Articles | Volume 15, issue 3
https://doi.org/10.5194/amt-15-775-2022
© Author(s) 2022. 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-15-775-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Coincident in situ and triple-frequency radar airborne observations in the Arctic
Cuong M. Nguyen
CORRESPONDING AUTHOR
Flight Research Laboratory, National Research Council Canada, Ottawa,
K1A 0R6, Canada
Mengistu Wolde
Flight Research Laboratory, National Research Council Canada, Ottawa,
K1A 0R6, Canada
Alessandro Battaglia
DIATI, Politecnico di Torino, Turin, Italy
Earth Observation Science, Department of Physics and Astronomy,
University of Leicester, Leicester, United Kingdom
National Centre for Earth Observation, University of Leicester,
Leicester, United Kingdom
Leonid Nichman
Flight Research Laboratory, National Research Council Canada, Ottawa,
K1A 0R6, Canada
Natalia Bliankinshtein
Flight Research Laboratory, National Research Council Canada, Ottawa,
K1A 0R6, Canada
Samuel Haimov
Department of Atmospheric Science, University of Wyoming, Laramie, WY, USA
Kenny Bala
Flight Research Laboratory, National Research Council Canada, Ottawa,
K1A 0R6, Canada
Dirk Schuettemeyer
European Space Agency, Noordwijk, the Netherlands
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Katia Lamer, Mariko Oue, Alessandro Battaglia, Richard J. Roy, Ken B. Cooper, Ranvir Dhillon, and Pavlos Kollias
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Alexis Merlaud, Livio Belegante, Daniel-Eduard Constantin, Mirjam Den Hoed, Andreas Carlos Meier, Marc Allaart, Magdalena Ardelean, Maxim Arseni, Tim Bösch, Hugues Brenot, Andreea Calcan, Emmanuel Dekemper, Sebastian Donner, Steffen Dörner, Mariana Carmelia Balanica Dragomir, Lucian Georgescu, Anca Nemuc, Doina Nicolae, Gaia Pinardi, Andreas Richter, Adrian Rosu, Thomas Ruhtz, Anja Schönhardt, Dirk Schuettemeyer, Reza Shaiganfar, Kerstin Stebel, Frederik Tack, Sorin Nicolae Vâjâiac, Jeni Vasilescu, Jurgen Vanhamel, Thomas Wagner, and Michel Van Roozendael
Atmos. Meas. Tech., 13, 5513–5535, https://doi.org/10.5194/amt-13-5513-2020, https://doi.org/10.5194/amt-13-5513-2020, 2020
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The AROMAT campaigns took place in Romania in 2014 and 2015. They aimed to test airborne observation systems dedicated to air quality studies and to verify the concept of such campaigns in support of the validation of space-borne atmospheric missions. We show that airborne measurements of NO2 can be valuable for the validation of air quality satellites. For H2CO and SO2, the validation should involve ground-based measurement systems at key locations that the AROMAT measurements help identify.
Frédéric Tridon, Alessandro Battaglia, and Stefan Kneifel
Atmos. Meas. Tech., 13, 5065–5085, https://doi.org/10.5194/amt-13-5065-2020, https://doi.org/10.5194/amt-13-5065-2020, 2020
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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.
Mario Simon, Lubna Dada, Martin Heinritzi, Wiebke Scholz, Dominik Stolzenburg, Lukas Fischer, Andrea C. Wagner, Andreas Kürten, Birte Rörup, Xu-Cheng He, João Almeida, Rima Baalbaki, Andrea Baccarini, Paulus S. Bauer, Lisa Beck, Anton Bergen, Federico Bianchi, Steffen Bräkling, Sophia Brilke, Lucia Caudillo, Dexian Chen, Biwu Chu, António Dias, Danielle C. Draper, Jonathan Duplissy, Imad El-Haddad, Henning Finkenzeller, Carla Frege, Loic Gonzalez-Carracedo, Hamish Gordon, Manuel Granzin, Jani Hakala, Victoria Hofbauer, Christopher R. Hoyle, Changhyuk Kim, Weimeng Kong, Houssni Lamkaddam, Chuan P. Lee, Katrianne Lehtipalo, Markus Leiminger, Huajun Mai, Hanna E. Manninen, Guillaume Marie, Ruby Marten, Bernhard Mentler, Ugo Molteni, Leonid Nichman, Wei Nie, Andrea Ojdanic, Antti Onnela, Eva Partoll, Tuukka Petäjä, Joschka Pfeifer, Maxim Philippov, Lauriane L. J. Quéléver, Ananth Ranjithkumar, Matti P. Rissanen, Simon Schallhart, Siegfried Schobesberger, Simone Schuchmann, Jiali Shen, Mikko Sipilä, Gerhard Steiner, Yuri Stozhkov, Christian Tauber, Yee J. Tham, António R. Tomé, Miguel Vazquez-Pufleau, Alexander L. Vogel, Robert Wagner, Mingyi Wang, Dongyu S. Wang, Yonghong Wang, Stefan K. Weber, Yusheng Wu, Mao Xiao, Chao Yan, Penglin Ye, Qing Ye, Marcel Zauner-Wieczorek, Xueqin Zhou, Urs Baltensperger, Josef Dommen, Richard C. Flagan, Armin Hansel, Markku Kulmala, Rainer Volkamer, Paul M. Winkler, Douglas R. Worsnop, Neil M. Donahue, Jasper Kirkby, and Joachim Curtius
Atmos. Chem. Phys., 20, 9183–9207, https://doi.org/10.5194/acp-20-9183-2020, https://doi.org/10.5194/acp-20-9183-2020, 2020
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Highly oxygenated organic compounds (HOMs) have been identified as key vapors involved in atmospheric new-particle formation (NPF). The molecular distribution, HOM yield, and NPF from α-pinene oxidation experiments were measured at the CLOUD chamber over a wide tropospheric-temperature range. This study shows on a molecular scale that despite the sharp reduction in HOM yield at lower temperatures, the reduced volatility counteracts this effect and leads to an overall increase in the NPF rate.
Katia Lamer, Pavlos Kollias, Alessandro Battaglia, and Simon Preval
Atmos. Meas. Tech., 13, 2363–2379, https://doi.org/10.5194/amt-13-2363-2020, https://doi.org/10.5194/amt-13-2363-2020, 2020
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According to ground-based radar observations, 50 % of liquid low-level clouds over the Atlantic extend below 1.2 km and are thinner than 400 m, thus limiting their detection from space. Using an emulator, we estimate that a 250 m resolution radar would capture cloud base better than the CloudSat radar which misses about 52 %. The more sensitive EarthCARE radar is expected to capture cloud cover but stretch cloud. This calls for the operation of interlaced pulse modes for future space missions.
Alexei Korolev, Ivan Heckman, Mengistu Wolde, Andrew S. Ackerman, Ann M. Fridlind, Luis A. Ladino, R. Paul Lawson, Jason Milbrandt, and Earle Williams
Atmos. Chem. Phys., 20, 1391–1429, https://doi.org/10.5194/acp-20-1391-2020, https://doi.org/10.5194/acp-20-1391-2020, 2020
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This study attempts identification of mechanisms of secondary ice production (SIP) based on the observation of small faceted ice crystals. It was found that in both mesoscale convective systems and frontal clouds, SIP was observed right above the melting layer and extended to the higher altitudes with colder temperatures. A principal conclusion of this work is that the freezing drop shattering mechanism is plausibly accounting for the measured ice concentrations in the observed condition.
Cuong M. Nguyen, Mengistu Wolde, and Alexei Korolev
Atmos. Meas. Tech., 12, 5897–5911, https://doi.org/10.5194/amt-12-5897-2019, https://doi.org/10.5194/amt-12-5897-2019, 2019
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This paper presents a methodology for high ice water content (HIWC) (up to 3.5 g m−3) retrieval from a dual-polarization side-looking X-band airborne radar. Zdr and Kdp are used to mitigate the effects of ice crystal shape and orientation on the variation in IWC – specific differential phase (Kdp) joint distribution. Empirical analysis shows that the proposed method improves the estimation bias by 35 % and increases the correlation by 4 % on average, compared to the method using Kdp alone.
Leonid Nichman, Martin Wolf, Paul Davidovits, Timothy B. Onasch, Yue Zhang, Doug R. Worsnop, Janarjan Bhandari, Claudio Mazzoleni, and Daniel J. Cziczo
Atmos. Chem. Phys., 19, 12175–12194, https://doi.org/10.5194/acp-19-12175-2019, https://doi.org/10.5194/acp-19-12175-2019, 2019
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Previous studies showed widespread ice nucleation activity of soot. In this systematic study we investigated the factors that affect the heterogeneous ice nucleation activity of soot surrogates in the cirrus cloud regime. Our observations are consistent with an ice nucleation mechanism of pore condensation followed by freezing. The results show significant variations in ice nucleation activity as a function of size, morphology, and surface chemistry of the black-carbon-containing particles.
Ralf Bennartz, Frank Fell, Claire Pettersen, Matthew D. Shupe, and Dirk Schuettemeyer
Atmos. Chem. Phys., 19, 8101–8121, https://doi.org/10.5194/acp-19-8101-2019, https://doi.org/10.5194/acp-19-8101-2019, 2019
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The Greenland Ice Sheet (GrIS) is rapidly melting. Snowfall is the only source of ice mass over the GrIS. We use satellite observations to assess how much snow on average falls over the GrIS and what the annual cycle and spatial distribution of snowfall is. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34 ± 7.5 cm yr−1 liquid equivalent.
Mengistu Wolde, Alessandro Battaglia, Cuong Nguyen, Andrew L. Pazmany, and Anthony Illingworth
Atmos. Meas. Tech., 12, 253–269, https://doi.org/10.5194/amt-12-253-2019, https://doi.org/10.5194/amt-12-253-2019, 2019
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This paper presents an implementation of polarization diversity pulse-pair processing (PDPP) on the National Research Council of Canada airborne W-band radar (NAW) system. A description of the NAW PDPP pulsing schemes and an analysis of comprehensive airborne data collected in diverse weather conditions in Canada is presented. The analysis shows a successful airborne measurement of Doppler velocity exceeding 100 m s−1 using PDPP approach, the first such measurement from a moving platform.
Frederik Tack, Alexis Merlaud, Andreas C. Meier, Tim Vlemmix, Thomas Ruhtz, Marian-Daniel Iordache, Xinrui Ge, Len van der Wal, Dirk Schuettemeyer, Magdalena Ardelean, Andreea Calcan, Daniel Constantin, Anja Schönhardt, Koen Meuleman, Andreas Richter, and Michel Van Roozendael
Atmos. Meas. Tech., 12, 211–236, https://doi.org/10.5194/amt-12-211-2019, https://doi.org/10.5194/amt-12-211-2019, 2019
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We present an intercomparison study of four airborne imaging DOAS instruments, dedicated to the retrieval and high-resolution mapping of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs). The AROMAPEX campaign took place in Berlin, Germany, in April 2016 with the primary objectives (1) to test and intercompare the performance of experimental airborne imagers and (2) to prepare the validation and calibration campaigns for the Sentinel-5 Precursor/TROPOMI mission.
Sabour Baray, Andrea Darlington, Mark Gordon, Katherine L. Hayden, Amy Leithead, Shao-Meng Li, Peter S. K. Liu, Richard L. Mittermeier, Samar G. Moussa, Jason O'Brien, Ralph Staebler, Mengistu Wolde, Doug Worthy, and Robert McLaren
Atmos. Chem. Phys., 18, 7361–7378, https://doi.org/10.5194/acp-18-7361-2018, https://doi.org/10.5194/acp-18-7361-2018, 2018
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Methane emissions from major oil sands facilities in the Athabasca Oil Sands Region (AOSR) of Alberta were measured in the summer of 2013 using two related aircraft mass-balance approaches. Tailings ponds and fugitive emissions of methane from open pit mines were found to be the major sources of methane in the region. Total methane emissions in the AOSR were measured to be ~ 20 tonnes of CH4 per hour, which is 48 % higher than the Canadian Greenhouse Gas Reporting Program Emissions Inventory.
Thomas Krings, Bruno Neininger, Konstantin Gerilowski, Sven Krautwurst, Michael Buchwitz, John P. Burrows, Carsten Lindemann, Thomas Ruhtz, Dirk Schüttemeyer, and Heinrich Bovensmann
Atmos. Meas. Tech., 11, 721–739, https://doi.org/10.5194/amt-11-721-2018, https://doi.org/10.5194/amt-11-721-2018, 2018
Alexis Merlaud, Frederik Tack, Daniel Constantin, Lucian Georgescu, Jeroen Maes, Caroline Fayt, Florin Mingireanu, Dirk Schuettemeyer, Andreas Carlos Meier, Anja Schönardt, Thomas Ruhtz, Livio Bellegante, Doina Nicolae, Mirjam Den Hoed, Marc Allaart, and Michel Van Roozendael
Atmos. Meas. Tech., 11, 551–567, https://doi.org/10.5194/amt-11-551-2018, https://doi.org/10.5194/amt-11-551-2018, 2018
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We present SWING-UAV, an atmospheric observation system based on a compact scanning spectrometer (SWING) mounted on an unmanned aerial vehicle (UAV). SWING-UAV was operated in the exhaust plume of a power plant in Romania in September 2014, during the AROMAT campaign. SWING quantified the NO2 emitted by the plant and the water vapour content in the boundary layer, in agreement with ancillary data. The system appears in particular promising to study emissions in rural areas.
Alexandra Tsekeri, Anton Lopatin, Vassilis Amiridis, Eleni Marinou, Julia Igloffstein, Nikolaos Siomos, Stavros Solomos, Panagiotis Kokkalis, Ronny Engelmann, Holger Baars, Myrto Gratsea, Panagiotis I. Raptis, Ioannis Binietoglou, Nikolaos Mihalopoulos, Nikolaos Kalivitis, Giorgos Kouvarakis, Nikolaos Bartsotas, George Kallos, Sara Basart, Dirk Schuettemeyer, Ulla Wandinger, Albert Ansmann, Anatoli P. Chaikovsky, and Oleg Dubovik
Atmos. Meas. Tech., 10, 4995–5016, https://doi.org/10.5194/amt-10-4995-2017, https://doi.org/10.5194/amt-10-4995-2017, 2017
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The Generalized Aerosol Retrieval from Radiometer and Lidar Combined data algorithm (GARRLiC) and the LIdar-Radiometer Inversion Code (LIRIC) provide the opportunity to study the aerosol vertical distribution by combining ground-based lidar and sun-photometric measurements. Here, we utilize the capabilities of both algorithms for the characterization of Saharan dust and marine particles, along with their mixtures, in the south-eastern Mediterranean.
Leonid Nichman, Emma Järvinen, James Dorsey, Paul Connolly, Jonathan Duplissy, Claudia Fuchs, Karoliina Ignatius, Kamalika Sengupta, Frank Stratmann, Ottmar Möhler, Martin Schnaiter, and Martin Gallagher
Atmos. Meas. Tech., 10, 3231–3248, https://doi.org/10.5194/amt-10-3231-2017, https://doi.org/10.5194/amt-10-3231-2017, 2017
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Optical probes are frequently used for the detection of cloud particles. The detected microphysical properties may affect particle growth and accretion mechanisms and the light scattering properties of cirrus clouds. In the CLOUD chamber study at CERN, we compared four optical measurement techniques. We show that shape derivation alone is not sufficient to determine the phase of the small cloud particles. None of the instruments were able to unambiguously determine the phase of small particles.
Tim Vlemmix, Xinrui (Jerry) Ge, Bryan T. G. de Goeij, Len F. van der Wal, Gerard C. J. Otter, Piet Stammes, Ping Wang, Alexis Merlaud, Dirk Schüttemeyer, Andreas C. Meier, J. Pepijn Veefkind, and Pieternel F. Levelt
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2017-257, https://doi.org/10.5194/amt-2017-257, 2017
Revised manuscript has not been submitted
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We present a first analysis of UV/VIS spectral measurements obtained with the Spectrolite Breadboard Instrument (developed by TNO, The Netherlands) during the AROMAPEX campaign held in Berlin in April 2016 (campaign supported by ESA and EUFAR). This new sensor was used to measure air pollution in the form of tropospheric NO2 columns. The study focuses specifically on the retrieval of surface reflectances, an important intermediate step towards the final product.
John Liggio, Samar G. Moussa, Jeremy Wentzell, Andrea Darlington, Peter Liu, Amy Leithead, Katherine Hayden, Jason O'Brien, Richard L. Mittermeier, Ralf Staebler, Mengistu Wolde, and Shao-Meng Li
Atmos. Chem. Phys., 17, 8411–8427, https://doi.org/10.5194/acp-17-8411-2017, https://doi.org/10.5194/acp-17-8411-2017, 2017
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The emission and formation of gaseous organic acids from the oil sands industry in Canada is explored through aircraft measurements directly over and downwind wind of industrial facilities. Results demonstrated that the formation of organic acids through atmospheric chemical reactions dominated over the direct emissions from mining activities but could not be explicitly modeled. The results highlight the need for improved understanding of photochemical mechanisms leading to these species.
Rachid Abida, Jean-Luc Attié, Laaziz El Amraoui, Philippe Ricaud, William Lahoz, Henk Eskes, Arjo Segers, Lyana Curier, Johan de Haan, Jukka Kujanpää, Albert Oude Nijhuis, Johanna Tamminen, Renske Timmermans, and Pepijn Veefkind
Atmos. Chem. Phys., 17, 1081–1103, https://doi.org/10.5194/acp-17-1081-2017, https://doi.org/10.5194/acp-17-1081-2017, 2017
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A detailed Observing System Simulation Experiment is performed to quantify the impact of future satellite instrument S-5P carbon monoxide (CO) on tropospheric analyses and forecasts. We focus on Europe for the period of northern summer 2003, when there was a severe heat wave episode. S-5P is able to capture the CO from forest fires that occurred in Portugal. Furthermore, our results provide evidence of S-5P CO benefits for monitoring processes contributing to atmospheric pollution.
Juha Lemmetyinen, Anna Kontu, Jouni Pulliainen, Juho Vehviläinen, Kimmo Rautiainen, Andreas Wiesmann, Christian Mätzler, Charles Werner, Helmut Rott, Thomas Nagler, Martin Schneebeli, Martin Proksch, Dirk Schüttemeyer, Michael Kern, and Malcolm W. J. Davidson
Geosci. Instrum. Method. Data Syst., 5, 403–415, https://doi.org/10.5194/gi-5-403-2016, https://doi.org/10.5194/gi-5-403-2016, 2016
Sarvesh Garimella, Thomas Bjerring Kristensen, Karolina Ignatius, Andre Welti, Jens Voigtländer, Gourihar R. Kulkarni, Frank Sagan, Gregory Lee Kok, James Dorsey, Leonid Nichman, Daniel Alexander Rothenberg, Michael Rösch, Amélie Catharina Ruth Kirchgäßner, Russell Ladkin, Heike Wex, Theodore W. Wilson, Luis Antonio Ladino, Jon P. D. Abbatt, Olaf Stetzer, Ulrike Lohmann, Frank Stratmann, and Daniel James Cziczo
Atmos. Meas. Tech., 9, 2781–2795, https://doi.org/10.5194/amt-9-2781-2016, https://doi.org/10.5194/amt-9-2781-2016, 2016
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The SPectrometer for Ice Nuclei (SPIN) is a commercially available ice nuclei counter manufactured by Droplet Measurement Technologies in Boulder, CO. This study characterizes the SPIN chamber, reporting data from laboratory measurements and quantifying uncertainties. Overall, we report that the SPIN is able to reproduce previous CFDC ice nucleation measurements.
Karoliina Ignatius, Thomas B. Kristensen, Emma Järvinen, Leonid Nichman, Claudia Fuchs, Hamish Gordon, Paul Herenz, Christopher R. Hoyle, Jonathan Duplissy, Sarvesh Garimella, Antonio Dias, Carla Frege, Niko Höppel, Jasmin Tröstl, Robert Wagner, Chao Yan, Antonio Amorim, Urs Baltensperger, Joachim Curtius, Neil M. Donahue, Martin W. Gallagher, Jasper Kirkby, Markku Kulmala, Ottmar Möhler, Harald Saathoff, Martin Schnaiter, Antonio Tomé, Annele Virtanen, Douglas Worsnop, and Frank Stratmann
Atmos. Chem. Phys., 16, 6495–6509, https://doi.org/10.5194/acp-16-6495-2016, https://doi.org/10.5194/acp-16-6495-2016, 2016
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Viscous solid or semi-solid secondary organic aerosol (SOA) may influence cloud properties through ice nucleation in the atmosphere. Here, we observed heterogeneous ice nucleation of viscous α-pinene SOA at temperatures between −39 °C and −37.2 °C with ice saturation ratios significantly below the homogeneous freezing limit. Global modelling suggests that viscous biogenic SOA are present in regions where cirrus formation takes place and could contribute to the global ice nuclei budget.
Emma Järvinen, Karoliina Ignatius, Leonid Nichman, Thomas B. Kristensen, Claudia Fuchs, Christopher R. Hoyle, Niko Höppel, Joel C. Corbin, Jill Craven, Jonathan Duplissy, Sebastian Ehrhart, Imad El Haddad, Carla Frege, Hamish Gordon, Tuija Jokinen, Peter Kallinger, Jasper Kirkby, Alexei Kiselev, Karl-Heinz Naumann, Tuukka Petäjä, Tamara Pinterich, Andre S. H. Prevot, Harald Saathoff, Thea Schiebel, Kamalika Sengupta, Mario Simon, Jay G. Slowik, Jasmin Tröstl, Annele Virtanen, Paul Vochezer, Steffen Vogt, Andrea C. Wagner, Robert Wagner, Christina Williamson, Paul M. Winkler, Chao Yan, Urs Baltensperger, Neil M. Donahue, Rick C. Flagan, Martin Gallagher, Armin Hansel, Markku Kulmala, Frank Stratmann, Douglas R. Worsnop, Ottmar Möhler, Thomas Leisner, and Martin Schnaiter
Atmos. Chem. Phys., 16, 4423–4438, https://doi.org/10.5194/acp-16-4423-2016, https://doi.org/10.5194/acp-16-4423-2016, 2016
Leonid Nichman, Claudia Fuchs, Emma Järvinen, Karoliina Ignatius, Niko Florian Höppel, Antonio Dias, Martin Heinritzi, Mario Simon, Jasmin Tröstl, Andrea Christine Wagner, Robert Wagner, Christina Williamson, Chao Yan, Paul James Connolly, James Robert Dorsey, Jonathan Duplissy, Sebastian Ehrhart, Carla Frege, Hamish Gordon, Christopher Robert Hoyle, Thomas Bjerring Kristensen, Gerhard Steiner, Neil McPherson Donahue, Richard Flagan, Martin William Gallagher, Jasper Kirkby, Ottmar Möhler, Harald Saathoff, Martin Schnaiter, Frank Stratmann, and António Tomé
Atmos. Chem. Phys., 16, 3651–3664, https://doi.org/10.5194/acp-16-3651-2016, https://doi.org/10.5194/acp-16-3651-2016, 2016
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Processes in the atmosphere are often governed by the physical and chemical properties of small cloud particles. Ice, water, and mixed clouds, as well as viscous aerosols, were formed under controlled conditions at the CLOUD-CERN facility. The experimental results show a link between cloud particle properties and their unique optical fingerprints. The classification map presented here allows easier discrimination between various particles such as viscous organic aerosol, salt, ice, and liquid.
C. R. Hoyle, C. Fuchs, E. Järvinen, H. Saathoff, A. Dias, I. El Haddad, M. Gysel, S. C. Coburn, J. Tröstl, A.-K. Bernhammer, F. Bianchi, M. Breitenlechner, J. C. Corbin, J. Craven, N. M. Donahue, J. Duplissy, S. Ehrhart, C. Frege, H. Gordon, N. Höppel, M. Heinritzi, T. B. Kristensen, U. Molteni, L. Nichman, T. Pinterich, A. S. H. Prévôt, M. Simon, J. G. Slowik, G. Steiner, A. Tomé, A. L. Vogel, R. Volkamer, A. C. Wagner, R. Wagner, A. S. Wexler, C. Williamson, P. M. Winkler, C. Yan, A. Amorim, J. Dommen, J. Curtius, M. W. Gallagher, R. C. Flagan, A. Hansel, J. Kirkby, M. Kulmala, O. Möhler, F. Stratmann, D. R. Worsnop, and U. Baltensperger
Atmos. Chem. Phys., 16, 1693–1712, https://doi.org/10.5194/acp-16-1693-2016, https://doi.org/10.5194/acp-16-1693-2016, 2016
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A significant portion of sulphate, an important constituent of atmospheric aerosols, is formed via the aqueous phase oxidation of sulphur dioxide by ozone. The rate of this reaction has previously only been measured over a relatively small temperature range. Here, we use the state of the art CLOUD chamber at CERN to perform the first measurements of this reaction rate in super-cooled droplets, confirming that the existing extrapolation of the reaction rate to sub-zero temperatures is accurate.
M. W. Shephard, C. A. McLinden, K. E. Cady-Pereira, M. Luo, S. G. Moussa, A. Leithead, J. Liggio, R. M. Staebler, A. Akingunola, P. Makar, P. Lehr, J. Zhang, D. K. Henze, D. B. Millet, J. O. Bash, L. Zhu, K. C. Wells, S. L. Capps, S. Chaliyakunnel, M. Gordon, K. Hayden, J. R. Brook, M. Wolde, and S.-M. Li
Atmos. Meas. Tech., 8, 5189–5211, https://doi.org/10.5194/amt-8-5189-2015, https://doi.org/10.5194/amt-8-5189-2015, 2015
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This study provides direct validations of Tropospheric Emission Spectrometer (TES) satellite retrieved profiles against coincident aircraft profiles of carbon monoxide, ammonia, methanol, and formic acid, all of which are of interest for air quality. The comparisons are performed over the Canadian oil sands region during an intensive field campaign in support of the Joint Canada-Alberta Implementation Plan for the Oil Sands Monitoring (JOSM). Initial model evaluations are also provided.
M. Gordon, S.-M. Li, R. Staebler, A. Darlington, K. Hayden, J. O'Brien, and M. Wolde
Atmos. Meas. Tech., 8, 3745–3765, https://doi.org/10.5194/amt-8-3745-2015, https://doi.org/10.5194/amt-8-3745-2015, 2015
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Aircraft-based measurements of air pollutants from sources in the Canadian oil sands were made during a summer intensive field campaign in 2013. This paper describes the top-down emission rate retrieval algorithm (TERRA) to determine facility emissions of pollutants, using SO2 and CH4 as examples. Uncertainty of the emission rates estimated with TERRA is estimated as less than 30%, which is primarily due to the unknown SO2 and CH4 mixing ratios near the surface below the lowest flight level.
W. Woiwode, O. Sumińska-Ebersoldt, H. Oelhaf, M. Höpfner, G. V. Belyaev, A. Ebersoldt, F. Friedl-Vallon, J.-U. Grooß, T. Gulde, M. Kaufmann, A. Kleinert, M. Krämer, E. Kretschmer, T. Kulessa, G. Maucher, T. Neubert, C. Piesch, P. Preusse, M. Riese, H. Rongen, C. Sartorius, G. Schardt, A. Schönfeld, D. Schuettemeyer, M. K. Sha, F. Stroh, J. Ungermann, C. M. Volk, and J. Orphal
Atmos. Meas. Tech., 8, 2509–2520, https://doi.org/10.5194/amt-8-2509-2015, https://doi.org/10.5194/amt-8-2509-2015, 2015
M. Kaufmann, J. Blank, T. Guggenmoser, J. Ungermann, A. Engel, M. Ern, F. Friedl-Vallon, D. Gerber, J. U. Grooß, G. Guenther, M. Höpfner, A. Kleinert, E. Kretschmer, Th. Latzko, G. Maucher, T. Neubert, H. Nordmeyer, H. Oelhaf, F. Olschewski, J. Orphal, P. Preusse, H. Schlager, H. Schneider, D. Schuettemeyer, F. Stroh, O. Suminska-Ebersoldt, B. Vogel, C. M. Volk, W. Woiwode, and M. Riese
Atmos. Meas. Tech., 8, 81–95, https://doi.org/10.5194/amt-8-81-2015, https://doi.org/10.5194/amt-8-81-2015, 2015
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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
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
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
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
Using machine learning algorithm to retrieve cloud fraction based on FY-4A AGRI observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Severe hail detection with C-band dual-polarisation radars using convolutional neural networks
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
Retrieval of cloud fraction and optical thickness from multi-angle polarization observations
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
PEAKO and peakTree: Tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
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
3-D Cloud Masking Across a Broad Swath using Multi-angle Polarimetry and Deep Learning
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
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
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.
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.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2024-2090, https://doi.org/10.5194/egusphere-2024-2090, 2024
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This article presents a novel technique to estimate the liquid water content (LWC) in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows to retrieve the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of the LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Jinyi Xia and Li Guan
EGUsphere, https://doi.org/10.5194/egusphere-2024-977, https://doi.org/10.5194/egusphere-2024-977, 2024
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This study presents a method for estimating cloud cover from FY4A AGRI observations using LSTM neural networks. The results demonstrate excellent performance in distinguishing clear sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
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This study demonstrates the potential for enhancing severe hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-87, https://doi.org/10.5194/amt-2024-87, 2024
Revised manuscript accepted for AMT
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1180, https://doi.org/10.5194/egusphere-2024-1180, 2024
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We introduce an innovative method to retrieve cloud fraction and optical thickness based on polarimetry, well-suited for satellite observations providing multi-angle polarization measurements. The cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and a second in the sun-glint region or at a scattering angle of approximately 90°.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40, https://doi.org/10.5194/amt-2024-40, 2024
Revised manuscript accepted for AMT
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we measured that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-837, https://doi.org/10.5194/egusphere-2024-837, 2024
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In this study, we present a toolkit of two Python algorithms to extract information about the cloud and precipitation particles present in clouds from data measured by ground-based radar instruments. The data consist of Doppler spectra, in which several peaks are formed by hydrometeor populations with different fall velocities. The detection of the specific peaks makes it possible to assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles, and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
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Short summary
An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in situ data from an Arctic storm confirms the main findings of modeling work with radar dual-frequency ratios (DFRs) at different zones of the DFR plane associated with different ice habits. High-resolution CPI images provide accurate identification of rimed particles within the DFR plane. The relationships between the triple-frequency signals and cloud microphysical properties are also presented.
An analysis of airborne triple-frequency radar and almost perfectly co-located coincident in...