Articles | Volume 13, issue 3
https://doi.org/10.5194/amt-13-1575-2020
© 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-13-1575-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Apr 2020
Research article |
| 01 Apr 2020
| 01 Apr 2020
Cloud detection over snow and ice with oxygen A- and B-band observations from the Earth Polychromatic Imaging Camera (EPIC)
NASA Goddard Space Flight Center, Greenbelt, MD, USA
The Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA
Yuekui Yang
NASA Goddard Space Flight Center, Greenbelt, MD, USA
NASA Goddard Space Flight Center, Greenbelt, MD, USA
SSAI/NASA Goddard Space Flight Center, Ocean Ecology Laboratory,
Greenbelt, MD, USA
Peng-Wang Zhai
The Joint Center for Earth Systems Technology, Department of Physics, University of Maryland Baltimore County,
Baltimore, MD 21250, USA
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Chenxi Wang, Steven Platnick, Kerry Meyer, Zhibo Zhang, and Yaping Zhou
Atmos. Meas. Tech., 13, 2257–2277, https://doi.org/10.5194/amt-13-2257-2020, https://doi.org/10.5194/amt-13-2257-2020, 2020
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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.
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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.
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Meng Gao, Bryan A. Franz, Peng-Wang Zhai, Kirk Knobelspiesse, Andrew M. Sayer, Xiaoguang Xu, J. Vanderlei Martins, Brian Cairns, Patricia Castellanos, Guangliang Fu, Neranga Hannadige, Otto Hasekamp, Yongxiang Hu, Amir Ibrahim, Frederick Patt, Anin Puthukkudy, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 5863–5881, https://doi.org/10.5194/amt-16-5863-2023, https://doi.org/10.5194/amt-16-5863-2023, 2023
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This study evaluated the retrievability and uncertainty of aerosol and ocean properties from PACE's HARP2 instrument using enhanced neural network models with the FastMAPOL algorithm. A cascading retrieval method is developed to improve retrieval performance. A global set of simulated HARP2 data is generated and used for uncertainty evaluations. The performance assessment demonstrates that the FastMAPOL algorithm is a viable approach for operational application to HARP2 data after PACE launch.
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Atmos. Meas. Tech., 16, 2067–2087, https://doi.org/10.5194/amt-16-2067-2023, https://doi.org/10.5194/amt-16-2067-2023, 2023
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Meng Gao, Kirk Knobelspiesse, Bryan A. Franz, Peng-Wang Zhai, Andrew M. Sayer, Amir Ibrahim, Brian Cairns, Otto Hasekamp, Yongxiang Hu, Vanderlei Martins, P. Jeremy Werdell, and Xiaoguang Xu
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Kirk Knobelspiesse, Henrique M. J. Barbosa, Christine Bradley, Carol Bruegge, Brian Cairns, Gao Chen, Jacek Chowdhary, Anthony Cook, Antonio Di Noia, Bastiaan van Diedenhoven, David J. Diner, Richard Ferrare, Guangliang Fu, Meng Gao, Michael Garay, Johnathan Hair, David Harper, Gerard van Harten, Otto Hasekamp, Mark Helmlinger, Chris Hostetler, Olga Kalashnikova, Andrew Kupchock, Karla Longo De Freitas, Hal Maring, J. Vanderlei Martins, Brent McBride, Matthew McGill, Ken Norlin, Anin Puthukkudy, Brian Rheingans, Jeroen Rietjens, Felix C. Seidel, Arlindo da Silva, Martijn Smit, Snorre Stamnes, Qian Tan, Sebastian Val, Andrzej Wasilewski, Feng Xu, Xiaoguang Xu, and John Yorks
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Guangliang Fu, Otto Hasekamp, Jeroen Rietjens, Martijn Smit, Antonio Di Noia, Brian Cairns, Andrzej Wasilewski, David Diner, Felix Seidel, Feng Xu, Kirk Knobelspiesse, Meng Gao, Arlindo da Silva, Sharon Burton, Chris Hostetler, John Hair, and Richard Ferrare
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Meng Gao, Peng-Wang Zhai, Bryan A. Franz, Yongxiang Hu, Kirk Knobelspiesse, P. Jeremy Werdell, Amir Ibrahim, Brian Cairns, and Alison Chase
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Xiaomei Lu, Yongxiang Hu, Yuekui Yang, Mark Vaughan, Zhaoyan Liu, Sharon Rodier, William Hunt, Kathy Powell, Patricia Lucker, and Charles Trepte
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Feng Xu, Oleg Dubovik, Peng-Wang Zhai, David J. Diner, Olga V. Kalashnikova, Felix C. Seidel, Pavel Litvinov, Andrii Bovchaliuk, Michael J. Garay, Gerard van Harten, and Anthony B. Davis
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We developed an algorithm for aerosol and water-leaving radiance retrieval in a simultaneous way.
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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.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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Jinyi Xia and Li Guan
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-141, https://doi.org/10.5194/amt-2024-141, 2024
Revised manuscript accepted for AMT
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In satellite remote sensing, estimating cloud base height (CBH) is more challenging than estimating cloud top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI instrument on JAXA’s GCOM-C satellite to estimate CBH together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-894, https://doi.org/10.5194/egusphere-2024-894, 2024
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We present a technique to discriminate between the Cloudnet target classification of "Drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for the measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over two years at the BCO is presented.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-99, https://doi.org/10.5194/amt-2024-99, 2024
Revised manuscript accepted for AMT
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This study introduces the JAXA EarthCARE 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 will be 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.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Ho Yi Lydia Mak and Christine Unal
EGUsphere, https://doi.org/10.5194/egusphere-2024-1232, https://doi.org/10.5194/egusphere-2024-1232, 2024
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The dynamics of thunderclouds is 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.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40, https://doi.org/10.5194/amt-2024-40, 2024
Revised manuscript accepted for AMT
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we measured that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests 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 observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Cited articles
Ackerman, S., Strabala, K., Menzel, P., Frey, R., Moeller, C., and Gumley,
L.: Discriminating clear-sky from cloud with MODIS algorithm theoretical
basis document (MOD35), MODIS Cloud Mask Team, Cooperative Institute for
Meteorological Satellite Studies, University of Wisconsin, 2010.
Barker, H. W., Stephens, G. L., Partain, P. T., Bergman, J. W., Bonnel, B.,
Campana, K., Clothiaux, E. E., Clough, S., Cusak, S., Delamere, J.,
Edwards, J., Evans, K. F., Fouquart, Y., Freidenreich, S., Galin, V., Hou,
Y., Kato, S., Li, J., Mlawer, E., Morcrette, J.-J., O'Hirok, W.,
Raisanen, P., Ramaswamy, V., Ritter, B., Rozanov, E., Schlesinger, M.,
Shibata, K., Sporyshev, P., Sun, Z., Wendisch, M., Wood, N., and Yang,
F.: Assessing 1D atmospheric solar radiative transfer models:
Interpretation and handling of unresolved clouds, J. Climate, 16, 2676–2699,
2003.
Brodzik, M. J. and Stewart, J. S.: Near-Real-Time SSM/I-SSMIS EASE-Grid Daily Global Ice Concentration and Snow Extent, Version 5. [Indicate subset used], Boulder,
Colorado USA, NASA National Snow and Ice Data Center Distributed Active
Archive Center, https://doi.org/10.5067/3KB2JPLFPK3R, 2016.
Buehler, S. A., Eriksson, P., and Lemke, O.: Absorption lookup tables in the
radiative transfer model ARTS, J. Quant. Spectrosc. Ra., 112, 1559–1567, https://doi.org/10.1016/j.jqsrt.2011.03.008, 2011.
Cesana, G., Kay, J. E., Chepfer, H., English, J. M., and de Boer, G.:
Ubiquitous low-level liquid-containing Arctic clouds: New observations and
climate model constraints from CALIPSO-GOCCP, Geophys. Res. Lett., 39,
L20804, https://doi.org/10.1029/2012GL053385, 2012.
Chetwynd H. J., Wang, J., and Anderson, G. P.: Fast Atmospheric Signature
CODE (FASCODE): an update and applications in atmospheric remote sensing,
Proc. SPIE 2266, Optical Spectroscopic Techniques and Instrumentation for
Atmospheric and Space Research, https://doi.org/10.1117/12.187599, 1994.
Ding, S., Wang, J., and Xu, X.: Polarimetric remote sensing in oxygen A and B bands: sensitivity study and information content analysis for vertical profile of aerosols, Atmos. Meas. Tech., 9, 2077–2092, https://doi.org/10.5194/amt-9-2077-2016, 2016.
Ferlay, N., Thieuleux, F., Cornet, C., and Davis, A. B.: Toward New
Inferences about Cloud Structures from Multidirectional Measurements in the
Oxygen A Band: Middle-of-Cloud Pressure and Cloud Geometrical Thickness from
POLDER-3/PARASOL, J. Appl. Meteorol. Clim., 49, 2492–2507, https://doi.org/10.1175/2010JAMC2550.1, 2010.
Fischer, J. and Grassl, H.: Detection of cloud-top height from backscattered
radiances within the oxygen A band. Part 1: Theoretical study, J. Appl.
Meteorol., 30, 1245–1259, 1991.
Frey, R. A., Ackerman, S. A., Liu, Y., Strabala, K. I., Zhang, H., Key, J. R.,
and Wang, X.: Cloud Detection with MODIS. Part I: Improvements in the MODIS
Cloud Mask for Collection 5, J. Atmos. Ocean.
Tech., 25, 1057–1072, https://doi.org/10.1175/2008JTECHA1052.1, 2008.
Gao, M., Zhai, P.-W., Yang, Y., and Hu. Y.: Cloud remote sensing with
EPIC/DSCOVR observations: A sensitivity study with radiative transfer
simulations, J. Quant. Spectrosc. Ra., 230, 56–60,
https://doi.org/10.1016/j.jqsrt.2019.03.022, 2019.
Geogdzhayev, I. V. and Marshak, A.: Calibration of the DSCOVR EPIC visible and NIR channels using MODIS Terra and Aqua data and EPIC lunar observations, Atmos. Meas. Tech., 11, 359–368, https://doi.org/10.5194/amt-11-359-2018, 2018 (data available at: https://doi.org/10.5067/EPIC/DSCOVR/L1B.002).
Grechko, Y. I., Dianov-Klokov, V. I., and Malkov, I. P.: Aircraft
measurements of photon paths in reflection and transmission of light by
clouds in the 0.76 mm oxygen band, Atmos. Ocean Phys., 9, 262–265, 1973.
Herman, J., Huang, L., McPeters, R., Ziemke, J., Cede, A., and Blank, K.: Synoptic ozone, cloud reflectivity, and erythemal irradiance from sunrise to sunset for the whole earth as viewed by the DSCOVR spacecraft from the earth–sun Lagrange 1 orbit, Atmos. Meas. Tech., 11, 177–194, https://doi.org/10.5194/amt-11-177-2018, 2018.
Karlsson, K.-G. and Håkansson, N.: Characterization of AVHRR global cloud detection sensitivity based on CALIPSO-CALIOP cloud optical thickness information: demonstration of results based on the CM SAF CLARA-A2 climate data record, Atmos. Meas. Tech., 11, 633–649, https://doi.org/10.5194/amt-11-633-2018, 2018.
Khlopenkov, K., Duda, D., Thieman, M., Minnis, P., Su, W., and Bedka, K.:
Development of multi-sensor global cloud and radiance composites for earth
radiation budget monitoring from DSCOVR, Proc. SPIE 10424, Remote Sens.,
104240K, Warsaw, 2 October 2017, https://doi.org/10.1117/12.2278645, 2017.
Koelemeijer, R. B. Stammes, A. P., Hovenier, J. W., and de Haan, J. F.: A fast
method for retrieval of cloud parameters using oxygen A band measurements
from the Global Ozone Monitoring Experiment, J. Geophys. Res., 106,
3475–3490, 2001.
Marshak, A., Herman, J., Szabo, A., Blank, K., Cede, A., Carn, S., Geogdzhayev, I., Huang, D., Huang, L-K, Knyazikhin, Y., Kowalewski, M., Krotkov, N., Lyapustin, A., McPeters, R., Meyer, K., Torres, O., and Yang, Y.: Earth observations from DSCOVR/EPIC instrument, B. Am. Meteorol. Soc., 99, 1829–1850, https://doi.org/10.1175/BAMS-D-17-0223.1, 2018.
Meyer, K., Yang, Y., and Platnick, S.: Uncertainties in cloud phase and optical thickness retrievals from the Earth Polychromatic Imaging Camera (EPIC), Atmos. Meas. Tech., 9, 1785–1797, https://doi.org/10.5194/amt-9-1785-2016, 2016.
Min, Q. L., Harrison, L. C., Kierdron, P., Berndt, J., and Joseph, E.: A
high-resolution oxygen A-band and water vapor band spectrometer, J. Geophys.
Res., 109, D02202, https://doi.org/10.1029/2003JD003540, 2004.
Minnis, P., Sun-Mack, S., Young, D. F., Heck, P. W., Garber, D. P., Chen,
Y., Spangenberg, D. A., Arduini, R. F., Trepte, Q. Z., Smith, W. L. Jr.,
Ayers, J. K., Gibson, S. C., Miller, W. F., Chakrapani, V., Takano, Y.,
Liou, K.-N., Xie, Y., and Yang, P.: CERES Edition-2 cloud property retrievals
using TRMM VIRS and Terra and Aqua MODIS data, Part I: Algorithms, IEEE
T. Geosci. Remote, 49, 4374–4400, https://doi.org/10.1109/TGRS.2011.2144601,
2011.
Petty, W. G.: A first course in atmospheric radiation, Sundog Pub.,
ISBN 9780972903318, 2006.
Pirazzini, R.: Surface albedo measurements over Antarctic sites in summer,
J. Geophys. Res., 109, D20118, https://doi.org/10.1029/2004JD004617, 2004.
Richardson, M., Leinonen, J., Cronk, H. Q., McDuffie, J., Lebsock, M. D., and Stephens, G. L.: Marine liquid cloud geometric thickness retrieved from OCO-2's oxygen A-band spectrometer, Atmos. Meas. Tech., 12, 1717–1737, https://doi.org/10.5194/amt-12-1717-2019, 2019.
Rossow, W. B. and Garder, L. C.: Cloud detection using satellite
measurements of infrared and visible radiances for ISCCP, J. Climate, 6,
2341–2369, 1993.
Rothman, L., Gordon, I., Babikov, Y., Barbe, A., Benner, D.C.,
Bernath, P., Birk , M., Bizzocchi, L., Boudon, V. , Brown, L.R., Campargue, A., Chance, K., Cohen, E. A. , Coudert, L. H., Devi, V. M., Drouin, B. J., Fayt, A.,Flaud, J.-M., Gamache, R. R., Harrison, J. J. , Hartmann, J.-M., Hill, C., Hodges, J. T., Jacquemart, D., Jolly, A., Lamouroux, J., Le Roy, R. J., Li, G., Long, D. A., Lyulin, O. M., Mackie, C. J., Massie, S. T., Mikhailenko, S., Müller, H. S. P., Naumenko, O. V., Nikitin, A. V., Orphal, J., Perevalov, V., Perrin, A., Polovtseva, E. R., Richard, C., Smith, M. A. H., Starikova, E., Sung, K., Tashkun, S., Tennyson, J. , Toon, G. C., Tyuterev, V. G., and Wagner, G.: The hitran2012 molecular spectroscopic database, J. Quant.
Spectrosc. Ra., 130, 4–50, https://doi.org/10.1016/j.jqsrt.2013.07.002, 2013.
Row III, L. W. and Hastings, D. A.: TerrainBase worldwide digital terrain
data, release 1.0 NOAA/National Geophysical Data Center, available at: ftp://ftp.ngdc.noaa.gov/Solid_Earth/cdroms/TerrainBase_94/data/global/tbase/ (last access: 26 March 2020), 1994.
Saunders, R. W. and Kriebel, K. T. : An improved method for detecting clear
sky and cloudy radiances from AVHRR data, Int. J. Remote Sens., 9, 123–150,
1988.
Shang, H., Letu, H., Nakajima, T. Y., Wang, Z., Ma, R., Wang, T., Lei, Y., Ji, D., Li, S., and Shi, J.: Diurnal cycle and seasonal variation
of cloud cover over the Tibetan Plateau as determined from Himawari-8
new-generation geostationary satellite data, Sci. Rep.-UK, 8, 1105,
https://doi.org/10.1038/s41598-018-19431-w, 2018.
Stammes, P., Sneep, M., de Haan, J. F., Veefkind, J. P., Wang, P., and Levelt, P. F.: Effective
cloud fractions from the Ozone Monitoring Instrument: Theoretical framework
and validation, J. Geophys. Res., 113, D16S38, https://doi.org/10.1029/2007JD008820, 2008.
Su, W., Liang, L., Doelling, D. R., Minnis, P., Duda, D. P., Khlopenkov, K. V., Thieman, M., Loeb, N. G., Kato, S., Valero, F. P. J., Wang, H., and Rose, F. G.: Determining the Shortwave Radiative Flux from Earth Polychromatic Imaging Camera, J. Geophys. Res., 123, 11479–11491, https://doi.org/10.1029/2018JD029390, 2018.
Su, W., Minnis, P., Liang, L., Duda, D. P., Khlopenkov, K., Thieman, M. M., Yu, Y., Smith, A., Lorentz, S., Feldman, D., and Valero, F. P. J.: Determining the daytime Earth radiative flux from National Institute of Standards and Technology Advanced Radiometer (NISTAR) measurements, Atmos. Meas. Tech., 13, 429–443, https://doi.org/10.5194/amt-13-429-2020, 2020.
Vasilkov, A., Joiner, J., Spurr, R., Bhartia, P. K., Levelt, P., and
Stephens, G.: Evaluation of the OMI cloud pressures derived from rotational
Raman scattering by comparisons with other satellite data and radiative
transfer simulations, J. Geophys. Res., 113, D15S19,
https://doi.org/10.1029/2007JD008689, 2008.
Wang, P., Stammes, P., van der A, R., Pinardi, G., and van Roozendael, M.: FRESCO+: an improved O2 A-band cloud retrieval algorithm for tropospheric trace gas retrievals, Atmos. Chem. Phys., 8, 6565–6576, https://doi.org/10.5194/acp-8-6565-2008, 2008.
Wang, T., Fetzer, E. J., Wong, S., Kahn, B. H., and Yue, Q.: Validation of
MODIS cloud mask and multilayer flag using CloudSat-CALIPSO cloud profiles
and a cross-reference of their cloud classifica- tions, J. Geophys. Res.-Atmos., 121, 11620–11635, https://doi.org/10.1002/2016JD025239, 2016.
Warren, S. G.: Optical properties of snow, Rev. Geophys., 20, 67–89, 1982.
Yang, P., Bi, L., Baum, B. A., Liou, K.-N., Kattawar, G. W., Mishchenko, M.
I., and Cole, B.: Spectrally Consistent Scattering, Absorption, and
Polarization Properties of Atmospheric Ice Crystals at Wavelengths from 0.2
to 100 µm, J. Atmos. Sci., 70, 330–347,
https://doi.org/10.1175/JAS-D-12-039.1, 2013.
Yang, Y., Di Girolamo, L., and Mazzoni, D.: Selection of the automated
thresholding algorithm for Multi-angle Imaging SpectroRadiometer Radiometric
Camera-by-Camera Cloud Mask over land, Remote Sens. Environ., 107, 159–171,
https://doi.org/10.1016/j.rse.2006.05.020, 2007.
Yang, Y., Marshak, A., Mao, J., Lyapustin, A., and Herman, J.: A Method of
Retrieving Cloud Top Height and Cloud Geometrical Thickness with Oxygen A
and B bands for the Deep Space Climate Observatory (DSCOVR) Mission:
Radiative Transfer Simulations, J. Quant. Spectrosc. Ra., 122,
141–149, https://doi.org/10.1016/j.jqsrt.2012.09.017, 2013.
Yang, Y., Meyer, K., Wind, G., Zhou, Y., Marshak, A., Platnick, S., Min, Q., Davis, A. B., Joiner, J., Vasilkov, A., Duda, D., and Su, W.: Cloud products from the Earth Polychromatic Imaging Camera (EPIC): algorithms and initial evaluation, Atmos. Meas. Tech., 12, 2019–2031, https://doi.org/10.5194/amt-12-2019-2019, 2019 (data available at: https://doi.org/10.5067/EPIC/DSCOVR/L2_Cloud_01).
Zhai, P., Hu, Y., Trepte, C. R., and Lucker, P. L.: A vector radiative
transfer model for coupled atmosphere and ocean systems based on successive
order of scattering method, Opt. Express 17, 2057–2079, 2009.
Zhai, P., Hu, Y., Chowdhary, J., Trepte, C. R., Lucker, P. L., and Josset, D.
B.: A vector radiative transfer model for coupled atmosphere and ocean
systems with a rough interface, J. Quant. Spectrosc. Ra., 111,
1025–1040, 2010.
Zhao, M. and Wang, Z.: Comparison of Arctic clouds between European Center
for Medium-Range Weather Forecasts simulations and Atmospheric Radiation
Measurement Climate Research Facility long-term observations at the North
Slope of Alaska Barrow site, J. Geophys. Res., 115, D23202,
https://doi.org/10.1029/2010JD014285, 2010.
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.
Satellite cloud detection over snow and ice has been difficult for passive remote sensing...
