Articles | Volume 18, issue 8
https://doi.org/10.5194/amt-18-1859-2025
© 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-18-1859-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Apr 2025
Research article |
| 25 Apr 2025
| 25 Apr 2025
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Department of Atmospheric and Oceanic Science, University of Colorado, Boulder, Boulder, CO, USA
Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Boulder, CO, USA
K. Sebastian Schmidt
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Colorado, Boulder, Boulder, CO, USA
Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Boulder, CO, USA
Hong Chen
Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Boulder, CO, USA
Steven T. Massie
Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Boulder, CO, USA
Susan S. Kulawik
Bay Area Environmental Research Institute, Earth Science Division, NASA Ames Research Center, Moffett Field, CA, USA
Hironobu Iwabuchi
Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Sendai, Miyagi, Japan
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Vivienne H. Payne, Susan S. Kulawik, Emily V. Fischer, Jared F. Brewer, L. Gregory Huey, Kazuyuki Miyazaki, John R. Worden, Kevin W. Bowman, Eric J. Hintsa, Fred Moore, James W. Elkins, and Julieta Juncosa Calahorrano
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We compare new satellite measurements of peroxyacetyl nitrate (PAN) with reference aircraft measurements from two different instruments flown on the same platform. While there is a systematic difference between the two aircraft datasets, both show the same large-scale distribution of PAN and the discrepancy between aircraft datasets is small compared to the satellite uncertainties. The satellite measurements show skill in capturing large-scale variations in PAN.
Matthew S. Norgren, John Wood, K. Sebastian Schmidt, Bastiaan van Diedenhoven, Snorre A. Stamnes, Luke D. Ziemba, Ewan C. Crosbie, Michael A. Shook, A. Scott Kittelman, Samuel E. LeBlanc, Stephen Broccardo, Steffen Freitag, and Jeffrey S. Reid
Atmos. Meas. Tech., 15, 1373–1394, https://doi.org/10.5194/amt-15-1373-2022, https://doi.org/10.5194/amt-15-1373-2022, 2022
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Jennifer D. Hegarty, Karen E. Cady-Pereira, Vivienne H. Payne, Susan S. Kulawik, John R. Worden, Valentin Kantchev, Helen M. Worden, Kathryn McKain, Jasna V. Pittman, Róisín Commane, Bruce C. Daube Jr., and Eric A. Kort
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Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Michal Segal Rozenhaimer, Meloë Kacenelenbogen, Yohei Shinozuka, Connor Flynn, Rich Ferrare, Sharon Burton, Chris Hostetler, Marc Mallet, and Paquita Zuidema
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Yu-Wen Chen, Yi-Chun Chen, Charles C.-K. Chou, Hui-Ming Hung, Shih-Yu Chang, Lisa Eirenschmalz, Michael Lichtenstern, Helmut Ziereis, Hans Schlager, Greta Stratmann, Katharina Kaiser, Johannes Schneider, Stephan Borrmann, Florian Obersteiner, Eric Förster, Andreas Zahn, Wei-Nai Chen, Po-Hsiung Lin, Shuenn-Chin Chang, Maria Dolores Andrés Hernández, Pao-Kuan Wang, and John P. Burrows
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-788, https://doi.org/10.5194/acp-2021-788, 2021
Preprint withdrawn
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By presenting an approach using EMeRGe-Asia airborne field measurements and surface observations, this study shows that the fraction of OH reactivity due to SO2-OH reaction has a significant correlation with the sulfate concentration. Approximately 30 % of sulfate is produced by SO2-OH reaction. Our results underline the importance of SO2-OH gas-phase oxidation in sulfate formation, and demonstrate that the method can be applied to other regions and under different meteorological conditions.
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 T. Massie, Heather Cronk, Aronne Merrelli, Christopher O'Dell, K. Sebastian Schmidt, Hong Chen, and David Baker
Atmos. Meas. Tech., 14, 1475–1499, https://doi.org/10.5194/amt-14-1475-2021, https://doi.org/10.5194/amt-14-1475-2021, 2021
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The OCO-2 science team is working to retrieve CO2 measurements that can be used by the carbon cycle community to calculate regional sources and sinks of CO2. The retrieved data, however, are in need of improvements in accuracy. This paper discusses several ways in which 3D cloud metrics (such as the distance of a measurement to the nearest cloud) can be used to account for cloud effects in the OCO-2 CO2 data files.
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
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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.
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Meloë Kacenelenbogen, Michal Segal Rozenhaimer, Yohei Shinozuka, Connor Flynn, Amie Dobracki, Paquita Zuidema, Steven Howell, Steffen Freitag, and Sarah Doherty
Atmos. Meas. Tech., 14, 567–593, https://doi.org/10.5194/amt-14-567-2021, https://doi.org/10.5194/amt-14-567-2021, 2021
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Based on observations from the 2016 and 2017 field campaigns of ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS), this work establishes an observationally driven link from mid-visible aerosol optical depth (AOD) and other scene parameters to broadband shortwave irradiance (and by extension the direct aerosol radiative effect, DARE). The majority of the case-to-case DARE variability within the ORACLES dataset is attributable to the dependence on AOD and scene albedo.
Susan S. Kulawik, John R. Worden, Vivienne H. Payne, Dejian Fu, Steven C. Wofsy, Kathryn McKain, Colm Sweeney, Bruce C. Daube Jr., Alan Lipton, Igor Polonsky, Yuguang He, Karen E. Cady-Pereira, Edward J. Dlugokencky, Daniel J. Jacob, and Yi Yin
Atmos. Meas. Tech., 14, 335–354, https://doi.org/10.5194/amt-14-335-2021, https://doi.org/10.5194/amt-14-335-2021, 2021
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This paper shows comparisons of a new single-footprint methane product from the AIRS satellite to aircraft-based observations. We show that this AIRS methane product provides useful information to study seasonal and global methane trends of this important greenhouse gas.
Yohei Shinozuka, Meloë S. Kacenelenbogen, Sharon P. Burton, Steven G. Howell, Paquita Zuidema, Richard A. Ferrare, Samuel E. LeBlanc, Kristina Pistone, Stephen Broccardo, Jens Redemann, K. Sebastian Schmidt, Sabrina P. Cochrane, Marta Fenn, Steffen Freitag, Amie Dobracki, Michal Segal-Rosenheimer, and Connor J. Flynn
Atmos. Chem. Phys., 20, 11275–11285, https://doi.org/10.5194/acp-20-11275-2020, https://doi.org/10.5194/acp-20-11275-2020, 2020
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To help satellite retrieval of aerosols and studies of their radiative effects, we demonstrate that daytime aerosol optical depth over low-level clouds is similar to that in neighboring clear skies at the same heights. Based on recent airborne lidar and sun photometer observations above the southeast Atlantic, the mean AOD difference at 532 nm is between 0 and -0.01, when comparing the cloudy and clear sides of cloud edges, with each up to 20 km wide.
Robert L. Herman, John Worden, David Noone, Dean Henze, Kevin Bowman, Karen Cady-Pereira, Vivienne H. Payne, Susan S. Kulawik, and Dejian Fu
Atmos. Meas. Tech., 13, 1825–1834, https://doi.org/10.5194/amt-13-1825-2020, https://doi.org/10.5194/amt-13-1825-2020, 2020
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This study is the first assessment and validation of AIRS HDO / H2O retrieved by optimal estimation. Initial comparisons with in situ measurements from NASA ORACLES are promising: the small bias and consistent rms of AIRS suggest that AIRS has well-characterized HDO / H2O. This analysis opens the possibility of a new 17-year long-term data record of global tropospheric HDO / H2O measured from space.
Bin Yao, Chao Liu, Yan Yin, Zhiquan Liu, Chunxiang Shi, Hironobu Iwabuchi, and Fuzhong Weng
Atmos. Meas. Tech., 13, 1033–1049, https://doi.org/10.5194/amt-13-1033-2020, https://doi.org/10.5194/amt-13-1033-2020, 2020
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Due to the complex spatiotemporal and physical properties of clouds, their quantitative depictions in different atmospheric reanalysis datasets are still highly uncertain. A radiance-based evaluation approach is developed to evaluate the quality of cloud properties by directly comparing them with satellite radiance observations. ERA5 and CRA are found to have great capability in representing the cloudy atmosphere over East Asia, and MERRA-2 tends to slightly overestimate clouds over the region.
Le Kuai, Kevin W. Bowman, Kazuyuki Miyazaki, Makoto Deushi, Laura Revell, Eugene Rozanov, Fabien Paulot, Sarah Strode, Andrew Conley, Jean-François Lamarque, Patrick Jöckel, David A. Plummer, Luke D. Oman, Helen Worden, Susan Kulawik, David Paynter, Andrea Stenke, and Markus Kunze
Atmos. Chem. Phys., 20, 281–301, https://doi.org/10.5194/acp-20-281-2020, https://doi.org/10.5194/acp-20-281-2020, 2020
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The tropospheric ozone increase from pre-industrial to the present day leads to a radiative forcing. The top-of-atmosphere outgoing fluxes at the ozone band are controlled by ozone, water vapor, and temperature. We demonstrate a method to attribute the models’ flux biases to these key players using satellite-constrained instantaneous radiative kernels. The largest spread between models is found in the tropics, mainly driven by ozone and then water vapor.
Sabrina P. Cochrane, K. Sebastian Schmidt, Hong Chen, Peter Pilewskie, Scott Kittelman, Jens Redemann, Samuel LeBlanc, Kristina Pistone, Meloë Kacenelenbogen, Michal Segal Rozenhaimer, Yohei Shinozuka, Connor Flynn, Steven Platnick, Kerry Meyer, Rich Ferrare, Sharon Burton, Chris Hostetler, Steven Howell, Steffen Freitag, Amie Dobracki, and Sarah Doherty
Atmos. Meas. Tech., 12, 6505–6528, https://doi.org/10.5194/amt-12-6505-2019, https://doi.org/10.5194/amt-12-6505-2019, 2019
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For two cases from the NASA ORACLES experiments, we retrieve aerosol and cloud properties and calculate a direct aerosol radiative effect (DARE). We investigate the relationship between DARE and the cloud albedo by specifying the albedo for which DARE transitions from a cooling to warming radiative effect. Our new aerosol retrieval algorithm is successful despite complexities associated with scenes that contain aerosols above clouds and decreases the uncertainty on retrieved aerosol parameters.
Pradeep Khatri, Hironobu Iwabuchi, Tadahiro Hayasaka, Hitoshi Irie, Tamio Takamura, Akihiro Yamazaki, Alessandro Damiani, Husi Letu, and Qin Kai
Atmos. Meas. Tech., 12, 6037–6047, https://doi.org/10.5194/amt-12-6037-2019, https://doi.org/10.5194/amt-12-6037-2019, 2019
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In an attempt to make cloud retrievals from the surface more common and convenient, we developed a cloud retrieval algorithm applicable for sky radiometers. It is based on an optimum method by fitting measured transmittances with modeled values. Further, a cost-effective and easy-to-use calibration procedure is proposed and validated using data obtained from the standard method. A detailed error analysis and quality assessment are also performed.
Steffen Mauceri, Bruce Kindel, Steven Massie, and Peter Pilewskie
Atmos. Meas. Tech., 12, 6017–6036, https://doi.org/10.5194/amt-12-6017-2019, https://doi.org/10.5194/amt-12-6017-2019, 2019
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Aerosols are fine particles that are suspended in Earth’s atmosphere. A better understanding of aerosols is important to lower uncertainties in climate predictions. We propose measuring aerosols from satellites and airplanes equipped with hyperspectral cameras using an artificial neural network, a form of machine learning. We applied our neural network to hyperspectral observations from a recent airplane flight over India and find general agreement with independent aerosol measurements.
Susan S. Kulawik, Sean Crowell, David Baker, Junjie Liu, Kathryn McKain, Colm Sweeney, Sebastien C. Biraud, Steve Wofsy, Christopher W. O'Dell, Paul O. Wennberg, Debra Wunch, Coleen M. Roehl, Nicholas M. Deutscher, Matthäus Kiel, David W. T. Griffith, Voltaire A. Velazco, Justus Notholt, Thorsten Warneke, Christof Petri, Martine De Mazière, Mahesh K. Sha, Ralf Sussmann, Markus Rettinger, Dave F. Pollard, Isamu Morino, Osamu Uchino, Frank Hase, Dietrich G. Feist, Sébastien Roche, Kimberly Strong, Rigel Kivi, Laura Iraci, Kei Shiomi, Manvendra K. Dubey, Eliezer Sepulveda, Omaira Elena Garcia Rodriguez, Yao Té, Pascal Jeseck, Pauli Heikkinen, Edward J. Dlugokencky, Michael R. Gunson, Annmarie Eldering, David Crisp, Brendan Fisher, and Gregory B. Osterman
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-257, https://doi.org/10.5194/amt-2019-257, 2019
Publication in AMT not foreseen
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This paper provides a benchmark of OCO-2 v8 and ACOS-GOSAT v7.3 XCO2 and lowermost tropospheric (LMT) errors. The paper focuses on the systematic errors and subtracts out validation, co-location, and random errors, looks at the correlation scale-length (spatially and temporally) of systematic errors, finding that the scale lengths are similar to bias correction scale-lengths. The assimilates of the bias correction term is used to place an error on fluxes estimates.
Susan S. Kulawik, Chris O'Dell, Robert R. Nelson, and Thomas E. Taylor
Atmos. Meas. Tech., 12, 5317–5334, https://doi.org/10.5194/amt-12-5317-2019, https://doi.org/10.5194/amt-12-5317-2019, 2019
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This work investigates errors in CO2 near-infrared estimates retrieved from simulated radiances. We find that interferent errors are underpredicted and that nonlinearity causes significant errors.
Kristina Pistone, Jens Redemann, Sarah Doherty, Paquita Zuidema, Sharon Burton, Brian Cairns, Sabrina Cochrane, Richard Ferrare, Connor Flynn, Steffen Freitag, Steven G. Howell, Meloë Kacenelenbogen, Samuel LeBlanc, Xu Liu, K. Sebastian Schmidt, Arthur J. Sedlacek III, Michal Segal-Rozenhaimer, Yohei Shinozuka, Snorre Stamnes, Bastiaan van Diedenhoven, Gerard Van Harten, and Feng Xu
Atmos. Chem. Phys., 19, 9181–9208, https://doi.org/10.5194/acp-19-9181-2019, https://doi.org/10.5194/acp-19-9181-2019, 2019
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Understanding how smoke particles interact with sunlight is important in calculating their effects on climate, since some smoke is more scattering (cooling) and some is more absorbing (heating). Knowing this proportion is important for both satellite observations and climate models. We measured smoke properties in a recent aircraft-based field campaign off the west coast of Africa and present a comparison of these properties as measured using the six different, independent techniques available.
John R. Worden, Susan S. Kulawik, Dejian Fu, Vivienne H. Payne, Alan E. Lipton, Igor Polonsky, Yuguang He, Karen Cady-Pereira, Jean-Luc Moncet, Robert L. Herman, Fredrick W. Irion, and Kevin W. Bowman
Atmos. Meas. Tech., 12, 2331–2339, https://doi.org/10.5194/amt-12-2331-2019, https://doi.org/10.5194/amt-12-2331-2019, 2019
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In this paper we take the first steps towards generating a multi-decadal record of the deuterium content of water vapor, useful for evaluating the moisture sources and processes affecting water vapor, by estimating the deuterium content from thermal IR radiances from the AIRS instrument. We find the AIRS-based measurements are sensitive to the deuterium content of water vapor in the middle and lower troposphere with a single measurement uncertainty of ~ 3 % and an accuracy of ~ 0.7 %.
Marc Mallet, Pierre Nabat, Paquita Zuidema, Jens Redemann, Andrew Mark Sayer, Martin Stengel, Sebastian Schmidt, Sabrina Cochrane, Sharon Burton, Richard Ferrare, Kerry Meyer, Pablo Saide, Hiren Jethva, Omar Torres, Robert Wood, David Saint Martin, Romain Roehrig, Christina Hsu, and Paola Formenti
Atmos. Chem. Phys., 19, 4963–4990, https://doi.org/10.5194/acp-19-4963-2019, https://doi.org/10.5194/acp-19-4963-2019, 2019
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The model is able to represent LWP but not the LCF. AOD is consistent over the continent but also over ocean (ACAOD). Differences are observed in SSA due to the absence of internal mixing in ALADIN-Climate. A significant regional gradient of the forcing at TOA is observed. An intense positive forcing is simulated over Gabon. Results highlight the significant effect of enhanced moisture on BBA extinction. The surface dimming modifies the energy budget.
Dejian Fu, Susan S. Kulawik, Kazuyuki Miyazaki, Kevin W. Bowman, John R. Worden, Annmarie Eldering, Nathaniel J. Livesey, Joao Teixeira, Fredrick W. Irion, Robert L. Herman, Gregory B. Osterman, Xiong Liu, Pieternel F. Levelt, Anne M. Thompson, and Ming Luo
Atmos. Meas. Tech., 11, 5587–5605, https://doi.org/10.5194/amt-11-5587-2018, https://doi.org/10.5194/amt-11-5587-2018, 2018
Emily V. Fischer, Liye Zhu, Vivienne H. Payne, John R. Worden, Zhe Jiang, Susan S. Kulawik, Steven Brey, Arsineh Hecobian, Daniel Gombos, Karen Cady-Pereira, and Frank Flocke
Atmos. Chem. Phys., 18, 5639–5653, https://doi.org/10.5194/acp-18-5639-2018, https://doi.org/10.5194/acp-18-5639-2018, 2018
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PAN is an atmospheric reservoir for nitrogen oxide radicals, and it plays a lead role in their redistribution in the troposphere. We analyze new Tropospheric Emission Spectrometer (TES) PAN observations over North America during July 2006 to 2009. We identify smoke-impacted TES PAN retrievals by co-location with NOAA Hazard Mapping System (HMS) smoke plumes. Depending on the year, 15–32 % of cases where elevated PAN is identified in TES observations overlap with smoke plumes.
Rintaro Okamura, Hironobu Iwabuchi, and K. Sebastian Schmidt
Atmos. Meas. Tech., 10, 4747–4759, https://doi.org/10.5194/amt-10-4747-2017, https://doi.org/10.5194/amt-10-4747-2017, 2017
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Three-dimensional (3-D) radiative transfer effects are a major source of retrieval errors in satellite-based optical remote sensing of clouds. Multi-pixel, multispectral approaches based on deep learning are proposed for retrieval of cloud optical thickness and droplet effective radius. A feasibility test shows that proposed retrieval methods are effective to obtain accurate cloud properties. Use of the convolutional neural network is effective to reduce 3-D radiative transfer effects.
Karen E. Cady-Pereira, Vivienne H. Payne, Jessica L. Neu, Kevin W. Bowman, Kazuyuki Miyazaki, Eloise A. Marais, Susan Kulawik, Zitely A. Tzompa-Sosa, and Jennifer D. Hegarty
Atmos. Chem. Phys., 17, 9379–9398, https://doi.org/10.5194/acp-17-9379-2017, https://doi.org/10.5194/acp-17-9379-2017, 2017
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Air quality is a major issue for megacities. Our paper looks at satellite measurements over Mexico City and Lagos of several trace gases gases related to air quality to determine the temporal and spatial variability of these gases, and it relates this variability to local conditions, such as topography, winds and biomass burning events. We find that, while Mexico City is known for severe pollution events, the levels of of pollution in Lagos are much higher and more persistent.
John R. Worden, Gary Doran, Susan Kulawik, Annmarie Eldering, David Crisp, Christian Frankenberg, Chris O'Dell, and Kevin Bowman
Atmos. Meas. Tech., 10, 2759–2771, https://doi.org/10.5194/amt-10-2759-2017, https://doi.org/10.5194/amt-10-2759-2017, 2017
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This paper evaluates the uncertainties of the total column carbon dioxide (XCO2) measurements from the NASA OCO-2 instrument by comparing observed variations in small geographical regions to the calculated uncertainties of the data within this region. In general we find that the reported XCO2 precision is related to that expected from the XCO2 radiance noise. However, the reported accuracy is at least smaller than the actual accuracy by a factor of 2–4.
Vivienne H. Payne, Emily V. Fischer, John R. Worden, Zhe Jiang, Liye Zhu, Thomas P. Kurosu, and Susan S. Kulawik
Atmos. Chem. Phys., 17, 6341–6351, https://doi.org/10.5194/acp-17-6341-2017, https://doi.org/10.5194/acp-17-6341-2017, 2017
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Peroxyacetyl nitrate (PAN) plays a key role in atmospheric chemistry and long-range transport of pollution. In this paper, we present measurements of PAN from the Tropospheric Emission Spectrometer, an instrument on board the NASA Aura satellite since 2004. We focus on measurements of PAN in tropical regions, where data from ground-based and aircraft campaigns are particularly sparse. We observe temporal changes in PAN associated with changes in fires, convection and emissions.
Susan S. Kulawik, Chris O'Dell, Vivienne H. Payne, Le Kuai, Helen M. Worden, Sebastien C. Biraud, Colm Sweeney, Britton Stephens, Laura T. Iraci, Emma L. Yates, and Tomoaki Tanaka
Atmos. Chem. Phys., 17, 5407–5438, https://doi.org/10.5194/acp-17-5407-2017, https://doi.org/10.5194/acp-17-5407-2017, 2017
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We introduce new vertically resolved GOSAT products that better separate locally and remotely influenced CO2. Current GOSAT column results for CO2 (XCO2) are sensitive to fluxes on continental scales, whereas flux estimates from surface and tower measurements are affected by sampling bias and model transport uncertainty. These new GOSAT measurements of boundary layer CO2 are validated against aircraft and surface observations of CO2 and are compared to vertically resolved MOPITT CO.
Shi Song, K. Sebastian Schmidt, Peter Pilewskie, Michael D. King, Andrew K. Heidinger, Andi Walther, Hironobu Iwabuchi, Gala Wind, and Odele M. Coddington
Atmos. Chem. Phys., 16, 13791–13806, https://doi.org/10.5194/acp-16-13791-2016, https://doi.org/10.5194/acp-16-13791-2016, 2016
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The radiative effects of spatially complex cloud fields are notoriously difficult to estimate and are afflicted with errors up to ±50 % of the incident solar radiation. We find that horizontal photon transport, the leading cause for these three-dimensional effects, manifests itself through a spectral fingerprint – a new observable that holds promise for reducing the errors associated with spatial complexity by moving the problem to the spectral dimension.
Hilke Oetjen, Vivienne H. Payne, Jessica L. Neu, Susan S. Kulawik, David P. Edwards, Annmarie Eldering, Helen M. Worden, and John R. Worden
Atmos. Chem. Phys., 16, 10229–10239, https://doi.org/10.5194/acp-16-10229-2016, https://doi.org/10.5194/acp-16-10229-2016, 2016
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We developed and tested a strategy for combining TES and IASI free-tropospheric ozone data. A time series of the merged ozone data is presented for regional monthly means over the western US, Europe, and eastern Asia. We show that free-tropospheric ozone over Europe and the western US has remained relatively constant over the past decade but that, contrary to expectations, ozone over Asia in recent years does not continue the rapid rate of increase observed from 2004–2010.
Christian Frankenberg, Susan S. Kulawik, Steven C. Wofsy, Frédéric Chevallier, Bruce Daube, Eric A. Kort, Christopher O'Dell, Edward T. Olsen, and Gregory Osterman
Atmos. Chem. Phys., 16, 7867–7878, https://doi.org/10.5194/acp-16-7867-2016, https://doi.org/10.5194/acp-16-7867-2016, 2016
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We use observations from the HIAPER Pole-to-Pole Observations (HIPPO) flights from January 2009 through September 2011 to validate CO2 measurements from satellites (GOSAT, TES, AIRS) and atmospheric inversion models (CarbonTracker CT2013B, MACC v13r1).
Steven T. Massie, Julien Delanoë, Charles G. Bardeen, Jonathan H. Jiang, and Lei Huang
Atmos. Chem. Phys., 16, 6091–6105, https://doi.org/10.5194/acp-16-6091-2016, https://doi.org/10.5194/acp-16-6091-2016, 2016
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Changes in cloud vertical structure (i.e. the shape of cloud ice water content (IWC) vertical structure) due to variations in aerosol, observed by three different satellite experiments (MODIS, OMI, and MLS) are calculated in the Tropics during 2007–2010. This topic is of interest because aerosol-cloud interactions are the largest source of uncertainty in climate models. Analysis of the effects of MODIS aerosol, OMI absorptive aerosol, and MLS CO (an absorptive aerosol proxy) upon deep convective
Susan Kulawik, Debra Wunch, Christopher O'Dell, Christian Frankenberg, Maximilian Reuter, Tomohiro Oda, Frederic Chevallier, Vanessa Sherlock, Michael Buchwitz, Greg Osterman, Charles E. Miller, Paul O. Wennberg, David Griffith, Isamu Morino, Manvendra K. Dubey, Nicholas M. Deutscher, Justus Notholt, Frank Hase, Thorsten Warneke, Ralf Sussmann, John Robinson, Kimberly Strong, Matthias Schneider, Martine De Mazière, Kei Shiomi, Dietrich G. Feist, Laura T. Iraci, and Joyce Wolf
Atmos. Meas. Tech., 9, 683–709, https://doi.org/10.5194/amt-9-683-2016, https://doi.org/10.5194/amt-9-683-2016, 2016
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To accurately estimate source and sink locations of carbon dioxide, systematic errors in satellite measurements and models must be characterized. This paper examines two satellite data sets (GOSAT, launched 2009, and SCIAMACHY, launched 2002), and two models (CarbonTracker and MACC) vs. the TCCON CO2 validation data set. We assess biases and errors by season and latitude, satellite performance under averaging, and diurnal variability. Our findings are useful for assimilation of satellite data.
M. Saito and H. Iwabuchi
Atmos. Meas. Tech., 8, 4295–4311, https://doi.org/10.5194/amt-8-4295-2015, https://doi.org/10.5194/amt-8-4295-2015, 2015
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A new algorithm for aerosol retrievals from twilight photographs taken by a digital single reflex-lens camera is developed. A radiative transfer model taking spherical-shell atmosphere, multiple scattering and refraction into account is used as a forward model, and the optimal estimation is used as an inversion calculation to infer the aerosol optical and radiative properties. The AOTs are inferred with small uncertainties and agree very well with that from the skyradiometer.
J. R. Worden, A. J. Turner, A. Bloom, S. S. Kulawik, J. Liu, M. Lee, R. Weidner, K. Bowman, C. Frankenberg, R. Parker, and V. H. Payne
Atmos. Meas. Tech., 8, 3433–3445, https://doi.org/10.5194/amt-8-3433-2015, https://doi.org/10.5194/amt-8-3433-2015, 2015
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Here we demonstrate the potential for estimating lower tropospheric CH4 concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH4 (dry-mole air fraction of methane) from the Greenhouse Gases Observing Satellite - Thermal And Near-infrared for carbon Observation (GOSAT TANSO).
B. C. Kindel, P. Pilewskie, K. S. Schmidt, T. Thornberry, A. Rollins, and T. Bui
Atmos. Meas. Tech., 8, 1147–1156, https://doi.org/10.5194/amt-8-1147-2015, https://doi.org/10.5194/amt-8-1147-2015, 2015
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Measurements of upper tropospheric-lower stratospheric water vapor amounts in the tropics were made using the 1400 and 1900nm water vapor bands present in airborne solar spectral irradiance data. These were validated with radiative transfer modeling using in situ profiles of water vapor, temperature, and pressure. An approach to extending these types of measurements from aircraft altitudes to the top of the atmosphere to infer stratospheric water vapor amount is outlined.
M. J. Alvarado, V. H. Payne, K. E. Cady-Pereira, J. D. Hegarty, S. S. Kulawik, K. J. Wecht, J. R. Worden, J. V. Pittman, and S. C. Wofsy
Atmos. Meas. Tech., 8, 965–985, https://doi.org/10.5194/amt-8-965-2015, https://doi.org/10.5194/amt-8-965-2015, 2015
H. Oetjen, V. H. Payne, S. S. Kulawik, A. Eldering, J. Worden, D. P. Edwards, G. L. Francis, H. M. Worden, C. Clerbaux, J. Hadji-Lazaro, and D. Hurtmans
Atmos. Meas. Tech., 7, 4223–4236, https://doi.org/10.5194/amt-7-4223-2014, https://doi.org/10.5194/amt-7-4223-2014, 2014
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We apply the TES ozone retrieval algorithm to IASI radiances and characterise the uncertainties and information content of the retrieved ozone profiles. We find that our biases with respect to sondes and our degrees of freedom for signal for ozone are comparable to previously published results from other IASI ozone algorithms. We find that predicted and empirical errors are consistent. In general, the precision of the IASI ozone profiles is better than 20%.
V. H. Payne, M. J. Alvarado, K. E. Cady-Pereira, J. R. Worden, S. S. Kulawik, and E. V. Fischer
Atmos. Meas. Tech., 7, 3737–3749, https://doi.org/10.5194/amt-7-3737-2014, https://doi.org/10.5194/amt-7-3737-2014, 2014
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Peroxyacetyl nitrate (PAN) plays an important role in the distribution of lower-atmospheric ozone. PAN can be transported far from the original pollution source, leading to ozone formation and degraded air quality in remote areas. Satellite observations from the Tropospheric Emission Spectrometer (TES) are sensitive to PAN at lower altitude than previous global data sets. We describe characteristics of the data and show elevated PAN associated with boreal fires and outflow of Asian pollution.
R. L. Herman, J. E. Cherry, J. Young, J. M. Welker, D. Noone, S. S. Kulawik, and J. Worden
Atmos. Meas. Tech., 7, 3127–3138, https://doi.org/10.5194/amt-7-3127-2014, https://doi.org/10.5194/amt-7-3127-2014, 2014
Y. Kanaya, H. Irie, H. Takashima, H. Iwabuchi, H. Akimoto, K. Sudo, M. Gu, J. Chong, Y. J. Kim, H. Lee, A. Li, F. Si, J. Xu, P.-H. Xie, W.-Q. Liu, A. Dzhola, O. Postylyakov, V. Ivanov, E. Grechko, S. Terpugova, and M. Panchenko
Atmos. Chem. Phys., 14, 7909–7927, https://doi.org/10.5194/acp-14-7909-2014, https://doi.org/10.5194/acp-14-7909-2014, 2014
E. Hache, J.-L. Attié, C. Tourneur, P. Ricaud, L. Coret, W. A. Lahoz, L. El Amraoui, B. Josse, P. Hamer, J. Warner, X. Liu, K. Chance, M. Höpfner, R. Spurr, V. Natraj, S. Kulawik, A. Eldering, and J. Orphal
Atmos. Meas. Tech., 7, 2185–2201, https://doi.org/10.5194/amt-7-2185-2014, https://doi.org/10.5194/amt-7-2185-2014, 2014
L. Kuai, J. Worden, S. S. Kulawik, S. A. Montzka, and J. Liu
Atmos. Meas. Tech., 7, 163–172, https://doi.org/10.5194/amt-7-163-2014, https://doi.org/10.5194/amt-7-163-2014, 2014
X. Jiang, M. C. Barth, C. Wiedinmyer, and S. T. Massie
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-21383-2013, https://doi.org/10.5194/acpd-13-21383-2013, 2013
Revised manuscript not accepted
H. M. Worden, D. P. Edwards, M. N. Deeter, D. Fu, S. S. Kulawik, J. R. Worden, and A. Arellano
Atmos. Meas. Tech., 6, 1633–1646, https://doi.org/10.5194/amt-6-1633-2013, https://doi.org/10.5194/amt-6-1633-2013, 2013
K. W. Bowman, D. T. Shindell, H. M. Worden, J.F. Lamarque, P. J. Young, D. S. Stevenson, Z. Qu, M. de la Torre, D. Bergmann, P. J. Cameron-Smith, W. J. Collins, R. Doherty, S. B. Dalsøren, G. Faluvegi, G. Folberth, L. W. Horowitz, B. M. Josse, Y. H. Lee, I. A. MacKenzie, G. Myhre, T. Nagashima, V. Naik, D. A. Plummer, S. T. Rumbold, R. B. Skeie, S. A. Strode, K. Sudo, S. Szopa, A. Voulgarakis, G. Zeng, S. S. Kulawik, A. M. Aghedo, and J. R. Worden
Atmos. Chem. Phys., 13, 4057–4072, https://doi.org/10.5194/acp-13-4057-2013, https://doi.org/10.5194/acp-13-4057-2013, 2013
J. Worden, K. Wecht, C. Frankenberg, M. Alvarado, K. Bowman, E. Kort, S. Kulawik, M. Lee, V. Payne, and H. Worden
Atmos. Chem. Phys., 13, 3679–3692, https://doi.org/10.5194/acp-13-3679-2013, https://doi.org/10.5194/acp-13-3679-2013, 2013
D. Fu, J. R. Worden, X. Liu, S. S. Kulawik, K. W. Bowman, and V. Natraj
Atmos. Chem. Phys., 13, 3445–3462, https://doi.org/10.5194/acp-13-3445-2013, https://doi.org/10.5194/acp-13-3445-2013, 2013
H. M. Worden, M. N. Deeter, C. Frankenberg, M. George, F. Nichitiu, J. Worden, I. Aben, K. W. Bowman, C. Clerbaux, P. F. Coheur, A. T. J. de Laat, R. Detweiler, J. R. Drummond, D. P. Edwards, J. C. Gille, D. Hurtmans, M. Luo, S. Martínez-Alonso, S. Massie, G. Pfister, and J. X. Warner
Atmos. Chem. Phys., 13, 837–850, https://doi.org/10.5194/acp-13-837-2013, https://doi.org/10.5194/acp-13-837-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Vertical Wind and Drop Size Distribution Retrieval with the CloudCube G-band Doppler Radar
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Harmonized Cloud Datasets for OMI and TROPOMI Using the O2‐O2 477 nm Absorption Band
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Assessment of horizontally-oriented ice crystals with a combination of multiangle polarization lidar and cloud Doppler radar
Benchmarking and improving algorithms for attributing satellite-observed contrails to flights
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Simulations of Spectral Polarimetric Variables measured in rain at W-band
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025, https://doi.org/10.5194/amt-18-3287-2025, 2025
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Accurate measurements of ice water content (IWC) and snowfall rate (SR) are challenging due to high spatial variability and limitations of our measurement techniques. Here, we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path (LWP) as a proxy for the occurrence of riming. LWP is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025, https://doi.org/10.5194/amt-18-3095-2025, 2025
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Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared, and the likelihood of them differing by 10 W m2 defines the closure.
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
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The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025, https://doi.org/10.5194/amt-18-2083-2025, 2025
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests based on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observation experiments.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
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Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
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
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
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This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
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The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Keiichi Ohara and Hirohiko Masunaga
EGUsphere, https://doi.org/10.5194/egusphere-2025-173, https://doi.org/10.5194/egusphere-2025-173, 2025
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Ice particles (e.g., cloud ice, snow and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
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This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Christian Heske, Florian Ewald, and Silke Groß
EGUsphere, https://doi.org/10.5194/egusphere-2025-691, https://doi.org/10.5194/egusphere-2025-691, 2025
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This study proposes a new technique to augment polarimetric radar measurements of the national German operational radar network with vertically looking cloud radars. The method is tested in two different case studies by comparison to dedicated scanning radars revealing promising results. Future usage of the method is motivated by analyzing the coverage of the operational radar network finding advantageous locations with good radar coverage.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
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The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
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Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
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Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
EGUsphere, https://doi.org/10.5194/egusphere-2025-478, https://doi.org/10.5194/egusphere-2025-478, 2025
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We introduce a new cloud retrieval algorithm using the O2-O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2-O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
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In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
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The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3841, https://doi.org/10.5194/egusphere-2024-3841, 2025
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This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a year-long dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve the atmospheric model.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
EGUsphere, https://doi.org/10.5194/egusphere-2024-3664, https://doi.org/10.5194/egusphere-2024-3664, 2025
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Contrails, the linear clouds formed by aircraft, are have a substantial climate impact. Flight deviations to avoid forming contrails should decrease this impact. We introduce a method for matching contrails seen by satellites to the flights that made them. This can determine if avoidance was successful and improve contrail forecasts. We also introduce a synthetic contrail dataset to evaluate the accuracy of the matches. We show that our attributions are much more accurate than previous methods.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Ioanna Tsikoudi, Alessandro Battaglia, Christine Unal, and Eleni Marinou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3164, https://doi.org/10.5194/egusphere-2024-3164, 2025
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The study simulates spectral polarimetric variables for raindrops as observed by a cloud radar. Raindrops are modelled as oblate spheroids and backscattering properties are computed via the T-matrix method including noise, turbulence and spectral averaging effects. When comparing simulations to measurements, differences on the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to mm wavelengths.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
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We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
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Short summary
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like...
