine stratiform clouds, J. Geophys. Res.-Atmos., 121, 14525–14537, https://doi.org/10.1002/2016JD025090, 2016.
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.
Castro, E., Ishida, T., Takahashi, Y., Kubota, H., Perez, G. J., and Marciano, J. S.: Determination of Cloud-top Height through Three-dimensional Cloud Reconstruction using DIWATA-1 Data, Sci. Rep., 10, 7570, https://doi.org/10.1038/s41598-020-64274-z, 2020.
Chambers, L. H., Wielicki, B. A., and Evans, K. F.: Accuracy of the independent pixel approximation for satellite estimates of oceanic boundary layer cloud optical depth, J. Geophys. Res.-Atmos., 102, 1779–1794, https://doi.org/10.1029/96JD02995, 1997.
Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., Di Girolamo, L., C.-Labonnote, L., Cornet, C., Riedi, J., and Holz, R. E.: Frequency and causes of failed MODIS cloud property retrievals for liquid phase clouds over global oceans, J. Geophys. Res.-Atmos., 120, 4132–4154, https://doi.org/10.1002/2015JD023161, 2015.
Considine, G., Curry, J. A., and Wielicki, B.: Modeling cloud fraction and horizontal variability in marine boundary layer clouds, J. Geophys. Res.-Atmos., 102, 13517–13525, https://doi.org/10.1029/97JD00261, 1997.
Dandini, P., Cornet, C., Binet, R., Fenouil, L., Holodovsky, V., Y. Schechner, Y., Ricard, D., and Rosenfeld, D.: 3D cloud envelope and cloud development velocity from simulated CLOUD (C3IEL) stereo images, Atmos. Meas. Tech., 15, 6221–6242, https://doi.org/10.5194/amt-15-6221-2022, 2022.
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.
Davies, R., Jovanovic, V. M., and Moroney, C. M.: Cloud heights measured by MISR from 2000 to 2015, J. Geophys. Res.-Atmos., 122, 3975–3986, https://doi.org/10.1002/2017JD026456, 2017.
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., Marshak, A., Gerber, H., and Wiscombe, W. J.: Horizontal structure of marine boundary layer clouds from centimeter to kilometer scales, J. Geophys. Res.-Atmos., 104, 6123–6144, https://doi.org/10.1029/1998JD200078, 1999.
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 2021a.
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 2021b.
De Vera, M. V., Di Girolamo, L., Zhao, G., Rauber, R. M., Nesbitt, S. W., and McFarquhar, G. M.: Observations of the macrophysical properties of cumulus cloud fields over the tropical western Pacific and their connection to meteorological variables, Atmos. Chem. Phys., 24, 5603–5623, https://doi.org/10.5194/acp-24-5603-2024, 2024.
Di Girolamo, L. and Davies, R.: Cloud fraction errors caused by finite resolution measurements, J. Geophys. Res.-Atmos., 102, 1739–1756, https://doi.org/10.1029/96JD02663, 1997.
Di Giuseppe, F. and Tompkins, A. M.: Effect of Spatial Organization on Solar Radiative Transfer in Three-Dimensional Idealized Stratocumulus Cloud Fields, J. Atmos. Sci., 60, 1774–1794, https://doi.org/10.1175/1520-0469(2003)060<1774:EOSOOS>2.0.CO;2, 2003.
Doicu, A., Doicu, A., Efremenko, D., and Trautmann, T.: Cloud tomographic retrieval algorithms. II: Adjoint method, J. Quant. Spectrosc Ra., 285, 108177, https://doi.org/10.1016/j.jqsrt.2022.108177, 2022.
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.
Ewald, F., Zinner, T., Kölling, T., and Mayer, B.: Remote sensing of cloud droplet radius profiles using solar reflectance from cloud sides – Part 1: Retrieval development and characterization, Atmos. Meas. Tech., 12, 1183–1206, https://doi.org/10.5194/amt-12-1183-2019, 2019.
Facciolo, G., de Franchis, C., and Meinhardt, E.: MGM: A Significantly More Global Matching for Stereovision, in: Procedings of the British Machine Vision Conference 2015, 7–10 September 2015, Swansea, UK, edited by: Xianghua Xie, X., Jones, M. W., and Tam, G. K. L., BMVA Press, 90.1–90.12, https://doi.org/10.5244/C.29.90, 2015.
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.
Fisher, D., Poulsen, C. A., Thomas, G. E., and Muller, J.-P.: Synergy of stereo cloud top height and ORAC optimal estimation cloud retrieval: evaluation and application to AATSR, Atmos. Meas. Tech., 9, 909–928, https://doi.org/10.5194/amt-9-909-2016, 2016.
Foley, S. R., Knobelspiesse, K. D., Sayer, A. M., Gao, M., Hays, J., and Hoffman, J.: 3D cloud masking across a broad swath using multi-angle polarimetry and deep learning, Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, 2024.
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R., and Wang, X.: Cloud Detection with MODIS. Part I: Improvements in the MODIS Cloud Mask for Collection 5, J. Atmos. Ocean. Tech., 25, 1057–1072, https://doi.org/10.1175/2008JTECHA1052.1, 2008.
Fu, D., Di Girolamo, L., Rauber, R. M., McFarquhar, G. M., Nesbitt, S. W., Loveridge, J., Hong, Y., van Diedenhoven, B., Cairns, B., Alexandrov, M. D., Lawson, P., Woods, S., Tanelli, S., Schmidt, S., Hostetler, C., and Scarino, A. J.: An evaluation of the liquid cloud droplet effective radius derived from MODIS, airborne remote sensing, and in situ measurements from CAMP2Ex, Atmos. Chem. Phys., 22, 8259–8285, https://doi.org/10.5194/acp-22-8259-2022, 2022.
Garrett, T. J., Glenn, I. B., and Krueger, S. K.: Thermodynamic Constraints on the Size Distributions of Tropical Clouds, J. Geophys. Res.-Atmos., 123, 8832–8849, https://doi.org/10.1029/2018JD028803, 2018.
Gasteiger, J., Emde, C., Mayer, B., Buras, R., Buehler, S. A., and Lemke, O.: Representative wavelengths absorption parameterization applied to satellite channels and spectral bands, J. Quant. Spectrosc. Ra., 148, 99–115, https://doi.org/10.1016/j.jqsrt.2014.06.024, 2014.
Goldbergs, G., Maier, S. W., Levick, S. R., and Edwards, A.: Limitations of high resolution satellite stereo imagery for estimating canopy height in Australian tropical savannas, Int. J. Appl. Earth Obs., 75, 83–95, https://doi.org/10.1016/j.jag.2018.10.021, 2019.
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. T., 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.
Hess, M., Koepke, P., and Schult, I.: Optical Properties of Aerosols and Clouds: The Software Package OPAC, B. Am. Meteorol. Soc., 79, 831–844, https://doi.org/10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2, 1998.
Holz, R. E., Ackerman, S. A., Nagle, F. W., Frey, R., Dutcher, S., Kuehn, R. E., Vaughan, M. A., and Baum, B.: Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res.-Atmos., 112, D11205, https://doi.org/10.1029/2008JD009837, 2008.
Horváth, Á. and Davies, R.: Simultaneous retrieval of cloud motion and height from polar-orbiter multiangle measurements, Geophys. Res. Lett., 28, 2915–2918, https://doi.org/10.1029/2001GL012951, 2001.
Iwabuchi, H. and Hayasaka, T.: A multi-spectral non-local method for retrieval of boundary layer cloud properties from optical remote sensing data, Remote Sens. Environ., 88, 294–308, https://doi.org/10.1016/j.rse.2003.08.005, 2003.
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.
Kazil, J., Narenpitak, P., Yamaguchi, T., and Feingold, G.: On Climate Change and Trade Cumulus Organization, J. Adv. Model. Earth Sy., 16, e2023MS004057, https://doi.org/10.1029/2023MS004057, 2024.
Killen, R. M. and Ellingson, R. G.: The Effect of Shape and Spatial Distribution of Cumulus Clouds on Longwave Irradiance, J. Atmos. Sci., 51, 2123–2136, https://doi.org/10.1175/1520-0469(1994)051<2123:TEOSAS>2.0.CO;2, 1994.
Klein, S. A., Hall, A., Norris, J. R., and Pincus, R.: Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review, Surv. Geophys., 38, 1307–1329, https://doi.org/10.1007/s10712-017-9433-3, 2017.
Kobayashi, T.: Reflected Solar Flux for Horizontally Inhomogeneous Atmospheres, J. Atmos. Sci., 48, 2436–2447, https://doi.org/10.1175/1520-0469(1991)048<2436:RSFFHI>2.0.CO;2, 1991.
Kokhanovsky, A.: Optical properties of terrestrial clouds, Earth-Sci. Rev., 64, 189–241, https://doi.org/10.1016/S0012-8252(03)00042-4, 2004.
Kölling, T., Zinner, T., and Mayer, B.: Aircraft-based stereographic reconstruction of 3-D cloud geometry, Atmos. Meas. Tech., 12, 1155–1166, https://doi.org/10.5194/amt-12-1155-2019, 2019.
Koren, I., Oreopoulos, L., Feingold, G., Remer, L. A., and Altaratz, O.: How small is a small cloud?, Atmos. Chem. Phys., 8, 3855–3864, https://doi.org/10.5194/acp-8-3855-2008, 2008.
Lamer, K., Kollias, P., Battaglia, A., and Preval, S.: Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars, Atmos. Meas. Tech., 13, 2363–2379, https://doi.org/10.5194/amt-13-2363-2020, 2020.
Lean, P., Migliorini, S., and Kelly, G.: Understanding Atmospheric Motion Vector Vertical Representativity Using a Simulation Study and First-Guess Departure Statistics, J. Appl. Meteorol. Clim., 54, 2479–2500, https://doi.org/10.1175/JAMC-D-15-0030.1, 2015.
Lehmann, K., Siebert, H., and Shaw, R. A.: Homogeneous and Inhomogeneous Mixing in Cumulus Clouds: Dependence on Local Turbulence Structure, J. Atmos. Sci., 66, 3641–3659, https://doi.org/10.1175/2009JAS3012.1, 2009.
Levis, A., Schechner, Y. Y., Davis, A. B., and Loveridge, J.: Multi-View Polarimetric Scattering Cloud Tomography and Retrieval of Droplet Size, Remote Sensing, 12, 2831, https://doi.org/10.3390/rs12172831, 2020.
Loeb, N. G., Várnai, T., and Winker, D. M.: Influence of Subpixel-Scale Cloud-Top Structure on Reflectances from Overcast Stratiform Cloud Layers, J. Atmos. Sci., 55, 2960–2973, https://doi.org/10.1175/1520-0469(1998)055<2960:IOSSCT>2.0.CO;2, 1998.
Loveridge, J.: Simulated Stereo Cloud Top Height Retrievals, Version v1.0, Zenodo [data set], https://doi.org/10.5281/zenodo.14509808, 2024.
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 1: Model description and Jacobian calculation, Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, 2023a.
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., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, 2023b.
Loveridge, J. R. and Di Girolamo, L.: Do Subsampling Strategies Reduce the Confounding Effect of Errors in Bispectral Retrievals on Estimates of Aerosol Cloud Interactions?, J. Geophys. Res.-Atmos., 129, e2023JD040189, https://doi.org/10.1029/2023JD040189, 2024.
Mace, G. G. and Zhang, Q.: The CloudSat radar-lidar geometrical profile product (RL-GeoProf): Updates, improvements, and selected results, J. Geophys. Res.-Atmos., 119, 9441–9462, https://doi.org/10.1002/2013JD021374, 2014.
Marchand, R. T., Ackerman, T. P., and Moroney, C.: An assessment of Multiangle Imaging Spectroradiometer (MISR) stereo-derived cloud top heights and cloud top winds using ground-based radar, lidar, and microwave radiometers, J. Geophys. Res.-Atmos., 112, D06204, https://doi.org/10.1029/2006JD007091, 2007.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Marchant, B., Platnick, S., Meyer, K., and Wind, G.: Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products, Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, 2020.
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.
Martins, J. P. A., Teixeira, J., Soares, P. M. M., Miranda, P. M. A., Kahn, B. H., Dang, V. T., Irion, F. W., Fetzer, E. J., and Fishbein, E.: Infrared sounding of the trade-wind boundary layer: AIRS and the RICO experiment, Geophys. Res. Lett., 37, L24806, https://doi.org/10.1029/2010GL045902, 2010.
Mason, S. L., Barker, H. W., Cole, J. N. S., Docter, N., Donovan, D. P., Hogan, R. J., Hünerbein, A., Kollias, P., Puigdomènech Treserras, B., Qu, Z., Wandinger, U., and van Zadelhoff, G.-J.: An intercomparison of EarthCARE cloud, aerosol, and precipitation retrieval products, Atmos. Meas. Tech., 17, 875–898, https://doi.org/10.5194/amt-17-875-2024, 2024.
McKim, B., Bony, S., and Dufresne, J.-L.: Weak anvil cloud area feedback suggested by physical and observational constraints, Nat. Geosci., 17, 392–397, https://doi.org/10.1038/s41561-024-01414-4, 2024.
Mitra, A., Di Girolamo, L., Hong, Y., Zhan, Y., and Mueller, K. J.: Assessment and Error Analysis of Terra-MODIS and MISR Cloud-Top Heights Through Comparison With ISS-CATS Lidar, J. Geophys. Res.-Atmos., 126, e2020JD034281, https://doi.org/10.1029/2020JD034281, 2021.
Mitra, A., Loveridge, J. R., and Di Girolamo, L.: Fusion of MISR Stereo Cloud Heights and Terra-MODIS Thermal Infrared Radiances to Estimate Two-Layered Cloud Properties, J. Geophys. Res.-Atmos., 128, e2022JD038135, https://doi.org/10.1029/2022JD038135, 2023.
Mueller, K. J., Wu, D. L., Horváth, Á., Jovanovic, V. M., Muller, J.-P., Di Girolamo, L., Garay, M. J., Diner, D. J., Moroney, C. M., and Wanzong, S.: Assessment of MISR Cloud Motion Vectors (CMVs) Relative to GOES and MODIS Atmospheric Motion Vectors (AMVs), J. Appl. Meteorol. Clim., 56, 555–572, https://doi.org/10.1175/JAMC-D-16-0112.1, 2017.
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.
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.
Naud, C., Muller, J.-P., Haeffelin, M., Morille, Y., and Delaval, A.: Assessment of MISR and MODIS cloud top heights through inter-comparison with a back-scattering lidar at SIRTA, Geophys. Res. Lett., 31, L04114, https://doi.org/10.1029/2003GL018976, 2004.
Naud, C. M., Muller, J.-P., Clothiaux, E. E., Baum, B. A., and Menzel, W. P.: Intercomparison of multiple years of MODIS, MISR and radar cloud-top heights, Ann. Geophys., 23, 2415–2424, https://doi.org/10.5194/angeo-23-2415-2005, 2005.
Neggers, R. A. J., Griewank, P. J., and Heus, T.: Power-Law Scaling in the Internal Variability of Cumulus Cloud Size Distributions due to Subsampling and Spatial Organization, J. Atmos. Sci., 76, 1489–1503, https://doi.org/10.1175/JAS-D-18-0194.1, 2019.
Niclòs, R., Valor, E., Caselles, V., Coll, C., and Sánchez, J. M.: In situ angular measurements of thermal infrared sea surface emissivity–Validation of models, Remote Sens. Environ., 94, 83–93, https://doi.org/10.1016/j.rse.2004.09.002, 2005.
Nicodemus, F. E., Richmond, J. C., Hsia, J. J., Ginsberg, I. W., and Limperis, T.: Geometrical considerations and nomenclature for reflectance, NBS Monograph 160, https://doi.org/10.6028/NBS.MONO.160, 1977.
O'Hirok, W. and Gautier, C.: A Three-Dimensional Radiative Transfer Model to Investigate the Solar Radiation within a Cloudy Atmosphere. Part I: Spatial Effects, J. Atmos. Sci., 55, 2162–2179, https://doi.org/10.1175/1520-0469(1998)055<2162:ATDRTM>2.0.CO;2, 1998.
Ohring, G., Wielicki, B., Spencer, R., Emery, B., and Datla, R.: Satellite Instrument Calibration for Measuring Global Climate Change: Report of a Workshop, B. Am. Meteorol. Soc., 86, 1303–1314, https://doi.org/10.1175/BAMS-86-9-1303, 2005.
Peng, Z., Yu, D., Huang, D., Heiser, J., Yoo, S., and Kalb, P.: 3D cloud detection and tracking system for solar forecast using multiple sky imagers, Sol. Energy, 118, 496–519, https://doi.org/10.1016/j.solener.2015.05.037, 2015.
Peters, J. M., Morrison, H., Varble, A. C., Hannah, W. M., and Giangrande, S. E.: Thermal Chains and Entrainment in Cumulus Updrafts. Part II: Analysis of Idealized Simulations, J. Atmos. Sci., 77, 3661–3681, https://doi.org/10.1175/JAS-D-19-0244.1, 2020.
Peters, J. M., Morrison, H., Zhang, G. J., and Powell, S. W.: Improving the Physical Basis for Updraft Dynamics in Deep Convection Parameterizations, J. Adv. Model. Earth Sy., 13, e2020MS002282, https://doi.org/10.1029/2020MS002282, 2021.
Platnick, S.: Approximations for horizontal photon transport in cloud remote sensing problems, J. Quant. Spectrosc. Ra., 68, 75–99, https://doi.org/10.1016/S0022-4073(00)00016-9, 2001.
Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699–4718, https://doi.org/10.5194/amt-8-4699-2015, 2015.
Prasad, A. A. and Davies, R.: Detecting tropical thin cirrus using Multiangle Imaging SpectroRadiometer's oblique cameras and modeled outgoing longwave radiation, J. Geophys. Res.-Atmos., 117, D06208, https://doi.org/10.1029/2011JD016798, 2012.
Prasad, A. A. and Davies, R.: An assessment of cirrus heights from MISR oblique stereo using ground-based radar and lidar at the Tropical Western Pacific ARM sites, J. Geophys. Res.-Atmos., 118, 5588–5599, https://doi.org/10.1002/jgrd.50454, 2013.
Radtke, J., Vogel, R., Ament, F., and Naumann, A. K.: Spatial Organisation Affects the Pathway to Precipitation in Simulated Trade-Wind Convection, Geophys. Res. Lett., 50, e2023GL103579, https://doi.org/10.1029/2023GL103579, 2023.
Reid, J. S., Maring, H. B., Narisma, G. T., Heever, S. van den, Girolamo, L. D., Ferrare, R., Lawson, P., Mace, G. G., Simpas, J. B., Tanelli, S., Ziemba, L., Diedenhoven, B. van, Bruintjes, R., Bucholtz, A., Cairns, B., Cambaliza, M. O., Chen, G., Diskin, G. S., Flynn, J. H., Hostetler, C. A., Holz, R. E., Lang, T. J., Schmidt, K. S., Smith, G., Sorooshian, A., Thompson, E. J., Thornhill, K. L., Trepte, C., Wang, J., Woods, S., Yoon, S., Alexandrov, M., Alvarez, S., Amiot, C. G., Bennett, J. R., M., B., Burton, S. P., Cayanan, E., Chen, H., Collow, A., Crosbie, E., DaSilva, A., DiGangi, J. P., Flagg, D. D., Freeman, S. W., Fu, D., Fukada, E., Hilario, M. R. A., Hong, Y., Hristova-Veleva, S. M., Kuehn, R., Kowch, R. S., Leung, G. R., Loveridge, J., Meyer, K., Miller, R. M., Montes, M. J., Moum, J. N., Nenes, T., Nesbitt, S. W., Norgren, M., Nowottnick, E. P., Rauber, R. M., Reid, E. A., Rutledge, S., Schlosser, J. S., Sekiyama, T. T., Shook, M. A., Sokolowsky, G. A., Stamnes, S. A., Tanaka, T. Y., Wasilewski, A., Xian, P., Xiao, Q., Xu, Z., and Zavaleta, J.: The coupling between tropical meteorology, aerosol lifecycle, convection, and radiation, during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex), B. Am. Meteorol. Soc., 104, E1179–E1205, https://doi.org/10.1175/BAMS-D-21-0285.1, 2023.
Smalley, K. M. and Rapp, A. D.: A-Train estimates of the sensitivity of the cloud-to-rainwater ratio to cloud size, relative humidity, and aerosols, Atmos. Chem. Phys., 21, 2765–2779, https://doi.org/10.5194/acp-21-2765-2021, 2021.
Stein, M.: Large Sample Properties of Simulations Using Latin Hypercube Sampling, Technometrics, 29, 143–151, https://doi.org/10.1080/00401706.1987.10488205, 1987.
Tan, Z., Kaul, C. M., Pressel, K. G., Cohen, Y., Schneider, T., and Teixeira, J.: An Extended Eddy-Diffusivity Mass-Flux Scheme for Unified Representation of Subgrid-Scale Turbulence and Convection, J. Adv. Model. Earth Sy., 10, 770–800, https://doi.org/10.1002/2017MS001162, 2018.
Tao, H., Sawhney, H. S., and Kumar, R.: A global matching framework for stereo computation, in: Proceedings Eighth IEEE International Conference on Computer Vision, ICCV 2001, Vancouver, BC, Canada, 7–14 July 2001, IEEE, 1, 532–539, https://doi.org/10.1109/ICCV.2001.937562, 2001.
Várnai, T.: Influence of Three-Dimensional Radiative Effects on the Spatial Distribution of Shortwave Cloud Reflection, J. Atmos. Sci., 57, 216–229, https://doi.org/10.1175/1520-0469(2000)057<0216:IOTDRE>2.0.CO;2, 2000.
Vaughan, M. A., Powell, K. A., Winker, D. M., Hostetler, C. A., Kuehn, R. E., Hunt, W. H., Getzewich, B. J., Young, S. A., Liu, Z., and McGill, M. J.: Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements, J. Atmos. Ocean. Tech., 26, 2034–2050, https://doi.org/10.1175/2009JTECHA1228.1, 2009.
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., and Morcette, J.-J.: Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview, IEEE T. Geosci. Remote, 35, 675–686, https://doi.org/10.1109/36.581987, 1997.
Vial, J., Bony, S., Stevens, B., and Vogel, R.: Mechanisms and Model Diversity of Trade-Wind Shallow Cumulus Cloud Feedbacks: A Review, Surv. Geophys., 38, 1331–1353, https://doi.org/10.1007/s10712-017-9418-2, 2017.
Vial, J., Albright, A. L., Vogel, R., Musat, I., and Bony, S.: Cloud transition across the daily cycle illuminates model responses of trade cumuli to warming, P. Natl. Acad. Sci. USA, 120, e2209805120, https://doi.org/10.1073/pnas.2209805120, 2023.
Volkmer, L., Kölling, T., Zinner, T., and Mayer, B.: Consideration of the cloud motion for aircraft-based stereographically derived cloud geometry and cloud top heights, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2024-19, 2024a.
Volkmer, L., Pörtge, V., Jakub, F., and Mayer, B.: Model-based evaluation of cloud geometry and droplet size retrievals from two-dimensional polarized measurements of specMACS, Atmos. Meas. Tech., 17, 1703–1719, https://doi.org/10.5194/amt-17-1703-2024, 2024b.
Wielicki, B. A. and Parker, L.: On the determination of cloud cover from satellite sensors: The effect of sensor spatial resolution, J. Geophys. Res.-Atmos., 97, 12799–12823, https://doi.org/10.1029/92JD01061, 1992.
Yang, Y. and Di Girolamo, L.: Impacts of 3-D radiative effects on satellite cloud detection and their consequences on cloud fraction and aerosol optical depth retrievals, J. Geophys. Res.-Atmos., 113, D04213, https://doi.org/10.1029/2007JD009095, 2008.
Yin, B., Min, Q., Morgan, E., Yang, Y., Marshak, A., and Davis, A. B.: Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations, Atmos. Meas. Tech., 13, 5259–5275, https://doi.org/10.5194/amt-13-5259-2020, 2020.
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, M. and Austin, P. H.: Life Cycle of Numerically Simulated Shallow Cumulus Clouds. Part I: Transport, J. Atmos. Sci., 62, 1269–1290, https://doi.org/10.1175/JAS3414.1, 2005.
Zhao, M., Golaz, J.-C., Held, I. M., Ramaswamy, V., Lin, S.-J., Ming, Y., Ginoux, P., Wyman, B., Donner, L. J., Paynter, D., and Guo, H.: Uncertainty in Model Climate Sensitivity Traced to Representations of Cumulus Precipitation Microphysics, J. Climate, 29, 543–560, https://doi.org/10.1175/JCLI-D-15-0191.1, 2016.
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.
Satellites can measure cloud geometry using stereoscopy. However, clouds are transparent and...
