Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7729-2021
© Author(s) 2021. 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-14-7729-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2021
Research article |
| 09 Dec 2021
| 09 Dec 2021
Inpainting radar missing data regions with deep learning
Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division, Richland, WA, USA
Joseph C. Hardin
Pacific Northwest National Laboratory, Atmospheric Sciences and Global Change Division, Richland, WA, USA
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Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
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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.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
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This article presents a novel technique to estimate the liquid water content (LWC) 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 to retrieve the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of the LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
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.
Jinyi Xia and Li Guan
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This study presents a method for estimating cloud cover from FY4A AGRI observations using LSTM neural networks. 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.
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.
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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.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
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This study demonstrates the potential for 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.
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.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-87, https://doi.org/10.5194/amt-2024-87, 2024
Revised manuscript accepted for AMT
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1180, https://doi.org/10.5194/egusphere-2024-1180, 2024
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We introduce an innovative method to retrieve cloud fraction and optical thickness based on polarimetry, well-suited for satellite observations providing multi-angle polarization measurements. The cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and a second in the sun-glint region or at a scattering angle of approximately 90°.
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.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-837, https://doi.org/10.5194/egusphere-2024-837, 2024
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In this study, we present a toolkit of two Python algorithms to extract information about the cloud and precipitation particles present in clouds from data measured by ground-based radar instruments. The data consist of Doppler spectra, in which several peaks are formed by hydrometeor populations with different fall velocities. The detection of the specific peaks makes it possible to assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
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.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles, and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
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.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Cited articles
Agrawal, S., Barrington, L., Bromberg, C., Burge, J., Gazen, C., and Hickey,
J.: Machine learning for precipitation nowcasting from radar images, arXiv [preprint], arXiv:1912.12132, 11 December 2019. a
Arjovsky, M., Chintala, S., and Bottou, L.: Wasserstein generative adversarial networks, in: International conference on machine learning, PMLR, 70, 214–223, available at: https://proceedings.mlr.press/v70/arjovsky17a.html (last access: 22 March 2020), 2017. a
Bertalmio, M., Sapiro, G., Caselles, V., and Ballester, C.: Image Inpainting,
in: Proceedings of the 27th annual conference on Computer graphics and
interactive techniques, SIGGRAPH '00, Addison-Wesley Publishing Co., USA, 417–424, https://doi.org/10.1145/344779.344972, 2000. a
Bertalmio, M., Bertozzi, A. L., and Sapiro, G.: Navier-stokes, fluid
dynamics, and image and video inpainting, in: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Kauai, HI, USA, 8–14 December 2001, IEEE CVPR, 1, https://doi.org/10.1109/CVPR.2001.990497, 2001. a
Bugeau, A., Bertalmío, M., Caselles, V., and Sapiro, G.: A Comprehensive
Framework for Image Inpainting, IEEE T. Image Process., 19,
2634–2645, https://doi.org/10.1109/TIP.2010.2049240, 2010. a
Criminisi, A., Perez, P., and Toyama, K.: Region filling and object
removal by exemplar-based image inpainting, IEEE T. Image Process., 13, 1200–1212, https://doi.org/10.1109/TIP.2004.833105, 2004. a
Efros, A. A. and Leung, T. K.: Texture Synthesis by Non-parametric Sampling,
IEEE I. Conf. Comp. Vis., 2, 1033–1038, https://doi.org/10.1109/ICCV.1999.790383, 1999. a, b
Elharrouss, O., Almaadeed, N., Al-Maadeed, S., and Akbari, Y.: Image
Inpainting: A Review, Neural Process. Lett., 51, 2007–2028,
https://doi.org/10.1007/s11063-019-10163-0, 2020. a
Feng, Z., Leung, L. R., Houze Jr., R. A., Hagos, S., Hardin, J., Yang, Q., Han, B., and Fan, J.: Structure and evolution of mesoscale convective systems: Sensitivity to cloud microphysics in convection-permitting simulations over the United States, J. Adv. Model. Earth Sy., 10, 1470–1494, https://doi.org/10.1029/2018MS001305, 2018. a
Geiss, A. and Hardin, J. C.: avgeiss/radar_inpainting: AMT Supplementary Code, Zenodo [code], https://doi.org/10.5281/zenodo.5643624, 2021a. a
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D.,
Ozair, S., Courville, A., and Bengio, Y.: Generative Adversarial Networks,
arXiv, arXiv:1406.2661, 10 June 2014. a, b, c
Guillemot, C. and Le Meur, O.: Image Inpainting : Overview and Recent
Advances, IEEE Signal Proc. Mag., 31, 127–144,
https://doi.org/10.1109/MSP.2013.2273004, 2014. a
Hardin, J., Hunzinger, A., Schuman, E., Matthews, A., Bharadwaj, N., Varble, A., Johnson, K., and Giangrande, S.: Ka ARM Zenith Radar (KAZRCFRGEQC), Atmospheric Radiation Measurement (ARM) User Facility, ARM [dataset], https://doi.org/10.5439/1615726, 2019a. a, b, c
Hardin, J., Hunzinger, A., Schuman, E., Matthews, A., Bharadwaj, N., Varble, A., Johnson, K., and Giangrande, S: C-Band Scanning ARM Precipitation Radar (CSAPR2CFRPPIQC), Atmospheric Radiation Measurement (ARM) User Facility, ARM [dataset], https://doi.org/10.5439/1615604, 2019b. a
Hardin, J., Hunzinger, A., Schuman, E., Matthews, A., Bharadwaj, N., Varble,
A., Johnson, K., and Giangrande, S.: CACTI Radar b1 Processing: Corrections, Calibrations, and Processing Report, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, Technical Report, DOE/SC-ARM-TR-244, 1–55, https://arm.gov/publications/brochures/doe-sc-arm-tr-244.pdf, last access: 19 May 2020. a, b
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q.: Densely Connected Convolutional Networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, IEEE CVPR, 2261–2269, https://doi.org/10.1109/CVPR.2017.243, 2017. a, b, c
Hubbert, J. C., Dixon, M., Ellis, S. M., and Meymaris, G.: Weather Radar Ground Clutter. Part I: Identification, Modeling, and Simulation, J. Atmos. Ocean. Techn., 26, 1165–1180, https://doi.org/10.1175/2009JTECHA1159.1, 2009a. a
Hubbert, J. C., Dixon, M., and Ellis, S. M.: Weather Radar Ground Clutter. Part II: Real-Time Identification and Filtering, J. Atmos. Ocean. Techn., 26, 1181–1197, https://doi.org/10.1175/2009JTECHA1160.1, 2009b. a
Isola, P., Zhu, J., Zhou, T., and Efros, A. A.: Image-to-Image
Translation with Conditional Adversarial Networks, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, IEEE CVPR, 5967–5976, https://doi.org/10.1109/CVPR.2017.632, 2017. a, b, c, d
Isom, B., Palmer, R., Secrest, G., Rhoton, R., Saxion, D., Allmon, T., Reed,
J., Crum, T., and Vogt, R.: Detailed observations of wind turbine clutter
with scanning weather radars, J. Atmos. Ocean. Techn., 26, 894–910, https://doi.org/10.1175/2008JTECHA1136.1, 2009. a
Jain, V. and Seung, H.: Natural Image Denoising with Convolutional Networks,
in: Advances in Neural Information Processing Systems 21–Proceedings of the 2008 Conference, NIPS'08, Curran Associates Inc., Red Hook, NY, USA, 769–776, https://dl.acm.org/doi/10.5555/2981780.2981876 (last access: 22 March 2020), 2008. a
Jam, J., Kendrick, C., Walker, K., Drouard, V., Hsu, J. G.-S., and Yap, M. H.: A comprehensive review of past and present image inpainting methods, Computer Vision and Image Understanding, 203, 103147, https://doi.org/10.1016/j.cviu.2020.103147, 2021. a, b
Johnson, K., Fairless, T., and Giangrande, S.: Ka-Band ARM Zenith Radar
Corrections (KAZRCOR, KAZRCFRCOR) Value-Added Products, Technical Report, U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research, 18 pp., available at:
https://www.arm.gov/publications/tech_reports/doe-sc-arm-tr-203.pdf (last access: 22 March 2021), 2020. a
Lang, T. J., Nesbitt, S. W., and Carey, L. D.: On the Correction of Partial
Beam Blockage in Polarimetric Radar Data, J. Atmos. Ocean. Techn., 26, 943–957, https://doi.org/10.1175/2008JTECHA1133.1, 2009. a
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A.,
Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., and Shi, W.: Photo-Realistic Single Image Super-Resolution Using a Generative
Adversarial Network, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, IEEE CVPR, Honolulu, HI, USA, 21–26 July 2017, 105–114, https://doi.org/10.1109/CVPR.2017.19, 2017. a
Liu, S., DiMego, G., Guan, S., Kumar, V. K., Keyser, D., Xu, Q., Nai, K.,
Zhang, P., Liu, L., Zhang, J., Howard, K., and Ator, J.: WSR-88D Radar Data
Processing at NCEP, Weather Forecast., 31, 2047–2055,
https://doi.org/10.1175/WAF-D-16-0003.1, 2016. a
Manabe, T. and Ihara, T.: A feasibility study of rain radar for the tropical rainfall measuring mission, 5. Effects of surface clutter on rain
measurements from satellite, J. Radio Res. Lab., 35, 163–181, http://www.nict.go.jp/publication/journal/35/145/Journal_Vol35_No145_pp163-181.pdf (last access: 22 March 2021), 1988. a
Moszkowicz, S., Ciach, G. J., and Krajewski, W. F.: Statistical Detection of
Anomalous Propagation in Radar Reflectivity Patterns, J. Atmos. Ocean. Techn., 11, 1026–1034,
https://doi.org/10.1175/1520-0426(1994)011<1026:SDOAPI>2.0.CO;2, 1994. a
Odena, A., Dumoulin, V., and Olah, C.: Deconvolution and Checkerboard
Artifacts, Distill, https://doi.org/10.23915/distill.00003, 2016. a
Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., and Efros, A. A.: Context Encoders: Feature Learning by Inpainting, in: 2016 IEEE Conference on
Computer Vision and Pattern Recognition, IEEE CVPR, Las Vegas, NV, USA, 27–30 June 2016, 2536–2544, https://doi.org/10.1109/CVPR.2016.278, 2016. a
Prudden, R., Adams, S., Kangin, D., Robinson, N., Ravuri, S., Mohamed, S., and Arribas, A.: A review of radar-based nowcasting of precipitation and
applicable machine learning techniques, arXiv [preprint], arXiv:2005.04988, 11 May 2020. a
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for
Biomedical Image Segmentation, in: Medical Image Computing and
Computer-Assisted Intervention, MICCAI, 9351, 234–241,
https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a, b
Shih, T. K., Liang-Chen Lu, Ying-Hong Wang, and Rong-Chi Chang:
Multi-resolution image inpainting, in: 2003 International Conference on
Multimedia and Expo, ICME'03, Proceedings, Baltimore, MD, USA, 6–9 July 2003, 485, https://doi.org/10.1109/ICME.2003.1220960, 2003. a
Tagawa, T. and Okamoto, K.: Calculations of surface clutter interference
with precipitation measurement from space by 35.5 GHz radar for Global
Precipitation Measurement Mission, in: 2003 IEEE International
Geoscience and Remote Sensing Symposium, IGARSS, Proceedings, Toulouse, France, Int. Geosci. Remote. Se., 21–25 July 2003, 5, 3172–3174, https://doi.org/10.1109/IGARSS.2003.1294719, 2003. a, b
Telea, A.: An Image Inpainting Technique Based on the Fast Marching Method,
Journal of Graphics Tools, 9, 23–24, https://doi.org/10.1080/10867651.2004.10487596, 2004. a
Varble, A., Nesbitt, S., Salio, P., Zipser, E., van den Heever, S., MdFarquhar, G., Kollias, P., Kreidenweis, S., DeMott, P., Jensen, M., Houze, Jr, R., Rasmussen, K., Leung, R., Romps, D., Gochis, D., Avila, E., Williams, C. R., and Borque, P.: Cloud, Aerosol, and Complex Terrain Interactions (CACTI), Science Plan, DOE Technical Report, DOE/SC-ARM-17-004, https://www.arm.gov/publications/programdocs/doe-sc-arm-17-004.pdf (last access: 15 May 2020), 2018. a
Veillette, M. S., Hassey, E. P., Mattioli, C. J., Iskenderian, H., and Lamey,
P. M.: Creating Synthetic Radar Imagery Using Convolutional Neural Networks, J. Atmos. Ocean. Techn., 35, 2323–2338, https://doi.org/10.1175/JTECH-D-18-0010.1, 2018. a, b
Westrick, K. J., Mass, C. F., and Colle, B. A.: The Limitations of the WSR-88D Radar Network for Quantitative Precipitation Measurement over the Coastal Western United States, B. Am. Meteorol. Soc., 80, 2289–2298, https://doi.org/10.1175/1520-0477(1999)080<2289:TLOTWR>2.0.CO;2, 1999. a
Widener, K., Bharadwaj, N., and Johnson, K.: Ka-Band ARM Zenith Radar (KAZR)
Instrument Handbook, DOE Office of Science Atmospheric Radiation Measurement (ARM) Program, United States, Technical Report, DOE/SC-ARM/TR-106, https://doi.org/10.2172/1035855, 2012. a, b, c
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., and Li, H.:
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis,
in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, IEEE CVPR, 4076–4084, https://doi.org/10.1109/CVPR.2017.434, 2017. a
Young, C. B., Nelson, B. R., Bradley, A. A., Smith, J. A., Peters-Lidard,
C. D., Kruger, A., and Baeck, M. L.: An evaluation of NEXRAD precipitation
estimates in complex terrain, J. Geophys. Res.-Atmos., 104, 19691–19703, https://doi.org/10.1029/1999JD900123, 1999. a, b
Zhang, J., Howard, K., Langston, C., Vasiloff, S., Kaney, B., Arthur, A.,
Cooten, S. V., Kelleher, K., Kitzmiller, D., Ding, F., Seo, D.-J., Wells, E., and Dempsey, C.: National Mosaic and Multi-Sensor QPE (NMQ) System:
Description, Results, and Future Plans, B. Am. Meteorol. Soc., 92, 1321–1338, https://doi.org/10.1175/2011BAMS-D-11-00047.1, 2011.
a
Zhang, P., Zrnic, D., and Ryzhkov, A.: Partial Beam Blockage Correction Using
Polarimetric Radar Measurements, J. Atmos. Ocean. Techn., 30, 861–872, https://doi.org/10.1175/JTECH-D-12-00075.1, 2013. a
Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J.: UNet++: A Nested
U-Net Architecture for Medical Image Segmentation, in: Deep Learning in
Medical Image Analysis and Multimodal Learning for Clinical Decision Support, DLMIA, 11045, 3–1, https://doi.org/10.1007/978-3-030-00889-5_1, 2018. a, b
Short summary
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage, instrument failure, and near-ground blind zones, etc. Here, we demonstrate how deep convolutional neural networks can be used for filling in radar-missing data regions and that they can significantly outperform conventional approaches in terms of realism and accuracy.
Radars can suffer from missing or poor-quality data regions for several reasons: beam blockage,...
