Articles | Volume 13, issue 9
https://doi.org/10.5194/amt-13-5065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-13-5065-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Sep 2020
Research article |
| 28 Sep 2020
| 28 Sep 2020
Estimating total attenuation using Rayleigh targets at cloud top: applications in multilayer and mixed-phase clouds observed by ground-based multifrequency radars
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
Alessandro Battaglia
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
Department of Physics and Astronomy, University of Leicester, Leicester, UK
Stefan Kneifel
Institute for Geophysics and Meteorology, University of Cologne, Cologne, Germany
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Shannon L. Mason, Robin J. Hogan, Christopher D. Westbrook, Stefan Kneifel, Dmitri Moisseev, and Leonie von Terzi
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Claudia Acquistapace, Stefan Kneifel, Ulrich Löhnert, Pavlos Kollias, Maximilian Maahn, and Matthias Bauer-Pfundstein
Atmos. Meas. Tech., 10, 1783–1802, https://doi.org/10.5194/amt-10-1783-2017, https://doi.org/10.5194/amt-10-1783-2017, 2017
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Francesco De Angelis, Domenico Cimini, James Hocking, Pauline Martinet, and Stefan Kneifel
Geosci. Model Dev., 9, 2721–2739, https://doi.org/10.5194/gmd-9-2721-2016, https://doi.org/10.5194/gmd-9-2721-2016, 2016
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Heike Kalesse, Wanda Szyrmer, Stefan Kneifel, Pavlos Kollias, and Edward Luke
Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, https://doi.org/10.5194/acp-16-2997-2016, 2016
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Mixed-phase clouds are ubiquitous. Process-level understanding is needed to address the complexity of mixed-phase clouds and to improve their representation in models. This study illustrates steps to identify the impact of a microphysical process (riming) on cloud Doppler radar observations. It suggests that in situ observations of key ice properties are needed to complement radar observations before process-oriented studies can effectively evaluate ice microphysical parameterizations in models.
I. V. Gorodetskaya, S. Kneifel, M. Maahn, K. Van Tricht, W. Thiery, J. H. Schween, A. Mangold, S. Crewell, and N. P. M. Van Lipzig
The Cryosphere, 9, 285–304, https://doi.org/10.5194/tc-9-285-2015, https://doi.org/10.5194/tc-9-285-2015, 2015
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Our paper presents a new cloud-precipitation-meteorological observatory established in the escarpment zone of Dronning Maud Land, East Antarctica. The site is characterised by bimodal cloud occurrence (clear sky or overcast) with liquid-containing clouds occurring 20% of the cloudy periods. Local surface mass balance strongly depends on rare intense snowfall events. A substantial part of the accumulated snow is removed by surface and drifting snow sublimation and wind-driven snow erosion.
A. Battaglia, C. D. Westbrook, S. Kneifel, P. Kollias, N. Humpage, U. Löhnert, J. Tyynelä, and G. W. Petty
Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, https://doi.org/10.5194/amt-7-1527-2014, 2014
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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
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
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This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
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The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
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This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
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The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
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In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Andrzej Zbigniew Kotarba and Izabela Wojciechowska
EGUsphere, https://doi.org/10.5194/egusphere-2024-3693, https://doi.org/10.5194/egusphere-2024-3693, 2025
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The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar-radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study discovers how small threshold changes significantly affect climatology of severe storms.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
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We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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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
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1651, https://doi.org/10.5194/egusphere-2024-1651, 2024
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Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared and the likelihood of them differing by 10 W/m2 defines the closure.
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.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1936, https://doi.org/10.5194/egusphere-2024-1936, 2024
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Retrievals of CO2 column-averaged dry air mole fractions from space can be done with spaceborne spectrometers such as OCO-2. Clouds in the vicinity of a footprint lead to spectral perturbations that bias those retrievals well beyond the required accuracy for global assessments of CO2 sources and sinks. This paper presents two physics-based mitigation techniques for these biases based on accompanying imagery, which can be used operationally.
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.
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.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
EGUsphere, https://doi.org/10.5194/egusphere-2023-2885, https://doi.org/10.5194/egusphere-2023-2885, 2024
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Fog and low stratus (FLS) are both persistent clouds close to the Earth’s surface. In the context of photovoltaic power production, FLS is particularly important, as FLS, impact large regions simultaneously, making regional power grid balancing hard. This study introduces a new machine leanring based algorithm developed for the MSG geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 H day cycle.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Cited articles
Barrett, A. I., Westbrook, C. D., Nicol, J. C., and Stein, T. H. M.: Rapid ice aggregation process revealed through triple-wavelength Doppler spectrum radar analysis, Atmos. Chem. Phys., 19, 5753–5769, https://doi.org/10.5194/acp-19-5753-2019, 2019. a
Battaglia, A., Kummerow, C., Shin, D.-B., and Williams, C.: Toward
characterizing the effect of radar bright bands on microwave brightness
temperatures, J. Atmos. Ocean. Technol., 20, 856–871,
https://doi.org/10.1175/1520-0426(2003)020<0856:CMBTBR>2.0.CO;2, 2003. a
Battaglia, A., Westbrook, C. D., Kneifel, S., Kollias, P., Humpage, N., Löhnert, U., Tyynelä, J., and Petty, G. W.: G band atmospheric radars: new frontiers in cloud physics, Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, 2014. a, b, c
Battaglia, A., Mroz, K., Lang, T., Tridon, F., Tanelli, S., Tian, L., and
Heymsfield, G. M.: Using a multiwavelength suite of microwave instruments to
investigate the microphysical structure of deep convective cores, J. Geophys. Res.-Atmos., 121,
9356–9381, https://doi.org/10.1002/2016JD025269, 2016. a, b, c
Battaglia, A., Kollias, P., Dhillon, R., Roy, R., Tanelli, S., Lamer, K.,
Grecu, M., Lebsock, M., Watters, D., Mroz, K., Heymsfield, G., Li, L., and
Furukawa, K.: Spaceborne Cloud and Precipitation Radars: Status, Challenges,
and Ways Forward, Rev. Geophys., 58, e2019RG000686,
https://doi.org/10.1029/2019RG000686, 2020a. a, b, c, d, e, f, g
Cadeddu, M. and Ghate, V.: Microwave Radiometer, 3 Channel (MWR3C),
2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP) University of Helsinki Research
Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), Atmospheric Radiation
Measurement (ARM) user facility, https://doi.org/10.5439/1025248, 2014a. a, b
Cadeddu, M. and Ghate, V.: Microwave Radiometer (MWRLOS), 2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP) U. of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), Atmospheric Radiation Measurement (ARM) user facility, https://doi.org/10.5439/1046211,
2014b. a, b
Chase, R. J., Finlon, J. A., Borque, P., McFarquhar, G. M., Nesbitt, S. W.,
Tanelli, S., Sy, O. O., Durden, S. L., and Poellot, M. R.: Evaluation of
Triple-Frequency Radar Retrieval of Snowfall Properties Using Coincident
Airborne In Situ Observations During OLYMPEX, Geophys. Res. Lett., 45,
5752–5760, https://doi.org/10.1029/2018GL077997, 2018. a
Cooper, K. B., Rodriguez Monje, R., Millán, L., Lebsock, M.,
Tanelli, S., Siles, J. V., Lee, C., and Brown, A.: Atmospheric
Humidity Sounding Using Differential Absorption Radar Near 183 GHz, IEEE
Geosci. Remote Sens. Lett., 15, 163–167,
https://doi.org/10.1109/LGRS.2017.2776078, 2018. a
Delrieu, G., Caoudal, S., and Creutin, J. D.: Feasibility of Using Mountain
Return for the Correction of Ground-Based X-Band Weather Radar Data, J.
Atmos. Ocean. Technol., 14, 368–385,
https://doi.org/10.1175/1520-0426(1997)014<0368:FOUMRF>2.0.CO;2, 1997. a
Dias Neto, J., Kneifel, S., Ori, D., Trömel, S., Handwerker, J., Bohn, B., Hermes, N., Mühlbauer, K., Lenefer, M., and Simmer, C.: The TRIple-frequency and Polarimetric radar Experiment for improving process observations of winter precipitation, Earth Syst. Sci. Data, 11, 845–863, https://doi.org/10.5194/essd-11-845-2019, 2019. a, b, c, d, e, f
Duncan, D. I. and Eriksson, P.: An update on global atmospheric ice estimates from satellite observations and reanalyses, Atmos. Chem. Phys., 18, 11205–11219, https://doi.org/10.5194/acp-18-11205-2018, 2018. a, b
Ellison, W. J.: Permittivity of Pure Water, at Standard Atmospheric Pressure,
over the Frequency Range 0–25THz and the Temperature Range 0–100 ∘C, J. Phys. Chem. Ref. Data, 36, 1–18,
https://doi.org/10.1063/1.2360986, 2007. a, b, c, d
Firda, J. M., Sekelsky, S. M., and McIntosh, R. E.: Application of
Dual-Frequency Millimeter-Wave Doppler Spectra for the Retrieval of Drop Size
Distributions and Vertical Air Motion in Rain, J. Atmos.
Ocean. Technol., 16, 216–236,
https://doi.org/10.1175/1520-0426(1999)016<0216:AODFMW>2.0.CO;2, 1999. a
Grecu, M., Tian, L., Heymsfield, G. M., Tokay, A., Olson, W. S., Heymsfield,
A. J., and Bansemer, A.: Nonparametric Methodology to Estimate Precipitating
Ice from Multiple-Frequency Radar Reflectivity Observations, J.
Appl. Meteorol. Climatol., 57, 2605–2622,
https://doi.org/10.1175/JAMC-D-18-0036.1, 2018. a
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.-Atmos., 114, D00A22, https://doi.org/10.1029/2008JD009973, 2009. a, b
Hitschfeld, W. and Bordan, J.: Errors Inherent in the Radar Measurement of
Rainfall at Attenuating Wavelengths, J. Meteorology, 11, 58–67,
https://doi.org/10.1175/1520-0469(1954)011<0058:EIITRM>2.0.CO;2, 1954. a
Hogan, R. J. and Illingworth, A. J.: The Potential of Spaceborne
Dual-Wavelength Radar to Make Global Measurements of Cirrus Clouds, J. Atmos. Ocean. Technol., 16, 518–531,
https://doi.org/10.1175/1520-0426(1999)016<0518:TPOSDW>2.0.CO;2, 1999. a
Hogan, R. J., Illingworth, A. J., and Sauvageot, H.: Measuring Crystal Size in Cirrus Using 35- and 94-GHz Radars, J. Atmos. Ocean.
Technol., 17, 27–37,
https://doi.org/10.1175/1520-0426(2000)017<0027:MCSICU>2.0.CO;2, 2000. a
Hogan, R. J., Mittermaier, M. P., and Illingworth, A. J.: The retrieval of ice
water content from radar reflectivity factor and temperature and its use in
the evaluation of a mesoscale model, J. Appl. Meteorol., 45, 301–317,
https://doi.org/10.1175/JAM2340.1, 2006. a
Houze, R. A.: Cloud Dynamics, International Geophysics Series, Academic
Press, 2014. a
Huang, D., Johnson, K., Liu, Y., and Wiscombe, W.: High resolution retrieval of
liquid water vertical distributions using collocated Ka-band and W-band cloud
radars, Geophys. Res. Lett., 36, L24807, https://doi.org/10.1029/2009GL041364, 2009. a, b
Iguchi, T. and Matsui, T.: Remote Sensing of Clouds and Precipitation, chap.
Advances in Clouds and Precipitation Modeling Supported by Remote Sensing
Measurements, Springer Remote Sensing/Photogrammetry, Springer, Cham, 2018. a
Iguchi, T. and Meneghini, R.: Intercomparison of Single-Frequency Methods for
Retrieving a Vertical Rain Profile from Airborne or Spaceborne Radar Data,
J. Atmos. Ocean. Technol., 11, 1507–1516,
https://doi.org/10.1175/1520-0426(1994)011<1507:IOSFMF>2.0.CO;2, 1994. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working
Group I to the Fifth Assessment Report of the Intergovernmental Panel on
Climate Change, Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, https://doi.org/10.1017/CBO9781107415324, 2013. a
Isom, B., Lindenmaier, I., and Matthews, A.: Marine W-Band (95 GHz) ARM Cloud Radar (MWACR), 2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP) University of Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1), Atmospheric Radiation Measurement (ARM) user facility, https://doi.org/10.5439/1150242, 2014a. a, b
Isom, B., Lindenmaier, I., Nelson, D., and Matthews, A.: Ka ARM Zenith
Radar (KAZR), 2014-02-21 to 2014-03-22, ARM Mobile Facility (TMP), Univrsity of
Helsinki Research Station (SMEAR II), Hyytiala, Finland; AMF2 (M1),
Atmospheric Radiation Measurement (ARM) user facility, https://doi.org/10.5439/1095601, 2014b. a, b
Kalesse, H., Szyrmer, W., Kneifel, S., Kollias, P., and Luke, E.: Fingerprints of a riming event on cloud radar Doppler spectra: observations and modeling, Atmos. Chem. Phys., 16, 2997–3012, https://doi.org/10.5194/acp-16-2997-2016, 2016. a, b, c
Kneifel, S., Löhnert, U., Battaglia, A., Crewell, S., and Siebler, D.: Snow
scattering signals in ground-based passive microwave radiometer measurements,
J. Geophys. Res., 115, D16214, https://doi.org/10.1029/2010JD013856, 2010. a
Kneifel, S., von Lerber, A., Tiira, J., Moisseev, D., Kollias, P.,
and Leinonen, J.: Observed relations between snowfall microphysics and
triple-frequency radar measurements, J. Geophys. Res., 120, 6034–6055,
https://doi.org/10.1002/2015JD023156, 2015. a, b
Kneifel, S., Kollias, P., Battaglia, A., Leinonen, J., Maahn, M., Kalesse, H.,
and Tridon, F.: First observations of triple-frequency radar Doppler spectra
in snowfall: Interpretation and applications, Geophys. Res. Lett., 43,
2225–2233, https://doi.org/10.1002/2015GL067618, 2016. a
Kneifel, S., Leinonen, J., Tyynelä, J., Ori, D., and Battaglia, A.: Scattering of Hydrometeors, in: Satellite Precipitation Measurement, Advances in Global Change Research, edited by: Levizzani, V., Kidd, C., Kirschbaum, D., Kummerow, C., Nakamura, K., and Turk, F., Vol 67. Springer, Cham., https://doi.org/10.1007/978-3-030-24568-9_15, 2020. a, b
Kollias, P., Clothiaux, E. E., Miller, M. A., Albrecht, B. A., Stephens, G. L.,
and Ackerman, T. P.: 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. a
Kollias, P., Bharadwaj, N., Clothiaux, E. E., Lamer, K., Oue, M., Hardin, J.,
Isom, B., Lindenmaier, I., Matthews, A., Luke, E. P., Giangrande, S. E.,
Johnson, K., Collis, S., Comstock, J., and Mather, J. H.: The ARM Radar
Network: At the Leading Edge of Cloud and Precipitation Observations,
B. Am. Meteorol. Soc., 101, E588–E607,
https://doi.org/10.1175/BAMS-D-18-0288.1, 2020. a
Küchler, N., Kneifel, S., Löhnert, U., Kollias, P., Czekala, H., and Rose,
T.: A W-Band Radar-Radiometer System for Accurate and Continuous
Monitoring of Clouds and Precipitation, J. Atmos. Ocean. Technol., 34,
2375–2392, https://doi.org/10.1175/JTECH-D-17-0019.1, 2017. a
Kulie, M. S., Hiley, M. J., Bennartz, R., Kneifel, S., and Tanelli, S.:
Triple-Frequency Radar Reflectivity Signatures of Snow: Observations and
Comparisons with Theoretical Ice Particle Scattering Models, J. Appl. Meteor. Climatol., 53, 1080–1098,
https://doi.org/10.1175/JAMC-D-13-066.1, 2014. a
Kumjian, M. R., Rutledge, S. A., Rasmussen, R. M., Kennedy, P. C., and Dixon,
M.: High-Resolution Polarimetric Radar Observations of Snow-Generating Cells, J. Appl. Meteor. Climatol.,
53, 1636–1658, https://doi.org/10.1175/JAMC-D-13-0312.1, 2014. a, b
L'Ecuyer, T. S. and Stephens, G. L.: An Estimation-Based Precipitation
Retrieval Algorithm for Attenuating Radars, J. Appl. Meteorol., 41,
272–285, https://doi.org/10.1175/1520-0450(2002)041<0272:AEBPRA>2.0.CO;2, 2002. a, b
L’Ecuyer, T. S., Beaudoing, H. K., Rodell, M., Olson, W., Lin, B., Kato, S.,
Clayson, C. A., Wood, E., Sheffield, J., Adler, R., Huffman, G., Bosilovich,
M., Gu, G., Robertson, F., Houser, P. R., Chambers, D., Famiglietti, J. S.,
Fetzer, E., Liu, W. T., Gao, X., Schlosser, C. A., Clark, E., Lettenmaier,
D. P., and Hilburn, K.: The Observed State of the Energy Budget in the Early
Twenty-First Century, J. Climate, 28, 8319–8346, https://doi.org/10.1175/JCLI-D-14-00556.1, 2015. a
Leinonen, J., Lebsock, M. D., Tanelli, S., Sy, O. O., Dolan, B., Chase, R. J., Finlon, J. A., von Lerber, A., and Moisseev, D.: Retrieval of snowflake microphysical properties from multifrequency radar observations, Atmos. Meas. Tech., 11, 5471–5488, https://doi.org/10.5194/amt-11-5471-2018, 2018. a, b
Lhermitte, R.: Attenuation and Scattering of Millimeter Wavelength Radiation
by Clouds and Precipitation, J. Atmos. Ocean. Technol., 7, 464–479,
https://doi.org/10.1175/1520-0426(1990)007<0464:AASOMW>2.0.CO;2, 1990. a, b
Li, H. and Moisseev, D.: Melting Layer Attenuation at Ka- and W-Bands as
Derived From Multifrequency Radar Doppler Spectra Observations, J.
Geophys. Res.-Atmos., 124, 9520–9533,
https://doi.org/10.1029/2019JD030316, 2019. a, b, c, d
Liao, L. and Meneghini, R.: A Study on the Feasibility of Dual-Wavelength
Radar for Identification of Hydrometeor Phases, 50, 449–456,
https://doi.org/10.1175/2010JAMC2499.1, 2011. a
Liao, L. and Meneghini, R.: Physical Evaluation of GPM DPR Single- and
Dual-Wavelength Algorithms, J. Atmos. Ocean. Technol., 36, 883–902,
https://doi.org/10.1175/JTECH-D-18-0210.1, 2019. a, b
Löhnert, U. and Crewell, S.: Accuracy of cloud liquid water path from
ground-based microwave radiometry 1. Dependency on cloud model statistics,
Radio Sci., 38, 8041, https://doi.org/10.1029/2002RS002654, 2003. a
Löhnert, U., Schween, J. H., Acquistapace, C., Ebell, K., Maahn, M.,
Barrera-Verdejo, M., Hirsikko, A., Bohn, B., Knaps, A., O'Connor, E., Simmer,
C., Wahner, A., and Crewell, S.: JOYCE: Jülich Observatory for Cloud
Evolution, B. Am. Meteorol. Soc., 96, 1157–1174,
https://doi.org/10.1175/BAMS-D-14-00105.1, 2015. a, b
Lubin, D., Zhang, D., Silber, I., Scott, R. C., Kalogeras, P., Battaglia, A.,
Bromwich, D. H., Cadeddu, M., Eloranta, E., Fridlind, A., Frossard, A.,
Hines, K. M., Kneifel, S., Leaitch, W. R., Lin, W., Nicolas, J., Powers, H.,
Quinn, P. K., Rowe, P., Russell, L. M., Sharma, S., Verlinde, J., and
Vogelmann, A. M.: AWARE: The Atmospheric Radiation Measurement (ARM) West
Antarctic Radiation Experiment, B. Am. Meteorol.
Soc., B. Am. Meteorol. Soc., 101, E1069–E1091, https://doi.org/10.1175/BAMS-D-18-0278.1, 2020. a
Luke, E. P., Kollias, P., and Shupe, M. D.: Detection of supercooled liquid in
mixed-phase clouds using radar Doppler spectra, J. Geophys.
Res.-Atmos., 115, D19201, https://doi.org/10.1029/2009JD012884, 2010. a
Marzoug, M. and Amayenc, P.: A Class of Single- and Dual-Frequency Algorithms
for Rain-Rate Profiling from a Spaceborne Radar. Pad I: Principle and Tests
from Numerical Simulations, J. Atmos. Ocean. Technol., 11, 1480–1506,
https://doi.org/10.1175/1520-0426(1994)011<1480:ACOSAD>2.0.CO;2, 1994. a
Mason, S. L., Chiu, J. C., Hogan, R. J., and Tian, L.: Improved rain rate and drop size retrievals from airborne Doppler radar, Atmos. Chem. Phys., 17, 11567–11589, https://doi.org/10.5194/acp-17-11567-2017, 2017. a
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. a
Matrosov, S.: Attenuation-Based Estimates of Rainfall Rates Aloft with
Vertically Pointing Ka-Band Radars, J. Atmos. Ocean. Technol., 22, 43–54,
https://doi.org/10.1175/JTECH-1677.1, 2005. a, b
Matrosov, S.: Assessment of Radar Signal Attenuation Caused by the Melting
Hydrometeor Layer, IEEE Trans. Geosci. Remote Sens., 46, 1039–1047, https://doi.org/10.1109/TGRS.2008.915757, 2008. a
Matrosov, S., May, P., and Shupe, M.: Rainfall Profiling Using Atmospheric
Radiation Measurement Program Vertically Pointing 8-mm Wavelength Radars, J.
Atmos. Ocean. Technol., 23, 1478–1491, https://doi.org/10.1175/JTECH1957.1, 2006. a, b
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. a
Matrosov, S. Y.: Feasibility of using radar differential Doppler velocity and
dual-frequency ratio for sizing particles in thick ice clouds, J.
Geophys. Res.-Atmos., 116, D17202, https://doi.org/10.1029/2011JD015857, 2011. a
Matrosov, S. Y.: Characteristic Raindrop Size Retrievals from Measurements of
Differences in Vertical Doppler Velocities at Ka- and W-Band Radar
Frequencies, J. Atmos. Ocean. Technol., 34, 65–71,
https://doi.org/10.1175/JTECH-D-16-0181.1, 2017. a
Matrosov, S. Y. and Turner, D. D.: Retrieving Mean Temperature of Atmospheric
Liquid Water Layers Using Microwave Radiometer Measurements, J. Atmos.
Ocean. Technol., 35, 1091–1102, https://doi.org/10.1175/JTECH-D-17-0179.1, 2018. a
Matrosov, S. Y., Maahn, M., and de Boer, G.: Observational and Modeling Study
of Ice Hydrometeor Radar Dual-Wavelength Ratios, J. Appl. Meteor. Climatol., 58, 2005–2017,
https://doi.org/10.1175/JAMC-D-19-0018.1, 2019. a
Meneghini, R., Iguchi, T., Kozu, T., Liao, L., Okamoto, K., Jones, J. A., and
Kwiatkowski, J.: Use of the Surface Reference Technique for Path Attenuation
Estimates from the TRMM Precipitation Radar, J. Appl. Meteorol., 39,
2053–2070, https://doi.org/10.1175/1520-0450(2001)040<2053:UOTSRT>2.0.CO;2, 2000. a
Meneghini, R., Kim, H., Liao, L., Jones, J. A., and Kwiatkowski, J. M.: An
Initial Assessment of the Surface Reference Technique Applied to Data from
the Dual-Frequency Precipitation Radar (DPR) on the GPM Satellite, J. Atmos.
Ocean. Technol., 32, 2281–2296, https://doi.org/10.1175/JTECH-D-15-0044.1, 2015. a
Mróz, K., Battaglia, A., Kneifel, S., D'Adderio, L. P., and Dias Neto, J.:
Triple-Frequency Doppler Retrieval of Characteristic Raindrop Size, Earth
Space Sci., 7, e2019EA000789, https://doi.org/10.1029/2019EA000789, 2020. a
Nemarich, J., Wellman, R. J., and Lacombe, J.: Backscatter and
attenuation by falling snow and rain at 96, 140, and 225 GHz, IEEE Trans. Geosci. Remote Sens., 26, 319–329,
https://doi.org/10.1109/36.3034, 1988. a, b
Petäjä, T., O’Connor, E. J., Moisseev, D., Sinclair, V. A., Manninen,
A. J., Väänänen, R., von Lerber, A., Thornton, J. A., Nicoll, K.,
Petersen, W., Chandrasekar, V., Smith, J. N., Winkler, P. M., Krüger, O.,
Hakola, H., Timonen, H., Brus, D., Laurila, T., Asmi, E., Riekkola, M.-L.,
Mona, L., Massoli, P., Engelmann, R., Komppula, M., Wang, J., Kuang, C.,
Bäck, J., Virtanen, A., Levula, J., Ritsche, M., and Hickmon, N.: BAECC: A
Field Campaign to Elucidate the Impact of Biogenic Aerosols on Clouds and
Climate, B. Am. Meteorol. Soc., 97, 1909–1928,
https://doi.org/10.1175/BAMS-D-14-00199.1, 2016. a
Protat, A., Delanoë, J., Bouniol, D., Heymsfield, A. J.,
Bansemer, A., and Brown, P.: Evaluation of Ice Water Content Retrievals
from Cloud Radar Reflectivity and Temperature Using a Large Airborne In Situ
Microphysical Database, J. Appl. Meteorol. Climatol., 46,
557–572, https://doi.org/10.1175/JAM2488.1, 2007. a, b
Protat, A., Rauniyar, S., Delanoë, J., Fontaine, E., and Schwarzenboeck, A.:
W-Band (95 GHz) Radar Attenuation in Tropical Stratiform Ice Anvils, J.
Atmos. Ocean. Technol., 36, 1463–1476, https://doi.org/10.1175/JTECH-D-18-0154.1,
2019. a, b
Rose, T., Crewell, S., Löhnert, U., and Simmer, C.: A network
suitable microwave radiometer for operational monitoring of the cloudy
atmosphere, Atmos. Res., 75, 183–200,
https://doi.org/10.1016/j.atmosres.2004.12.005, 2005. a
Rosenkranz, P. W.: A Model for the Complex Dielectric Constant of
Supercooled Liquid Water at Microwave Frequencies, IEEE Trans.
Geosci. Remote Sens., 53, 1387–1393,
https://doi.org/10.1109/TGRS.2014.2339015, 2015. a, b, c
Roy, R. J., Lebsock, M., Millán, L., Dengler, R., Rodriguez Monje, R., Siles, J. V., and Cooper, K. B.: Boundary-layer water vapor profiling using differential absorption radar, Atmos. Meas. Tech., 11, 6511–6523, https://doi.org/10.5194/amt-11-6511-2018, 2018. a
Serrar, S., Delrieu, G., Creutin, J.-D., and Uijlenhoet, R.: Mountain
reference technique: Use of mountain returns to calibrate weather radars
operating at attenuating wavelengths, J. Geophys. Res.-Atmos., 105, 2281–2290,
https://doi.org/10.1029/1999JD901025, 2000. a
Shupe, M. D., Kollias, P., Matrosov, S. Y., and Schneider, T. L.: Deriving
Mixed-Phase Cloud Properties from Doppler Radar Spectra, J.
Atmos. Ocean. Technol., 21, 660–670,
https://doi.org/10.1175/1520-0426(2004)021<0660:DMCPFD>2.0.CO;2, 2004. a
Shupe, M. D., Daniel, J. S., de Boer, G., Eloranta, E. W., Kollias, P., Long,
C. N., Luke, E. P., Turner, D. D., and Verlinde, J.: A Focus On Mixed-Phase
Clouds, B. Am. Meteorol. Soc., 89, 1549–1562,
https://doi.org/10.1175/2008BAMS2378.1, 2008. a
Simmer, C., Thiele-Eich, I., Masbou, M., Amelung, W., Bogena, H., Crewell, S.,
Diekkrüger, B., Ewert, F., Hendricks Franssen, H.-J., Huisman, J. A., Kemna,
A., Klitzsch, N., Kollet, S., Langensiepen, M., Löhnert, U., Rahman, A. S.
M. M., Rascher, U., Schneider, K., Schween, J., Shao, Y., Shrestha, P.,
Stiebler, M., Sulis, M., Vanderborght, J., Vereecken, H., van der Kruk, J.,
Waldhoff, G., and Zerenner, T.: Monitoring and Modeling the Terrestrial
System from Pores to Catchments: The Transregional Collaborative Research
Center on Patterns in the Soil–Vegetation–Atmosphere System, B.
Am. Meteorol. Soc., 96, 1765–1787,
https://doi.org/10.1175/BAMS-D-13-00134.1, 2015. a
Stephens, G. L., Li, J., Wild, M., Clayson, C., Loeb, N., Kato, S., L'Ecuyer,
T., Stackhouse, P., Lebsock, M., and Andrews, T.: An update on Earth's
energy balance in light of the latest global observations, Nat.
Geosci., 5, 691–696, https://doi.org/10.1038/ngeo1580, 2012. a
Tridon, F. and Battaglia, A.: Dual-frequency radar Doppler spectral retrieval
of rain drop size distributions and entangled dynamics variables, J. Geophys. Res.-Atmos., 120,
5585–5601, https://doi.org/10.1002/2014JD023023, 2015. a, b
Tridon, F., Battaglia, A., and Kollias, P.: Disentangling Mie and attenuation
effects in rain using a Ka-W dual-wavelength Doppler spectral ratio
technique, Geophys. Res. Lett., 40, 5548–5552, https://doi.org/10.1002/2013GL057454,
2013a. a, b
Tridon, F., Battaglia, A., Kollias, P., Luke, E., and Williams, C. R.: Signal
Postprocessing and Reflectivity Calibration of the Atmospheric Radiation
Measurement Program 915-MHz Wind Profilers, J. Atmos. Ocean. Technol., 30,
1038–1054, https://doi.org/10.1175/JTECH-D-12-00146.1, 2013b. a
Tridon, F., Battaglia, A., Luke, E., and Kollias, P.: Rain retrieval from
dual-frequency radar Doppler spectra: validation and potential for a
midlatitude precipitating case-study, Q. J. Roy. Meteorol. Soc., 143,
1364–1380, https://doi.org/10.1002/qj.3010, 2017a. a, b, c, d
Tridon, F., Battaglia, A., and Watters, D.: Evaporation in action sensed by
multiwavelength Doppler radars, J. Geophys. Res.-Atmos., 122, 9379–9390, https://doi.org/10.1002/2016JD025998,
2017b. a
Tridon, F., Battaglia, A., Chase, R. J., Turk, F. J., Leinonen, J., Kneifel,
S., Mroz, K., Finlon, J., Bansemer, A., Tanelli, S., Heymsfield, A. J., and
Nesbitt, S. W.: The Microphysics of Stratiform Precipitation During OLYMPEX:
Compatibility Between Triple-Frequency Radar and Airborne In Situ
Observations, J. Geophys. Res.-Atmos., 124, 8764–8792, https://doi.org/10.1029/2018JD029858,
2019a. a
Tridon, F., Planche, C., Mroz, K., Banson, S., Battaglia, A., Van Baelen, J.,
and Wobrock, W.: On the Realism of the Rain Microphysics Representation of a
Squall Line in the WRF Model. Part I: Evaluation with Multifrequency Cloud
Radar Doppler Spectra Observations, Mon. Weather Rev., 147, 2787–2810,
https://doi.org/10.1175/MWR-D-18-0018.1, 2019b.
a
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. Climatol.,
56, 1561–1582, https://doi.org/10.1175/JAMC-D-16-0379.1, 2017. a, b, c
Wallace, H. B.: Millimeter-wave propagation measurements at the Ballistic
Research Laboratory, IEEE Trans. Geosci. Remote Sens., 26, 253–258, https://doi.org/10.1109/36.3028, 1988. a
Wild, M., Folini, D., Schär, C., Loeb, N., Dutton, E. G., and
König-Langlo, G.: The global energy balance from a surface perspective,
Clim. Dynam., 40, 3107–3134, https://doi.org/10.1007/s00382-012-1569-8, 2013. a
Williams, C. R., Beauchamp, R. M., and Chandrasekar, V.: Vertical Air
Motions and Raindrop Size Distributions Estimated Using Mean Doppler Velocity
Difference From 3- and 35-GHz Vertically Pointing Radars, IEEE Trans. Geosci. Remote Sens., 54, 6048–6060,
https://doi.org/10.1109/TGRS.2016.2580526, 2016. a
Zelinka, M. D., Randall, D. A., Webb, M. J., and Klein, S. A.: Clearing clouds
of uncertainty, Nat. Clim. Change, 7, 674–678,
https://doi.org/10.1038/nclimate3402, 2017. a
Zhu, Z., Lamer, K., Kollias, P., and Clothiaux, E. E.: The Vertical Structure
of Liquid Water Content in Shallow Clouds as Retrieved From Dual-Wavelength
Radar Observations, J. Geophys. Res.-Atmos., 124, 14184–14197, https://doi.org/10.1029/2019JD031188, 2019. a, b
Short summary
The droplets and ice crystals composing clouds and precipitation interact with microwaves and can therefore be observed by radars, but they can also attenuate the signal they emit. By combining the observations made by two ground-based radars, this study describes an original approach for estimating such attenuation. As a result, the latter can be not only corrected in the radar observations but also exploited for providing an accurate characterization of droplet and ice crystal properties.
The droplets and ice crystals composing clouds and precipitation interact with microwaves and...
