Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-811-2019
© Author(s) 2019. 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-12-811-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Feb 2019
Research article |
| 06 Feb 2019
| 06 Feb 2019
X-band dual-polarization radar-based hydrometeor classification for Brazilian tropical precipitation systems
Jean-François Ribaud
CORRESPONDING AUTHOR
National Institute of Space Research (INPE), Center for Weather Forecast and Climate Studies (CPTEC),
Rodovia Presidente Dutra, km 40, Cachoeira Paulista, SP, 12 630-000, Brazil
Luiz Augusto Toledo Machado
National Institute of Space Research (INPE), Center for Weather Forecast and Climate Studies (CPTEC),
Rodovia Presidente Dutra, km 40, Cachoeira Paulista, SP, 12 630-000, Brazil
Thiago Biscaro
National Institute of Space Research (INPE), Center for Weather Forecast and Climate Studies (CPTEC),
Rodovia Presidente Dutra, km 40, Cachoeira Paulista, SP, 12 630-000, Brazil
Related authors
Luiz A. T. Machado, Alan J. P. Calheiros, Thiago Biscaro, Scott Giangrande, Maria A. F. Silva Dias, Micael A. Cecchini, Rachel Albrecht, Meinrat O. Andreae, Wagner F. Araujo, Paulo Artaxo, Stephan Borrmann, Ramon Braga, Casey Burleyson, Cristiano W. Eichholz, Jiwen Fan, Zhe Feng, Gilberto F. Fisch, Michael P. Jensen, Scot T. Martin, Ulrich Pöschl, Christopher Pöhlker, Mira L. Pöhlker, Jean-François Ribaud, Daniel Rosenfeld, Jaci M. B. Saraiva, Courtney Schumacher, Ryan Thalman, David Walter, and Manfred Wendisch
Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018, https://doi.org/10.5194/acp-18-6461-2018, 2018
Short summary
Short summary
This overview discuss the main precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin. It presents a review of the knowledge acquired about cloud processes and rainfall formation in Amazonas. In addition, this study provides a characterization of the seasonal variation and rainfall sensitivities to topography, surface cover, and aerosol concentration. Airplane measurements were evaluated to characterize and contrast cloud microphysical properties.
Camila da Cunha Lopes, Rachel Ifanger Albrecht, Douglas Messias Uba, Thiago Souza Biscaro, and Ivan Saraiva
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-438, https://doi.org/10.5194/essd-2024-438, 2024
Preprint under review for ESSD
Short summary
Short summary
This study used observations collected during The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment to create a database of storms and thunderstorms characteristics with weather radar and lightning measurements. These storms have different sizes and durations between wet and dry seasons as well as throughout the day, with the most intense ones occurring in the dry-to-wet transition. This database is useful in future studies on Amazonian clouds.
Toshi Matsui, Daniel Hernandez-Deckers, Scott E. Giangrande, Thiago S. Biscaro, Ann Fridlind, and Scott Braun
Atmos. Chem. Phys., 24, 10793–10814, https://doi.org/10.5194/acp-24-10793-2024, https://doi.org/10.5194/acp-24-10793-2024, 2024
Short summary
Short summary
Using computer simulations and real measurements, we discovered that storms over the Amazon were narrower but more intense during the dry periods, producing heavier rain and more ice particles in the clouds. Our research showed that cumulus bubbles played a key role in creating these intense storms. This study can improve the representation of the effect of continental and ocean environments on tropical regions' rainfall patterns in simulations.
Luiz A. T. Machado, Jürgen Kesselmeier, Santiago Botía, Hella van Asperen, Meinrat O. Andreae, Alessandro C. de Araújo, Paulo Artaxo, Achim Edtbauer, Rosaria R. Ferreira, Marco A. Franco, Hartwig Harder, Sam P. Jones, Cléo Q. Dias-Júnior, Guido G. Haytzmann, Carlos A. Quesada, Shujiro Komiya, Jost Lavric, Jos Lelieveld, Ingeborg Levin, Anke Nölscher, Eva Pfannerstill, Mira L. Pöhlker, Ulrich Pöschl, Akima Ringsdorf, Luciana Rizzo, Ana M. Yáñez-Serrano, Susan Trumbore, Wanda I. D. Valenti, Jordi Vila-Guerau de Arellano, David Walter, Jonathan Williams, Stefan Wolff, and Christopher Pöhlker
Atmos. Chem. Phys., 24, 8893–8910, https://doi.org/10.5194/acp-24-8893-2024, https://doi.org/10.5194/acp-24-8893-2024, 2024
Short summary
Short summary
Composite analysis of gas concentration before and after rainfall, during the day and night, gives insight into the complex relationship between trace gas variability and precipitation. The analysis helps us to understand the sources and sinks of trace gases within a forest ecosystem. It elucidates processes that are not discernible under undisturbed conditions and contributes to a deeper understanding of the trace gas life cycle and its intricate interactions with cloud dynamics in the Amazon.
Siddhant Gupta, Dié Wang, Scott E. Giangrande, Thiago S. Biscaro, and Michael P. Jensen
Atmos. Chem. Phys., 24, 4487–4510, https://doi.org/10.5194/acp-24-4487-2024, https://doi.org/10.5194/acp-24-4487-2024, 2024
Short summary
Short summary
We examine the lifecycle of isolated deep convective clouds (DCCs) in the Amazon rainforest. Weather radar echoes from the DCCs are tracked to evaluate their lifecycle. The DCC size and intensity increase, reach a peak, and then decrease over the DCC lifetime. Vertical profiles of air motion and mass transport from different seasons are examined to understand the transport of energy and momentum within DCC cores and to address the deficiencies in simulating DCCs using weather and climate models.
Gabriela R. Unfer, Luiz A. T. Machado, Paulo Artaxo, Marco A. Franco, Leslie A. Kremper, Mira L. Pöhlker, Ulrich Pöschl, and Christopher Pöhlker
Atmos. Chem. Phys., 24, 3869–3882, https://doi.org/10.5194/acp-24-3869-2024, https://doi.org/10.5194/acp-24-3869-2024, 2024
Short summary
Short summary
Amazonian aerosols and their interactions with precipitation were studied by understanding them in a 3D space based on three parameters that characterize the concentration and size distribution of aerosols. The results showed characteristic arrangements regarding seasonal and diurnal cycles, as well as when interacting with precipitation. The use of this 3D space appears to be a promising tool for aerosol population analysis and for model validation and parameterization.
Scott E. Giangrande, Thiago S. Biscaro, and John M. Peters
Atmos. Chem. Phys., 23, 5297–5316, https://doi.org/10.5194/acp-23-5297-2023, https://doi.org/10.5194/acp-23-5297-2023, 2023
Short summary
Short summary
Our study tracks thunderstorms observed during the wet and dry seasons of the Amazon Basin using weather radar. We couple this precipitation tracking with opportunistic overpasses of a wind profiler and other ground observations to add unique insights into the upwards and downwards air motions within these clouds at various stages in the storm life cycle. The results of a simple updraft model are provided to give physical explanations for observed seasonal differences.
Marco A. Franco, Florian Ditas, Leslie A. Kremper, Luiz A. T. Machado, Meinrat O. Andreae, Alessandro Araújo, Henrique M. J. Barbosa, Joel F. de Brito, Samara Carbone, Bruna A. Holanda, Fernando G. Morais, Janaína P. Nascimento, Mira L. Pöhlker, Luciana V. Rizzo, Marta Sá, Jorge Saturno, David Walter, Stefan Wolff, Ulrich Pöschl, Paulo Artaxo, and Christopher Pöhlker
Atmos. Chem. Phys., 22, 3469–3492, https://doi.org/10.5194/acp-22-3469-2022, https://doi.org/10.5194/acp-22-3469-2022, 2022
Short summary
Short summary
In Central Amazonia, new particle formation in the planetary boundary layer is rare. Instead, there is the appearance of sub-50 nm aerosols with diameters larger than about 20 nm that eventually grow to cloud condensation nuclei size range. Here, 254 growth events were characterized which have higher predominance in the wet season. About 70 % of them showed direct relation to convective downdrafts, while 30 % occurred partly under clear-sky conditions, evidencing still unknown particle sources.
Luiz A. T. Machado, Marco A. Franco, Leslie A. Kremper, Florian Ditas, Meinrat O. Andreae, Paulo Artaxo, Micael A. Cecchini, Bruna A. Holanda, Mira L. Pöhlker, Ivan Saraiva, Stefan Wolff, Ulrich Pöschl, and Christopher Pöhlker
Atmos. Chem. Phys., 21, 18065–18086, https://doi.org/10.5194/acp-21-18065-2021, https://doi.org/10.5194/acp-21-18065-2021, 2021
Short summary
Short summary
Several studies evaluate aerosol–cloud interactions, but only a few attempted to describe how clouds modify aerosol properties. This study evaluates the effect of weather events on the particle size distribution at the ATTO, combining remote sensing and in situ data. Ultrafine, Aitken and accumulation particles modes have different behaviors for the diurnal cycle and for rainfall events. This study opens up new scientific questions that need to be pursued in detail in new field campaigns.
Ramon Campos Braga, Barbara Ervens, Daniel Rosenfeld, Meinrat O. Andreae, Jan-David Förster, Daniel Fütterer, Lianet Hernández Pardo, Bruna A. Holanda, Tina Jurkat-Witschas, Ovid O. Krüger, Oliver Lauer, Luiz A. T. Machado, Christopher Pöhlker, Daniel Sauer, Christiane Voigt, Adrian Walser, Manfred Wendisch, Ulrich Pöschl, and Mira L. Pöhlker
Atmos. Chem. Phys., 21, 17513–17528, https://doi.org/10.5194/acp-21-17513-2021, https://doi.org/10.5194/acp-21-17513-2021, 2021
Short summary
Short summary
Interactions of aerosol particles with clouds represent a large uncertainty in estimates of climate change. Properties of aerosol particles control their ability to act as cloud condensation nuclei. Using aerosol measurements in the Amazon, we performed model studies to compare predicted and measured cloud droplet number concentrations at cloud bases. Our results confirm previous estimates of particle hygroscopicity in this region.
Alice Henkes, Gilberto Fisch, Luiz A. T. Machado, and Jean-Pierre Chaboureau
Atmos. Chem. Phys., 21, 13207–13225, https://doi.org/10.5194/acp-21-13207-2021, https://doi.org/10.5194/acp-21-13207-2021, 2021
Short summary
Short summary
The Amazonian boundary layer is investigated during the dry season in order to better understand the processes that occur between night and day until the stage where shallow cumulus clouds become deep. Observations show that shallow to deep clouds are characterized by a shorter morning transition stage (e.g., the time needed to eliminate the stable boundary layer inversion), while higher humidity above the boundary layer favors the evolution from shallow to deep cumulus clouds.
Thiago S. Biscaro, Luiz A. T. Machado, Scott E. Giangrande, and Michael P. Jensen
Atmos. Chem. Phys., 21, 6735–6754, https://doi.org/10.5194/acp-21-6735-2021, https://doi.org/10.5194/acp-21-6735-2021, 2021
Short summary
Short summary
This study suggests that there are two distinct modes driving diurnal precipitating convective clouds over the central Amazon. In the wet season, local factors such as turbulence and nighttime cloud coverage are the main controls of daily precipitation, while dry-season daily precipitation is modulated primarily by the mesoscale convective pattern. The results imply that models and parameterizations must consider different formulations based on the seasonal cycle to correctly resolve convection.
Bruna A. Holanda, Mira L. Pöhlker, David Walter, Jorge Saturno, Matthias Sörgel, Jeannine Ditas, Florian Ditas, Christiane Schulz, Marco Aurélio Franco, Qiaoqiao Wang, Tobias Donth, Paulo Artaxo, Henrique M. J. Barbosa, Stephan Borrmann, Ramon Braga, Joel Brito, Yafang Cheng, Maximilian Dollner, Johannes W. Kaiser, Thomas Klimach, Christoph Knote, Ovid O. Krüger, Daniel Fütterer, Jošt V. Lavrič, Nan Ma, Luiz A. T. Machado, Jing Ming, Fernando G. Morais, Hauke Paulsen, Daniel Sauer, Hans Schlager, Johannes Schneider, Hang Su, Bernadett Weinzierl, Adrian Walser, Manfred Wendisch, Helmut Ziereis, Martin Zöger, Ulrich Pöschl, Meinrat O. Andreae, and Christopher Pöhlker
Atmos. Chem. Phys., 20, 4757–4785, https://doi.org/10.5194/acp-20-4757-2020, https://doi.org/10.5194/acp-20-4757-2020, 2020
Short summary
Short summary
Biomass burning smoke from African savanna and grassland is transported across the South Atlantic Ocean in defined layers within the free troposphere. The combination of in situ aircraft and ground-based measurements aided by satellite observations showed that these layers are transported into the Amazon Basin during the early dry season. The influx of aged smoke, enriched in black carbon and cloud condensation nuclei, has important implications for the Amazonian aerosol and cloud cycling.
Fan Mei, Jian Wang, Jennifer M. Comstock, Ralf Weigel, Martina Krämer, Christoph Mahnke, John E. Shilling, Johannes Schneider, Christiane Schulz, Charles N. Long, Manfred Wendisch, Luiz A. T. Machado, Beat Schmid, Trismono Krisna, Mikhail Pekour, John Hubbe, Andreas Giez, Bernadett Weinzierl, Martin Zoeger, Mira L. Pöhlker, Hans Schlager, Micael A. Cecchini, Meinrat O. Andreae, Scot T. Martin, Suzane S. de Sá, Jiwen Fan, Jason Tomlinson, Stephen Springston, Ulrich Pöschl, Paulo Artaxo, Christopher Pöhlker, Thomas Klimach, Andreas Minikin, Armin Afchine, and Stephan Borrmann
Atmos. Meas. Tech., 13, 661–684, https://doi.org/10.5194/amt-13-661-2020, https://doi.org/10.5194/amt-13-661-2020, 2020
Short summary
Short summary
In 2014, the US DOE G1 aircraft and the German HALO aircraft overflew the Amazon basin to study how aerosols influence cloud cycles under a clean condition and around a tropical megacity. This paper describes how to meaningfully compare similar measurements from two research aircraft and identify the potential measurement issue. We also discuss the uncertainty range for each measurement for further usage in model evaluation and satellite data validation.
Lianet Hernández Pardo, Luiz Augusto Toledo Machado, Micael Amore Cecchini, and Madeleine Sánchez Gácita
Atmos. Chem. Phys., 19, 7839–7857, https://doi.org/10.5194/acp-19-7839-2019, https://doi.org/10.5194/acp-19-7839-2019, 2019
Short summary
Short summary
This work analyzes the effects of changes in the environmental aerosol population on Amazonian clouds. The results confirm that the clouds can be very sensitive to changes in the aerosol properties, but the relative importance of each property is variable and depends on the values of the other aerosol characteristics, especially aerosol size. This is controlled by the degree to which the cloud mixes with the surrounding air and by the efficiency with which aerosols are consumed by droplets.
John E. Shilling, Mikhail S. Pekour, Edward C. Fortner, Paulo Artaxo, Suzane de Sá, John M. Hubbe, Karla M. Longo, Luiz A. T. Machado, Scot T. Martin, Stephen R. Springston, Jason Tomlinson, and Jian Wang
Atmos. Chem. Phys., 18, 10773–10797, https://doi.org/10.5194/acp-18-10773-2018, https://doi.org/10.5194/acp-18-10773-2018, 2018
Short summary
Short summary
We report aircraft observations of the evolution of organic aerosol in the Manaus urban plume as it ages. We observe dynamic changes in the organic aerosol. The mean carbon oxidation state of the OA increases from −0.6 to −0.45. Hydrocarbon-like organic aerosol (HOA) mass is lost and is balanced out by formation of oxygenated organic aerosol (OOA). Because HOA loss is balanced by OOA formation, we observe little change in the net Δorg / ΔCO values with aging.
Jorge Saturno, Florian Ditas, Marloes Penning de Vries, Bruna A. Holanda, Mira L. Pöhlker, Samara Carbone, David Walter, Nicole Bobrowski, Joel Brito, Xuguang Chi, Alexandra Gutmann, Isabella Hrabe de Angelis, Luiz A. T. Machado, Daniel Moran-Zuloaga, Julian Rüdiger, Johannes Schneider, Christiane Schulz, Qiaoqiao Wang, Manfred Wendisch, Paulo Artaxo, Thomas Wagner, Ulrich Pöschl, Meinrat O. Andreae, and Christopher Pöhlker
Atmos. Chem. Phys., 18, 10391–10405, https://doi.org/10.5194/acp-18-10391-2018, https://doi.org/10.5194/acp-18-10391-2018, 2018
Short summary
Short summary
This study uses satellite observations to track volcanic emissions in eastern Congo and their subsequent transport across the Atlantic Ocean into the Amazon Basin. Aircraft and ground-based observations are used to characterize the influence of volcanogenic aerosol on the chemical and microphysical properties of Amazonian aerosols. Further, this work is an illustrative example of the conditions and dynamics driving the transatlantic transport of African emissions to South America.
Lindsay D. Yee, Gabriel Isaacman-VanWertz, Rebecca A. Wernis, Meng Meng, Ventura Rivera, Nathan M. Kreisberg, Susanne V. Hering, Mads S. Bering, Marianne Glasius, Mary Alice Upshur, Ariana Gray Bé, Regan J. Thomson, Franz M. Geiger, John H. Offenberg, Michael Lewandowski, Ivan Kourtchev, Markus Kalberer, Suzane de Sá, Scot T. Martin, M. Lizabeth Alexander, Brett B. Palm, Weiwei Hu, Pedro Campuzano-Jost, Douglas A. Day, Jose L. Jimenez, Yingjun Liu, Karena A. McKinney, Paulo Artaxo, Juarez Viegas, Antonio Manzi, Maria B. Oliveira, Rodrigo de Souza, Luiz A. T. Machado, Karla Longo, and Allen H. Goldstein
Atmos. Chem. Phys., 18, 10433–10457, https://doi.org/10.5194/acp-18-10433-2018, https://doi.org/10.5194/acp-18-10433-2018, 2018
Short summary
Short summary
Biogenic volatile organic compounds react in the atmosphere to form secondary organic aerosol, yet the chemical pathways remain unclear. We collected filter samples and deployed a semi-volatile thermal desorption aerosol gas chromatograph in the central Amazon. We measured 30 sesquiterpenes and 4 diterpenes and find them to be important for reactive ozone loss. We estimate that sesquiterpene oxidation contributes at least 0.4–5 % (median 1 %) of observed submicron organic aerosol mass.
Die Wang, Scott E. Giangrande, Mary Jane Bartholomew, Joseph Hardin, Zhe Feng, Ryan Thalman, and Luiz A. T. Machado
Atmos. Chem. Phys., 18, 9121–9145, https://doi.org/10.5194/acp-18-9121-2018, https://doi.org/10.5194/acp-18-9121-2018, 2018
Luiz A. T. Machado, Alan J. P. Calheiros, Thiago Biscaro, Scott Giangrande, Maria A. F. Silva Dias, Micael A. Cecchini, Rachel Albrecht, Meinrat O. Andreae, Wagner F. Araujo, Paulo Artaxo, Stephan Borrmann, Ramon Braga, Casey Burleyson, Cristiano W. Eichholz, Jiwen Fan, Zhe Feng, Gilberto F. Fisch, Michael P. Jensen, Scot T. Martin, Ulrich Pöschl, Christopher Pöhlker, Mira L. Pöhlker, Jean-François Ribaud, Daniel Rosenfeld, Jaci M. B. Saraiva, Courtney Schumacher, Ryan Thalman, David Walter, and Manfred Wendisch
Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018, https://doi.org/10.5194/acp-18-6461-2018, 2018
Short summary
Short summary
This overview discuss the main precipitation processes and their sensitivities to environmental conditions in the Central Amazon Basin. It presents a review of the knowledge acquired about cloud processes and rainfall formation in Amazonas. In addition, this study provides a characterization of the seasonal variation and rainfall sensitivities to topography, surface cover, and aerosol concentration. Airplane measurements were evaluated to characterize and contrast cloud microphysical properties.
Trismono C. Krisna, Manfred Wendisch, André Ehrlich, Evelyn Jäkel, Frank Werner, Ralf Weigel, Stephan Borrmann, Christoph Mahnke, Ulrich Pöschl, Meinrat O. Andreae, Christiane Voigt, and Luiz A. T. Machado
Atmos. Chem. Phys., 18, 4439–4462, https://doi.org/10.5194/acp-18-4439-2018, https://doi.org/10.5194/acp-18-4439-2018, 2018
Short summary
Short summary
The optical thickness and particle effective radius of a cirrus above liquid water clouds and a DCC topped by an anvil cirrus are retrieved based on SMART and MODIS radiance measurements. For the cirrus, retrieved particle effective radius are validated with corresponding in situ data using a vertical weighting method. This approach allows to assess the measurements, retrieval algorithms, and derived cloud products.
Lianet Hernández Pardo, Luiz Augusto Toledo Machado, and Micael Amore Cecchini
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-190, https://doi.org/10.5194/acp-2018-190, 2018
Revised manuscript not accepted
Meinrat O. Andreae, Armin Afchine, Rachel Albrecht, Bruna Amorim Holanda, Paulo Artaxo, Henrique M. J. Barbosa, Stephan Borrmann, Micael A. Cecchini, Anja Costa, Maximilian Dollner, Daniel Fütterer, Emma Järvinen, Tina Jurkat, Thomas Klimach, Tobias Konemann, Christoph Knote, Martina Krämer, Trismono Krisna, Luiz A. T. Machado, Stephan Mertes, Andreas Minikin, Christopher Pöhlker, Mira L. Pöhlker, Ulrich Pöschl, Daniel Rosenfeld, Daniel Sauer, Hans Schlager, Martin Schnaiter, Johannes Schneider, Christiane Schulz, Antonio Spanu, Vinicius B. Sperling, Christiane Voigt, Adrian Walser, Jian Wang, Bernadett Weinzierl, Manfred Wendisch, and Helmut Ziereis
Atmos. Chem. Phys., 18, 921–961, https://doi.org/10.5194/acp-18-921-2018, https://doi.org/10.5194/acp-18-921-2018, 2018
Short summary
Short summary
We made airborne measurements of aerosol particle concentrations and properties over the Amazon Basin. We found extremely high concentrations of very small particles in the region between 8 and 14 km altitude all across the basin, which had been recently formed by gas-to-particle conversion at these altitudes. This makes the upper troposphere a very important source region of atmospheric particles with significant implications for the Earth's climate system.
Micael A. Cecchini, Luiz A. T. Machado, Manfred Wendisch, Anja Costa, Martina Krämer, Meinrat O. Andreae, Armin Afchine, Rachel I. Albrecht, Paulo Artaxo, Stephan Borrmann, Daniel Fütterer, Thomas Klimach, Christoph Mahnke, Scot T. Martin, Andreas Minikin, Sergej Molleker, Lianet H. Pardo, Christopher Pöhlker, Mira L. Pöhlker, Ulrich Pöschl, Daniel Rosenfeld, and Bernadett Weinzierl
Atmos. Chem. Phys., 17, 14727–14746, https://doi.org/10.5194/acp-17-14727-2017, https://doi.org/10.5194/acp-17-14727-2017, 2017
Short summary
Short summary
This study introduces and explores the concept of gamma phase space. This space is able to represent all possible variations in the cloud droplet size distributions (DSDs). The methodology was applied to recent in situ aircraft measurements over the Amazon. It is shown that the phase space is able to represent several processes occurring in the clouds in a simple manner. The consequences for cloud studies, modeling, and the representation of the transition from warm to mixed phase are discussed.
Scott E. Giangrande, Zhe Feng, Michael P. Jensen, Jennifer M. Comstock, Karen L. Johnson, Tami Toto, Meng Wang, Casey Burleyson, Nitin Bharadwaj, Fan Mei, Luiz A. T. Machado, Antonio O. Manzi, Shaocheng Xie, Shuaiqi Tang, Maria Assuncao F. Silva Dias, Rodrigo A. F de Souza, Courtney Schumacher, and Scot T. Martin
Atmos. Chem. Phys., 17, 14519–14541, https://doi.org/10.5194/acp-17-14519-2017, https://doi.org/10.5194/acp-17-14519-2017, 2017
Short summary
Short summary
The Amazon forest is the largest tropical rain forest on the planet, featuring
prolific and diverse cloud conditions. The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) experiment was motivated by demands to gain a better understanding of aerosol and cloud interactions on climate and the global circulation. The routine DOE ARM observations from this 2-year campaign are summarized to help quantify controls on clouds and precipitation over this undersampled region.
Ramon Campos Braga, Daniel Rosenfeld, Ralf Weigel, Tina Jurkat, Meinrat O. Andreae, Manfred Wendisch, Ulrich Pöschl, Christiane Voigt, Christoph Mahnke, Stephan Borrmann, Rachel I. Albrecht, Sergej Molleker, Daniel A. Vila, Luiz A. T. Machado, and Lucas Grulich
Atmos. Chem. Phys., 17, 14433–14456, https://doi.org/10.5194/acp-17-14433-2017, https://doi.org/10.5194/acp-17-14433-2017, 2017
Micael A. Cecchini, Luiz A. T. Machado, Meinrat O. Andreae, Scot T. Martin, Rachel I. Albrecht, Paulo Artaxo, Henrique M. J. Barbosa, Stephan Borrmann, Daniel Fütterer, Tina Jurkat, Christoph Mahnke, Andreas Minikin, Sergej Molleker, Mira L. Pöhlker, Ulrich Pöschl, Daniel Rosenfeld, Christiane Voigt, Bernadett Weinzierl, and Manfred Wendisch
Atmos. Chem. Phys., 17, 10037–10050, https://doi.org/10.5194/acp-17-10037-2017, https://doi.org/10.5194/acp-17-10037-2017, 2017
Short summary
Short summary
We study the effects of aerosol particles and updraft speed on the warm phase of Amazonian clouds. We expand the sensitivity analysis usually found in the literature by concomitantly considering cloud evolution and the effects on droplet size distribution (DSD) shape. The quantitative results show that particle concentration is the primary driver for the vertical profiles of effective diameter and droplet concentration in the warm phase of Amazonian convective clouds.
Evelyn Jäkel, Manfred Wendisch, Trismono C. Krisna, Florian Ewald, Tobias Kölling, Tina Jurkat, Christiane Voigt, Micael A. Cecchini, Luiz A. T. Machado, Armin Afchine, Anja Costa, Martina Krämer, Meinrat O. Andreae, Ulrich Pöschl, Daniel Rosenfeld, and Tianle Yuan
Atmos. Chem. Phys., 17, 9049–9066, https://doi.org/10.5194/acp-17-9049-2017, https://doi.org/10.5194/acp-17-9049-2017, 2017
Short summary
Short summary
Vertical profiles of the cloud particle phase state in tropical deep convective clouds (DCCs) were investigated using airborne imaging spectrometer measurements during the ACRIDICON-CHUVA campaign, which was conducted over the Brazilian rainforest in September 2014. A phase discrimination retrieval was applied to observations of clouds formed in different aerosol conditions. The profiles were compared to in situ and satellite measurements.
Ramon Campos Braga, Daniel Rosenfeld, Ralf Weigel, Tina Jurkat, Meinrat O. Andreae, Manfred Wendisch, Mira L. Pöhlker, Thomas Klimach, Ulrich Pöschl, Christopher Pöhlker, Christiane Voigt, Christoph Mahnke, Stephan Borrmann, Rachel I. Albrecht, Sergej Molleker, Daniel A. Vila, Luiz A. T. Machado, and Paulo Artaxo
Atmos. Chem. Phys., 17, 7365–7386, https://doi.org/10.5194/acp-17-7365-2017, https://doi.org/10.5194/acp-17-7365-2017, 2017
Luiz F. Sapucci, Luiz A. T. Machado, Eniuce Menezes de Souza, and Thamiris B. Campos
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-378, https://doi.org/10.5194/amt-2016-378, 2016
Revised manuscript not accepted
Short summary
Short summary
This study employs precipitable water vapor from a Global Positioning System (GPS-PWV) signal, in high time resolution, to be used as precursor information of intense rainfall events. A typical jump in the GPS-PWV values before the occurrence of more intense rainfalls has been found, it is probably related to humid convergence occurring before intense rainfall events. The results from this manuscript create the physical basis for further development of a nowcasting tool in future studies.
Micael A. Cecchini, Luiz A. T. Machado, Jennifer M. Comstock, Fan Mei, Jian Wang, Jiwen Fan, Jason M. Tomlinson, Beat Schmid, Rachel Albrecht, Scot T. Martin, and Paulo Artaxo
Atmos. Chem. Phys., 16, 7029–7041, https://doi.org/10.5194/acp-16-7029-2016, https://doi.org/10.5194/acp-16-7029-2016, 2016
Short summary
Short summary
This work focuses on the analysis of anthropogenic impacts on Amazonian clouds. The experiment was conducted around Manaus (Brazil), which is a city with 2 million inhabitants and is surrounded by the Amazon forest in every direction. The clouds that form over the pristine atmosphere of the forest are understood as the background clouds and the ones that form over the city pollution are the anthropogenically impacted ones. The paper analyses microphysical characteristics of both types of clouds.
S. T. Martin, P. Artaxo, L. A. T. Machado, A. O. Manzi, R. A. F. Souza, C. Schumacher, J. Wang, M. O. Andreae, H. M. J. Barbosa, J. Fan, G. Fisch, A. H. Goldstein, A. Guenther, J. L. Jimenez, U. Pöschl, M. A. Silva Dias, J. N. Smith, and M. Wendisch
Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, https://doi.org/10.5194/acp-16-4785-2016, 2016
Short summary
Short summary
The Observations and Modeling of the Green Ocean Amazon (GoAmazon2014/5) Experiment took place in central Amazonia throughout 2014 and 2015. The experiment focused on the complex links among vegetation, atmospheric chemistry, and aerosol production on the one hand and their connections to aerosols, clouds, and precipitation on the other, especially when altered by urban pollution. This article serves as an introduction to the special issue of publications presenting findings of this experiment.
W. A. Gonçalves, L. A. T. Machado, and P.-E. Kirstetter
Atmos. Chem. Phys., 15, 6789–6800, https://doi.org/10.5194/acp-15-6789-2015, https://doi.org/10.5194/acp-15-6789-2015, 2015
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
3D cloud masking across a broad swath using multi-angle polarimetry and deep learning
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Retrieval of cloud fraction and optical thickness of liquid water clouds over the ocean from multi-angle polarization observations
Severe-hail detection with C-band dual-polarisation radars using convolutional neural networks
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Retrieving cloud base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
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
Discriminating between "Drizzle or rain" and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE CPR, ATLID and MSI
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
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of 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
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
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
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
Short summary
Short summary
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-141, https://doi.org/10.5194/amt-2024-141, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
In satellite remote sensing, estimating cloud base height (CBH) is more challenging than estimating cloud top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI instrument on JAXA’s GCOM-C satellite to estimate CBH together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-894, https://doi.org/10.5194/egusphere-2024-894, 2024
Short summary
Short summary
We present a technique to discriminate between the Cloudnet target classification of "Drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for the measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over two years at the BCO is presented.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-99, https://doi.org/10.5194/amt-2024-99, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This study introduces the JAXA EarthCARE L2 cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation will be quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-87, https://doi.org/10.5194/amt-2024-87, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40, https://doi.org/10.5194/amt-2024-40, 2024
Revised manuscript accepted for AMT
Short summary
Short summary
Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we measured that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Cited articles
Al-Sakka, H., Boumahmoud, A. A., Fradon, B., Frasier, S. J., and Tabary, P.:
A New Fuzzy Logic Hydrometeor Classification Scheme Applied to the French
X-, C-, and S-Band Polarimetric Radars, J. Appl. Meteor. Climatol., 52,
2328–2344, 2013.
American Meteorological Society: Rain. Glossary of Meteorology,
available at: http://glossary.ametsoc.org/wiki/rain, last access: 2018.
Artaxo, P., Martins, J. V., Yamasoe, M. A., Procópio, A. S., Pauliquevis, T. M.,
Andreae,
M. O., Guyon, P., Gatti, L. V., and Leal, A. M.: Physical and chemical properties of
aerosols in the wet and dry seasons in Rondônia, Amazonia, J.
Geophys. Res.-Atmos., 107, 8081, https://doi.org/10.1029/2001JD000666, 2002.
Augros, C., Caumont, O., Ducrocq, V., Gaussiat, N., and Tabary, P.:
Comparisons between S-, C-and X-band polarimetric radar observations and
convective-scale simulations of the HyMeX first special observing period,
Q. J. Roy. Meteor. Soc., 142, 347–362,
2016.
Aydin, K., Seliga, T. A., and Balaji, V.: Remote sensing of hail with a dual linear
polarization radar, J. Clim. Appl. Meteorol., 25, 1475–1484, 1986.
Bechini, R. and Chandrasekar V.: A Semisupervised Robust Hydrometeor
Classification Method for Dual-Polarization Radar Applications, J. Atmos.
Ocean. Tech., 32, 22–47, https//doi.org/10.1175/JTECH-D-14-00097.1,
2015.
Bechini, R., Chandrasekar, V., Cremonini, R., and Lim, S.: Radome attenuation at X-band
radar operations, Proc. Sixth European Conf. on Radar in Meteorology and Hydrology,
Sibiu, Romania, ERAD, p. 15.1, available at: http://www.erad2010.org/pdf/POSTER/Thursday/02_Xband/01_ERAD2010_0346_extended.pdf
(last access: November 2016), 2010.
Besic, N., Figueras i Ventura, J., Grazioli, J., Gabella, M., Germann, U., and Berne, A.:
Hydrometeor classification through statistical clustering of polarimetric radar measurements:
a semi-supervised approach, Atmos. Meas. Tech., 9, 4425–4445, https://doi.org/10.5194/amt-9-4425-2016, 2016.
Braga, R. C., Rosenfeld, D., Weigel, R., Jurkat, T., Andreae, M. O., Wendisch, M., Pöschl, U.,
Voigt, C., Mahnke, C., Borrmann, S., Albrecht, R. I., Molleker, S., Vila, D. A.,
Machado, L. A. T., and Grulich, L.: Further evidence for CCN aerosol concentrations
determining the height of warm rain and ice initiation in convective clouds over the
Amazon basin, Atmos. Chem. Phys., 17, 14433–14456, https://doi.org/10.5194/acp-17-14433-2017, 2017.
Bringi, V. N. and Chandrasekar V.: Polarimetric Doppler weather radar:
principles and applications, Cambridge University Press, Cambridge, UK, 2001.
Bringi, V. N., Rasmussen, R. M., and Vivekanandan, J.: Multiparameter radar measurements
in Colorado convective storms. Part I: Graupel melting studies, J. Atmos. Sci., 43, 2545–2563, 1986.
Bringi, V. N., Thurai, R., and Hannesen, R.: Dual-Polarization Weather Radar
Handbook, AMS-Gematronik GmbH, Germany, 2007.
Cazenave, F., Gosset, M., Kacou, M., Alcoba, M., Fontaine, E., Duroure, C.,
and Dolan, B.: Characterization of hydrometeors in Sahelian convective
systems with an X-band radar and comparison with in situ measurements. Part
I: Sensitivity of polarimetric radar particle identification retrieval and
case study evaluation, J. Appl. Meteorol. Clim.,
55, 231–249, 2016.
Cecchini, M. A., Machado, L. A. T., Wendisch, M., Costa, A., Krämer, M., Andreae, M. O.,
Afchine, A., Albrecht, R. I., Artaxo, P., Borrmann, S., Fütterer, D., Klimach, T.,
Mahnke, C., Martin, S. T., Minikin, A., Molleker, S., Pardo, L. H.,
Pöhlker, C., Pöhlker, M. L., Pöschl, U., Rosenfeld, D., and Weinzierl, B.:
Illustration of microphysical processes in Amazonian deep convective clouds in the gamma
phase space: introduction and potential applications, Atmos. Chem. Phys., 17, 14727–14746, https://doi.org/10.5194/acp-17-14727-2017, 2017.
Dolan, B. and Rutledge, S. A.: A Theory-Based Hydrometeor Identification
Algorithm for X-Band Polarimetric Radars, J. Atmos. Ocean. Tech., 26,
2071–2088, 2009.
Dolan, B., Rutledge, S. A., Lim, S., Chandrasekar, V., and Thurai, M.: A robust
C-Band hydrometeor identification algorithm and application to a long-term
polarimetric radar dataset, J. Appl. Meteorol. Clim., 52, 2162–2186, 2013.
Durden, S. L., Kitlyakara, A., Im, E., Tanner, A. B., Haddad, Z. S., Li, F.
K., and Wilson, W. J.: ARMAR observations of the melting layer during TOGA
COARE, IEEE T. Geosci. Remote, 35, 1453–1456,
1997.
El-Magd, A., Chandrasekar, V., Bringi, V., and Strapp, W.: Multiparameter radar
and in situ aircraft observation of graupel and hail, IEEE T. Geosci.
Remote, 38, 570–578, 2000.
Giangrande, S. E., Krause, J. M., and Ryzhkov, A. V.: Automatic designation
of the melting layer with a polarimetric prototype of the WSR-88D radar,
J. Appl. Meteorol. Clim., 47, 1354–1364, 2008.
Giangrande, S. E., Toto, T., Jensen, M. P., Bartholomew, M. J., Feng, Z.,
Protat, A., Williams, C. R., Schumacher, C., and Machado, L.: Convective cloud
vertical velocity and mass-flux characteristics from radar wind profiler
observations during GoAmazon2014/5, J. Geophys. Res.-Atmos., 121,
12891–12913, https://doi.org/10.1002/2016JD025303, 2017.
Grazioli, J., Tuia, D., and Berne, A.: Hydrometeor classification from polarimetric
radar measurements: a clustering approach, Atmos. Meas. Tech., 8, 149–170, https://doi.org/10.5194/amt-8-149-2015, 2015.
Grazioli, J., Genthon, C., Boudevillain, B., Duran-Alarcon, C., Del Guasta, M.,
Madeleine, J.-B., and Berne, A.: Measurements of precipitation in Dumont d'Urville,
Adélie Land, East Antarctica, The Cryosphere, 11, 1797–1811, https://doi.org/10.5194/tc-11-1797-2017, 2017.
Hall, M. P. M., Goddard, J. W. F., and Cherry, S. M.: Identification of hydrometeors
and other targets by dual-polarization radar, Radio Sci., 19, 132–140,
1984.
Hallett, J. and Mossop, S. C. C.: Production of secondary ice particles
during the riming process, Nature, 249, 26–28, 1974.
Heymsfield, A. J., Bansemer, A., Poellot, M. R., and Wood, N.: Observations of
Ice Microphysics through the Melting Layer, J. Atmos. Sci., 72, 2902–2928,
https://doi.org/10.1175/JAS-D-14-0363.1, 2015.
Höller, H., Hagen, M., Meischner, P. F., Bringi, V. N., and Hubbert, J.: Life
Cycle and Precipitation Formation in a Hybrid-Type Hailstorm Revealed by
Polarimetric and Doppler Radar Measurements, J. Atmos. Sci., 51, 2500–2522,
1994.
Houze, R. A.: Stratiform Precipitation in Regions of Convection: A
Meteorological Paradox?, B. Am. Meteorol. Soc., 78, 2179–2196,
https://doi.org/10.1175/1520-0477(1997)078<2179:SPIROC>2.0.CO;2, 1997.
Hubbert, J. and Bringi, V. N.: An iterative filtering technique for the
analysis of copolar differential phase and dual-frequency radar
measurements, J. Atmos. Ocean. Tech., 12.3, 643–648,
1995.
Illingworth, A. J. and Blackman, T. M.: The need to represent raindrop size
spectra as normalized gamma distributions for the interpretation of
polarization radar observations, J. Appl. Meteorol., 41, 286–297, 2002.
Jain, A. K., Duin, R. P. W., and Mao, J. C.: Statistical pattern
recognition: A review, IEEE T. Pattern Analysis Machine Intell., 22,
4–37, https://doi.org/10.1109/34.824819, 2000.
Jäkel, E., Wendisch, M., Krisna, T. C., Ewald, F., Kölling, T., Jurkat, T.,
Voigt, C., Cecchini, M. A., Machado, L. A. T., Afchine, A., Costa, A., Krämer, M.,
Andreae, M. O., Pöschl, U., Rosenfeld, D., and Yuan, T.: Vertical distribution of
the particle phase in tropical deep convective clouds as derived from cloud-side
reflected solar radiation measurements, Atmos. Chem. Phys., 17, 9049–9066, https://doi.org/10.5194/acp-17-9049-2017, 2017.
Kumjian, M. R. and Ryzhkov, A. V.: Polarimetric Signatures in Supercell
Thunderstorms, J. Appl. Meteorol. Clim., 47, 1940–1961, https://doi.org/10.1175/2007JAMC1874.1, 2008.
Kumjian, M. R. and Ryzhkov, A. V.: The impact of evaporation on
polarimetric characteristics of rain: Theoretical model and practical
implications, J. Appl. Meteorol. Clim., 49,
1247–1267, 2010.
Leary, C. A. and Houze Jr., R. A.: Melting and evaporation of hydrometeors
in precipitation from the anvil clouds of deep tropical convection, J. Atmos. Sci., 36, 669–679,
1979.
Liu, H. and Chandrasekar, V.: Classification of Hydrometeors Based on
Polarimetric Radar Measurements: Development of Fuzzy Logic and Neuro-Fuzzy
Systems, and In Situ Verification, J. Atmos. Ocean. Tech., 17, 140–164,
2000.
Machado, L. A. T., Calheiros, A. J. P., Biscaro, T., Giangrande, S., Silva Dias, M. A. F.,
Cecchini, M. A., Albrecht, R., Andreae, M. O., Araujo, W. F., Artaxo, P., Borrmann, S.,
Braga, R., Burleyson, C., Eichholz, C. W., Fan, J., Feng, Z., Fisch, G. F.,
Jensen, M. P., Martin, S. T., Pöschl, U., Pöhlker, C., Pöhlker, M. L.,
Ribaud, J.-F., Rosenfeld, D., Saraiva, J. M. B., Schumacher, C., Thalman, R.,
Walter, D., and Wendisch, M.: Overview: Precipitation characteristics and
sensitivities to environmental conditions during GoAmazon2014/5 and
ACRIDICON-CHUVA, Atmos. Chem. Phys., 18, 6461–6482, https://doi.org/10.5194/acp-18-6461-2018, 2018.
Martin, S. T., Artaxo, P., Machado, L. A. T., Manzi, A. O., Souza, R. A. F.,
Schumacher, C., Wang, J., Andreae, M. O., Barbosa, H. M. J., Fan, J., Fisch, G.,
Goldstein, A. H., Guenther, A., Jimenez, J. L., Pöschl, U., Silva Dias, M. A.,
Smith, J. N., and Wendisch, M.: Introduction: Observations and Modeling of the
Green Ocean Amazon (GoAmazon2014/5), Atmos. Chem. Phys., 16, 4785–4797, https://doi.org/10.5194/acp-16-4785-2016, 2016.
Martin, S. T., Artaxo, P., Machado, L., Manzi, A. O., Souza, R. A., Schumacher, C.,
Wang, J., Biscaro, T., Brito, J., Calheiros, A., Jardine, K., Medeiros, A.,
Portela, B., de Sá, S. S., Adachi, K., Aiken, A. C., Albrecht, R., Alexander, L.,
Andreae, M. O., Barbosa, H. M., Buseck, P., Chand, D., Comstock, J. M., Day, D. A.,
Dubey, M., Fan, J., Fast, J., Fisch, G., Fortner, E., Giangrande, S., Gilles, M.,
Goldstein, A. H., Guenther, A., Hubbe, J., Jensen, M., Jimenez, J. L., Keutsch, F. N.,
Kim, S., Kuang, C., Laskin, A., McKinney, K., Mei, F., Miller, M., Nascimento, R.,
Pauliquevis, T., Pekour, M., Peres, J., Petäjä, T., Pöhlker, C., Pöschl, U.,
Rizzo, L., Schmid, B., Shilling, J. E., Dias, M. A., Smith, J. N., Tomlinson, J. M., Tóta, J.,
and Wendisch, M.: The Green Ocean Amazon Experiment (GoAmazon2014/5) Observes
Pollution Affecting Gases, Aerosols, Clouds, and Rainfall over the Rain Forest, B. Am. Meteorol. Soc., 98, 981–997, 2017.
Marzano, F., Scaranari, D., Celano, M., Alberoni, P. P., Vulpiani, G., and Montopoli, M.:
Hydrometeor classification from dual-polarized weather radar: extending
fuzzy logic from S-band to C-band data, Adv. Geosci., 7,
109–114, 2006.
Marzano, F., Scaranari, D., Montopoli, M., and Vulpiani, G.: Supervised
classification and estimation of hydrometeors from C-band dual-polarized
radars: A Bayesian approach, IEEE T. Geosci. Remote, 46, 85–98,
https://doi.org/10.1109/TGRS.2007.906476, 2008.
Marzano, F. S., Botta, G., and Montopoli, M.: Iterative Bayesian retrieval
of hydrometeor content from X-band polarimetric weather radar, IEEE T.
Geosci. Remote, 48, 3059–3074, 2010.
Matrosov, S. Y., Clark, K. A., and Kingsmill, D. E.: A polarimetric radar
approach to identify rain, melting-layer, and snow regions for applying
corrections to vertical profiles of reflectivity, J. Appl. Meteorol. Clim., 46, 154–166, 2007.
Mendel, J. M.: Fuzzy logic systems for engineering: A tutorial, Proc. IEEE,
83, 345–377, 1995.
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. Spectr.
Ra., 60, 309–324, 1998.
Müllner, D.: Modern hierarchical, agglomerative clustering algorithms,
arXiv preprint, arXiv:1109.2378, 2011.
Oliveira, R., Maggioni, V., Vila, D., and Morales, C.: Characteristics and
diurnal cycle of GPM rainfall estimates over the central amazon region,
Remote Sens., 8, 544, 2016.
Park, H. S., Ryzhkov, A. V., Zrnić, D., and Kim, K. E.: The Hydrometeor
Classification Algorithm for the Polarimetric WSR-88D: Description and
Application to an MCS, Weather Forecast., 24, 730–748, 2009.
Penide, G., Kumar, V. V., Protat, A., and May, P. T.: Statistics of drop
size distribution parameters and rain rates for stratiform and convective
precipitation during the north Australian wet season, Mon. Weather
Rev., 141, 3222–3237, 2013.
Ribaud, J.-F., Bousquet, O., and Coquillat, S.: Relationships between total
lightning activity, microphysics and kinematics during the 24 September 2012
HyMeX bow-echo system, Q. J. Roy. Meteor. Soc., 142, 298–309, 2016a.
Ribaud, J.-F., Bousquet, O., Coquillat, S., Al-Sakka, H., Lambert, D., Ducrocq, V.,
and Fontaine, E.: Evaluation and application of hydrometeor classification
algorithm outputs inferred from multi-frequency dual-polarimetric radar
observations collected during HyMeX, Q. J. Roy. Meteor. Soc., 142, 95–107,
https://doi.org/10.1002/qj.2589, 2016b.
Rosenfeld, D. and Lensky, I. M.: Satellite-based insights into precipitation formation
processes in continental and maritime convective clouds, B. Am. Meteorol. Soc., 79, 2457–2476, 1998.
Rosenfeld, D. and Ulbrich, C. W.: Cloud microphysical properties, processes,
and rainfall estimation opportunities. Radar and Atmospheric Science: A
Collection of Essays in Honor of David Atlas, Meteor. Monogr., 52, 237–258, 2003.
Ryzhkov, A. V., Schuur, T. J., Burgess, D. W., Heinselman, P. L.,
Giangrande, S. E., and Zrnic, D. S.: The joint polarization experiment,
polarimetric rainfall measurements and hydrometeor classification, B.
Am. Meteorol. Soc., 86, 809–824, https://doi.org/10.1175/BAMS-86-6-809, 2005.
Ryzhkov, A. V., Kumjian, M. R., Ganson, S. M., and Khain, A. P.:
Polarimetric radar characteristics of melting hail. Part I: Theoretical
simulations using spectral microphysical modeling, J. Appl. Meteorol. Clim., 52, 2849–2870, 2013.
Schneebeli, M., Sakuragi, J., Biscaro, T., Angelis, C. F., Carvalho da Costa, I.,
Morales, C., Baldini, L., and Machado, L. A. T.: Polarimetric X-band weather
radar measurements in the tropics: radome and rain attenuation
correction, Atmos. Meas. Tech., 5, 2183–2199, https://doi.org/10.5194/amt-5-2183-2012, 2012.
Segond, M.-L., Tabary, P., and Parent du Châtelet, J.: Quantitative
precipitation estimations from operational polarimetric radars for
hydrological applications, Preprints, 33rd Int. Conf. on Radar
Meteorology, AMS, Cairns, Australia, August 2007.
Seliga, T. A. and Bringi, V. N.: Potential use of radar differential
reflectivity measurements at orthogonal polarizations for measuring
precipitation, J. Appl. Meteorol., 15, 69–76,
https://doi.org/10.1175/1520-0450(1976)015%3C0069:PUORDR%3E2.0.CO;2, 1976.
Smyth, T. J. and Illingworth, A. J.: Radar estimates of rainfall rates at the
ground in bright band and non-bright band events, Q. J. Roy. Meteor. Soc., 124, 2417–2434, 1998.
Steiner, M., Houze Jr., R. A., and Yuter, S. E.: Climatological characterization
of three-dimensional storm structure from operational radar and rain gauge
data, J. Appl. Meteorol., 34, 1978–2007, 1995.
Straka, J. M.: Hydrometeor fields in a supercell storm as deduced from dual
polarization radar. Preprints, 18th Conf. on Severe Local Storms, San
Francisco, CA, Amer. Meteor. Soc., 551–554, 1996.
Straka, J. and Zrnić, D. S.: An algorithm to deduce hydrometeor types and
contents from multiparameter radar data, Preprints, 26th Conf. on Radar
Meteorology, Norman, OK, Amer. Meteor. Soc., 513–515, 1993.
Straka, J. M., Zrnić, D. S., and Ryzhkov, A. V.: Bulk hydrometeor
classification and quantification using polarimetric radar data: Synthesis
of relations, J. Appl. Meteorol., 39.8, 1341–1372, 2000.
Testud, J., Le Bouar, E., Obligis, E., and Ali-Meheni, M.: The rain profiling
algorithm applied to polarimetric weather radar, J. Atmos. Ocean. Tech.,
17, 332–356, 2000.
Thompson, E. J., Rutledge, S. A., Dolan, B., Chandrasekar, V., and Cheong, B.
L.: A dual-polarization radar hydrometeor classification algorithm for
winter precipitation, J. Atmos. Ocean. Tech., 31, 1457–1481,
https://doi.org/10.1175/JTECH-D-13-00119.1, 2014.
Vivekanandan, J., Ellis, S. M., Oye, R., Zrnić, D. S., Ryzhkov, A. V., and
Straka, J.: Cloud Microphysics Retrieval Using S-band Dual-Polarization Radar
Measurements, B. Am. Meteorol. Soc., 80, 381–388, 1999.
Wang, D., Giangrande, S. E., Bartholomew, M. J., Hardin, J., Feng, Z., Thalman, R.,
and Machado, L. A. T.: The Green Ocean: precipitation insights from the
GoAmazon2014/5 experiment, Atmos. Chem. Phys., 18, 9121–9145, https://doi.org/10.5194/acp-18-9121-2018, 2018.
Ward, J.: Hierarchical grouping to optimize an objective function, J. Am.
Stat. Assoc., 58, 236–244, 1963.
Wen, G., Protat, A., May, P. T., Wang, X., and Moran, W.: A cluster-based method
for hydrometeor classification using polarimetric variables. Part I:
Interpretation and analysis, J. Atmos. Ocean. Tech.,
32, 1320–1340, 2015.
Wen, G., Protat, A., May, P. T., Moran, W., and Dixon, M.: A cluster-based method
for hydrometeor classification using polarimetric variables. Part II:
Classification, J. Atmos. Ocean. Tech., 33, 45–60,
2016.
Wendisch, M., Pöschl, U., Andreae, M. O., Machado, L. A. T., Albrecht, R., Schlager, H.,
Rosenfeld, D., Martin, S. T., Abdelmonem, A., Afchine, A., Araújo, A., Artaxo, P., Aufmhoff, H.,
Barbosa, H. M. J., Borrmann, S., Braga, R., Buchholz, B., Cecchini, M. A., Costa, A.,
Curtius, J., Dollner, M., Dorf, M., Dreiling, V., Ebert, V., Ehrlich, A., Ewald, F.,
Fisch, G., Fix, A., Frank, F., Fütterer, D., Heckl, C., Heidelberg, F., Hüneke, T., Jäkel, E.,
Járvinen, E., Jurkat, T., Kanter, S., Kästner, U., Kenntner, M.,
Kesselmeier, J., Klimach, T., Knecht, M., Kohl, R., Kölling, T., Krämer, M., Krüger, M.,
Krisna, T. C., Lavric, J. V., Longo, K., Mahnke, C., Manzi, A. O., Mayer, B.,
Mertes, S., Minikin, A., Molleker, S., Münch, S., Nillius, B., Pfeilsticker, K.,
Pöhlker, C., Roiger, A., Rose, D., Rosenow, D., Sauer, D., Schnaiter, M.,
Schneider, J., Schulz, C., de Souza, R. A. F., Spanu, A., Stock, P., Vila, D.,
Voigt, C., Walser, A., Walter, D., Weigel, R., Weinzierl, B., Werner, F.,
Yamasoe, M. A., Ziereis, H., Zinner, T., and Zöger, M.: The ACRIDICON-CHUVA
campaign: Studying tropical deep convective clouds and precipitation over
Amazonia using the new German research aircraft HALO, B. Am. Meteorol. Soc., 97, 1885–1908, https://doi.org/10.1175/BAMS-D-14-00255.1, 2016.
Williams, E., Rosenfeld, D., Madden, N., Gerlach, J., Gears, N., Atkinson, L.,
Dunnemann, N., Frostrom, G., Antonio, M., Biazon, B., and Camargo, R.: Contrasting convective
regimes over the Amazon: Implications for cloud electrification, J. Geophys. Res.-Atmos., 27, 107, LBA-50, 2002.
Wolfensberger, D. and Berne, A.: From model to radar variables: a new forward
polarimetric radar operator for COSMO, Atmos. Meas. Tech., 11, 3883–3916, https://doi.org/10.5194/amt-11-3883-2018,
2018.
Wolfensberger, D., Scipion, D., and Berne, A.: Detection and
characterization of the melting layer based on polarimetric radar scans,
Q. J. Roy. Meteor. Soc., 142, 108–124,
2016.
Zrnić, D. S., Ryzhkov, A., Straka, J., Liu, Y., and Vivekanandan, J.:
Testing a procedure for automatic classification of hydrometeor types, J.
Atmos. Ocean. Tech., 18, 892–913,
https://doi.org/10.1175/1520-0426(2001)018%3C0892:TAPFAC%3E2.0.CO;2, 2001.
Short summary
The dominant hydrometeor types associated with Brazilian tropical precipitation systems are identified for the Amazon region during both the wet and dry seasons. Overall the stratiform regions are composed of five hydrometeor classes: drizzle, rain, wet snow, aggregates, and ice crystals, whereas convective echoes are generally associated with light rain, moderate rain, heavy rain, graupel, aggregates and ice crystals.
The dominant hydrometeor types associated with Brazilian tropical precipitation systems are...
