Articles | Volume 16, issue 6
https://doi.org/10.5194/amt-16-1723-2023
© Author(s) 2023. 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-16-1723-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Mar 2023
Research article |
| 31 Mar 2023
| 31 Mar 2023
Simulation and sensitivity analysis for cloud and precipitation measurements via spaceborne millimeter-wave radar
Leilei Kou
CORRESPONDING AUTHOR
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of
China Meteorological Administration, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Zhengjian Lin
School of Atmospheric Physics, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Haiyang Gao
Collaborative Innovation Center on Forecast and Evaluation of
Meteorological Disasters, Key Laboratory for Aerosol-Cloud-Precipitation of
China Meteorological Administration, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Shujun Liao
School of Atmospheric Physics, Nanjing University of Information
Science and Technology, Nanjing 210044, China
Piman Ding
The First Research Department, Shanghai Institute of Satellite Engineering, Shanghai 201109, China
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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.
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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.
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Severe hail detection with C-band dual-polarisation radars using convolutional neural networks
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
An advanced spatial co-registration of cloud properties for the atmospheric Sentinel missions: Application to TROPOMI
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Information Content of Brightness Temperature Differences of Spaceborne Imagers with respect to Cloud Phase
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
Marine cloud base height retrieval from MODIS cloud properties using machine learning
The EarthCARE mission: science data processing chain overview
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Supercooled liquid water cloud classification using lidar backscatter peak properties
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Cloud mask algorithm from the EarthCARE Multi-Spectral Imager: the M-CM products
Across-track extension of retrieved cloud and aerosol properties for the EarthCARE mission: the ACMB-3D product
Insights into 3D cloud radiative transfer effects for the Orbiting Carbon Observatory
Evaluation of polarimetric ice microphysical retrievals with OLYMPEX campaign data
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 1: Model description and Jacobian calculation
The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations
Near-global distributions of overshooting tops derived from Terra and Aqua MODIS observations
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
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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.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R.H. Barrett
EGUsphere, https://doi.org/10.5194/egusphere-2024-961, https://doi.org/10.5194/egusphere-2024-961, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2024-829, https://doi.org/10.5194/egusphere-2024-829, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-28, https://doi.org/10.5194/amt-2024-28, 2024
Revised manuscript accepted for AMT
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This manuscript describes a new treatment of the spatial mis-registration 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.
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.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
EGUsphere, https://doi.org/10.5194/egusphere-2024-540, https://doi.org/10.5194/egusphere-2024-540, 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 the SEVIRI imager) 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.
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.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
EGUsphere, https://doi.org/10.5194/egusphere-2024-327, https://doi.org/10.5194/egusphere-2024-327, 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, 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.
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.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
EGUsphere, https://doi.org/10.5194/egusphere-2023-1085, https://doi.org/10.5194/egusphere-2023-1085, 2023
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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.
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
Short summary
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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.
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.
Cited articles
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K.,
Grecu, M., Lebsock, M., Watters, D., Mroz, K, Heymsfield, G., Li, L. H., and
Furukawa, K.: Spaceborne cloud and precipitation radars status challenges
and ways forward, Rev. Geophys., 58, e2019RG000686, https://doi.org/10.1029/2019RG000686, 2020.
Behrangi, A., Tian, Y. D., Lambrigtsen, B., and Stephens, G. L.: What does
CloudSat reveal about global land precipitation detection by other
spaceborne sensors?, Water Resour. Res., 50, 4893–4905,
https://doi.org/10.1002/2013WR014566, 2013.
Bohren, C. F. and Huffman, D. R.: Absorption and scattering of light by small
particles, Wiley, New York, https://doi.org/10.1002/9783527618156, 1983.
Brandes, E., Zhang, G. F., and Vivekanandan, J.: Experiments in rainfall
estimation with polarimetric radar in subtropical environment, J. Appl.
Meteorol., 41, 2245–2264, https://doi.org/10.1175/1520-0450(2002)041<0674:EIREWA>2.0.CO;2, 2002.
Brandes, E., Ikeda, K., Zhang, G., Schoenhuber, M., and Rasmussen, R.: A
statistical and physical description of hydrometeor distributions in
Colorado snowstorms using a video disdrometer, J. Appl. Meteorol. Clim.,
46, 634–650, https://doi.org/10.1175/JAM2489.1, 2007.
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler weather radar:
principles and applications, Cambridge Univ., New York, NY, https://doi.org/10.1017/CBO9780511541094, 2001.
Chase, R. J., Nesbitt, S. W., and McFarquhar, G. M.: Evaluation of the
microphysical assumptions within GPM-DPR using ground-based observations of
rain and snow, Atmosphere, 11, 169, https://doi.org/10.3390/atmos11060619, 2020.
Chen, B. J., Yang, J., and Pu, J. P.: Statistical characteristics of raindrop
size distribution in the Meiyu season observed in Eastern China, J.
Meteorol. Soc. Jpn., 91, 215–227, https://doi.org/10.2151/jmsj.2013-208, 2013.
Dodson, J. B., Taylor, P. C., and Branson, M.: Microphysical variability of Amazonian deep convective cores observed by CloudSat and simulated by a multi-scale modeling framework, Atmos. Chem. Phys., 18, 6493–6510, https://doi.org/10.5194/acp-18-6493-2018, 2018.
Ellis, T. D., L'Ecuyer, T., Haynes, J. M., and Stephens, G. L.: How often
does it rain over the global oceans? The perspective from CloudSat, Geophys.
Res. Lett., 36, L03815, https://doi.org/10.1029/2008GL036728, 2009.
Erfani, E. and Mitchell, D. L.: Growth of ice particle mass and projected area during riming, Atmos. Chem. Phys., 17, 1241–1257, https://doi.org/10.5194/acp-17-1241-2017, 2017.
Fu, Q.: An accurate parameterization of the solar radiative properties of
cirrus clouds for climate models, J. Climate, 9, 2058–2082, https://doi.org/10.1175/1520-0442(1996)009<2058:AAPOTS>2.0.CO;2, 1996.
Garrett, T., Yuter, S., Fallgatter, C., Shkurko, K., Rhodes, S., and
Endries, J.: Orientations and aspect ratios of falling snow, Geophys. Res.
Lett., 42, 4617-4622, https://doi.org/10.1002/2015GL064040, 2015.
Hayden, L. and Liu, C. T.: A multiyear analysis of global precipitation
combining CloudSat and GPM precipitation retrievals, J. Hydrometeorol., 19,
1925–1944, https://doi.org/10.1175/JHM-D-18-0053.1, 2018.
Haynes, J. M., Marchand, R. T., Luo, Z., Bodas-Salcedo, A., and Stephens, G.
L.: A multipurpose radar simulation package: quick beam, B. Am. Meteorol.
Soc., 88, 1723–1727, https://doi.org/10.1175/BAMS-88-11-1723, 2007.
Haynes, J. M., L'Ecuyer, T. S., Stephens, G. L., Miller, S. D., Mitrescu, C., Wood, N. B., and Tanelli, S.: Rainfall retrieval over the ocean with spaceborne W-band radar, J. Geophys. Res., 114, D00A22, https://doi.org/10.1029/2008JD009973, 2009.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Moñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., Chiara, G.D., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803,
2020.
Heymsfield, A. J., Schmitt, C., Bansemer, A., and Twohy, C. H.: Improved
representation of ice particle masses based on observations in natural
clouds, J. Atmos. Sci., 67, 3303–3318, https://doi.org/10.1175/2010JAS3507.1, 2010.
Heymsfield, A. J., Schmitt, C., and Bansemer, A.: Ice cloud particle size
distributions and pressure-dependent terminal velocities from in situ
observations at temperatures from 0∘ to −86 ∘C, J. Atmos. Sci., 70, 4123–4154, https://doi.org/10.1175/JAS-D-12-0124.1, 2013.
Heymsfield, A., Szakáll, M., Jost, A., Giammanco, I., and Wright, R.: A
comprehensive observational study of graupel and hail terminal velocity,
mass flux, and kinetic energy, J. Atmos. Sci., 75, 3861–3885, https://doi.org/10.1175/JAS-D-18-0035.s1, 2018.
Hogan, R. J., Tian, L., Brown, P. R. A., Westbrook, C. D., Heymsfield, A. J., and Eastment, J. D.: Radar scattering from ice aggregates using the horizontally aligned oblate spheroid approximation, J. Appl. Meteorol. Clim., 51, 655–671, https://doi.org/10.1175/JAMC-D-11-074.1, 2012.
Horie, H., Takahashi, N., Ohno, Y., Sato, K., Hayasaka, T., Nakamura, K.,
and Im, E.: Simulation for spaceborne cloud profiling Doppler Radar:
EarthCARE/CPR, Proc. SPIE 8523, Remote Sensing of the Atmosphere, Clouds,
and Precipitation IV, 8 November 2012, Kyoto, Japan, https://doi.org/10.1117/12.977253,
2012.
Huang, G.-J., Bringi, V. N., Newman, A. J., Lee, G., Moisseev, D., and Notaroš, B. M.: Dual-wavelength radar technique development for snow rate estimation: a case study from GCPEx, Atmos. Meas. Tech., 12, 1409–1427, https://doi.org/10.5194/amt-12-1409-2019, 2019.
Iguchi, T., Matsui, T., Shi, J. J., Tao, W.-K., Khain, A. P., Hou, A.,
Cifelli, R., Heymsfield, A., and Tokay, A.: Numerical analysis using WRF-SBM
for the cloud microphysical structures in the C3VP field campaign: impacts
of supercooled droplets and resultant riming on snow microphysics, J.
Geophys. Res., 117, D23206, https://doi.org/10.1029/2012JD018101, 2012.
Illingworth, A. J., Barker, H. W., Beljaars, A., Ceccaldi, M., Chepfer, H.,
Clerbaux, N., Cole, J., Delanoë, J., Domenech, C., Donovan, D. P.,
Fukuda, S., Hirakata, M., Hogan, R. J., Huenerbein, A., Kollias, P., Kubota,
T., Nakajima, T., Nakajima, T. Y., Nishizawa, T., Ohno, Y., Okamoto, H., Oki,
R., Sato, K., Satoh, M., Shephard, M. W., Velázquez-Blázquez, A.,
Wandinger, U., Wehr, T., and Van, Z. G.: The EarthCARE Satellite: The Next
Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and
Radiation, B. Am. Meteorol. Soc., 96, 1311–1332, https://doi.org/10.1175/BAMS-D-12-00227.1, 2015.
Jung, Y., Zhang, G., and Xue, M.: Assimilation of simulated polarimetric
radar data for a convective storm using the ensemble Kalman filter. Part I:
observation operators for reflectivity and polarimetric variables, Mon. Weather Rev., 136, 2228–2245, https://doi.org/10.1175/2007mwr2083.1, 2008.
Khain, A. and Pinsky, M.: Physical processes in clouds and cloud modeling, Cambridge University Press, UK, https://doi.org/10.1017/9781139049481, 2018.
Kneifel, S., Maahn, M., Peters, G., and Simmer, C.: Observations of snowfall
with a low-power FM-CW K-band radar (Micro Rain Radar), Meteorol. Atmos.
Phys., 113, 75–87, https://doi.org/10.1007/s00703-011-0142-z, 2011.
Kollias, P. and Albrecht, B. A.: Why the melting layer reflectivity is not
bright at 94-GHz, Geophys. Res. Lett., 32, L24818, https://doi.org/10.1029/2005GL024074, 2005.
Kollias, P., Clothiaux, E. E., and Miller, M. A.: Millimeter-wavelength radars: new frontier in atmospheric cloud and precipitation research, B. Am.
Meteorol. Soc., 88, 1608–1624, https://doi.org/10.1175/BAMS-88-10-1608, 2007.
Lamer, K., Oue, M., Battaglia, A., Roy, R. J., Cooper, K. B., Dhillon, R., and Kollias, P.: Multifrequency radar observations of clouds and precipitation including the G-band, Atmos. Meas. Tech., 14, 3615–3629, https://doi.org/10.5194/amt-14-3615-2021, 2021.
Leinonen, J., Lebsock, M. D., Tanelli, S., Suzuki, K., Yashiro, H., and Miyamoto, Y.: Performance assessment of a triple-frequency spaceborne cloud–precipitation radar concept using a global cloud-resolving model, Atmos. Meas. Tech., 8, 3493–3517, https://doi.org/10.5194/amt-8-3493-2015, 2015.
Li, R., Min, Q. L., Wu, X. Q., and Fu, Y. F.: Retrieving latent heating
vertical structure from cloud and precipitation profiles – part II: deep
convective and stratiform rain processes, J. Quant. Spectrosc. Ra., 122, 47–63, https://doi.org/10.1016/j.jqsrt.2012.11.029, 2013.
Liebe, H. J.: Modeling attenuation and phase of radio waves in air at
frequencies below 1000 GHz, Radio Sci., 16, 1183–1199, https://doi.org/10.1029/RS016i006p01183, 1981.
Lin, Y. L. and Colle, B. A.: A new bulk microphysical scheme that includes
riming intensity and temperature-dependent ice characteristics, Mon. Weather
Rev., 139, 1013–1035, https://doi.org/10.1175/2010MWR3293.1, 2011.
Liu, G. S.: A database of microwave single-scattering properties for
nonspherical ice particles, B. Am. Meteorol. Soc., 89, 1563–1570, https://doi.org/10.1175/2008BAMS2486.1, 2008.
Liu, L. P. and Zhou, M.: Characteristics of bright band and automatic detection algorithm with vertical pointed Ka band cloud radar, Plateau Meteor., 35, 734–744, http://www.gyqx.ac.cn/EN/10.7522/j.issn.1000-0534.2014.00002 (last access: 26 March 2023), 2016.
Luo, Z. Z., Liu, G. Y., and Stephens, G. L.: CloudSat adding new insight into
tropical penetrating convection, Geophys. Res. Lett., 35, L19819, https://doi.org/10.1029/2008GL035330, 2008.
Mace, G. and Benson, S.: Diagnosing cloud microphysical process information
from remote sensing measurements-A feasibility study using aircraft data.
Part I: Tropical anvils measured during TC4, J. Appl. Meteorol. Clim.,
56, 633–649, https://doi.org/10.1175/JAMC-D-16-0083.1, 2017.
Marchand, R. and Mace, G.: Level 2 GEOPROF product process description and interface control document version P1_R05, CloudSat mission and the Data Processing Center (CloudSat DPC) [data set], https://www.cloudsat.cira.colostate.edu/data-products/2b-geoprof (last access: 25 March 2023), 2018.
Marra, A. C., Marra, G. P., and Prodi, F.: Numerical scattering simulations
for interpreting simultaneous observations of clouds by a W-band spaceborne
and a C-band ground radar, Eur. J. Remote Sens., 46, 909–927, https://doi.org/10.5721/EuJRS20134654, 2013.
Mason, B. J.: The physics of clouds, Oxford University Press, London, https://doi.org/10.1007/978-3-642-45881-1_9, 1971.
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., and Kneifel, S.:
Retrievals of riming and snow density from vertically pointing Doppler
radars, J. Geophys. Res., 123, 807–834, https://doi.org/10.1029/2018JD028603, 2018.
Mason, S. L., Hogan, R. J., Westbrook, C. D., Kneifel, S., Moisseev, D., and von Terzi, L.: The importance of particle size distribution and internal structure for triple-frequency radar retrievals of the morphology of snow, Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, 2019.
Masunaga, H., Matsui, T., Tao, W.-K., Hou, A. Y., Kummerow, C. D., Nakajima,
T., Bauer, P., Olson, W. S., Sekiguchi, M., and Nakajima, T. Y.: Satellite
data simulator unit: a multisensor, multispectral satellite simulator
package, B. Am. Meteorol. Soc., 91, 1625–1632, https://doi.org/10.1175/2010BAMS2809.1, 2010.
Matrosov, S. Y.: Modeling backscatter properties of snowfall at millimeter
wavelengths, J. Atmos. Sci., 64, 1727–1736, https://doi.org/10.1175/JAS3904.1, 2007.
Matsui, T., Iguchi, T., Li, X. W., Han, M., Tao, W. K., Petersen, W.,
L'Ecuyer, T., Meneghini, R., Olson, W., Kummerow, C. D., Hou, A. Y.,
Schwaller, M. R., Stocker, E. F., and Kwiatkowski, J.: GPM satellite
simulator over ground validation sites, B. Am. Meteorol. Soc., 94,
1653–1660, https://doi.org/10.1175/BAMS-D-12-00160.1, 2013.
Melnikov, V. and Straka, J.: Axis ratios and flutter angles of cloud ice
particles: Retrievals from radar data, J. Atmos. Ocean. Tech., 30,
1691–1703, https://doi.org/10.1175/JTECH-D-12-00212.1, 2013.
Menzel, W. P., Frey, R. A., Zhang, H., Wylie, D. P., Moeller, C. C., Holz, R. E., Maddux, B., Baum, B. A., Strabala, K. I., and Gumley, L. E.: MODIS global
cloud-top pressure and amount estimation: algorithm description and results,
J. Appl. Meteorol. Clim., 47, 1175–1198, https://doi.org/10.1175/2007JAMC1705.1, 2008.
Miles, N. L., Verlinde, J., and Clothiaux, E. E.: Cloud droplet size
distributions in low-level stratiform clouds, J. Atmos. Sci., 57, 295–311,
https://doi.org/10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2, 2000.
Mishchenko, M. I. and Travis, L. D.: Capabilities and limitations of a
current FORTRAN implementation of the T-matrix method for randomly oriented,
rotationally symmetric scatterers, J. Quant. Spectrosc. Ra.,
60, 309–324, https://doi.org/10.1016/S0022-4073(98)00008-9, 1998.
Moisseev, D., von Lerber, A., and Tiira, J.: Quantifying the effect of
riming on snowfall using ground-based observations, J. Geophys. Res., 122,
4019–4037, https://doi.org/10.1002/2016JD026272, 2017.
National Centers for Environmental Prediction/National Weather Service/NOAA/U.S. Department of Commerce: NCEP FNL Operational Model Global Tropospheric Analyses, continuing from July 1999, updated daily, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6M043C6, 2000.
Niu, S. J. and He, Z. Z.: Study on the water drop size distribution (WDSD)
in precipitous stratiform clouds, Plateau Meteor., 14, 114–120, http://www.gyqx.ac.cn/EN/Y1995/V14/I1/114 (last access: 26 March 2023), 1995.
Nowell, H., Liu, G. S., and Honeyage, R.: Modeling the microwave
single-scattering properties of aggregate snowflakes, J. Geophys. Res., 118,
7873–7885, https://doi.org/10.1002/jgrd.50620, 2013.
Ray, P.: Broadband complex refractive indices of ice and water, Appl. Opt.,
11, 1836–1844, https://doi.org/10.1364/AO.11.001836, 1972.
Ryzhkov, A., Pinsky, M., Pokrovsky, A., and Khain, A.: Polarimetric radar
observation operator for a cloud model with spectral microphysics, J. Appl.
Meteorol. Clim., 50, 873–894, https://doi.org/10.1175/2010JAMC2363.1, 2011.
Ryzhkov, A. V. and Zrnic, D. S.: Radar polarimetry for weather
observations, Springer Press, Cham, Switzerland, https://doi.org/10.1007/978-3-030-05093-1, 2019.
Sassen, K., Matrosov, S., and Campbell, J.: CloudSat spaceborne 94 GHz radar
bright bands in the melting layer: an attenuation-driven upside-down lidar
analog, Geophys. Res. Lett., 34, L16818, https://doi.org/10.1029/2007GL030291, 2007.
Seto, S., Iguchi, T., Meneghini, R., Awaka, J., Kubota, T., Masaki, T., and
Takahashi, N.: The precipitation rate retrieval algorithms for the GPM
dual-frequency precipitation radar, J. Meteorol. Soc. Jpn., 99, 205–237, https://doi.org/10.2151/jmsj.2020-010, 2021.
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Liu, Z. Q., Berner,
J., Wang, W., Powers, J. G., Duda, M. G., Barker, D. M., and Huang, X. Y.: A
description of the advanced research WRF version 4, Tech. Note Rep.,
NCAR/TN-556+STR, NCAR, Boulder, CO, USA, https://doi.org/10.5065/1dfh-6p97, 2019.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang,
Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., and Durden, S. L.: The CloudSat mission and the A-train: a new dimension of space-based
observations of clouds and precipitation, B. Am. Meteorol. Soc., 83,
1771–1790, https://doi.org/10.1175/BAMS-83-12-1771, 2002.
Stephens, G. L., Vane, D. G., Tanelli, S., Im, E., and Suzuki, K.: CloudSat
mission: Performance and early science after the first year of operation, J.
Geophys. Res., 113, D00A18, https://doi.org/10.1029/2008JD009982, 2008.
Stephens, G. L., Winker, D., Pelon, J., Trepte, C., Vane, D., Yuhas, C.,
L'Ecuyer, T., and Lebsock, M.: CloudSat and CALIPSO within the A-Train: Ten
years of actively observing the Earth system, B. Am. Meteorol. Soc., 99,
569–581, https://doi.org/10.1175/BAMS-D-16-0324.1, 2018.
Sy, O. O., Tanelli, S., Durden, S. L., Heymsfield, A., Bansemer, A., Kuo,
K.-S., Niamsuwan, N., Beauchamp, R. M., Chandrasekar, V., Vega, M., and
Johnson, M. P.: Impact of mass-size parameterizations of frozen hydrometeors
on microphysical retrievals: evaluation by matching radar to in situ
observations from GCPEX and OLYMPEx, J. Atmos. Ocean. Tech., 37, 93–112,
https://doi.org/10.1175/JTECH-D-19-0104.1, 2020.
Szyrmer, W. and Zawadzki, I.: Snow studies. Part II: Average relationship
between mass of snowflakes and their terminal fall velocity, J. Atmos. Sci.,
67, 3319–3335, https://doi.org/10.1175/2010JAS3390.1, 2010.
Tanelli, S., Durden, S. L., and Im, E.: CloudSat's cloud profiling radar after two years in orbit: performance, calibration, and processing, IEEE T.
Geosci. Remote, 46, 3560–3573, https://doi.org/10.1109/TGRS.2008.2002030, 2008.
Tanelli, S., Haddad, Z. S., Im, E., and Durden, S. L.: Radar concepts for
the next generation of spaceborne observations of cloud and precipitation
processes, IEEE Radar Conference, 23–27 April 2018, Oklahoma City, OK, USA,
https://doi.org/10.1109/RADAR.2018.8378741, 2018.
Tiira, J., Moisseev, D. N., von Lerber, A., Ori, D., Tokay, A., Bliven, L. F., and Petersen, W.: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland, Atmos. Meas. Tech., 9, 4825–4841, https://doi.org/10.5194/amt-9-4825-2016, 2016.
Tinel, C., Testud, J., Pelon, J., Hogan, R. J., Protat, A., Delano, J., and
Bouniol, D.: The retrieval of ice-cloud properties from cloud radar and
lidar synergy, J. Appl. Meteorol., 44, 860–875, https://doi.org/10.1175/JAM2229.1,
2005.
Tomita, H.: New microphysical schemes with five and six categories by
diagnostic generation of cloud ice, J. Meteorol. Soc. Jpn., 86A, 121–142,
https://doi.org/10.2151/jmsj.86A.121, 2008.
von Lerber, A., Moisseev, D., Bliven, L. F., Petersen, W., Harri, A.-M., and
Chandrasekar, V.: Microphysical properties of snow and their link to Ze-S
relations during BAECC 2014, J. Appl. Meteorol. Clim., 56, 1561–1582,
https://doi.org/10.1175/JAMC-D-16-0379.1, 2017.
Wang, D. W., Liu, L. P., Zhong, L. Z., Wei, Y. Q., and Wang, X. B.: Analysis of the characters of melting layer of cloud radar data and its identification, Meteorol. Mon., 38, 712–721, https://doi.org/10.7519/j.issn.1000-0526.2012.06.009, 2012.
Wang, Y., Han, T., Guo, J. C., Jiang, K., Li, R., Shao, W. C., and Liu, G. S.: Simulation of the capability of Ku, Ka and W tri-frequency satellite-borne radar measuring the three-dimensional structure of cloud and precipitation, Chin. Sci. Bull., 64, 430–443, https://doi.org/10.1360/N972018-00089, 2019.
Wen, L., Zhao, K., Yang, Z. L., Chen, H. N., Huang, H., Chen, G., and Yang,
Z. W.: Microphysics of stratiform and convective precipitation during Meiyu
season in eastern China, J. Geophys. Res., 125, e2020JD032677, https://doi.org/10.1029/2020JD032677, 2020.
Wood, N. B. and L'Ecuyer, T. S.: What millimeter-wavelength radar reflectivity reveals about snowfall: an information-centric analysis, Atmos. Meas. Tech., 14, 869–888, https://doi.org/10.5194/amt-14-869-2021, 2021.
Wood, N. B., L'Ecuyer, T. S., Bliven, F. L., and Stephens, G. L.: Characterization of video disdrometer uncertainties and impacts on estimates of snowfall rate and radar reflectivity, Atmos. Meas. Tech., 6, 3635–3648, https://doi.org/10.5194/amt-6-3635-2013, 2013.
Wood, N. B., L'Ecuyer, T. S., Heymsfield, A. J., and Stephens, G. L.:
Microphysical constraints on millimeter-wavelength scattering properties of
snow particles, J. Appl. Meteorol. Clim., 54, 909–931, https://doi.org/10.1175/JAMC-D-14-0137.1, 2015.
Woods, C. P., Stoelinga, M. T., and Locatelli, J. D.: Size spectra of snow
particles measured in wintertime precipitation in the Pacific Northwest, J.
Atmos. Sci., 65, 189–205, https://doi.org/10.1175/2007JAS2243.1, 2008.
Wu, Q., Hu, Y., and Lu, N. M.: A simulation research on the wave bands selection of a satellite-borne rain measuring radar, Acta Meteorol. Sin., 69, 344–351, https://doi.org/10.11676/qxxb2011.029, 2011.
Wu, Q., Yang, M. L., Dou, F. L., Guo, Y., and An, D. W.: A study of cloud
parameters retrieval algorithm for spaceborne millimeter wavelength cloud
radar, Acta Meteorol. Sin., 76, 160–168, https://doi.org/10.11676/qxxb2017.069, 2018.
Yin, J. F., Wang, D. H., and Zhai, G. Q.: Long-term in situ measurements of
the cloud-precipitation microphysical properties over East Asia, Atmos.
Res., 102, 206–217, https://doi.org/10.1016/j.atmosres.2011.07.002, 2011.
Yin, M. T., Liu, G. S., Honeyager, R., and Turk, F. J.: Observed differences of triple-frequency radar signatures between snowflakes in stratiform and
convective clouds, J. Quant. Spectrosc. Ra., 193, 13–20, https://doi.org/10.1016/j.jqsrt.2017.02.017, 2017.
Zhang, G. F.: Weather radar polarimetry, CRC Press, New York, NY, https://doi.org/10.1201/9781315374666, 2017.
Short summary
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.
Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented....
