Articles | Volume 13, issue 5
https://doi.org/10.5194/amt-13-2257-2020
© Author(s) 2020. 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-13-2257-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
11 May 2020
Research article | Highlight paper |
| 11 May 2020
| 11 May 2020
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, Baltimore, MD, USA
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Steven Platnick
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Kerry Meyer
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Zhibo Zhang
Department of Physics, University of Maryland, Baltimore County,
Baltimore, MD, USA
Yaping Zhou
Joint Center for Earth Systems Technology, University of Maryland
Baltimore County, Baltimore, MD, USA
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt,
MD, USA
Related authors
Adeleke S. Ademakinwa, Zahid H. Tushar, Jianyu Zheng, Chenxi Wang, Sanjay Purushotham, Jianwu Wang, Kerry G. Meyer, Tamas Várnai, and Zhibo Zhang
Atmos. Chem. Phys., 24, 3093–3114, https://doi.org/10.5194/acp-24-3093-2024, https://doi.org/10.5194/acp-24-3093-2024, 2024
Short summary
Short summary
Clouds play a critical role in our climate system. At present and in the near future, satellite-based remote sensing is the only means to obtain regional and global observations of cloud properties. The current satellite remote sensing algorithms are mostly based on the so-called 1D radiative transfer. This deviation from the 3D world reality can lead to large errors. In this study we investigate how this error affects our estimation of cloud radiative effects.
Jianyu Zheng, Zhibo Zhang, Hongbin Yu, Anne Garnier, Qianqian Song, Chenxi Wang, Claudia Di Biagio, Jasper F. Kok, Yevgeny Derimian, and Claire Ryder
Atmos. Chem. Phys., 23, 8271–8304, https://doi.org/10.5194/acp-23-8271-2023, https://doi.org/10.5194/acp-23-8271-2023, 2023
Short summary
Short summary
We developed a multi-year satellite-based retrieval of dust optical depth at 10 µm and the coarse-mode dust effective diameter over global oceans. It reveals climatological coarse-mode dust transport patterns and regional differences over the North Atlantic, the Indian Ocean and the North Pacific.
Meloë S. F. Kacenelenbogen, Ralph Kuehn, Nandana Amarasinghe, Kerry Meyer, Edward Nowottnick, Mark Vaughan, Hong Chen, Sebastian Schmidt, Richard Ferrare, John Hair, Robert Levy, Hongbin Yu, Paquita Zuidema, Robert Holz, and Willem Marais
EGUsphere, https://doi.org/10.5194/egusphere-2025-1403, https://doi.org/10.5194/egusphere-2025-1403, 2025
Short summary
Short summary
Aerosols perturb the radiation balance of the Earth-atmosphere system. To reduce the uncertainty in quantifying present-day climate change, we combine two satellite sensors and a model to assess the aerosol effects on radiation in all-sky conditions. Satellite-based and coincident aircraft measurements of aerosol radiative effects agree well over the Southeast Atlantic. This constitutes a crucial first evaluation before we apply our method to more years and regions of the world.
Anthony La Luna, Zhibo Zhang, Jianyu Zheng, Qianqian Song, Hongbin Yu, Jiachen Ding, Ping Yang, and Masanori Saito
EGUsphere, https://doi.org/10.5194/egusphere-2025-1117, https://doi.org/10.5194/egusphere-2025-1117, 2025
Short summary
Short summary
The lidar backscattering properties of Asian dust particles were studied using a discrete dipole approximation (DDA) model. Both the lidar ratio (LR) and depolarization ratio (DPR) exhibit an asymptotic trend with dust particle size. Two parameterization schemes were developed: one to estimate the DPR of a single dust particle given its size, and the other to estimate the DPR of dust particles with a lognormal particle size distribution given the effective radius.
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.
Adeleke S. Ademakinwa, Zahid H. Tushar, Jianyu Zheng, Chenxi Wang, Sanjay Purushotham, Jianwu Wang, Kerry G. Meyer, Tamas Várnai, and Zhibo Zhang
Atmos. Chem. Phys., 24, 3093–3114, https://doi.org/10.5194/acp-24-3093-2024, https://doi.org/10.5194/acp-24-3093-2024, 2024
Short summary
Short summary
Clouds play a critical role in our climate system. At present and in the near future, satellite-based remote sensing is the only means to obtain regional and global observations of cloud properties. The current satellite remote sensing algorithms are mostly based on the so-called 1D radiative transfer. This deviation from the 3D world reality can lead to large errors. In this study we investigate how this error affects our estimation of cloud radiative effects.
Ruth A. R. Digby, Nathan P. Gillett, Adam H. Monahan, Knut von Salzen, Antonis Gkikas, Qianqian Song, and Zhibo Zhang
Atmos. Chem. Phys., 24, 2077–2097, https://doi.org/10.5194/acp-24-2077-2024, https://doi.org/10.5194/acp-24-2077-2024, 2024
Short summary
Short summary
The COVID-19 lockdowns reduced aerosol emissions. We ask whether these reductions affected regional aerosol optical depth (AOD) and compare the observed changes to predictions from Earth system models. Only India has an observed AOD reduction outside of typical variability. Models overestimate the response in some regions, but when key biases have been addressed, the agreement is improved. Our results suggest that current models can realistically predict the effects of future emission changes.
Jianyu Zheng, Zhibo Zhang, Hongbin Yu, Anne Garnier, Qianqian Song, Chenxi Wang, Claudia Di Biagio, Jasper F. Kok, Yevgeny Derimian, and Claire Ryder
Atmos. Chem. Phys., 23, 8271–8304, https://doi.org/10.5194/acp-23-8271-2023, https://doi.org/10.5194/acp-23-8271-2023, 2023
Short summary
Short summary
We developed a multi-year satellite-based retrieval of dust optical depth at 10 µm and the coarse-mode dust effective diameter over global oceans. It reveals climatological coarse-mode dust transport patterns and regional differences over the North Atlantic, the Indian Ocean and the North Pacific.
Nikolas Ovaskainen, Pietari Skyttä, Nicklas Nordbäck, and Jon Engström
Solid Earth, 14, 603–624, https://doi.org/10.5194/se-14-603-2023, https://doi.org/10.5194/se-14-603-2023, 2023
Short summary
Short summary
We studied bedrock fracturing at Åland Islands from bedrock outcrops,
digital elevation models and geophysics using multiple scales of observation.
Using the results we can compare properties of the fractures of different sizes
to find similarities and differences; e.g. we found that glacial erosion has a
probable effect on the study of larger bedrock structures. Furthermore, we
collected data from 100 to 500 m long fractures, which have previously
proved to be difficult to sample.
Robert Pincus, Paul A. Hubanks, Steven Platnick, Kerry Meyer, Robert E. Holz, Denis Botambekov, and Casey J. Wall
Earth Syst. Sci. Data, 15, 2483–2497, https://doi.org/10.5194/essd-15-2483-2023, https://doi.org/10.5194/essd-15-2483-2023, 2023
Short summary
Short summary
This paper describes a new global dataset of cloud properties observed by a specific satellite program created to facilitate comparison with a matching observational proxy used in climate models. Statistics are accumulated over daily and monthly timescales on an equal-angle grid. Statistics include cloud detection, cloud-top pressure, and cloud optical properties. Joint histograms of several variable pairs are also available.
Ian Chang, Lan Gao, Connor J. Flynn, Yohei Shinozuka, Sarah J. Doherty, Michael S. Diamond, Karla M. Longo, Gonzalo A. Ferrada, Gregory R. Carmichael, Patricia Castellanos, Arlindo M. da Silva, Pablo E. Saide, Calvin Howes, Zhixin Xue, Marc Mallet, Ravi Govindaraju, Qiaoqiao Wang, Yafang Cheng, Yan Feng, Sharon P. Burton, Richard A. Ferrare, Samuel E. LeBlanc, Meloë S. Kacenelenbogen, Kristina Pistone, Michal Segal-Rozenhaimer, Kerry G. Meyer, Ju-Mee Ryoo, Leonhard Pfister, Adeyemi A. Adebiyi, Robert Wood, Paquita Zuidema, Sundar A. Christopher, and Jens Redemann
Atmos. Chem. Phys., 23, 4283–4309, https://doi.org/10.5194/acp-23-4283-2023, https://doi.org/10.5194/acp-23-4283-2023, 2023
Short summary
Short summary
Abundant aerosols are present above low-level liquid clouds over the southeastern Atlantic during late austral spring. The model simulation differences in the proportion of aerosol residing in the planetary boundary layer and in the free troposphere can greatly affect the regional aerosol radiative effects. This study examines the aerosol loading and fractional aerosol loading in the free troposphere among various models and evaluates them against measurements from the NASA ORACLES campaign.
Huilin Huang, Yun Qian, Ye Liu, Cenlin He, Jianyu Zheng, Zhibo Zhang, and Antonis Gkikas
Atmos. Chem. Phys., 22, 15469–15488, https://doi.org/10.5194/acp-22-15469-2022, https://doi.org/10.5194/acp-22-15469-2022, 2022
Short summary
Short summary
Using a clustering method developed in the field of artificial neural networks, we identify four typical dust transport patterns across the Sierra Nevada, associated with the mesoscale and regional-scale wind circulations. Our results highlight the connection between dust transport and dominant weather patterns, which can be used to understand dust transport in a changing climate.
Qianqian Song, Zhibo Zhang, Hongbin Yu, Jasper F. Kok, Claudia Di Biagio, Samuel Albani, Jianyu Zheng, and Jiachen Ding
Atmos. Chem. Phys., 22, 13115–13135, https://doi.org/10.5194/acp-22-13115-2022, https://doi.org/10.5194/acp-22-13115-2022, 2022
Short summary
Short summary
This study developed a dataset that enables us to efficiently calculate dust direct radiative effect (DRE, i.e., cooling or warming our planet) for any given dust size distribution in addition to three sets of dust mineral components and two dust shapes. We demonstrate and validate the method of using this dataset to calculate dust DRE. Moreover, using this dataset we found that dust mineral composition is a more important factor in determining dust DRE than dust size and shape.
Po-Lun Ma, Bryce E. Harrop, Vincent E. Larson, Richard B. Neale, Andrew Gettelman, Hugh Morrison, Hailong Wang, Kai Zhang, Stephen A. Klein, Mark D. Zelinka, Yuying Zhang, Yun Qian, Jin-Ho Yoon, Christopher R. Jones, Meng Huang, Sheng-Lun Tai, Balwinder Singh, Peter A. Bogenschutz, Xue Zheng, Wuyin Lin, Johannes Quaas, Hélène Chepfer, Michael A. Brunke, Xubin Zeng, Johannes Mülmenstädt, Samson Hagos, Zhibo Zhang, Hua Song, Xiaohong Liu, Michael S. Pritchard, Hui Wan, Jingyu Wang, Qi Tang, Peter M. Caldwell, Jiwen Fan, Larry K. Berg, Jerome D. Fast, Mark A. Taylor, Jean-Christophe Golaz, Shaocheng Xie, Philip J. Rasch, and L. Ruby Leung
Geosci. Model Dev., 15, 2881–2916, https://doi.org/10.5194/gmd-15-2881-2022, https://doi.org/10.5194/gmd-15-2881-2022, 2022
Short summary
Short summary
An alternative set of parameters for E3SM Atmospheric Model version 1 has been developed based on a tuning strategy that focuses on clouds. When clouds in every regime are improved, other aspects of the model are also improved, even though they are not the direct targets for calibration. The recalibrated model shows a lower sensitivity to anthropogenic aerosols and surface warming, suggesting potential improvements to the simulated climate in the past and future.
Qing Yue, Eric J. Fetzer, Likun Wang, Brian H. Kahn, Nadia Smith, John M. Blaisdell, Kerry G. Meyer, Mathias Schreier, Bjorn Lambrigtsen, and Irina Tkatcheva
Atmos. Meas. Tech., 15, 2099–2123, https://doi.org/10.5194/amt-15-2099-2022, https://doi.org/10.5194/amt-15-2099-2022, 2022
Short summary
Short summary
The self-consistency and continuity of cloud retrievals from infrared sounders and imagers aboard Aqua and SNPP (Suomi National Polar-orbiting Partnership) are examined at the pixel scale. Cloud products are found to be consistent with each other. Differences between sounder products are mainly due to cloud clearing and the treatment of clouds in scenes with unsuccessful atmospheric retrievals. The impact of algorithm and instrument differences is clearly seen in the imager cloud retrievals.
Justin A. Covert, David B. Mechem, and Zhibo Zhang
Atmos. Chem. Phys., 22, 1159–1174, https://doi.org/10.5194/acp-22-1159-2022, https://doi.org/10.5194/acp-22-1159-2022, 2022
Short summary
Short summary
Stratocumulus play an important role in Earth's radiative balance. The simulation of these cloud systems in climate models is difficult due to the scale at which cloud microphysical processes occur compared with model grid sizes. In this study, we use large-eddy simulation to analyze subgrid-scale variability of cloud water and its implications on a cloud water to drizzle model enhancement factor E. We find current values of E may be too large and that E should be vertically dependent in models.
Galina Wind, Arlindo M. da Silva, Kerry G. Meyer, Steven Platnick, and Peter M. Norris
Geosci. Model Dev., 15, 1–14, https://doi.org/10.5194/gmd-15-1-2022, https://doi.org/10.5194/gmd-15-1-2022, 2022
Short summary
Short summary
This is the third paper in series about the Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS). In this paper we use MCARS to create a set of constraints that might be used to assimilate a new above-cloud aerosol retrieval product developed for the MODIS instrument into a general circulation model. We executed the above-cloud aerosol retrieval over a series of synthetic MODIS granules and found the product to be of excellent quality.
Sarah J. Doherty, Pablo E. Saide, Paquita Zuidema, Yohei Shinozuka, Gonzalo A. Ferrada, Hamish Gordon, Marc Mallet, Kerry Meyer, David Painemal, Steven G. Howell, Steffen Freitag, Amie Dobracki, James R. Podolske, Sharon P. Burton, Richard A. Ferrare, Calvin Howes, Pierre Nabat, Gregory R. Carmichael, Arlindo da Silva, Kristina Pistone, Ian Chang, Lan Gao, Robert Wood, and Jens Redemann
Atmos. Chem. Phys., 22, 1–46, https://doi.org/10.5194/acp-22-1-2022, https://doi.org/10.5194/acp-22-1-2022, 2022
Short summary
Short summary
Between July and October, biomass burning smoke is advected over the southeastern Atlantic Ocean, leading to climate forcing. Model calculations of forcing by this plume vary significantly in both magnitude and sign. This paper compares aerosol and cloud properties observed during three NASA ORACLES field campaigns to the same in four models. It quantifies modeled biases in properties key to aerosol direct radiative forcing and evaluates how these biases propagate to biases in forcing.
Qianqian Song, Zhibo Zhang, Hongbin Yu, Paul Ginoux, and Jerry Shen
Atmos. Chem. Phys., 21, 13369–13395, https://doi.org/10.5194/acp-21-13369-2021, https://doi.org/10.5194/acp-21-13369-2021, 2021
Short summary
Short summary
We present a satellite-derived global dust climatological record over the last two decades, including the monthly mean visible dust optical depth (DAOD) and vertical distribution of dust extinction coefficient at a 2º × 5º spatial resolution derived from CALIOP and MODIS. In addition, the CALIOP climatological dataset also includes dust vertical extinction profiles. Based on these two datasets, we carried out a comprehensive comparative study of the spatial and temporal climatology of dust.
Hongbin Yu, Qian Tan, Lillian Zhou, Yaping Zhou, Huisheng Bian, Mian Chin, Claire L. Ryder, Robert C. Levy, Yaswant Pradhan, Yingxi Shi, Qianqian Song, Zhibo Zhang, Peter R. Colarco, Dongchul Kim, Lorraine A. Remer, Tianle Yuan, Olga Mayol-Bracero, and Brent N. Holben
Atmos. Chem. Phys., 21, 12359–12383, https://doi.org/10.5194/acp-21-12359-2021, https://doi.org/10.5194/acp-21-12359-2021, 2021
Short summary
Short summary
This study characterizes a historic African dust intrusion into the Caribbean Basin in June 2020 using satellites and NASA GEOS. Dust emissions in West Africa were large albeit not extreme. However, a unique synoptic system accumulated the dust near the coast for about 4 d before it was ventilated. Although GEOS reproduced satellite-observed plume tracks well, it substantially underestimated dust emissions and did not lift up dust high enough for ensuing long-range transport.
Fanny Peers, Peter Francis, Steven J. Abel, Paul A. Barrett, Keith N. Bower, Michael I. Cotterell, Ian Crawford, Nicholas W. Davies, Cathryn Fox, Stuart Fox, Justin M. Langridge, Kerry G. Meyer, Steven E. Platnick, Kate Szpek, and Jim M. Haywood
Atmos. Chem. Phys., 21, 3235–3254, https://doi.org/10.5194/acp-21-3235-2021, https://doi.org/10.5194/acp-21-3235-2021, 2021
Short summary
Short summary
Satellite observations at high temporal resolution are a valuable asset to monitor the transport of biomass burning plumes and the cloud diurnal cycle in the South Atlantic, but they need to be validated. Cloud and above-cloud aerosol properties retrieved from SEVIRI are compared against MODIS and measurements from the CLARIFY-2017 campaign. While some systematic differences are observed between SEVIRI and MODIS, the overall agreement in the cloud and aerosol properties is very satisfactory.
Zhibo Zhang, Qianqian Song, David B. Mechem, Vincent E. Larson, Jian Wang, Yangang Liu, Mikael K. Witte, Xiquan Dong, and Peng Wu
Atmos. Chem. Phys., 21, 3103–3121, https://doi.org/10.5194/acp-21-3103-2021, https://doi.org/10.5194/acp-21-3103-2021, 2021
Short summary
Short summary
This study investigates the small-scale variations and covariations of cloud microphysical properties, namely, cloud liquid water content and cloud droplet number concentration, in marine boundary layer clouds based on in situ observation from the Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) campaign. We discuss the dependence of cloud variations on vertical location in cloud and the implications for warm-rain simulations in the global climate models.
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.
Tianle Yuan, Hua Song, Robert Wood, Johannes Mohrmann, Kerry Meyer, Lazaros Oreopoulos, and Steven Platnick
Atmos. Meas. Tech., 13, 6989–6997, https://doi.org/10.5194/amt-13-6989-2020, https://doi.org/10.5194/amt-13-6989-2020, 2020
Short summary
Short summary
We use deep transfer learning techniques to classify satellite cloud images into different morphology types. It achieves the state-of-the-art results and can automatically process a large amount of satellite data. The algorithm will help low-cloud researchers to better understand their mesoscale organizations.
Marc Mallet, Fabien Solmon, Pierre Nabat, Nellie Elguindi, Fabien Waquet, Dominique Bouniol, Andrew Mark Sayer, Kerry Meyer, Romain Roehrig, Martine Michou, Paquita Zuidema, Cyrille Flamant, Jens Redemann, and Paola Formenti
Atmos. Chem. Phys., 20, 13191–13216, https://doi.org/10.5194/acp-20-13191-2020, https://doi.org/10.5194/acp-20-13191-2020, 2020
Short summary
Short summary
This paper presents numerical simulations using two regional climate models to study the impact of biomass fire plumes from central Africa on the radiative balance of this region. The results indicate that biomass fires can either warm the regional climate when they are located above low clouds or cool it when they are located above land. They can also alter sea and land surface temperatures by decreasing solar radiation at the surface. Finally, they can also modify the atmospheric dynamics.
Benjamin Marchant, Steven Platnick, Kerry Meyer, and Galina Wind
Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, https://doi.org/10.5194/amt-13-3263-2020, 2020
Short summary
Short summary
Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the Earth's atmosphere and are quite difficult to detect from space. The detection of multilayer clouds is important to better understand how they interact with the light and their impact on the climate. So, for the instrument MODIS an algorithm has been developed to detect those clouds, and this paper presents an evaluation of this algorithm by comparing it with
other instruments.
Yaping Zhou, Yuekui Yang, Meng Gao, and Peng-Wang Zhai
Atmos. Meas. Tech., 13, 1575–1591, https://doi.org/10.5194/amt-13-1575-2020, https://doi.org/10.5194/amt-13-1575-2020, 2020
Short summary
Short summary
Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces; the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Samuel E. LeBlanc, Jens Redemann, Connor Flynn, Kristina Pistone, Meloë Kacenelenbogen, Michal Segal-Rosenheimer, Yohei Shinozuka, Stephen Dunagan, Robert P. Dahlgren, Kerry Meyer, James Podolske, Steven G. Howell, Steffen Freitag, Jennifer Small-Griswold, Brent Holben, Michael Diamond, Robert Wood, Paola Formenti, Stuart Piketh, Gillian Maggs-Kölling, Monja Gerber, and Andreas Namwoonde
Atmos. Chem. Phys., 20, 1565–1590, https://doi.org/10.5194/acp-20-1565-2020, https://doi.org/10.5194/acp-20-1565-2020, 2020
Short summary
Short summary
The southeast Atlantic during August–October experiences layers of smoke from biomass burning over marine stratocumulus clouds. Here we present the light attenuation of the smoke and its dependence in the spatial, vertical, and spectral domain through direct measurements from an airborne platform during September 2016. From our observations of this climatically important smoke, we found an average aerosol optical depth of 0.32 at 500 nm, slightly lower than comparative satellite measurements.
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
Short summary
Short summary
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.
Fanny Peers, Peter Francis, Cathryn Fox, Steven J. Abel, Kate Szpek, Michael I. Cotterell, Nicholas W. Davies, Justin M. Langridge, Kerry G. Meyer, Steven E. Platnick, and Jim M. Haywood
Atmos. Chem. Phys., 19, 9595–9611, https://doi.org/10.5194/acp-19-9595-2019, https://doi.org/10.5194/acp-19-9595-2019, 2019
Short summary
Short summary
The measurements from the geostationary satellite MSG/SEVIRI are used to retrieve the cloud and above-cloud aerosol properties over the South Atlantic. The technique relies on the spectral contrast and the magnitude of the signal in the visible to shortwave infrared region as well as the atmospheric correction based on forecasted water vapour profiles. The sensitivity analysis and the stability of the retrieval over time show great potential of the high-temporal-resolution observations.
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
Short summary
Short summary
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.
Yuekui Yang, Kerry Meyer, Galina Wind, Yaping Zhou, Alexander Marshak, Steven Platnick, Qilong Min, Anthony B. Davis, Joanna Joiner, Alexander Vasilkov, David Duda, and Wenying Su
Atmos. Meas. Tech., 12, 2019–2031, https://doi.org/10.5194/amt-12-2019-2019, https://doi.org/10.5194/amt-12-2019-2019, 2019
Short summary
Short summary
The physical basis of the EPIC cloud product algorithms and an initial evaluation of their performance are presented. EPIC cloud products include cloud mask, effective height, and optical depth. Comparison with co-located retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the NASA Langley Atmospheric Sciences Data Center.
Antonio Di Noia, Otto P. Hasekamp, Bastiaan van Diedenhoven, and Zhibo Zhang
Atmos. Meas. Tech., 12, 1697–1716, https://doi.org/10.5194/amt-12-1697-2019, https://doi.org/10.5194/amt-12-1697-2019, 2019
Short summary
Short summary
We present a neural network algorithm for the retrieval of cloud physical properties from multi-angle polarimetric measurements. We have trained the algorithm on a large dataset of synthetic measurements and applied it to a year of POLDER-3 data. A comparison against MODIS cloud products reveals that our algorithm is capable of performing cloud property retrievals on a global scale and possibly improves the estimates of cloud effective radius over land with respect to existing POLDER-3 products.
Zhibo Zhang, Hua Song, Po-Lun Ma, Vincent E. Larson, Minghuai Wang, Xiquan Dong, and Jianwu Wang
Atmos. Chem. Phys., 19, 1077–1096, https://doi.org/10.5194/acp-19-1077-2019, https://doi.org/10.5194/acp-19-1077-2019, 2019
Peng Wu, Baike Xi, Xiquan Dong, and Zhibo Zhang
Atmos. Chem. Phys., 18, 17405–17420, https://doi.org/10.5194/acp-18-17405-2018, https://doi.org/10.5194/acp-18-17405-2018, 2018
Short summary
Short summary
Prescribed autoconversion and accretion enhancement factors in GCM warm-rain parameterizations contribute partially to the too-frequent and too-light problem in precipitation simulation. The two factors should be regime- and resolution-dependent. A decreased autoconversion enhancement factor and increased accretion enhancement factor in the Morrison and Gettleman (2008) scheme can improve the simulated precipitation frequency and intensity. The two factors for other schemes are also suggested.
Qianqian Song, Zhibo Zhang, Hongbin Yu, Seiji Kato, Ping Yang, Peter Colarco, Lorraine A. Remer, and Claire L. Ryder
Atmos. Chem. Phys., 18, 11303–11322, https://doi.org/10.5194/acp-18-11303-2018, https://doi.org/10.5194/acp-18-11303-2018, 2018
Short summary
Short summary
Mineral dust is the most abundant atmospheric aerosol component in terms of dry mass. In this study, we integrate recent aircraft measurements of dust microphysical and optical properties with satellite retrievals of aerosol and radiative fluxes to quantify the dust direct radiative effects on the shortwave and longwave radiation at both the top of the atmosphere and the surface in the tropical North Atlantic during summer months.
Hua Song, Zhibo Zhang, Po-Lun Ma, Steven Ghan, and Minghuai Wang
Geosci. Model Dev., 11, 3147–3158, https://doi.org/10.5194/gmd-11-3147-2018, https://doi.org/10.5194/gmd-11-3147-2018, 2018
Daniel J. Miller, Zhibo Zhang, Steven Platnick, Andrew S. Ackerman, Frank Werner, Celine Cornet, and Kirk Knobelspiesse
Atmos. Meas. Tech., 11, 3689–3715, https://doi.org/10.5194/amt-11-3689-2018, https://doi.org/10.5194/amt-11-3689-2018, 2018
Short summary
Short summary
Prior satellite comparisons of bispectral and polarimetric cloud droplet size retrievals exhibited systematic biases. However, similar airborne instrument retrievals have been found to be quite similar to one another. This study explains this discrepancy in terms of differing sensitivity to vertical profile, as well as spatial and angular resolution. This is accomplished by using a satellite retrieval simulator – an LES cloud model coupled to radiative transfer and cloud retrieval algorithms.
Heming Bai, Cheng Gong, Minghuai Wang, Zhibo Zhang, and Tristan L'Ecuyer
Atmos. Chem. Phys., 18, 1763–1783, https://doi.org/10.5194/acp-18-1763-2018, https://doi.org/10.5194/acp-18-1763-2018, 2018
Short summary
Short summary
Precipitation susceptibility to aerosol perturbation plays a key role in understanding aerosol–cloud interactions and for constraining aerosol indirect effects. Here, multisensor aerosol and cloud products from A-Train satellites are analyzed to estimate precipitation susceptibility. Compared to precipitation intensity susceptibility, precipitation frequency susceptibility demonstrates relatively robust features across different retrieval products.
Thomas Fauchez, Steven Platnick, Kerry Meyer, Céline Cornet, Frédéric Szczap, and Tamás Várnai
Atmos. Chem. Phys., 17, 8489–8508, https://doi.org/10.5194/acp-17-8489-2017, https://doi.org/10.5194/acp-17-8489-2017, 2017
Short summary
Short summary
This study presents impact of cirrus cloud horizontal heterogeneity on simulated thermal infrared brightness temperatures at the top of the atmosphere for spatial resolutions ranging from 50 m to 10 km. The cirrus is generated by the 3DCLOUD code and the radiative transfer by the 3DMCPOL code. Brightness temperatures are mostly impacted by the horizontal transport effect and plane-parallel bias at high and coarse spatial resolutions, respectively, with a minimum around 100 m–250 m.
John Rausch, Kerry Meyer, Ralf Bennartz, and Steven Platnick
Atmos. Meas. Tech., 10, 2105–2116, https://doi.org/10.5194/amt-10-2105-2017, https://doi.org/10.5194/amt-10-2105-2017, 2017
Short summary
Short summary
This paper documents the observed differences in the aggregated (Level-3) cloud droplet effective radius and droplet number concentration estimates inferred from the Aqua–MODIS cloud product collections 5.1 and 6 for warm oceanic cloud scenes over the year 2008. We note significant differences in effective radius and droplet concentration between the two products and discuss the algorithmic and calibration changes which may contribute to observed results.
Frank Werner, Galina Wind, Zhibo Zhang, Steven Platnick, Larry Di Girolamo, Guangyu Zhao, Nandana Amarasinghe, and Kerry Meyer
Atmos. Meas. Tech., 9, 5869–5894, https://doi.org/10.5194/amt-9-5869-2016, https://doi.org/10.5194/amt-9-5869-2016, 2016
Short summary
Short summary
A research–level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. This yields reliable estimates of important cloud variables at a horizontal resolution of 30 m. Comparisons of the ASTER retrieval results with the operational cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) show a high agreement for 48 example cloud fields.
Kerry Meyer, Yuekui Yang, and Steven Platnick
Atmos. Meas. Tech., 9, 1785–1797, https://doi.org/10.5194/amt-9-1785-2016, https://doi.org/10.5194/amt-9-1785-2016, 2016
Short summary
Short summary
This paper presents the expected uncertainties of a single-channel cloud opacity retrieval technique and a temperature-based cloud phase approach in support of the Deep Space Climate Observatory (DSCOVR) mission; DSCOVR cloud products will be derived from Earth Polychromatic Imaging Camera (EPIC) observations. Results show that, for ice clouds, retrieval errors are minimal (< 2 %), while for liquid clouds the error is limited to within 10 %, although for thin clouds the error can be higher.
Kerry Meyer, Steven Platnick, G. Thomas Arnold, Robert E. Holz, Paolo Veglio, John Yorks, and Chenxi Wang
Atmos. Meas. Tech., 9, 1743–1753, https://doi.org/10.5194/amt-9-1743-2016, https://doi.org/10.5194/amt-9-1743-2016, 2016
Short summary
Short summary
Cirrus cloud optical and microphysical properties are retrieved from remote sensing solar reflectance measurements at two narrow wavelength channels within the broader water vapor absorption band at 1.88 µm. Results from this technique compare well with other solar reflectance, IR, and lidar-based retrievals. This approach is complementary to traditional remote sensing techniques and can extend cloud retrieval capabilities for thin cirrus clouds.
Zhibo Zhang, Kerry Meyer, Hongbin Yu, Steven Platnick, Peter Colarco, Zhaoyan Liu, and Lazaros Oreopoulos
Atmos. Chem. Phys., 16, 2877–2900, https://doi.org/10.5194/acp-16-2877-2016, https://doi.org/10.5194/acp-16-2877-2016, 2016
Short summary
Short summary
The frequency of occurrence and shortwave direct radiative effects (DRE) of above-cloud aerosols (ACAs) over global oceans are investigated using 8 years of collocated CALIOP and MODIS observations. We estimated that ACAs have a global ocean annual mean diurnally averaged cloudy-sky DRE of 0.015 W m−2 (range of −0.03 to 0.06 W m−2) at TOA. The DREs at surface and within atmosphere are −0.15 W m−2 (range of −0.09 to −0.21 W m−2), and 0.17 W m−2 (range of 0.11 to 0.24 W m−2), respectively.
T. Fauchez, P. Dubuisson, C. Cornet, F. Szczap, A. Garnier, J. Pelon, and K. Meyer
Atmos. Meas. Tech., 8, 633–647, https://doi.org/10.5194/amt-8-633-2015, https://doi.org/10.5194/amt-8-633-2015, 2015
Z. Zhang, K. Meyer, S. Platnick, L. Oreopoulos, D. Lee, and H. Yu
Atmos. Meas. Tech., 7, 1777–1789, https://doi.org/10.5194/amt-7-1777-2014, https://doi.org/10.5194/amt-7-1777-2014, 2014
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Extension of AVHRR-based climate data records: exploring ways to simulate AVHRR radiances from Suomi NPP VIIRS data
Improved simulation of thunderstorm characteristics and polarimetric signatures with LIMA two-moment microphysics in AROME
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
Riming-dependent snowfall rate and ice water content retrievals for W-band cloud radar
Radiative closure assessment of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-DF product
Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology
Infrared radiometric image classification and segmentation of cloud structures using a deep-learning framework from ground-based infrared thermal camera observations
Classifying Thermodynamic Cloud Phase Using Machine Learning Models
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Synergy of millimeter-wave radar and radiometer measurements for retrieving frozen hydrometeors in deep convective systems
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Augmenting the German weather radar network with vertically pointing cloud radars: implications of resolution and attenuation
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Vertical Wind and Drop Size Distribution Retrieval with the CloudCube G-band Doppler Radar
Harmonized Cloud Datasets for OMI and TROPOMI Using the O2‐O2 477 nm Absorption Band
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Retrieving Vertical Profiles of Cloud Droplet Effective Radius using Multispectral Measurements from MODIS: Examples and Limitations
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
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
Identification of multiple co-located hydrometeor types in Doppler spectra from scanning polarimetric cloud radar 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
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
Atmos. Meas. Tech., 18, 3833–3855, https://doi.org/10.5194/amt-18-3833-2025, https://doi.org/10.5194/amt-18-3833-2025, 2025
Short summary
Short summary
The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels, but some corrections are necessary to account for some differences. We have simulated AVHRR data from the Visible Infrared Imaging Radiometer Suite (VIIIRS) sensor on National Oceanic and Atmospheric Administration (NOAA) polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Cloé David, Clotilde Augros, Benoit Vié, François Bouttier, and Tony Le Bastard
Atmos. Meas. Tech., 18, 3715–3745, https://doi.org/10.5194/amt-18-3715-2025, https://doi.org/10.5194/amt-18-3715-2025, 2025
Short summary
Short summary
Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome some of these issues, different parameterizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
Atmos. Meas. Tech., 18, 3611–3634, https://doi.org/10.5194/amt-18-3611-2025, https://doi.org/10.5194/amt-18-3611-2025, 2025
Short summary
Short summary
This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a yearlong 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 atmospheric models.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
Atmos. Meas. Tech., 18, 3495–3532, https://doi.org/10.5194/amt-18-3495-2025, https://doi.org/10.5194/amt-18-3495-2025, 2025
Short summary
Short summary
Contrails, the clouds formed by aircraft, are have a substantial climate impact. Determining which flight formed each contrail is a critical step to decreasing this impact. We introduce a dataset of synthetic contrail observations with known flight attributions that can be used to develop and assess geostationary-satellite-based contrail-to-flight attribution systems. We additionally introduce a new attribution algorithm and show that it outperforms previous methods.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
Atmos. Meas. Tech., 18, 3287–3304, https://doi.org/10.5194/amt-18-3287-2025, https://doi.org/10.5194/amt-18-3287-2025, 2025
Short summary
Short summary
Accurate measurements of ice water content (IWC) and snowfall rate (SR) are challenging due to high spatial variability and limitations of our measurement techniques. Here, we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path (LWP) as a proxy for the occurrence of riming. LWP is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
Atmos. Meas. Tech., 18, 3095–3107, https://doi.org/10.5194/amt-18-3095-2025, https://doi.org/10.5194/amt-18-3095-2025, 2025
Short summary
Short summary
Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared, and the likelihood of them differing by 10 W m2 defines the closure.
Andrzej Z. Kotarba and Izabela Wojciechowska
Atmos. Meas. Tech., 18, 2721–2738, https://doi.org/10.5194/amt-18-2721-2025, https://doi.org/10.5194/amt-18-2721-2025, 2025
Short summary
Short summary
The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar–radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study shows how small threshold changes significantly affect the climatology of severe storms.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
Atmos. Meas. Tech., 18, 2083–2101, https://doi.org/10.5194/amt-18-2083-2025, https://doi.org/10.5194/amt-18-2083-2025, 2025
Short summary
Short summary
Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests based on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observation experiments.
Lexie Goldberger, Maxwell Levin, Carlandra Harris, Andrew Geiss, Matthew D. Shupe, and Damao Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1501, https://doi.org/10.5194/egusphere-2025-1501, 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, application of the model to another observatory, and quantify how the models respond to instrument outages.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
Short summary
Short summary
Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025, https://doi.org/10.5194/amt-18-1859-2025, 2025
Short summary
Short summary
CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
Short summary
Short summary
A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
Short summary
Short summary
This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
Short summary
Short summary
The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Keiichi Ohara and Hirohiko Masunaga
EGUsphere, https://doi.org/10.5194/egusphere-2025-173, https://doi.org/10.5194/egusphere-2025-173, 2025
Short summary
Short summary
Ice particles (e.g., cloud ice, snow and graupel) in convective clouds play key roles in cloud and precipitation formation. This study combines satellite millimeter-wave radar and radiometer observations to estimate the vertical distributions of physical parameters of ice particles such as mass, size, and number densities. CPR radar and GPM radiometer observations together reduce the estimation errors of the physical parameters and provide information on the optimal ice particle shape.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
Short summary
Short summary
This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Christian Heske, Florian Ewald, and Silke Groß
EGUsphere, https://doi.org/10.5194/egusphere-2025-691, https://doi.org/10.5194/egusphere-2025-691, 2025
Short summary
Short summary
This study proposes a new technique to augment polarimetric radar measurements of the national German operational radar network with vertically looking cloud radars. The method is tested in two different case studies by comparison to dedicated scanning radars revealing promising results. Future usage of the method is motivated by analyzing the coverage of the operational radar network finding advantageous locations with good radar coverage.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
Short summary
Short summary
The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Nitika Yadlapalli Yurk, Matthew Lebsock, Juan Socuellamos, Raquel Rodriguez Monje, Ken Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2025-618, https://doi.org/10.5194/egusphere-2025-618, 2025
Short summary
Short summary
Current knowledge of the link between clouds and climate is limited by lack of observations of the drop size distribution (DSD) within clouds, especially for the smallest drops. We demonstrate a method of retrieving DSDs down to small drop sizes using observations of drizzling marine layer clouds captured by the CloudCube millimeter-wave Doppler radar. We compare the shape of the observed spectra to theoretical expectations of radar echoes to solve for DSDs at each time and elevation.
Huan Yu, Isabelle De Smedt, Nicolas Theys, Maarten Sneep, Pepijn Veefkind, and Michel Van Roozendael
EGUsphere, https://doi.org/10.5194/egusphere-2025-478, https://doi.org/10.5194/egusphere-2025-478, 2025
Short summary
Short summary
We introduce a new cloud retrieval algorithm using the O2-O2 absorption band at 477 nm to generate harmonized cloud datasets from OMI and TROPOMI. The algorithm improves upon the OMI O2-O2 operational cloud algorithm in several aspects. The new approach improves consistency in cloud parameters and NO2 retrievals between two sensors.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
Short summary
Short summary
In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Andrew John Buggee and Peter Andrew Pilewskie
EGUsphere, https://doi.org/10.5194/egusphere-2025-546, https://doi.org/10.5194/egusphere-2025-546, 2025
Short summary
Short summary
A constrained optimal estimation technique was developed to utilize space-borne hyperspectral measurements of reflected solar radiation for retrieving a vertical profile of cloud droplet size, providing insight into the internal structure of a cloud. The improved accuracy and, to a lesser extent, the enhanced spectral sampling provided by next-generation space-borne spectrometers are essential for extracting vertically resolved droplet size information from moderately thick, warm clouds.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
Short summary
Short summary
We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Ioanna Tsikoudi, Alessandro Battaglia, Christine Unal, and Eleni Marinou
EGUsphere, https://doi.org/10.5194/egusphere-2024-3164, https://doi.org/10.5194/egusphere-2024-3164, 2025
Short summary
Short summary
The study simulates spectral polarimetric variables for raindrops as observed by a cloud radar. Raindrops are modelled as oblate spheroids and backscattering properties are computed via the T-matrix method including noise, turbulence and spectral averaging effects. When comparing simulations to measurements, differences on the amplitudes of polarimetric variables are noted. This shows the challenge of using simplified shapes to model raindrop polarimetric variables when moving to mm wavelengths.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
Short summary
Short summary
Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
Short summary
Short summary
This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
Short summary
Short summary
We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
Short summary
Short summary
This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
Short summary
Short summary
This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Majid Hajipour, Patric Seifert, Hannes Griesche, Kevin Ohneiser, and Martin Radenz
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-173, https://doi.org/10.5194/amt-2024-173, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This study presents an approach that enables the detection of the shape and orientation of multiple types of co-located hydrometeors in mixed-phase cloud systems. This information is key for improving the understanding of these clouds, as they do contain ice and liquid water simultaneously, making them relevant for the precipitation budget and radiative balance of the Earth's atmosphere. The retrieval is based on elevation scans of polarimetric cloud radars and can therefore be flexibly applied.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
Short summary
Short summary
In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
Short summary
Short summary
This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
Short summary
Short summary
Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
Short summary
Short summary
The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
Short summary
Short summary
Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
Short summary
Short summary
Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
Short summary
Short summary
The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
Short summary
Short summary
This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
Short summary
Short summary
ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
Short summary
Short summary
Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
Short summary
Short summary
The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
Short summary
Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
Short summary
Short summary
This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Cited articles
Ackerman, S. and Frey, R.: Continuity MODIS/Aqua Level-2 (L2) Cloud Mask Product, https://doi.org/10.5067/MODIS/CLDMSK_L2_MODIS_Aqua.001, 2019a.
Ackerman, S. and Frey, R.: Continuity VIIRS/SNPP Level-2 (L2) Cloud Mask Product, https://doi.org/10.5067/VIIRS/CLDMSK_L2_VIIRS_SNPP.001, 2019b.
Ackerman, S. A., Holz, R. E., Frey, R., Eloranta, E. W., Maddux, B. C., and
McGill, M.: Cloud detection with MODIS. Part II: Validation, J. Atmos. Ocean. Technol., 25,
1073–1086, https://doi.org/10.1175/2007JTECHA1053.1, 2008.
Ackerman, S., Menzel, P., Frey, R., and Baum, B.: MODIS Atmosphere L2 Cloud Mask Product. NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA, https://doi.org/10.5067/MODIS/MYD35_L2.061, 2017.
Ackerman, S. A., Frey, R., Heidinger, A., Li, Y., Walther, A., Platnick, S.,
Meyer, K., Wind, G., Amarasinghe, N., Wang, C., Marchant, B., Holz, R. E.,
Dutcher, S., and Hubanks, P.: EOS MODIS and SNPP VIIRS Cloud Properties: User
guide for climate data record continuity Level-2 cloud top and optical
properties product (CLDPROP), version 1, NASA MODIS Adaptive Processing System, Goddard Space Flight Center, USA, 2019.
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.
Breiman, L.: Random forests – random features, Technical report, University
of California at Berkeley, Berkeley, California, 1999.
Brodzik, M. J. and Stewart, J. S.: Near-Real-Time SSM/I-SSMIS EASE-Grid Daily
Global Ice Concentration and Snow Extent, Version 5,
https://doi.org/10.5067/3KB2JPLFPK3R, 2016.
Cao, C., Xiong, J., Blonski, S., Liu, Q., Uprety, S., Shao, X., Bai, Y., and
Weng, F.: Suomi NPP VIIRS sensor data record verification, validation, and
long-term performance monitoring, J. Geophys. Res.-Atmos., 118, 11664–11678,
https://doi.org/10.1002/2013JD020418, 2013.
Cho, H., Nasiri, S. L., and Yang, P.: Application of CALIOP Measurements to
the Evaluation of Cloud Phase Derived from MODIS Infrared Channels, J. Appl. Meteor. Climatol., 48,
2169–2180, https://doi.org/10.1175/2009JAMC2238.1, 2009.
Dietterich, T. G.: Ensemble methods in machine learning, International
Workshop on Multiple Classifier Systems, MCS 2000, Lecture Notes in Computer
Science, vol. 1857, Springer, Berlin, Heidelberg, 2000.
Freund, Y.: An Adaptive Version of the Boost by Majority Algorithm, Machine Learning, 43, 293–318, 2001.
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J.
R., and Wang, X.: Cloud detection with MODIS. Part I: Improvements in the
MODIS cloud mask for Collection 5, J. Atmos. Ocean. Technol., 25, 1057–1072, https://doi.org/10.1175/2008JTECHA1052.1, 2008.
Friedman, J. H.: Greedy function approximation: a gradient boosting machine,
Ann. Stat., 29, 1189–1232, 2001.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A., Gu, W., Kim, G., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: 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.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Daily L3 Global 500 m
SIN Grid, Version 6, NASA National Snow and Ice Data
Center Distributed Active Archive Center, Boulder, Colorado USA, https://doi.org/10.5067/MODIS/MYD10A1.006,
2016.
Haynes, J. M., Noh, Y. J., Miller, S. D., Heidinger, A., and Forsythe, J.
M.: Cloud geometric thickness and improved cloud boundary detection with
GEOS ABI, 15th Annual Symposium on New Generation Operational
Environment Satellite Systems, Phoenix, AZ, 6–10 January 2019.
Heidinger, A. K., Evan, A. T., Foster, M. J., and Walther, A.: A naive
bayesian cloud-detection scheme derived from CALIPSO and applied within
PATMOS-x, J. Appl. Meteor. Climatol., 51, 1129–1144, https://doi.org/10.1175/JAMC-D-11-02.1, 2012.
Ho, T. K: The random subspace method for constructing decision forests,
IEEE Trans. Pattern Anal. Mach. Intell, 20, 832–844, 1998.
Holz, R. E., Ackerman, S. A., Nagle, F. W., Frey, R., Dutcher, S., Kuehn, R.
E., Vaughan, M. A., and Baum, B.: Global Moderate Resolution Imaging
Spectroradiometer (MODIS) cloud detection and height evaluation using
CALIOP, J. Geophys. Res., 113, D00A19, https://doi.org/10.1029/2008JD009837, 2008.
Hu, X. F., Belle, J. H., Meng, X., Wildani, A., Waller, L. A., Strickland,
M. J., and Liu, Y.: Estimating PM2.5 concentrations in the conterminous
United States using the random forest approach, Environ. Sci. Technol., 51, 6936–6944, https://doi.org/10.1021/acs.est.7b01210, 2017.
Ji, C. and Ma, S.: Combinations of weak classifiers, IEEE T. Neural. Networ., 8, 32–42, 1997.
Joachims, T.: Text categorization with support vector machines: Learning
with many relevant features, in: Proceedings of the 10th European Conference
on Machine Learning, 137–142, Springer-Verlag, Chemnitz, Germany, 1998.
Justice, C. O., Vermote, E., Privette, J., and Sei, A.: The Evolution of U.S.
Moderate Resolution Optical Land Remote Sensing from AVHRR to VIIRS. Land
Remote Sensing and Global Environmental Change, in: Remote Sensing and Digital Image Processing, edited by: Ramachandran, B., Justice, C., and Abrams, M., 11,
Springer, New York, NY, 781–806, 2011.
Kox, S., Bugliaro, L., and Ostler, A.: Retrieval of cirrus cloud optical thickness and top altitude from geostationary remote sensing, Atmos. Meas. Tech., 7, 3233–3246, https://doi.org/10.5194/amt-7-3233-2014, 2014.
Latinne P., Debeir O., and Decaestecker C.: Limiting the number of trees in random forests, in Multiple Classifier Systems, 178–187, Springer-Verlag, Berlin, Germany, 2001.
Lee, T. E., Miller, S. D., Turk, F. J., Schueler, C., Julian, R., Deyo, S.,
Dills, P., and Wang, S.: The NPOESS VIIRS Day/Night Visible Sensor, B. Am. Meteorol. Soc., 87,
191–200, https://doi.org/10.1175/BAMS-87-2-191, 2006.
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013.
Liu, Y., Ackerman, S. A., Maddux, B. C., Key, J. R., and Frey, R. A.: Errors
in cloud detection over the Arctic using a satellite imager and implications
for observing feedback mechanisms, J. Climate, 23, 1894–1907,
https://doi.org/10.1175/2009JCLI3386.1, 2010.
Maddux, B. C., Ackerman, S. A., and Platnick, S.: Viewing geometry
dependencies in MODIS cloud products, J. Atmos. Ocean. Technol., 27, 1519–1528,
https://doi.org/10.1175/2010JTECHA1432.1, 2010.
Martins, J. V., Tanré, D., Remer, L., Kaufman, Y., Mattoo, S., and Levy, R.: MODIS Cloud screening for remote sensing of aerosols over oceans using spatial variability, Geophys. Res. Lett., 29, https://doi.org/10.1029/2001GL013252, 2002.
Marchant, B., Platnick, S., Meyer, K., Arnold, G. T., and Riedi, J.: MODIS Collection 6 shortwave-derived cloud phase classification algorithm and comparisons with CALIOP, Atmos. Meas. Tech., 9, 1587–1599, https://doi.org/10.5194/amt-9-1587-2016, 2016.
Marchant, B., Platnick, S., Meyer, K., and Wind, G.: Evaluation of the Aqua MODIS Collection 6.1 multilayer cloud detection algorithm through comparisons with CloudSat CPR and CALIPSO CALIOP products, Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-448, in review, 2020.
McGill, M. J., Yorks, J. E., Scott, V. S., Kupchock, A. W., and Selmer, P.
A.: The Cloud-Aerosol Transport System (CATS): A technology demonstration on
the International Space Station, Proc. SPIE, 9612, 96120A,
https://doi.org/10.1117/12.2190841, 2015.
Meyer, K., Platnick, S., Arnold, G. T., Holz, R. E., Veglio, P., Yorks, J., and Wang, C.: Cirrus cloud optical and microphysical property retrievals from eMAS during SEAC4RS using bi-spectral reflectance measurements within the 1.88 µm water vapor absorption band, Atmos. Meas. Tech., 9, 1743–1753, https://doi.org/10.5194/amt-9-1743-2016, 2016.
Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51∘ S and 51∘ N, seen by the CATS spaceborne lidar from the International Space Station, Atmos. Chem. Phys., 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, 2018.
Oshiro, T. M., Perez, P. S., and Baranauskas, J. A.: How many trees in a random
forest, in Machine Learning and Data Mining in Pattern Recognition, MLDM
2012, Lecture Notes in Computer Science, 7376, Springer, Berlin, Heidelberg,
2012.
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. Climatol., 44, 804–826, 2005.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., and Cournapeau, D.: Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011
Platnick, S., King, M., Wind, G., Ackerman, S., Menzel, P., and Frey, R.: MODIS/Aqua Clouds 5-Min L2 Swath 1 km and 5 km, https://doi.org/10.5067/MODIS/MYD06_L2.061, 2015.
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 T. Geosci. Remote, 55,
502–525, https://doi.org/10.1109/TGRS.2016.2610522, 2017a.
Platnick, S., Meyer, K., Wind, G., Arnold, T., Amrasinghe, N., Marchant, B., Wang, C., Ackerman, S., Heidinger, A., Holtz, B., Li, Y., and Frey, R.: Continuity VIIRS/SNPP Level-2 (L2) Cloud Properties Product, https://doi.org/10.5067/VIIRS/CLDPROP_L2_VIIRS_SNPP.011, 2017b.
Platnick, S., Meyer, K., Wind, G., Arnold, T., Amrasinghe, N., Marchant, B., Wang, C., Ackerman, S., Heidinger, A., Holtz, B., Li, Y., and Frey, R.: Continuity MODIS/Aqua Level-2 (L2) Cloud Properties Product, https://doi.org/10.5067/MODIS/CLDPROP_L2_MODIS_Aqua.011, 2017c.
Remer, L. A., Kaufman, Y. J., Tanré, D., Mattoo, S., Chu, D. A.,
Martins, J. V., Li, R., Ichoku, C., Levy, R. C., Kleidman, R. G., Eck, T.
F., Vermote, E., and Holben, B. N.: The MODIS aerosol algorithm, products,
and validation, J. Atmos. Sci., 62, 947–973, https://doi.org/10.1175/JAS3385.1, 2005.
Sassen, K. and Cho, B. S., Subvisual-thin cirrus lidar dataset for
satellite verification and climatological research, J. Appl. Meteor., 31, 1275–1285.
https://doi.org/10.1175/1520-0450(1992)031<1275:STCLDF>2.0.CO;2, 1992.
Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and
Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua's
e-Deep Blue, Dark Target, and “merged” data sets, and usage
recommendations, J. Geophys. Res.-Atmos., 119, 13965–13989, https://doi.org/10.1002/2014JD022453, 2014.
Sayer, A. M., Hsu, N. C., Lee, J., Bettenhausen, C., Kim, W. V., and
Smirnov, A.: Satellite Ocean Aerosol Retrieval (SOAR) algorithm extension to
S-NPP VIIRS as part of the “Deep Blue” aerosol project, J. Geophys. Res.-Atmos., 123, 380–400, https://doi.org/10.1002/2017JD027412, 2017.
Scornet, E.: Tuning parameters in random forests, ESAIM: Procs., 60, 144–162, 2018.
Seemann, S. W., Borbas, E. E., Knuteson, R. O., Stephenson, G. R., and
Huang, H.: Development of a global infrared land surface emissivity database
for application to clear sky sounding retrievals from multispectral
satellite radiance measurements, J. Appl. Meteor. Climatol., 47, 108–123, 2008.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C. M., and the CloudSat team: The CloudSat mission and the A-Train: A new dimension of space-based observations of clouds and precipitation, B. Am. Meteorol. Soc., 83, 1771–1790, 2002.
Strandgren, J., Bugliaro, L., Sehnke, F., and Schröder, L.: Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks, Atmos. Meas. Tech., 10, 3547–3573, https://doi.org/10.5194/amt-10-3547-2017, 2017.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao,
G.: Assessment of Global Cloud Datasets from Satellites: Project and
Database Initiated by the GEWEX Radiation Panel, B. Am. Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1, 2013.
Sulla-Menashe, D. and Friedl, M. A.: User Guide to Collection 6 MODIS Land
Cover (MCD12Q1 and MCD12C1) Product, USGS, Reston, VA, USA, 2018.
Tanelli, S., Durden, S. L., Im, E., Pak, K., Reinke, D., Partain, P.,
Haynes, J., and Marchand, R.: CloudSat's cloud profiling radar after two
years in orbit: Performance, calibration, and processing, IEEE T. Geosci. Remote, 46, 3560–3573,
https://doi.org/10.1109/TGRS.2008.2002030, 2008.
Thampi, B. V., Wong, T., Lukashin, C., and Loeb, N. G.: Determination of
CERES TOA fluxes using machine learning algorithms. Part I: Classification
and retrieval of CERES cloudy and clear scenes, J. Atmos. Ocean. Technol., 34, 2329–2345, https://doi.org/10.1175/JTECH-D-16-0183.1, 2017.
Tumer, K., and Ghosh, J.: Error correlation and error reduction in ensemble
classifiers, Connect. Sci., 8, 385–403, https://doi.org/10.1080/095400996116839, 1996.
Wan, Z., Zhang, Y., Zhang, Q., and Li, Z.-L.: Quality assessment and
validation of the MODIS global land surface temperature, Int. J. Remote Sens., 25, 261–274,
https://doi.org/10.1080/0143116031000116417, 2004.
Wang, C., Yang, P., Dessler, A., Baum, B. A., and Hu, Y.: Estimation of the
cirrus cloud scattering phase function from satellite observations, J. Quant. Spectrosc. Ra., 138,
36–49, https://doi.org/10.1016/j.jqsrt.2014.02.001, 2014.
Wang, C., Platnick, S., Zhang, Z., Meyer, K., and Yang, P.: Retrieval of ice
cloud properties using an optimal estimation algorithm and MODIS infrared
observations: 1. Forward model, error analysis, and information content, J. Geophys. Res-Atmos.,
121, 5809–5826, https://doi.org/10.1002/2015jd024526, 2016a.
Wang, C., Platnick, S., Zhang, Z., Meyer, K., Wind, G., and Yang, P.:
Retrieval of ice cloud properties using an optimal estimation algorithm and
MODIS infrared observations: 2. Retrieval evaluation, J. Geophys. Res.-Atmos., 121, 5827–5845, https://doi.org/10.1002/2015jd024528, 2016b.
Wang, C., Platnick, S., Fauchez, T., Meyer, K., Zhang, Z., Iwabuchi, H., and
Kahn, B. H.: An assessment of the impacts of cloud vertical heterogeneity on
global ice cloud data records from passive satellite retrievals, J. Geophys. Res.-Atmos., 124,
1578–1595, https://doi.org/10.1029/2018JD029681, 2019.
Winker, D. M., Tackett, J. L., Getzewich, B. J., Liu, Z., Vaughan, M. A., and Rogers, R. R.: The global 3-D distribution of tropospheric aerosols as characterized by CALIOP, Atmos. Chem. Phys., 13, 3345–3361, https://doi.org/10.5194/acp-13-3345-2013, 2013.
Wolters, E. L., Roebeling, R. A., and Feijt, A. J.: Evaluation of
cloud-phase retrieval methods for SEVIRI on Meteosat-8 using ground-based
lidar and cloud radar data, J. Appl. Meteor. Climatol., 47, 1723–1738, https://doi.org/10.1175/2007JAMC1591.1, 2008.
Wu, Y., de Graaf, M., and Menenti, M.: Improved MODIS Dark Target aerosol optical depth algorithm over land: angular effect correction, Atmos. Meas. Tech., 9, 5575–5589, https://doi.org/10.5194/amt-9-5575-2016, 2016.
Yuan, T., Wang, C., Song, H., Platnick, S., Meyer, K., and Oreopoulos,
L.: Automatically finding ship tracks to enable large-scale analysis of
aerosol-cloud interactions, Geophys. Res. Lett., 46, 7726–7733, https://doi.org/10.1029/2019GL083441,
2019.
Short summary
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is developed. An all-day model that uses infrared (IR) observations and a daytime model that uses shortwave and IR observations from a passive instrument are trained separately for different surface types. The training datasets are selected by using reference pixel types from collocated space lidar. The ML approach is validated carefully and the overall performance is better than traditional methods.
A machine-learning (ML)-based approach that can be used for cloud mask and phase detection is...
