Articles | Volume 18, issue 22
https://doi.org/10.5194/amt-18-6681-2025
© 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-18-6681-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2025
Research article |
| 18 Nov 2025
| 18 Nov 2025
Evaluation of cloud height, optical thickness, and phase retrievals from the CHROMA algorithm applied to Sentinel-3 OLCI data
Goddard Earth Sciences Technology And Research (GESTAR) II, University of Maryland Baltimore County, Baltimore, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Brian Cairns
NASA Goddard Institute for Space Studies, New York, NY, USA
Kirk D. Knobelspiesse
NASA Goddard Space Flight Center, Greenbelt, MD, USA
Luca Lelli
Remote Sensing Technology Institute, German Aerospace Centre (DLR), Wessling, Germany
Chamara Rajapakshe
NASA Goddard Space Flight Center, Greenbelt, MD, USA
SSAI, Lanham, MD, USA
Scott E. Giangrande
Environmental and Climate Sciences Department, Brookhaven National Laboratory, Upton, NY, USA
Gareth E. Thomas
STFC Rutherford Appleton Laboratory, Didcot, UK
Damao Zhang
Pacific Northwest National Laboratory, Richland, WA, USA
Related authors
Xin Xi, Jun Wang, Zhendong Lu, Andrew M. Sayer, Jaehwa Lee, Robert C. Levy, Yujie Wang, Alexei Lyapustin, Hongqing Liu, Istvan Laszlo, Changwoo Ahn, Omar Torres, Sabur Abdullaev, James Limbacher, and Ralph A. Kahn
Atmos. Chem. Phys., 25, 7403–7429, https://doi.org/10.5194/acp-25-7403-2025, https://doi.org/10.5194/acp-25-7403-2025, 2025
Short summary
Short summary
The Aralkum Desert is challenging for aerosol retrieval due to its bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on aerosol properties. The performance and consistency of satellite algorithms in observing Aralkum-generated saline dust remain unknown. This study compares multisensor UVAI (ultraviolet aerosol index), AOD (aerosol optical depth), and ALH (aerosol layer height) products and reveals inconsistencies and potential biases over the Aral Sea basin.
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.
Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023, https://doi.org/10.5194/amt-16-5863-2023, 2023
Short summary
Short summary
This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations. The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.
Edward Gryspeerdt, Adam C. Povey, Roy G. Grainger, Otto Hasekamp, N. Christina Hsu, Jane P. Mulcahy, Andrew M. Sayer, and Armin Sorooshian
Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, https://doi.org/10.5194/acp-23-4115-2023, 2023
Short summary
Short summary
The impact of aerosols on clouds is one of the largest uncertainties in the human forcing of the climate. Aerosol can increase the concentrations of droplets in clouds, but observational and model studies produce widely varying estimates of this effect. We show that these estimates can be reconciled if only polluted clouds are studied, but this is insufficient to constrain the climate impact of aerosol. The uncertainty in aerosol impact on clouds is currently driven by cases with little aerosol.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
Short summary
Short summary
This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022, https://doi.org/10.5194/amt-15-4859-2022, 2022
Short summary
Short summary
In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative these uncertainties are compared with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural networks.
Cheng Chen, Oleg Dubovik, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Fabrice Ducos, Yevgeny Derimian, Maurice Herman, Didier Tanré, Lorraine A. Remer, Alexei Lyapustin, Andrew M. Sayer, Robert C. Levy, N. Christina Hsu, Jacques Descloitres, Lei Li, Benjamin Torres, Yana Karol, Milagros Herrera, Marcos Herreras, Michael Aspetsberger, Moritz Wanzenboeck, Lukas Bindreiter, Daniel Marth, Andreas Hangler, and Christian Federspiel
Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, https://doi.org/10.5194/essd-12-3573-2020, 2020
Short summary
Short summary
Aerosol products obtained from POLDER/PARASOL processed by the GRASP algorithm have been released. The entire archive of PARASOL/GRASP aerosol products is evaluated against AERONET and compared with MODIS (DT, DB and MAIAC), as well as PARASOL/Operational products. PARASOL/GRASP aerosol products provide spectral 443–1020 nm AOD correlating well with AERONET with a maximum bias of 0.02. Finally, GRASP shows capability to derive detailed spectral properties, including aerosol absorption.
Kaiden Sookdar, Scott E. Giangrande, John Rausch, Lihong Ma, Meng Wang, Dié Wang, Michael P. Jensen, Ching-Shu Hung, and J. Christine Chiu
Atmos. Meas. Tech., 18, 6271–6289, https://doi.org/10.5194/amt-18-6271-2025, https://doi.org/10.5194/amt-18-6271-2025, 2025
Short summary
Short summary
Photometer observations of stratocumulus cloud properties are evaluated for a multiyear archive. Retrievals for cloud optical depth, cloud droplet effective radius, and liquid water path show solid agreement with collocated references. Continental stratocumulus clouds sorted by cloud thickness indicate double the cloud optical depth and liquid water path of their marine counterparts, while exhibiting similar bulk cloud droplet effective radius.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
Atmos. Meas. Tech., 18, 5393–5414, https://doi.org/10.5194/amt-18-5393-2025, https://doi.org/10.5194/amt-18-5393-2025, 2025
Short summary
Short summary
This study leverages machine learning models to classify cloud thermodynamic phases using multi-sensor remote sensing data collected at the Department of Energy Atmospheric Radiation Measurement North Slope of Alaska observatory. We evaluate model performance, feature importance, and application of the model to another observatory and quantify how the models respond to instrument outages.
Damao Zhang, Jennifer Comstock, Chitra Sivaraman, Kefei Mo, Raghavendra Krishnamurthy, Jingjing Tian, Tianning Su, Zhanqing Li, and Natalia Roldán-Henao
Atmos. Meas. Tech., 18, 3453–3475, https://doi.org/10.5194/amt-18-3453-2025, https://doi.org/10.5194/amt-18-3453-2025, 2025
Short summary
Short summary
Planetary boundary layer height (PBLHT) is an important parameter in atmospheric process studies and numerical model simulations. We use machine learning methods to produce a best-estimate planetary boundary layer height (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements. We demonstrated that PBLHT-BE-ML greatly improved the comparisons against sounding-derived PBLHT.
Xin Xi, Jun Wang, Zhendong Lu, Andrew M. Sayer, Jaehwa Lee, Robert C. Levy, Yujie Wang, Alexei Lyapustin, Hongqing Liu, Istvan Laszlo, Changwoo Ahn, Omar Torres, Sabur Abdullaev, James Limbacher, and Ralph A. Kahn
Atmos. Chem. Phys., 25, 7403–7429, https://doi.org/10.5194/acp-25-7403-2025, https://doi.org/10.5194/acp-25-7403-2025, 2025
Short summary
Short summary
The Aralkum Desert is challenging for aerosol retrieval due to its bright, heterogeneous, and dynamic surfaces and the lack of in situ constraints on aerosol properties. The performance and consistency of satellite algorithms in observing Aralkum-generated saline dust remain unknown. This study compares multisensor UVAI (ultraviolet aerosol index), AOD (aerosol optical depth), and ALH (aerosol layer height) products and reveals inconsistencies and potential biases over the Aral Sea basin.
Dan Lubin, Xun Zou, Johannes Mülmenstädt, Andrew Vogelmann, Maria Cadeddu, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2768, https://doi.org/10.5194/egusphere-2025-2768, 2025
Short summary
Short summary
The US Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska Facility has measured solar and atmospheric infrared radiation, and cloud properties, for the past 25 years. Statistically significant trends are emerging, including increasing infrared radiation due to a warming atmosphere, and decreasing solar radiation due to increasing liquid water content in clouds. These trends are influenced by large-scale atmospheric circulation patterns and by atmospheric rivers.
Tamanna Subba, Michael P. Jensen, Min Deng, Scott E. Giangrande, Mark C. Harvey, Ashish Singh, Die Wang, Maria Zawadowicz, and Chongai Kuang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2659, https://doi.org/10.5194/egusphere-2025-2659, 2025
Short summary
Short summary
This study highlights how sea breeze circulations influence aerosol concentrations and radiative effects in Southern Texas region. Using TRacking Aerosol Convection Interactions Experiment field campaign observations and model simulations, we show that sea breeze–aerosol interactions significantly impact cloud-relevant aerosols and regional air quality. These findings improve understanding of mesoscale controls on aerosols in complex coastal urban environments.
Florian Tornow, Ann Fridlind, George Tselioudis, Brian Cairns, Andrew Ackerman, Seethala Chellappan, David Painemal, Paquita Zuidema, Christiane Voigt, Simon Kirschler, and Armin Sorooshian
Atmos. Chem. Phys., 25, 5053–5074, https://doi.org/10.5194/acp-25-5053-2025, https://doi.org/10.5194/acp-25-5053-2025, 2025
Short summary
Short summary
The recent NASA campaign ACTIVATE (Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment) performed 71 tandem flights in mid-latitude marine cold-air outbreaks off the US eastern seaboard. We provide meteorological and cloud transition stage context, allowing us to identify days that are most suitable for Lagrangian modeling and analysis. Surveyed cloud properties show signatures of cloud microphysical processes, such as cloud-top entrainment and secondary ice formation.
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.
Fan Mei, Qi Zhang, Damao Zhang, Jerome D. Fast, Gourihar Kulkarni, Mikhail S. Pekour, Christopher R. Niedek, Susanne Glienke, Israel Silber, Beat Schmid, Jason M. Tomlinson, Hardeep S. Mehta, Xena Mansoura, Zezhen Cheng, Gregory W. Vandergrift, Nurun Nahar Lata, Swarup China, and Zihua Zhu
Atmos. Chem. Phys., 25, 3425–3444, https://doi.org/10.5194/acp-25-3425-2025, https://doi.org/10.5194/acp-25-3425-2025, 2025
Short summary
Short summary
This study highlights the unique capability of the ArcticShark, an uncrewed aerial system, in measuring vertically resolved atmospheric properties. Data from 32 research flights in 2023 reveal seasonal patterns and correlations with conventional measurements. The consistency and complementarity of in situ and remote sensing methods are highlighted. The study demonstrates the ArcticShark’s versatility in bridging data gaps and improving the understanding of vertical atmospheric structures.
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
Short summary
Short summary
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.
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.
Fan Mei, Jennifer M. Comstock, Mikhail S. Pekour, Jerome D. Fast, Krista L. Gaustad, Beat Schmid, Shuaiqi Tang, Damao Zhang, John E. Shilling, Jason M. Tomlinson, Adam C. Varble, Jian Wang, L. Ruby Leung, Lawrence Kleinman, Scot Martin, Sebastien C. Biraud, Brian D. Ermold, and Kenneth W. Burk
Earth Syst. Sci. Data, 16, 5429–5448, https://doi.org/10.5194/essd-16-5429-2024, https://doi.org/10.5194/essd-16-5429-2024, 2024
Short summary
Short summary
Our study explores a comprehensive dataset from airborne field studies (2013–2018) conducted using the US Department of Energy's Gulfstream 1 (G-1). The 236 flights span diverse regions, including the Arctic, US Southern Great Plains, US West Coast, eastern North Atlantic, Amazon Basin in Brazil, and Sierras de Córdoba range in Argentina. This dataset provides unique insights into atmospheric dynamics, aerosols, and clouds and makes data available in a more accessible format.
Sanja Dmitrovic, Joseph S. Schlosser, Ryan Bennett, Brian Cairns, Gao Chen, Glenn S. Diskin, Richard A. Ferrare, Johnathan W. Hair, Michael A. Jones, Jeffrey S. Reid, Taylor J. Shingler, Michael A. Shook, Armin Sorooshian, Kenneth L. Thornhill, Luke D. Ziemba, and Snorre Stamnes
EGUsphere, https://doi.org/10.5194/egusphere-2024-3088, https://doi.org/10.5194/egusphere-2024-3088, 2024
Short summary
Short summary
This study focuses on aerosol particles, which critically influence the atmosphere by scattering and absorbing light. To understand these interactions, airborne field campaigns deploy instruments that can measure these particles’ directly or indirectly via remote sensing. We introduce the In Situ Aerosol Retrieval Algorithm (ISARA) to ensure consistency between aerosol measurements and show that the two data sets generally align, with some deviation caused by the presence of larger particles.
Brent A. McBride, J. Vanderlei Martins, J. Dominik Cieslak, Roberto Fernandez-Borda, Anin Puthukkudy, Xiaoguang Xu, Noah Sienkiewicz, Brian Cairns, and Henrique M. J. Barbosa
Atmos. Meas. Tech., 17, 5709–5729, https://doi.org/10.5194/amt-17-5709-2024, https://doi.org/10.5194/amt-17-5709-2024, 2024
Short summary
Short summary
The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is a new Earth-observing instrument that provides highly accurate measurements of the atmosphere and surface. Using a physics-based calibration technique, we show that AirHARP achieves high measurement accuracy in laboratory and field environments and exceeds a benchmark accuracy requirement for modern aerosol and cloud climate observations. Therefore, the HARP design is highly attractive for upcoming NASA climate missions.
Toshi Matsui, Daniel Hernandez-Deckers, Scott E. Giangrande, Thiago S. Biscaro, Ann Fridlind, and Scott Braun
Atmos. Chem. Phys., 24, 10793–10814, https://doi.org/10.5194/acp-24-10793-2024, https://doi.org/10.5194/acp-24-10793-2024, 2024
Short summary
Short summary
Using computer simulations and real measurements, we discovered that storms over the Amazon were narrower but more intense during the dry periods, producing heavier rain and more ice particles in the clouds. Our research showed that cumulus bubbles played a key role in creating these intense storms. This study can improve the representation of the effect of continental and ocean environments on tropical regions' rainfall patterns in simulations.
Ewan Crosbie, Luke D. Ziemba, Michael A. Shook, Taylor Shingler, Johnathan W. Hair, Armin Sorooshian, Richard A. Ferrare, Brian Cairns, Yonghoon Choi, Joshua DiGangi, Glenn S. Diskin, Chris Hostetler, Simon Kirschler, Richard H. Moore, David Painemal, Claire Robinson, Shane T. Seaman, K. Lee Thornhill, Christiane Voigt, and Edward Winstead
Atmos. Chem. Phys., 24, 6123–6152, https://doi.org/10.5194/acp-24-6123-2024, https://doi.org/10.5194/acp-24-6123-2024, 2024
Short summary
Short summary
Marine clouds are found to clump together in regions or lines, readily discernible from satellite images of the ocean. While clustering is also a feature of deep storm clouds, we focus on smaller cloud systems associated with fair weather and brief localized showers. Two aircraft sampled the region around these shallow systems: one incorporated measurements taken within, adjacent to, and below the clouds, while the other provided a survey from above using remote sensing techniques.
Basudev Swain, Marco Vountas, Aishwarya Singh, Nidhi L. Anchan, Adrien Deroubaix, Luca Lelli, Yanick Ziegler, Sachin S. Gunthe, Hartmut Bösch, and John P. Burrows
Atmos. Chem. Phys., 24, 5671–5693, https://doi.org/10.5194/acp-24-5671-2024, https://doi.org/10.5194/acp-24-5671-2024, 2024
Short summary
Short summary
Arctic amplification (AA) accelerates the warming of the central Arctic cryosphere and affects aerosol dynamics. Limited observations hinder a comprehensive analysis. This study uses AEROSNOW aerosol optical density (AOD) data and GEOS-Chem simulations to assess AOD variability. Discrepancies highlight the need for improved observational integration into models to refine our understanding of aerosol effects on cloud microphysics, ice nucleation, and radiative forcing under evolving AA.
Leong Wai Siu, Joseph S. Schlosser, David Painemal, Brian Cairns, Marta A. Fenn, Richard A. Ferrare, Johnathan W. Hair, Chris A. Hostetler, Longlei Li, Mary M. Kleb, Amy Jo Scarino, Taylor J. Shingler, Armin Sorooshian, Snorre A. Stamnes, and Xubin Zeng
Atmos. Meas. Tech., 17, 2739–2759, https://doi.org/10.5194/amt-17-2739-2024, https://doi.org/10.5194/amt-17-2739-2024, 2024
Short summary
Short summary
An unprecedented 3-year aerosol dataset was collected from a recent NASA field campaign over the western North Atlantic Ocean, which offers a special opportunity to evaluate two state-of-the-art remote sensing instruments, one lidar and the other polarimeter, on the same aircraft. Special attention has been paid to validate aerosol optical depth data and their uncertainties when no reference dataset is available. Physical reasons for the disagreement between two instruments are discussed.
Siddhant Gupta, Dié Wang, Scott E. Giangrande, Thiago S. Biscaro, and Michael P. Jensen
Atmos. Chem. Phys., 24, 4487–4510, https://doi.org/10.5194/acp-24-4487-2024, https://doi.org/10.5194/acp-24-4487-2024, 2024
Short summary
Short summary
We examine the lifecycle of isolated deep convective clouds (DCCs) in the Amazon rainforest. Weather radar echoes from the DCCs are tracked to evaluate their lifecycle. The DCC size and intensity increase, reach a peak, and then decrease over the DCC lifetime. Vertical profiles of air motion and mass transport from different seasons are examined to understand the transport of energy and momentum within DCC cores and to address the deficiencies in simulating DCCs using weather and climate models.
Basudev Swain, Marco Vountas, Adrien Deroubaix, Luca Lelli, Yanick Ziegler, Soheila Jafariserajehlou, Sachin S. Gunthe, Andreas Herber, Christoph Ritter, Hartmut Bösch, and John P. Burrows
Atmos. Meas. Tech., 17, 359–375, https://doi.org/10.5194/amt-17-359-2024, https://doi.org/10.5194/amt-17-359-2024, 2024
Short summary
Short summary
Aerosols are suspensions of particles dispersed in the air. In this study, we use a novel retrieval of satellite data to investigate an optical property of aerosols, the aerosol optical depth, in the high Arctic to assess their direct and indirect roles in climate change. This study demonstrates that the presented approach shows good quality and very promising potential.
Yang Wang, Chanakya Bagya Ramesh, Scott E. Giangrande, Jerome Fast, Xianda Gong, Jiaoshi Zhang, Ahmet Tolga Odabasi, Marcus Vinicius Batista Oliveira, Alyssa Matthews, Fan Mei, John E. Shilling, Jason Tomlinson, Die Wang, and Jian Wang
Atmos. Chem. Phys., 23, 15671–15691, https://doi.org/10.5194/acp-23-15671-2023, https://doi.org/10.5194/acp-23-15671-2023, 2023
Short summary
Short summary
We report the vertical profiles of aerosol properties over the Southern Great Plains (SGP), a region influenced by shallow convective clouds, land–atmosphere interactions, boundary layer turbulence, and the aerosol life cycle. We examined the processes that drive the aerosol population and distribution in the lower troposphere over the SGP. This study helps improve our understanding of aerosol–cloud interactions and the model representation of aerosol processes.
Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023, https://doi.org/10.5194/amt-16-5863-2023, 2023
Short summary
Short summary
This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations. The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
Short summary
Short summary
Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Neranga K. Hannadige, Peng-Wang Zhai, Meng Gao, Yongxiang Hu, P. Jeremy Werdell, Kirk Knobelspiesse, and Brian Cairns
Atmos. Meas. Tech., 16, 5749–5770, https://doi.org/10.5194/amt-16-5749-2023, https://doi.org/10.5194/amt-16-5749-2023, 2023
Short summary
Short summary
We evaluated the impact of three ocean optical models with different numbers of free parameters on the performance of an aerosol and ocean color remote sensing algorithm using the multi-angle polarimeter (MAP) measurements. It was demonstrated that the three- and seven-parameter bio-optical models can be used to accurately represent both open and coastal waters, whereas the one-parameter model has smaller retrieval uncertainty over open water.
Elisa Carboni, Gareth E. Thomas, Richard Siddans, and Brian Kerridge
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-232, https://doi.org/10.5194/amt-2023-232, 2023
Revised manuscript not accepted
Short summary
Short summary
We analyzed different satellite datasets of cloud properties with a new approach to quantify and interpret their interannual variability based on singular vector decomposition (SVD). The spatial pattern and its temporal evolution are strikingly similar for all the satellite datasets and follow the El Nino Southern Oscillation. The SVD approach reported here has potential for application to satellite data sets and to evaluate consistency between models and observations.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Armin Sorooshian, Mikhail D. Alexandrov, Adam D. Bell, Ryan Bennett, Grace Betito, Sharon P. Burton, Megan E. Buzanowicz, Brian Cairns, Eduard V. Chemyakin, Gao Chen, Yonghoon Choi, Brian L. Collister, Anthony L. Cook, Andrea F. Corral, Ewan C. Crosbie, Bastiaan van Diedenhoven, Joshua P. DiGangi, Glenn S. Diskin, Sanja Dmitrovic, Eva-Lou Edwards, Marta A. Fenn, Richard A. Ferrare, David van Gilst, Johnathan W. Hair, David B. Harper, Miguel Ricardo A. Hilario, Chris A. Hostetler, Nathan Jester, Michael Jones, Simon Kirschler, Mary M. Kleb, John M. Kusterer, Sean Leavor, Joseph W. Lee, Hongyu Liu, Kayla McCauley, Richard H. Moore, Joseph Nied, Anthony Notari, John B. Nowak, David Painemal, Kasey E. Phillips, Claire E. Robinson, Amy Jo Scarino, Joseph S. Schlosser, Shane T. Seaman, Chellappan Seethala, Taylor J. Shingler, Michael A. Shook, Kenneth A. Sinclair, William L. Smith Jr., Douglas A. Spangenberg, Snorre A. Stamnes, Kenneth L. Thornhill, Christiane Voigt, Holger Vömel, Andrzej P. Wasilewski, Hailong Wang, Edward L. Winstead, Kira Zeider, Xubin Zeng, Bo Zhang, Luke D. Ziemba, and Paquita Zuidema
Earth Syst. Sci. Data, 15, 3419–3472, https://doi.org/10.5194/essd-15-3419-2023, https://doi.org/10.5194/essd-15-3419-2023, 2023
Short summary
Short summary
The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol–cloud–meteorology interactions. HU-25 Falcon and King Air aircraft conducted systematic and spatially coordinated flights over the northwest Atlantic Ocean. This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes.
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
Short summary
Short summary
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.
Basudev Swain, Marco Vountas, Adrien Deroubaix, Luca Lelli, Aishwarya Singh, Yanick Ziegler, Sachin S. Gunthe, and John P. Burrows
EGUsphere, https://doi.org/10.5194/egusphere-2023-730, https://doi.org/10.5194/egusphere-2023-730, 2023
Preprint archived
Short summary
Short summary
Aerosols are suspensions of particles distributed in the air. Depending on their chemical composition, they scatter and/or absorb sunlight and thus cool or warm the earth's atmosphere and its surface. They also provide as a surface in the atmosphere upon which ice or liquid clouds droplets nucleate and grow. In this study, we use satellite observations and model simulations to investigate the properties of aerosols with the goal of assessing their direct and indirect role in climate change.
Scott E. Giangrande, Thiago S. Biscaro, and John M. Peters
Atmos. Chem. Phys., 23, 5297–5316, https://doi.org/10.5194/acp-23-5297-2023, https://doi.org/10.5194/acp-23-5297-2023, 2023
Short summary
Short summary
Our study tracks thunderstorms observed during the wet and dry seasons of the Amazon Basin using weather radar. We couple this precipitation tracking with opportunistic overpasses of a wind profiler and other ground observations to add unique insights into the upwards and downwards air motions within these clouds at various stages in the storm life cycle. The results of a simple updraft model are provided to give physical explanations for observed seasonal differences.
Christopher R. Williams, Joshua Barrio, Paul E. Johnston, Paytsar Muradyan, and Scott E. Giangrande
Atmos. Meas. Tech., 16, 2381–2398, https://doi.org/10.5194/amt-16-2381-2023, https://doi.org/10.5194/amt-16-2381-2023, 2023
Short summary
Short summary
This study uses surface disdrometer observations to calibrate 8 years of 915 MHz radar wind profiler deployed in the central United States in northern Oklahoma. This study had two key findings. First, the radar wind profiler sensitivity decreased approximately 3 to 4 dB/year as the hardware aged. Second, this drift was slow enough that calibration can be performed using 3-month intervals. Calibrated radar wind profiler observations and Python processing code are available on public repositories.
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Brian Cairns, Xiaoguang Xu, and J. Vanderlei Martins
Atmos. Meas. Tech., 16, 2067–2087, https://doi.org/10.5194/amt-16-2067-2023, https://doi.org/10.5194/amt-16-2067-2023, 2023
Short summary
Short summary
Multi-angle polarimetric measurements have been shown to greatly improve the remote sensing capability of aerosols and help atmospheric correction for ocean color retrievals. However, the uncertainty correlations among different measurement angles have not been well characterized. In this work, we provided a practical framework to evaluate the impact of the angular uncertainty correlation in retrieval results and a method to directly estimate correlation strength from retrieval residuals.
Emily D. Lenhardt, Lan Gao, Jens Redemann, Feng Xu, Sharon P. Burton, Brian Cairns, Ian Chang, Richard A. Ferrare, Chris A. Hostetler, Pablo E. Saide, Calvin Howes, Yohei Shinozuka, Snorre Stamnes, Mary Kacarab, Amie Dobracki, Jenny Wong, Steffen Freitag, and Athanasios Nenes
Atmos. Meas. Tech., 16, 2037–2054, https://doi.org/10.5194/amt-16-2037-2023, https://doi.org/10.5194/amt-16-2037-2023, 2023
Short summary
Short summary
Small atmospheric particles, such as smoke from wildfires or pollutants from human activities, impact cloud properties, and clouds have a strong influence on climate. To better understand the distributions of these particles, we develop relationships to derive their concentrations from remote sensing measurements from an instrument called a lidar. Our method is reliable for smoke particles, and similar steps can be taken to develop relationships for other particle types.
Edward Gryspeerdt, Adam C. Povey, Roy G. Grainger, Otto Hasekamp, N. Christina Hsu, Jane P. Mulcahy, Andrew M. Sayer, and Armin Sorooshian
Atmos. Chem. Phys., 23, 4115–4122, https://doi.org/10.5194/acp-23-4115-2023, https://doi.org/10.5194/acp-23-4115-2023, 2023
Short summary
Short summary
The impact of aerosols on clouds is one of the largest uncertainties in the human forcing of the climate. Aerosol can increase the concentrations of droplets in clouds, but observational and model studies produce widely varying estimates of this effect. We show that these estimates can be reconciled if only polluted clouds are studied, but this is insufficient to constrain the climate impact of aerosol. The uncertainty in aerosol impact on clouds is currently driven by cases with little aerosol.
Luca Lelli, Marco Vountas, Narges Khosravi, and John Philipp Burrows
Atmos. Chem. Phys., 23, 2579–2611, https://doi.org/10.5194/acp-23-2579-2023, https://doi.org/10.5194/acp-23-2579-2023, 2023
Short summary
Short summary
Arctic amplification describes the recent period in which temperatures have been rising twice as fast as or more than the global average and sea ice and the Greenland ice shelf are approaching a tipping point. Hence, the Arctic ability to reflect solar energy decreases and absorption by the surface increases. Using 2 decades of complementary satellite data, we discover that clouds unexpectedly increase the pan-Arctic reflectance by increasing their liquid water content, thus cooling the Arctic.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
Short summary
Short summary
This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Bastiaan van Diedenhoven, Otto P. Hasekamp, Brian Cairns, Gregory L. Schuster, Snorre Stamnes, Michael Shook, and Luke Ziemba
Atmos. Meas. Tech., 15, 7411–7434, https://doi.org/10.5194/amt-15-7411-2022, https://doi.org/10.5194/amt-15-7411-2022, 2022
Short summary
Short summary
The strong variability in the chemistry of atmospheric particulate matter affects the amount of water aerosols absorb and their effect on climate. We present a remote sensing method to determine the amount of water in particulate matter. Its application to airborne instruments indicates that the observed aerosols have rather low water contents and low fractions of soluble particles. Future satellites will be able to yield global aerosol water uptake data.
Santiago Gassó and Kirk D. Knobelspiesse
Atmos. Chem. Phys., 22, 13581–13605, https://doi.org/10.5194/acp-22-13581-2022, https://doi.org/10.5194/acp-22-13581-2022, 2022
Short summary
Short summary
Atmospheric particles interact with light resulting in observable optical polarization. Thus, we can learn about their composition from space. New satellite sensor technology measures full polarization of reflected sunlight. This paper considers circular polarization, an overlooked category of polarization with distinctive features that could bring new insights. We review existing literature and make novel computations to consider this previously underappreciated category of polarization.
Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 15, 4859–4879, https://doi.org/10.5194/amt-15-4859-2022, https://doi.org/10.5194/amt-15-4859-2022, 2022
Short summary
Short summary
In this work, we assessed the pixel-wise retrieval uncertainties on aerosol and ocean color derived from multi-angle polarimetric measurements. Standard error propagation methods are used to compute the uncertainties. A flexible framework is proposed to evaluate how representative these uncertainties are compared with real retrieval errors. Meanwhile, to assist operational data processing, we optimized the computational speed to evaluate the retrieval uncertainties based on neural networks.
Damao Zhang, Jennifer Comstock, and Victor Morris
Atmos. Meas. Tech., 15, 4735–4749, https://doi.org/10.5194/amt-15-4735-2022, https://doi.org/10.5194/amt-15-4735-2022, 2022
Short summary
Short summary
The planetary boundary layer is the lowest part of the atmosphere. Its structure and depth (PBLHT) significantly impact air quality, global climate, land–atmosphere interactions, and a wide range of atmospheric processes. To test the robustness of the ceilometer-estimated PBLHT under different atmospheric conditions, we compared ceilometer- and radiosonde-estimated PBLHTs using multiple years of U.S. DOE ARM measurements at various ARM observatories located around the world.
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.
Annakaisa von Lerber, Mario Mech, Annette Rinke, Damao Zhang, Melanie Lauer, Ana Radovan, Irina Gorodetskaya, and Susanne Crewell
Atmos. Chem. Phys., 22, 7287–7317, https://doi.org/10.5194/acp-22-7287-2022, https://doi.org/10.5194/acp-22-7287-2022, 2022
Short summary
Short summary
Snowfall is an important climate indicator. However, microphysical snowfall processes are challenging for atmospheric models. In this study, the performance of a regional climate model is evaluated in modeling the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations. Excellent agreement in averaged annual snowfall rates is found, and the shown methodology offers a promising diagnostic tool to investigate the shown differences further.
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
Short summary
Short summary
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.
Michael P. Jensen, Virendra P. Ghate, Dié Wang, Diana K. Apoznanski, Mary J. Bartholomew, Scott E. Giangrande, Karen L. Johnson, and Mandana M. Thieman
Atmos. Chem. Phys., 21, 14557–14571, https://doi.org/10.5194/acp-21-14557-2021, https://doi.org/10.5194/acp-21-14557-2021, 2021
Short summary
Short summary
This work compares the large-scale meteorology, cloud, aerosol, precipitation, and thermodynamics of closed- and open-cell cloud organizations using long-term observations from the astern North Atlantic. Open-cell cases are associated with cold-air outbreaks and occur in deeper boundary layers, with stronger winds and higher rain rates compared to closed-cell cases. These results offer important benchmarks for model representation of boundary layer clouds in this climatically important region.
Christopher R. Williams, Karen L. Johnson, Scott E. Giangrande, Joseph C. Hardin, Ruşen Öktem, and David M. Romps
Atmos. Meas. Tech., 14, 4425–4444, https://doi.org/10.5194/amt-14-4425-2021, https://doi.org/10.5194/amt-14-4425-2021, 2021
Short summary
Short summary
In addition to detecting clouds, vertically pointing cloud radars detect individual insects passing over head. If these insects are not identified and removed from raw observations, then radar-derived cloud properties will be contaminated. This work identifies clouds in radar observations due to their continuous and smooth structure in time, height, and velocity. Cloud masks are produced that identify cloud vertical structure that are free of insect contamination.
Meng Gao, Bryan A. Franz, Kirk Knobelspiesse, Peng-Wang Zhai, Vanderlei Martins, Sharon Burton, Brian Cairns, Richard Ferrare, Joel Gales, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Brent McBride, Anin Puthukkudy, P. Jeremy Werdell, and Xiaoguang Xu
Atmos. Meas. Tech., 14, 4083–4110, https://doi.org/10.5194/amt-14-4083-2021, https://doi.org/10.5194/amt-14-4083-2021, 2021
Short summary
Short summary
Multi-angle polarimetric measurements can retrieve accurate aerosol properties over complex atmosphere and ocean systems; however, most retrieval algorithms require high computational costs. We propose a deep neural network (NN) forward model to represent the radiative transfer simulation of coupled atmosphere and ocean systems and then conduct simultaneous aerosol and ocean color retrievals on AirHARP measurements. The computational acceleration is 103 times with CPU or 104 times with GPU.
Thiago S. Biscaro, Luiz A. T. Machado, Scott E. Giangrande, and Michael P. Jensen
Atmos. Chem. Phys., 21, 6735–6754, https://doi.org/10.5194/acp-21-6735-2021, https://doi.org/10.5194/acp-21-6735-2021, 2021
Short summary
Short summary
This study suggests that there are two distinct modes driving diurnal precipitating convective clouds over the central Amazon. In the wet season, local factors such as turbulence and nighttime cloud coverage are the main controls of daily precipitation, while dry-season daily precipitation is modulated primarily by the mesoscale convective pattern. The results imply that models and parameterizations must consider different formulations based on the seasonal cycle to correctly resolve convection.
Kirk Knobelspiesse, Amir Ibrahim, Bryan Franz, Sean Bailey, Robert Levy, Ziauddin Ahmad, Joel Gales, Meng Gao, Michael Garay, Samuel Anderson, and Olga Kalashnikova
Atmos. Meas. Tech., 14, 3233–3252, https://doi.org/10.5194/amt-14-3233-2021, https://doi.org/10.5194/amt-14-3233-2021, 2021
Short summary
Short summary
We assessed atmospheric aerosol and ocean surface wind speed remote sensing capability with NASA's Multi-angle Imaging SpectroRadiometer (MISR), using synthetic data and a Bayesian inference technique called generalized nonlinear retrieval analysis (GENRA). We found success using three aerosol parameters plus wind speed. This shows that MISR can perform an atmospheric correction for the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same spacecraft (Terra).
Andrew M. Dzambo, Tristan L'Ecuyer, Kenneth Sinclair, Bastiaan van Diedenhoven, Siddhant Gupta, Greg McFarquhar, Joseph R. O'Brien, Brian Cairns, Andrzej P. Wasilewski, and Mikhail Alexandrov
Atmos. Chem. Phys., 21, 5513–5532, https://doi.org/10.5194/acp-21-5513-2021, https://doi.org/10.5194/acp-21-5513-2021, 2021
Short summary
Short summary
This work highlights a new algorithm using data collected from the 2016–2018 NASA ORACLES field campaign. This algorithm synthesizes cloud and rain measurements to attain estimates of cloud and precipitation properties over the southeast Atlantic Ocean. Estimates produced by this algorithm compare well against in situ estimates. Increased rain fractions and rain rates are found in regions of atmospheric instability. This dataset can be used to explore aerosol–cloud–precipitation interactions.
Jens Redemann, Robert Wood, Paquita Zuidema, Sarah J. Doherty, Bernadette Luna, Samuel E. LeBlanc, Michael S. Diamond, Yohei Shinozuka, Ian Y. Chang, Rei Ueyama, Leonhard Pfister, Ju-Mee Ryoo, Amie N. Dobracki, Arlindo M. da Silva, Karla M. Longo, Meloë S. Kacenelenbogen, Connor J. Flynn, Kristina Pistone, Nichola M. Knox, Stuart J. Piketh, James M. Haywood, Paola Formenti, Marc Mallet, Philip Stier, Andrew S. Ackerman, Susanne E. Bauer, Ann M. Fridlind, Gregory R. Carmichael, Pablo E. Saide, Gonzalo A. Ferrada, Steven G. Howell, Steffen Freitag, Brian Cairns, Brent N. Holben, Kirk D. Knobelspiesse, Simone Tanelli, Tristan S. L'Ecuyer, Andrew M. Dzambo, Ousmane O. Sy, Greg M. McFarquhar, Michael R. Poellot, Siddhant Gupta, Joseph R. O'Brien, Athanasios Nenes, Mary Kacarab, Jenny P. S. Wong, Jennifer D. Small-Griswold, Kenneth L. Thornhill, David Noone, James R. Podolske, K. Sebastian Schmidt, Peter Pilewskie, Hong Chen, Sabrina P. Cochrane, Arthur J. Sedlacek, Timothy J. Lang, Eric Stith, Michal Segal-Rozenhaimer, Richard A. Ferrare, Sharon P. Burton, Chris A. Hostetler, David J. Diner, Felix C. Seidel, Steven E. Platnick, Jeffrey S. Myers, Kerry G. Meyer, Douglas A. Spangenberg, Hal Maring, and Lan Gao
Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, https://doi.org/10.5194/acp-21-1507-2021, 2021
Short summary
Short summary
Southern Africa produces significant biomass burning emissions whose impacts on regional and global climate are poorly understood. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) is a 5-year NASA investigation designed to study the key processes that determine these climate impacts. The main purpose of this paper is to familiarize the broader scientific community with the ORACLES project, the dataset it produced, and the most important initial findings.
Robert Jackson, Scott Collis, Valentin Louf, Alain Protat, Die Wang, Scott Giangrande, Elizabeth J. Thompson, Brenda Dolan, and Scott W. Powell
Atmos. Meas. Tech., 14, 53–69, https://doi.org/10.5194/amt-14-53-2021, https://doi.org/10.5194/amt-14-53-2021, 2021
Short summary
Short summary
About 4 years of 2D video disdrometer data in Darwin are used to develop and validate rainfall retrievals for tropical convection in C- and X-band radars in Darwin. Using blended techniques previously used for Colorado and Manus and Gan islands, with modified coefficients in each estimator, provided the most optimal results. Using multiple radar observables to develop a rainfall retrieval provided a greater advantage than using a single observable, including using specific attenuation.
Cheng Chen, Oleg Dubovik, David Fuertes, Pavel Litvinov, Tatyana Lapyonok, Anton Lopatin, Fabrice Ducos, Yevgeny Derimian, Maurice Herman, Didier Tanré, Lorraine A. Remer, Alexei Lyapustin, Andrew M. Sayer, Robert C. Levy, N. Christina Hsu, Jacques Descloitres, Lei Li, Benjamin Torres, Yana Karol, Milagros Herrera, Marcos Herreras, Michael Aspetsberger, Moritz Wanzenboeck, Lukas Bindreiter, Daniel Marth, Andreas Hangler, and Christian Federspiel
Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, https://doi.org/10.5194/essd-12-3573-2020, 2020
Short summary
Short summary
Aerosol products obtained from POLDER/PARASOL processed by the GRASP algorithm have been released. The entire archive of PARASOL/GRASP aerosol products is evaluated against AERONET and compared with MODIS (DT, DB and MAIAC), as well as PARASOL/Operational products. PARASOL/GRASP aerosol products provide spectral 443–1020 nm AOD correlating well with AERONET with a maximum bias of 0.02. Finally, GRASP shows capability to derive detailed spectral properties, including aerosol absorption.
Johannes Quaas, Antti Arola, Brian Cairns, Matthew Christensen, Hartwig Deneke, Annica M. L. Ekman, Graham Feingold, Ann Fridlind, Edward Gryspeerdt, Otto Hasekamp, Zhanqing Li, Antti Lipponen, Po-Lun Ma, Johannes Mülmenstädt, Athanasios Nenes, Joyce E. Penner, Daniel Rosenfeld, Roland Schrödner, Kenneth Sinclair, Odran Sourdeval, Philip Stier, Matthias Tesche, Bastiaan van Diedenhoven, and Manfred Wendisch
Atmos. Chem. Phys., 20, 15079–15099, https://doi.org/10.5194/acp-20-15079-2020, https://doi.org/10.5194/acp-20-15079-2020, 2020
Short summary
Short summary
Anthropogenic pollution particles – aerosols – serve as cloud condensation nuclei and thus increase cloud droplet concentration and the clouds' reflection of sunlight (a cooling effect on climate). This Twomey effect is poorly constrained by models and requires satellite data for better quantification. The review summarizes the challenges in properly doing so and outlines avenues for progress towards a better use of aerosol retrievals and better retrievals of droplet concentrations.
Cited articles
Ackerman, T. P., Cress, T. S., Ferrell, W. R., Mather, J. H., and Turner, D. D.: The programmatic maturation of the ARM program, Meteor. Monogr., 3.1–3.19, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0054.1, 2016. a
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. Meteor. Climatol., 51, 1145–1163, https://doi.org/10.1175/JAMC-D-11-0203.1, 2012. a
Baum, B. A., Yang, P., Heymsfield, A. J., Bansemer, A., Cole, B. H., Merrelli, A., Schmitt, C., and Wang, C.: Ice cloud single-scattering property models with the full phase matrix at wavelengths from 0.2 to 100 μm, Journal of Quantitative Spectroscopy and Radiative Transfer, 146, 123–139, https://doi.org/10.1016/j.jqsrt.2014.02.029, 2014. a
Chiu, J. C., Huang, C.-H., Marshak, A., Slutsker, I., Giles, D. M., Holben, B. N., Knyazikhin, Y., and Wiscombe, W. J.: Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations, J. Geophys. Res., 115, https://doi.org/10.1029/2009JD013121, 2010. a, b
Chiu, J. C., Marshak, A., Huang, C.-H., Várnai, T., Hogan, R. J., Giles, D. M., Holben, B. N., O'Connor, E. J., Knyazikhin, Y., and Wiscombe, W. J.: Cloud droplet size and liquid water path retrievals from zenith radiance measurements: examples from the Atmospheric Radiation Measurement Program and the Aerosol Robotic Network, Atmos. Chem. Phys., 12, 10313–10329, https://doi.org/10.5194/acp-12-10313-2012, 2012. a
Cho, H.-M., Zhang, Z., Meyer, K., Lebsock, M., Platnick, S., Ackerman, A. S., Di Girolamo, L., Labonnote, L. C., 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. a
Clothiaux, E. E., Miller, M. A., Perez, R. C., Turner, D. D., Moran, K. P., Martner, B. E., Ackerman, T. P., Mace, G. G., Marchand, R. T., Widener, K. B., Rodriguez, D. J., Uttal, T., Mather, J. H., Flynn, C. J., Gaustad, K. L., and Ermold, B.: The ARM millimeter wave cloud radars (MMCRs) and the active remote sensing of clouds (ARSCL) value added product (VAP), Tech. rep., dOE Tech. Memo. ARM VAP-002. 1, 56 pp., https://www.arm.gov/publications/tech_reports/arm-vap-002-1.pdf (last access: 10 November 2025), 2001. a
Coddington, O. M., Vukicevic, T., Schmidt, K. S., and Platnick, S.: Characterizing the information content of cloud thermodynamic phase retrievals from the notional PACE OCI shortwave reflectance measurements, Journal of Geophysical Research: Atmospheres, 122, 8079–8100, https://doi.org/10.1002/2017JD026493, 2017. a
Compernolle, S., Argyrouli, A., Lutz, R., Sneep, M., Lambert, J.-C., Fjæraa, A. M., Hubert, D., Keppens, A., Loyola, D., O'Connor, E., Romahn, F., Stammes, P., Verhoelst, T., and Wang, P.: Validation of the Sentinel-5 Precursor TROPOMI cloud data with Cloudnet, Aura OMI O2–O2, MODIS, and Suomi-NPP VIIRS, Atmos. Meas. Tech., 14, 2451–2476, https://doi.org/10.5194/amt-14-2451-2021, 2021. a, b
Copernicus: Cloud properties global gridded monthly and daily data from 1982 to present derived from satellite observations, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68653055, 2022. a
Donlon, C., Berruti, B., Buongiorno, A., Ferreira, M.-H., Féménias, P., Frerick, J., Goryl, P., Klein, U., Laur, H., Mavrocordatos, C., Nieke, J., Rebhan, H., Seitz, B., Stroede, J., and Sciarra, R.: The Global Monitoring for Environment and Security (GMES) Sentinel-3 mission, Remote Sensing of Environment, 120, 37–57, https://doi.org/10.1016/j.rse.2011.07.024, 2012. a, b, c
Dubin, M., Hull, A. R., and Chapman, K. S. W. c.: U.S. Standard Atmosphere, 1976, Tech. rep., nOAA S/T 76-1562, U.S. Government Printing Office, Washington, D.C., https://ntrs.nasa.gov/citations/19770009539 (last access: 10 November 2025), 1976. a
ESA/EUMETSAT: S3 Product Notice – OLCI S3.PN-OLCI-L1.11, Tech. rep., https://sentinels.copernicus.eu/documents/247904/2702575/Sentinel-3-OLCI-Product-Notice-Level-1 (last access: 10 November 2025), version 1.1, 2023. a
Fischer, J. and Preusker, R.: Study on cloud top pressure development from Sentinel-3 OLCI OCTPO2, Tech. rep., eUMETSAT Algorithm Product Validation and Evolution Report Issue 2.0, 20.06.2021 EUM/CO/19/4600002221/AIBo, https://www-cdn.eumetsat.int/files/2021-09/OCTPO2_PVR_v2-1.pdf(last access: 10 November 2025), 2021. a, b
Gelaro, R. and Coauthors: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Giangrande, S., Comstock, J., Collis, S., Shilling, J., Gaustad, K., Kehoe, K., Xie, S., and Zhang, D.: Translator Plan: A Coordinated Vision for Fiscal Years 2023–2025, Tech. rep., Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), ARM Data Center, https://doi.org/10.2172/1893730, 2022. a
Giangrande, S. E., Feng, Z., Jensen, M. P., Comstock, J. M., Johnson, K. L., Toto, T., Wang, M., Burleyson, C., Bharadwaj, N., Mei, F., Machado, L. A. T., Manzi, A. O., Xie, S., Tang, S., Silva Dias, M. A. F., de Souza, R. A. F., Schumacher, C., and Martin, S. T.: Cloud characteristics, thermodynamic controls and radiative impacts during the Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment, Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, 2017. a
Gordon, I., Rothman, L., Hargreaves, R., Hashemi, R., Karlovets, E., Skinner, F., Conway, E., Hill, C., Kochanov, R., Tan, Y., Wcisło, P., Finenko, A., Nelson, K., Bernath, P., M. Birk, V. B., Campargue, A., Chance, K., Coustenis, A., B.J. Drouin, J. F., Gamache, R., Hodges, J., Jacquemart, D., Mlawer, E., Nikitin, A., Perevalov, V., Rotger, M., Tennyson, J., Toon, G., Tran, H., Tyuterev, V., Adkins, E., Baker, A., Barbe, A., Canè, E., Császár, A., Dudaryonok, A., Egorov, O., Fleisher, A., Fleurbaey, H., Foltynowicz, A., Furtenbacher, T., Harrison, J., Hartmann, J., Horneman, V., Huang, X., Karman, T., Karns, J., Kassi, S., Kleiner, I., Kofman, V., Kwabia-Tchana, F., Lavrentieva, N., Lee, T., Long, D., Lukashevskaya, A., Lyulin, O., Makhnev, V., Matt, W., Massie, S., Melosso, M., Mikhailenko, S., Mondelain, D., Müller, H., Naumenko, O., Perrin, A., Polyansky, O., Raddaoui, E., Raston, P., Reed, Z., Rey, M., Richard, C., Tóbiás, R., Sadiek, I., Schwenke, D., Starikova, E., Sung, K., Tamassia, F., Tashkun, S., Auwera, J. V., Vasilenko, I., Vigasin, A., Villanueva, G., Vispoel, B., Wagner, G., Yachmenev, A., and Yurchenko, S.: The HITRAN2020 molecular spectroscopic database, J. Quant. Spectrosc. Radiat. Transfer, 277, https://doi.org/10.1016/j.jqsrt.2021.107949, 2022. a, b
Heidinger, A. and Stephens, G. L.: Molecular Line Absorption in a Scattering Atmosphere. Part II: Application to Remote Sensing in the O2 A band, Journal of Atmospheric Science, 57, 1615–1634, https://doi.org/10.1175/1520-0469(2000)057<1615:MLAIAS>2.0.CO;2, 2000. a
Heidinger, A. K. and Stephens, G. L.: Molecular Line Absorption in a Scattering Atmosphere. Part III: Pathlength Characteristics and Effects of Spatially Heterogeneous CLouds, Journal of Atmospheric Science, 59, 1641–1654, https://doi.org/10.1175/1520-0469(2002)059<1641:MLAIAS>2.0.CO;2, 2002. a
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., 113, https://doi.org/10.1029/2008JD009837, 2008. a
Illingworth, A. J., Hogan, R, J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoé, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Klein Baltink, H., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations, Bulletin of the American Meteorological Society, 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007. a, b
Jones, A. L., Di Girolamo, L., and G., Z.: Reducing the resolution bias in cloud fraction from satellite derived clear-conservative cloud masks, J. Geophys. Res., 117, https://doi.org/10.1029/2011JD017195, 2012. a
Kollias, P., Clothiaux, E. E., Ackerman, T. P., Albrecht, B. A., Widener, K. B., Moran, K. P., Luke, E. P., Johnson, K. L., Bharadwaj, N., Mead, J. B., Miller, M. A., Verlinde, J., Marchand, R. T., and Mace, G. G.: Development and Applications of ARM Millimeter-Wavelength Cloud Radars, Meteorological Monographs, 57, 17.1–17.19, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0037.1, 2016. a, b
Lelli, L., Kokhanovsky, A. A., Rozanov, V. V., Vountas, M., Sayer, A. M., and Burrows, J. P.: Seven years of global retrieval of cloud properties using space-borne data of GOME, Atmos. Meas. Tech., 5, 1551–1570, https://doi.org/10.5194/amt-5-1551-2012, 2012. a, b, c
Lindstrot, R., Preusker, R., and Fischer, J.: The Retrieval of Land Surface Pressure from MERIS Measurements in the Oxygen A Band, J. Atmos. Oceanic Technol., 26, 1367–1377, https://doi.org/10.1175/2009JTECHA1212.1, 2009. a
Long, C. N., Ackerman, T. P., Gaustad, K. L., and Cole, J. N. S.: Estimation of fractional sky cover from broadband shortwave radiometer measurements, J. Geophys. Res. Atmos., 11, https://doi.org/10.1029/2005JD006475, 2006. a
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, https://doi.org/10.1029/2023JD040189, 2024. a
Maddux, B. C., Ackerman, S. A., and Platnick, S.: Viewing Geometry Dependencies in MODIS Cloud Products, Bull. Amer. Meteor. Soc., 27, 1519–1528, https://doi.org/10.1175/2010JTECHA1432.1, 2010. a
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. a, b
Martins, J. V., Fernandez-Borda, R., McBride, B., Remer, L., and Barbosa, H. M. J.: The HARP hyperangular imaging polarimeter and the need for small satellite payloads with high science payoff for earth science remote sensing, in: IGARSS 2018 – 2018 IEEE International Geoscience and Remote Sensing Symposium, 6304–6307, https://doi.org/10.1109/IGARSS.2018.8518823, 2018. a, b
Mather, J. H. and Voyles, J. W.: The ARM Climate Research Facility: A Review of Structure and Capabilities, Bull. Amer. Meteor. Soc., 94, 377–392, https://doi.org/10.1175/BAMS-D-11-00218.1, 2016. a
McFarlane, S. and Shi, Y.: Changes to MFRSRCLDOD1MIN Datastream, Tech. rep., U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, report DOE/SC-ARM/TR-112, https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-112.pdf?id=45 (last access: 10 November 2025), 2012. a
McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397–3431, https://doi.org/10.5194/amt-11-3397-2018, 2018. a
Mechem, D. B. and Giangrande, S. E.: The challenge of identifying controls on cloud properties and precipitation onset for cumulus congestus sampled during MC3E, J. Geophys. Res. Atmos., 123, 3126–3144, https://doi.org/10.1002/2017JD027457, 2018. a
Mechem, D. B., Giangrande, S. E., Wittman, C. S., Borque, P., Toto, T., and Kollias, P.: Insights from modeling and observational evaluation of a precipitating continental cumulus event observed during the MC3E field campaign, J. Geophys. Res. Atmos., 120, 1980–1995, https://doi.org/10.1002/2014JD022255, 2015. a
Meister, G., Knuble, J. J., Gliese, U., Bousquet, R., Chemerys, L. H., Choi, H., Eplee, R. E., Estep, R. H., Gorman, E. T., Kitchen-McKinley, S., Kubalak, D., Lee, S., McClain, C., McIntire, J. W., Patt, F. S., Rhodes, Z., and Werdell, P. J.: The Ocean Color Instrument (OCI) on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: System Design and Prelaunch Radiometric Performance, IEEE Transactions on Geoscience and Remote Sensing, 62, 1–18, https://doi.org/10.1109/TGRS.2024.3383812, 2024. a
Mendonca, J., Strong, K., Wunch, D., Toon, G. C., Long, D. A., Hodges, J. T., Sironneau, V. T., and Franklin, J. E.: Using a speed-dependent Voigt line shape to retrieve O2 from Total Carbon Column Observing Network solar spectra to improve measurements of XCO2, Atmos. Meas. Tech., 12, 35–50, https://doi.org/10.5194/amt-12-35-2019, 2019. a
Menzel, W. P., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C., Holz, R. E., Maddux, B., Baum, B. A., Strabala, K. I., and Gumley, L. E.: MODIS Global Cloud-Top Pressure and Amount Estimation: Algorithm Description and Results, Journal of Applied Meteorology and Climatology, 47, 1175–1198, https://doi.org/10.1175/2007JAMC1705.1, 2008. a
Miller, M. A., Nitschke, K., Ackerman, T. P., Ferrell, W. R., Hickmon, N., and Ivey, M.: The ARM Mobile Facilities, Meteor. Monogr., 57, 9.1–9.15, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0051.1, 2016. a
Min, Q.-L. and Harrison, L. C.: Cloud properties derived from surface MFRSR measurements and comparison with GOES results at the ARM SGP Site, Geophys. Res. Lett., 23, 1641–1644, https://doi.org/10.1029/96GL01488, 1996. a
Min, Q.-L., Duan, M., and Marchand, R.: Validation of surface retrieved cloud optical properties with in situ measurements at the Atmospheric Radiation Measurement Program (ARM) South Great Plains site, J. Geophys. Res., 108, https://doi.org/10.1029/2003JD003385, 2003. a
Mlawer, E., Payne, V. H., Moncet, J.-L., Delamere, J. S., Alvarado, M. J., and Tobin, D.: Development and recent evaluation of the MT_CKD model of continuum absorption, Phil. Trans. Roy. Soc. A, 360, 1–37, https://doi.org/10.1098/rsta.2011.0295, 2012. a
Nagao, T. M., Suzuki, K., and Kuji, M.: Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI, Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, 2025. a, b
Nakajima, T. and King, M. D.: Determination of the Optical Thickness and Effective Particle Radius of Clouds from Reflected Solar Radiation Measurements. Part I: Theory, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:DOTOTA>2.0.CO;2, 1990. a
Pavolonis, M. J. and Heidinger, A. K.: Daytime Cloud Overlap Detection from AVHRR and VIIRS, J. Appl. Meteor., 43, 762–778, https://doi.org/10.1175/2099.1, 2004. a
Pavolonis, M. J., Heidinger, A. K., and Uttal, T.: Daytime Global Cloud Typing from AVHRR and VIIRS: Algorithm Description, Validation, and Comparisons, J. Appl. Meteor., 44, 804–826, https://doi.org/10.1175/JAM2236.1, 2005. a
Platnick, S., King, M., Ackerman, S., Menzel, W., Baum, B., Riedi, J., and Frey, R.: The MODIS cloud products: algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003. a
Platnick, S., Meyer, K. G., King, M. D., Wind, G., Amarasinghe, N., Marchant, B., Arnold, G. T., Zhang, Z., Hubanks, P. A., Holz, R. E., Yang, P., Ridgway, W. L., and Riedi, J.: The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua, IEEE Trans, Geosci. Remote Sens., 55, 502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017. a, b
Platnick, S., Meyer, K., Wind, G., Holz, R. E., Amarasinghe, N., Hubanks, P. A., Marchant, B., Dutcher, S., and Veglio, P.: The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products, Remote Sensing, 13, https://doi.org/10.3390/rs13010002, 2021. a
Poulsen, C., McGarragh, G., Thomas, G., Stengel, M., Christensen, M., Povey, A., Proud, S., Carboni, E., Hollmann, R., and Grainger, D.: Cloud properties, CCI ICDR product version 3.1. Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.68653055, 2022. a
Poulsen, C. A., Siddans, R., Thomas, G. E., Sayer, A. M., Grainger, R. G., Campmany, E., Dean, S. M., Arnold, C., and Watts, P. D.: Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR, Atmos. Meas. Tech., 5, 1889–1910, https://doi.org/10.5194/amt-5-1889-2012, 2012. a
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. a
Richardson, M., Leinonen, J., Cronk, H. Q., McDuffie, J., Lebsock, M. D., and Stephens, G. L.: Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer, Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019, 2019. a
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding Theory and Practice, World Scientific (Singapore, London, New Jersey, Hong Kong, https://doi.org/10.1142/3171, 2000. a, b, c
Sayer, A. M., Poulsen, C. A., Arnold, C., Campmany, E., Dean, S., Ewen, G. B. L., Grainger, R. G., Lawrence, B. N., Siddans, R., Thomas, G. E., and Watts, P. D.: Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment, Atmos. Chem. Phys., 11, 3913–3936, https://doi.org/10.5194/acp-11-3913-2011, 2011. a, b, c, d
Sayer, A. M., Govaerts, Y., Kolmonen, P., Lipponen, A., Luffarelli, M., Mielonen, T., Patadia, F., Popp, T., Povey, A. C., Stebel, K., and Witek, M. L.: A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing, Atmos. Meas. Tech., 13, 373–404, https://doi.org/10.5194/amt-13-373-2020, 2020. a, b
Sayer, A. M., Lelli, L., Cairns, B., van Diedenhoven, B., Ibrahim, A., Knobelspiesse, K. D., Korkin, S., and Werdell, P. J.: The CHROMA cloud-top pressure retrieval algorithm for the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission, Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, 2023. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t
Schönbrodt, F. D. and Perugini, M.: At what sample size do correlations stabilize?, Journal of Research in Personality, 47, 609–612, https://doi.org/10.1016/j.jrp.2013.05.009, 2013. a
Shupe, M. D.: A ground-based multisensor cloud phase classifier, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL031008, 2007. a
Shupe, M. D.: Clouds at Arctic Atmospheric Observatories. Part II: Thermodynamic Phase Characteristics, J. Appl. Meteorol. Clim., 650, 645–661, https://doi.org/10.1175/2010JAMC2468.1, 2011. a
Shupe, M. D., Matrosov, S. Y., and Uttal, T.: Arctic Mixed-Phase Cloud Properties Derived from Surface-Based Sensors at SHEBA, J. Atmos. Sci., 63, 697–711, https://doi.org/10.1175/JAS3659.1, 2006. a
Shupe, M. D., Comstock, J. M., Turner, D. D., and Mace, G. G.: Cloud Property Retrievals in the ARM Program, Meteor. Monogr., 57, 19.1–19.20, https://doi.org/10.1175/AMSMONOGRAPHS-D-15-0030.1, 2016. a
Smith, Jr, W. L., Minnis, P., Finney, H., Palikona, R., and Khaiyer, M. M.: An evaluation of operational GOES-derived single-layer cloud top heights with ARSCL data over the ARM Southern Great Plains Site, Geophys. Res. Lett., 34, https://doi.org/10.1029/2008GL034275, 2008. a
Sneep, M., de Haan, J. F., Stammes, P., Wang, P., Vanbauce, C., Joiner, J., Vasilkov, A. P., and Levelt, P. F.: Three-way comparison between OMI and PARASOL cloud pressure products, J. Geophys. Res., 113, https://doi.org/10.1029/2007JD008694, 2008. a, b
Sookdar, K., Giangrande, S. E., Rausch, J., Ma, L., Wang, M., Wang, D., Jensen, M. P., Hung, C.-S., and Chiu, J. C.: Marine and continental stratocumulus cloud microphysical properties obtained from routine ARM Cimel sunphotometer observations, Atmos. Meas. Tech., 18, 6271–6289, https://doi.org/10.5194/amt-18-6271-2025, 2025. a, b
Stephens, G., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C., L'Ecuyer, T., and M., L.: CloudSat and CALIPSO within the A-Train: Ten Years of Actively Observing the Earth System, Bull. Amer. Meteor. Soc., 99, 569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018. a
Sus, O., Stengel, M., Stapelberg, S., McGarragh, G., Poulsen, C., Povey, A. C., Schlundt, C., Thomas, G., Christensen, M., Proud, S., Jerg, M., Grainger, R., and Hollmann, R.: The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors, Atmos. Meas. Tech., 11, 3373–3396, https://doi.org/10.5194/amt-11-3373-2018, 2018. a
Tilstra, L. G., de Graaf, M., Trees, V. J. H., Litvinov, P., Dubovik, O., and Stammes, P.: A directional surface reflectance climatology determined from TROPOMI observations, Atmos. Meas. Tech., 17, 2235–2256, https://doi.org/10.5194/amt-17-2235-2024, 2024. a, b
Tran, H. and Hartmann, J.-M.: An improved O2 A band absorption model and its consequences for retrievals of photon paths and surface pressures, J. Geophys. Res., 113, https://doi.org/10.1029/2008JD010011, 2008. a
Turner, D. D., Lo, C., Min, Q., Zhang, D., and Gaustad, K.: Cloud Optical Properties from the Multifilter Shadowband Radiometer (MFRSRCLDOD): An ARM Value-Added Product, Tech. rep., US Deparment of Energy technical report DOE/SC-ARM-TR-047, https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-047.pdf (last access: 10 November 2025), 2021. a
van Amerongen, A., Rietjens, J., Campo, J., Dogan, E., Dingjan, J., Nalla, R., Caron, J., and Hasekamp, O.: SPEXone: a compact multi-angle polarimeter, in: Proc. SPIE 11180, International Conference on Space Optics – ICSO 2018, 111800L, https://doi.org/10.1117/12.2535940, 2019. a, b
Varble, A. C., Nesbitt, S. W., Salio, P., Hardin, J. C., Bharadwaj, N., Borque, P., DeMott, P. J., Feng, Z., Hill, T. C. J., Marquis, J. N., Matthews, A., Mei, F., Ökten, R., Castro, V., Goldberger, L., Hunziger, A., Barry, K. R., Kriedenweis, S. M., McMurdie, L. A., Pekour, M., Powers, H., Romps, D. M., Saulo, C., Schmid, B., Tomlinson, J. M., van der Heever, S. C., Zelenyuk, A., Zhang, Z., and Zipser, E. H.: Utilizing a Storm-Generating Hotspot to Study Convective Cloud Transitions: The CACTI Experiment, Bull. Amer. Meteor. Soc., 192, E1597–E1620, https://doi.org/10.1175/BAMS-D-20-0030.1, 2021. a
Vinjamuri, K. S., Vountas, M., Lelli, L., Stengel, M., Shupe, M. D., Ebell, K., and Burrows, J. P.: Validation of the Cloud_CCI (Cloud Climate Change Initiative) cloud products in the Arctic, Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, 2023. a
Wang, P. and Stammes, P.: Evaluation of SCIAMACHY Oxygen A band cloud heights using Cloudnet measurements, Atmos. Meas. Tech., 7, 1331–1350, https://doi.org/10.5194/amt-7-1331-2014, 2014. a
Werdell, P. J., Behrenfeld, M. J., Bontempi, P. S., Boss, E., Cairns, B., Davis, G. T., Franz, B. A., Gliese, U. B., Gorman, E. T., Hasekamp, O., Knobelspiesse, K. D., Mannino, A., Martins, J. V., McClain, C. R., Meister, G., and Remer, L. A.: The Plankton, Aerosol, Cloud, Ocean Ecosystem Mission: Status, Science, Advances, Bulletin of the American Meteorological Society, 100, 1775–1794, https://doi.org/10.1175/BAMS-D-18-0056.1, 2019. a, b
Wevers, J., Müller, D., Scholze, J., Kirches, G., Quast, R., and Brockman, C.: IdePix for Sentinel-3 OLCI Algorithm Theoretical Basis Document (version 1.0), Zenodo, https://doi.org/10.5281/zenodo.6517333, 2021. a
Yang, J., Li, S., Gong, W., Min, Q., Mao, F., and Pan, Z.: A fast cloud geometrical thickness retrieval algorithm for single-layer marine liquid clouds using OCO-2 oxygen A-band measurements, Remote Sens. Environ., 256, https://doi.org/10.1016/j.rse.2021.112305, 2021. a
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G., and Mishchenko, M.: Spectrally consistent scattering, absorption, and polarization properties of atmospheric ice crystals at wavelengths from 0.2 μm to 100 μm, J. Atmos. Sci., 70, 330–347, https://doi.org/10.1175/JAS-D-12-039.1, 2013. a
Zhang, Z. and Platnick, S.: An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands, J. Geophys. Res., 116, https://doi.org/10.1029/2011JD016216, 2011. a
Short summary
Satellites can estimate cloud height in several ways: two include a thermal technique (colder clouds being higher up), and another looking at colours of light that oxygen in the atmosphere absorbs (darker clouds being lower down). It can also be measured (from ground or space) by radar and lidar. We compare satellite data we developed using the oxygen method with other estimates to help us refine our technique.
Satellites can estimate cloud height in several ways: two include a thermal technique (colder...
