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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Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
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The algorithm of microphysical parameter profiles of aerosol and small cloud droplets based on the dual wavelength Lidar data
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Gerald G. Mace
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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.
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 proposed an inversion method of atmosphere 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. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmosphere particles and has good robustness.
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EGUsphere, https://doi.org/10.5194/egusphere-2023-2345, https://doi.org/10.5194/egusphere-2023-2345, 2024
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We introduce ProPS – a new method to detect clouds and their thermodynamic phase using a geostationary satellite. It distinguishes between clear sky, ice, mixed-phase, supercooled and warm liquid clouds. ProPS uses a Bayesian approach with the lidar-radar product DARDAR as reference data. The new method allows studying different cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparisons with climate models.
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.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-252, https://doi.org/10.5194/amt-2023-252, 2024
Revised manuscript accepted for AMT
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The radar-lidar synergy is used to retrieve ice, supercooled water and mixed-phase clouds properties, making the most of the radar sensitivity to ice crystals and the lidar to the supercooled droplets. 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.
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.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1953, https://doi.org/10.5194/egusphere-2023-1953, 2023
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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 limited amount of observations of clouds that are currently available. Our work proposes to address this 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.
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.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
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The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over 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.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
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Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
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We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
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SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
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Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
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,...
