Articles | Volume 14, issue 10
https://doi.org/10.5194/amt-14-6885-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-6885-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Oct 2021
Research article |
| 26 Oct 2021
| 26 Oct 2021
Retrieving microphysical properties of concurrent pristine ice and snow using polarimetric radar observations
Nicholas J. Kedzuf
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
J. Christine Chiu
CORRESPONDING AUTHOR
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
V. Chandrasekar
Department of Electrical and Computer Engineering, Colorado State
University, Fort Collins, CO 80523, USA
Sounak Biswas
Department of Electrical and Computer Engineering, Colorado State
University, Fort Collins, CO 80523, USA
Shashank S. Joshil
Department of Electrical and Computer Engineering, Colorado State
University, Fort Collins, CO 80523, USA
Yinghui Lu
Department of Meteorology and Atmospheric Science, The Pennsylvania State University, University Park, PA 16802, USA
Center for Advanced Data Assimilation and Predictability Techniques, The Pennsylvania State University, University Park, PA 16802, USA
Peter Jan van Leeuwen
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Christopher Westbrook
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
Yann Blanchard
ESCER Centre, Department of Earth and Atmospheric Sciences, University of Québec at Montréal, Montréal, Quebec, H3C 3P8, Canada
Sebastian O'Shea
Department of Earth and Environmental Sciences, University of
Manchester, Manchester, M13 9PL, UK
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Atmos. Meas. Tech., 18, 1981–2003, https://doi.org/10.5194/amt-18-1981-2025, https://doi.org/10.5194/amt-18-1981-2025, 2025
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Water is a precious resource, and it is essential to monitor and predict the current and future occurrence of precipitation-producing clouds. We investigate the cloud characteristics related to precipitation using several cloud cases in the United Arab Emirates with data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support for the development of an applicable framework to examine cloud precipitation processes.
Marc Schneebeli, Andreas Leuenberger, Philipp J. Schmid, Jacopo Grazioli, Heather Corden, Alexis Berne, Patrick Kennedy, Jim George, Francesc Junyent, and V. Chandrasekar
EGUsphere, https://doi.org/10.5194/egusphere-2025-1702, https://doi.org/10.5194/egusphere-2025-1702, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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A new technique for the end-to-end calibration of weather radars is introduced. Highly precise artificial radar targets are generated with a radar target simulator and serve as a calibration reference for weather radar observables like reflectivity and Doppler velocity. The system allows to investigate and correct any biases associated with weather radar observations.
Kaiden Sookdar, Scott Edward Giangrande, John Rausch, Lihong Ma, Meng Wang, Dié Wang, Michael Jensen, Ching-Shu Hung, and J. Christine Chiu
EGUsphere, https://doi.org/10.5194/egusphere-2025-694, https://doi.org/10.5194/egusphere-2025-694, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Photometer observations of stratocumulus cloud properties are evaluated for a multiyear archive. Retrievals for cloud optical depth, cloud droplet effective radius, and liquid water path show solid agreement with collocated references. Continental stratocumulus clouds sorted by cloud thickness indicate double the cloud optical depth and liquid water path of their marine counterparts, while exhibiting similar bulk cloud droplet effective radius.
Jennifer R. Stout, Christopher D. Westbrook, Thorwald H. M. Stein, and Mark W. McCorquodale
Atmos. Chem. Phys., 24, 11133–11155, https://doi.org/10.5194/acp-24-11133-2024, https://doi.org/10.5194/acp-24-11133-2024, 2024
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This study uses 3D-printed ice crystal analogues falling in a water–glycerine mix and observed with multi-view cameras, simulating atmospheric conditions. Four types of motion are observed: stable, zigzag, transitional, and spiralling. Particle shape strongly influences motion; complex shapes have a wider range of conditions where they fall steadily compared to simple plates. The most common orientation of unstable particles is non-horizontal, contrary to prior assumptions of always horizontal.
Karina McCusker, Anthony J. Baran, Chris Westbrook, Stuart Fox, Patrick Eriksson, Richard Cotton, Julien Delanoë, and Florian Ewald
Atmos. Meas. Tech., 17, 3533–3552, https://doi.org/10.5194/amt-17-3533-2024, https://doi.org/10.5194/amt-17-3533-2024, 2024
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Polarised radiative transfer simulations are performed using an atmospheric model based on in situ measurements. These are compared to large polarisation measurements to explore whether such measurements can provide information on cloud ice, e.g. particle shape and orientation. We find that using oriented particle models with shapes based on imagery generally allows for accurate simulations. However, results are sensitive to shape assumptions such as the choice of single crystals or aggregates.
Nicholas Williams, Nicholas Byrne, Daniel Feltham, Peter Jan Van Leeuwen, Ross Bannister, David Schroeder, Andrew Ridout, and Lars Nerger
The Cryosphere, 17, 2509–2532, https://doi.org/10.5194/tc-17-2509-2023, https://doi.org/10.5194/tc-17-2509-2023, 2023
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Observations show that the Arctic sea ice cover has reduced over the last 40 years. This study uses ensemble-based data assimilation in a stand-alone sea ice model to investigate the impacts of assimilating three different kinds of sea ice observation, including the novel assimilation of sea ice thickness distribution. We show that assimilating ice thickness distribution has a positive impact on thickness and volume estimates within the ice pack, especially for very thick ice.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter Jan van Leeuwen, Gilad Lerman, and Efi Foufoula-Georgiou
Nonlin. Processes Geophys., 29, 77–92, https://doi.org/10.5194/npg-29-77-2022, https://doi.org/10.5194/npg-29-77-2022, 2022
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The outputs from Earth system models are optimally combined with satellite observations to produce accurate forecasts through a process called data assimilation. Many existing data assimilation methodologies have some assumptions regarding the shape of the probability distributions of model output and observations, which results in forecast inaccuracies. In this paper, we test the effectiveness of a newly proposed methodology that relaxes such assumptions about high-dimensional models.
Concetta Di Mauro, Renaud Hostache, Patrick Matgen, Ramona Pelich, Marco Chini, Peter Jan van Leeuwen, Nancy K. Nichols, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 4081–4097, https://doi.org/10.5194/hess-25-4081-2021, https://doi.org/10.5194/hess-25-4081-2021, 2021
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This study evaluates how the sequential assimilation of flood extent derived from synthetic aperture radar data can help improve flood forecasting. In particular, we carried out twin experiments based on a synthetically generated dataset with controlled uncertainty. Our empirical results demonstrate the efficiency of the proposed data assimilation framework, as forecasting errors are substantially reduced as a result of the assimilation.
Sagar K. Tamang, Ardeshir Ebtehaj, Peter J. van Leeuwen, Dongmian Zou, and Gilad Lerman
Nonlin. Processes Geophys., 28, 295–309, https://doi.org/10.5194/npg-28-295-2021, https://doi.org/10.5194/npg-28-295-2021, 2021
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Data assimilation aims to improve hydrologic and weather forecasts by combining available information from Earth system models and observations. The classical approaches to data assimilation usually proceed with some preconceived assumptions about the shape of their probability distributions. As a result, when such assumptions are invalid, the forecast accuracy suffers. In the proposed methodology, we relax such assumptions and demonstrate improved performance.
Sebastian O'Shea, Jonathan Crosier, James Dorsey, Louis Gallagher, Waldemar Schledewitz, Keith Bower, Oliver Schlenczek, Stephan Borrmann, Richard Cotton, Christopher Westbrook, and Zbigniew Ulanowski
Atmos. Meas. Tech., 14, 1917–1939, https://doi.org/10.5194/amt-14-1917-2021, https://doi.org/10.5194/amt-14-1917-2021, 2021
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The number, shape, and size of ice crystals in clouds are important properties that influence the Earth's radiation budget, cloud evolution, and precipitation formation. This work suggests that one of the most widely used methods for in situ measurements of these properties has significant uncertainties and biases. We suggest methods that dramatically improve these measurements, which can be applied to past and future datasets from these instruments.
Georgia Sotiropoulou, Étienne Vignon, Gillian Young, Hugh Morrison, Sebastian J. O'Shea, Thomas Lachlan-Cope, Alexis Berne, and Athanasios Nenes
Atmos. Chem. Phys., 21, 755–771, https://doi.org/10.5194/acp-21-755-2021, https://doi.org/10.5194/acp-21-755-2021, 2021
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Summer clouds have a significant impact on the radiation budget of the Antarctic surface and thus on ice-shelf melting. However, these are poorly represented in climate models due to errors in their microphysical structure, including the number of ice crystals that they contain. We show that breakup from ice particle collisions can substantially magnify the ice crystal number concentration with significant implications for surface radiation. This process is currently missing in climate models.
Richard J. Bantges, Helen E. Brindley, Jonathan E. Murray, Alan E. Last, Jacqueline E. Russell, Cathryn Fox, Stuart Fox, Chawn Harlow, Sebastian J. O'Shea, Keith N. Bower, Bryan A. Baum, Ping Yang, Hilke Oetjen, and Juliet C. Pickering
Atmos. Chem. Phys., 20, 12889–12903, https://doi.org/10.5194/acp-20-12889-2020, https://doi.org/10.5194/acp-20-12889-2020, 2020
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Understanding how ice clouds influence the Earth's energy balance remains a key challenge for predicting the future climate. These clouds are ubiquitous and are composed of ice crystals that have complex shapes that are incredibly difficult to model. This work exploits new measurements of the Earth's emitted thermal energy made from instruments flown on board an aircraft to test how well the latest ice cloud models can represent these clouds. Results indicate further developments are required.
Yu Ma, Guangheng Ni, Chandrasekar V. Chandra, Fuqiang Tian, and Haonan Chen
Hydrol. Earth Syst. Sci., 23, 4153–4170, https://doi.org/10.5194/hess-23-4153-2019, https://doi.org/10.5194/hess-23-4153-2019, 2019
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Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation. This study extensively investigates the DSD characteristics during rainy seasons in the Beijing urban area using 5-year DSD observations from a Parsivel2 disdrometer. The statistical distributions of DSD parameters are examined and the polarimetric radar rainfall algorithms are derived to support the ongoing development of an X-band radar network.
Steven D. Miller, Louie D. Grasso, Qijing Bian, Sonia M. Kreidenweis, Jack F. Dostalek, Jeremy E. Solbrig, Jennifer Bukowski, Susan C. van den Heever, Yi Wang, Xiaoguang Xu, Jun Wang, Annette L. Walker, Ting-Chi Wu, Milija Zupanski, Christine Chiu, and Jeffrey S. Reid
Atmos. Meas. Tech., 12, 5101–5118, https://doi.org/10.5194/amt-12-5101-2019, https://doi.org/10.5194/amt-12-5101-2019, 2019
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Satellite–based detection of lofted mineral via infrared–window channels, well established in the literature, faces significant challenges in the presence of atmospheric moisture. Here, we consider a case featuring the juxtaposition of two dust plumes embedded within dry and moist air masses. The case is considered from the vantage points of numerical modeling, multi–sensor observations, and radiative transfer theory arriving at a new method for mitigating the water vapor masking effect.
Jonathan K. P. Shonk, Jui-Yuan Christine Chiu, Alexander Marshak, David M. Giles, Chiung-Huei Huang, Gerald G. Mace, Sally Benson, Ilya Slutsker, and Brent N. Holben
Atmos. Meas. Tech., 12, 5087–5099, https://doi.org/10.5194/amt-12-5087-2019, https://doi.org/10.5194/amt-12-5087-2019, 2019
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Retrievals of cloud optical depth made using AERONET radiometers in “cloud mode” rely on the assumption that all cloud is liquid. The presence of ice cloud therefore introduces errors in the retrieved optical depth, which can be over 25 in optically thick ice clouds. However, such clouds are not frequent and the long-term mean optical depth error is about 3 for a sample of real clouds. A correction equation could improve the retrieval further, although this would require extra instrumentation.
Shannon L. Mason, Robin J. Hogan, Christopher D. Westbrook, Stefan Kneifel, Dmitri Moisseev, and Leonie von Terzi
Atmos. Meas. Tech., 12, 4993–5018, https://doi.org/10.5194/amt-12-4993-2019, https://doi.org/10.5194/amt-12-4993-2019, 2019
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The mass contents of snowflakes are critical to remotely sensed estimates of snowfall. The signatures of snow measured at three radar frequencies can distinguish fluffy, fractal snowflakes from dense and more homogeneous rimed snow. However, we show that the shape of the particle size spectrum also has a significant impact on triple-frequency radar signatures and must be accounted for when making triple-frequency radar estimates of snow that include variations in particle structure and density.
Emma Hopkin, Anthony J. Illingworth, Cristina Charlton-Perez, Chris D. Westbrook, and Sue Ballard
Atmos. Meas. Tech., 12, 4131–4147, https://doi.org/10.5194/amt-12-4131-2019, https://doi.org/10.5194/amt-12-4131-2019, 2019
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Ceilometers are laser cloud base recorders which retrieve information about atmospheric aerosol and differing cloud types. In order to ensure the information retrieved from the ceilometer is correct and comparable with other ceilometers in an observation network, a calibration is needed. Presented here is a novel automated calibration method, which includes a correction for the effects of water vapour in the atmosphere and shows its application on the UK Met Office's ceilometer network.
Sebastian J. O'Shea, Jonathan Crosier, James Dorsey, Waldemar Schledewitz, Ian Crawford, Stephan Borrmann, Richard Cotton, and Aaron Bansemer
Atmos. Meas. Tech., 12, 3067–3079, https://doi.org/10.5194/amt-12-3067-2019, https://doi.org/10.5194/amt-12-3067-2019, 2019
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Optical array probe measurements of clouds are widely used to inform and validate numerical weather and climate models. In this paper, we discuss artefacts which may bias data from these instruments. Using laboratory and synthetic datasets, we demonstrate how greyscale analysis can be used to filter data, constraining the sample volume and improving data quality particularly at small sizes where their measurements are considered unreliable.
Eoghan Darbyshire, William T. Morgan, James D. Allan, Dantong Liu, Michael J. Flynn, James R. Dorsey, Sebastian J. O'Shea, Douglas Lowe, Kate Szpek, Franco Marenco, Ben T. Johnson, Stephane Bauguitte, Jim M. Haywood, Joel F. Brito, Paulo Artaxo, Karla M. Longo, and Hugh Coe
Atmos. Chem. Phys., 19, 5771–5790, https://doi.org/10.5194/acp-19-5771-2019, https://doi.org/10.5194/acp-19-5771-2019, 2019
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A novel analysis of aerosol and gas-phase vertical profiles shows a marked regional pollution contrast: composition is driven by the fire regime and vertical distribution is driven by thermodynamics. These drivers ought to be well represented in simulations to ensure realistic prediction of climate and air quality impacts. The BC : CO ratio in haze and plumes increases with altitude – long-range transport or fire stage coupled to plume dynamics may be responsible. Further enquiry is advocated.
Andrew I. Barrett, Christopher D. Westbrook, John C. Nicol, and Thorwald H. M. Stein
Atmos. Chem. Phys., 19, 5753–5769, https://doi.org/10.5194/acp-19-5753-2019, https://doi.org/10.5194/acp-19-5753-2019, 2019
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We use radars at three wavelengths to study cloud properties. The full Doppler spectra (rather than calculated averages of the spectra) are compared for the radars. This allows us to estimate the size and number of ice particles within the cloud. By following the evolution of the ice particles, we observe a region where particles rapidly and consistently increase in size. The observations suggest that these large particles form through interlocking of branched arms of smaller ice particles.
Stuart Fox, Jana Mendrok, Patrick Eriksson, Robin Ekelund, Sebastian J. O'Shea, Keith N. Bower, Anthony J. Baran, R. Chawn Harlow, and Juliet C. Pickering
Atmos. Meas. Tech., 12, 1599–1617, https://doi.org/10.5194/amt-12-1599-2019, https://doi.org/10.5194/amt-12-1599-2019, 2019
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Airborne observations of ice clouds are used to validate radiative transfer simulations using a state-of-the-art database of cloud ice optical properties. Simulations at these wavelengths are required to make use of future satellite instruments such as the Ice Cloud Imager. We show that they can generally reproduce observed cloud signals, but for a given total ice mass there is considerable sensitivity to the cloud microphysics, including the particle shape and distribution of ice mass.
Paul I. Palmer, Simon O'Doherty, Grant Allen, Keith Bower, Hartmut Bösch, Martyn P. Chipperfield, Sarah Connors, Sandip Dhomse, Liang Feng, Douglas P. Finch, Martin W. Gallagher, Emanuel Gloor, Siegfried Gonzi, Neil R. P. Harris, Carole Helfter, Neil Humpage, Brian Kerridge, Diane Knappett, Roderic L. Jones, Michael Le Breton, Mark F. Lunt, Alistair J. Manning, Stephan Matthiesen, Jennifer B. A. Muller, Neil Mullinger, Eiko Nemitz, Sebastian O'Shea, Robert J. Parker, Carl J. Percival, Joseph Pitt, Stuart N. Riddick, Matthew Rigby, Harjinder Sembhi, Richard Siddans, Robert L. Skelton, Paul Smith, Hannah Sonderfeld, Kieran Stanley, Ann R. Stavert, Angelina Wenger, Emily White, Christopher Wilson, and Dickon Young
Atmos. Chem. Phys., 18, 11753–11777, https://doi.org/10.5194/acp-18-11753-2018, https://doi.org/10.5194/acp-18-11753-2018, 2018
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This paper provides an overview of the Greenhouse gAs Uk and Global Emissions (GAUGE) experiment. GAUGE was designed to quantify nationwide GHG emissions of the UK, bringing together measurements and atmospheric transport models. This novel experiment is the first of its kind. We anticipate it will inform the blueprint for countries that are building a measurement infrastructure in preparation for global stocktakes, which are a key part of the Paris Agreement.
Amy K. Hodgson, William T. Morgan, Sebastian O'Shea, Stéphane Bauguitte, James D. Allan, Eoghan Darbyshire, Michael J. Flynn, Dantong Liu, James Lee, Ben Johnson, Jim M. Haywood, Karla M. Longo, Paulo E. Artaxo, and Hugh Coe
Atmos. Chem. Phys., 18, 5619–5638, https://doi.org/10.5194/acp-18-5619-2018, https://doi.org/10.5194/acp-18-5619-2018, 2018
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We flew a large atmospheric research aircraft across a number of different biomass burning environments in the Amazon Basin in September and October 2012. In this paper, we focus on smoke sampled very close to fresh fires (only 600–900 m above the fires and smoke that was 4–6 min old) to examine the chemical components that make up the smoke and their abundance. We found substantial differences in the emitted smoke that are due to the fuel type and combustion processes driving the fires.
Jake J. Gristey, J. Christine Chiu, Robert J. Gurney, Cyril J. Morcrette, Peter G. Hill, Jacqueline E. Russell, and Helen E. Brindley
Atmos. Chem. Phys., 18, 5129–5145, https://doi.org/10.5194/acp-18-5129-2018, https://doi.org/10.5194/acp-18-5129-2018, 2018
James D. Lee, Stephen D. Mobbs, Axel Wellpott, Grant Allen, Stephane J.-B. Bauguitte, Ralph R. Burton, Richard Camilli, Hugh Coe, Rebecca E. Fisher, James L. France, Martin Gallagher, James R. Hopkins, Mathias Lanoiselle, Alastair C. Lewis, David Lowry, Euan G. Nisbet, Ruth M. Purvis, Sebastian O'Shea, John A. Pyle, and Thomas B. Ryerson
Atmos. Meas. Tech., 11, 1725–1739, https://doi.org/10.5194/amt-11-1725-2018, https://doi.org/10.5194/amt-11-1725-2018, 2018
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This work describes measurements, made from an aircraft platform, of the emission of methane and other organic gases from an uncontrolled leak from an oil platform in the North Sea (Total Elgin). The measurements made helped the platform operators to devise a strategy for repairing the leak and serve as a methodology for assessing future similar incidents.
Hazel M. Jones, Gillian Young, Thomas W. Choularton, Keith N. Bower, Thomas Lachlan-Cope, Sebastian O'Shea, James Dorsey, Russell Ladkin, Amelié Kirchgaessner, and Alexandra Weiss
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-283, https://doi.org/10.5194/acp-2018-283, 2018
Revised manuscript not accepted
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This paper presents new in-situ aerosol and cloud physics measurements from the Arctic during the summertime ACCACIA campaign. Data from eight flights in the vicinity of Svalbard are presented and compared to data from previous Arctic projects. It is hoped this dataset will be of use to modellers who wish to develop polar cloud parameterisations.
Zhao Shi, Fangqiang Wei, and Venkatachalam Chandrasekar
Nat. Hazards Earth Syst. Sci., 18, 765–780, https://doi.org/10.5194/nhess-18-765-2018, https://doi.org/10.5194/nhess-18-765-2018, 2018
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The aim of this paper is to evaluate the debris flow occurrence thresholds of the rainfall intensity–duration in the earthquake-affected areas of Sichuan province over the rainy seasons from 2012 to 2014. it is clear that radar-based rainfall estimate and threshold supplement the monitoring gap of EWS where rain gauge is scarce. A better understanding of relationship between rainfall and debris flow initiation can be enhanced by the radar with highly spatiotemporal resolution.
Sebastian J. O'Shea, Thomas W. Choularton, Michael Flynn, Keith N. Bower, Martin Gallagher, Jonathan Crosier, Paul Williams, Ian Crawford, Zoë L. Fleming, Constantino Listowski, Amélie Kirchgaessner, Russell S. Ladkin, and Thomas Lachlan-Cope
Atmos. Chem. Phys., 17, 13049–13070, https://doi.org/10.5194/acp-17-13049-2017, https://doi.org/10.5194/acp-17-13049-2017, 2017
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Few direct measurements have been made of Antarctic cloud and aerosol properties. As part of the 2015 Microphysics of Antarctic Clouds (MAC) field campaign, detailed airborne and ground-based measurements were made over the Weddell Sea and Antarctic coastal continent. This paper presents the first results from this campaign and discusses the cloud properties and processes important in this region.
Shannon L. Mason, J. Christine Chiu, Robin J. Hogan, and Lin Tian
Atmos. Chem. Phys., 17, 11567–11589, https://doi.org/10.5194/acp-17-11567-2017, https://doi.org/10.5194/acp-17-11567-2017, 2017
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Airborne Doppler radar measurements are used to estimate the properties of tropical stratiform rain. Doppler velocity measurements provide sufficient information to estimate the rain rate over land and also to retrieve the raindrop size distribution over ocean, addressing major uncertainties in current satellite measurements of rain. These results suggest that EarthCARE, with the first space-borne Doppler radar, will facilitate improved global measurements of rain.
Tom Lachlan-Cope, Constantino Listowski, and Sebastian O'Shea
Atmos. Chem. Phys., 16, 15605–15617, https://doi.org/10.5194/acp-16-15605-2016, https://doi.org/10.5194/acp-16-15605-2016, 2016
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The paper presents observations of clouds over the Antarctic Peninsula (a 2500 m high barrier separating the Weddell and Bellingshausen seas) during summer 2010 and 2011. The observations of ice and liquid particles in the clouds reveal that more particles were seen during 2011 and that this is associated with an air mass that has spent longer close to the sea ice surface. This suggests that sea ice is a source of cloud nuclei.
Mattia Vaccarono, Renzo Bechini, Chandra V. Chandrasekar, Roberto Cremonini, and Claudio Cassardo
Atmos. Meas. Tech., 9, 5367–5383, https://doi.org/10.5194/amt-9-5367-2016, https://doi.org/10.5194/amt-9-5367-2016, 2016
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The data quality of radars must be ensured and continuously monitored. The aim of this paper is to provide an integrated approach able to monitor the calibration of operational dual-polarization radars. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. This is considered adequate to automatically detect any unexpected change in the radar system requiring further investigations.
Roberto Cremonini, Dmitri Moisseev, and Venkatachalam Chandrasekar
Atmos. Meas. Tech., 9, 5063–5075, https://doi.org/10.5194/amt-9-5063-2016, https://doi.org/10.5194/amt-9-5063-2016, 2016
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Although high-spatial-resolution weather radar observations are of primary relevance for urban hydrology, weather radar siting and characterization are challenging in an urban environment. Buildings, masts and trees cause partial beam blockages and clutter that seriously affect the observations. For the first time, this paper investigates the benefits of using airborne laser scanner (ALS) data for quantitative estimations of partial beam blockages in an urban environment.
J. R. Pitt, M. Le Breton, G. Allen, C. J. Percival, M. W. Gallagher, S. J.-B. Bauguitte, S. J. O'Shea, J. B. A. Muller, M. S. Zahniser, J. Pyle, and P. I. Palmer
Atmos. Meas. Tech., 9, 63–77, https://doi.org/10.5194/amt-9-63-2016, https://doi.org/10.5194/amt-9-63-2016, 2016
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We present details of an Aerodyne quantum cascade laser absorption spectrometer (QCLAS) used to make airborne measurements of N2O and CH4, including its configuration for use on board an aircraft. Two different methods to correct for the influence of water vapour on the measurements are evaluated. We diagnose a sensitivity of the instrument to changes in pressure, introduce a new calibration procedure to account for this effect, and assess its performance.
M. D. Fielding, J. C. Chiu, R. J. Hogan, G. Feingold, E. Eloranta, E. J. O'Connor, and M. P. Cadeddu
Atmos. Meas. Tech., 8, 2663–2683, https://doi.org/10.5194/amt-8-2663-2015, https://doi.org/10.5194/amt-8-2663-2015, 2015
M. D. Jolleys, H. Coe, G. McFiggans, J. W. Taylor, S. J. O'Shea, M. Le Breton, S. J.-B. Bauguitte, S. Moller, P. Di Carlo, E. Aruffo, P. I. Palmer, J. D. Lee, C. J. Percival, and M. W. Gallagher
Atmos. Chem. Phys., 15, 3077–3095, https://doi.org/10.5194/acp-15-3077-2015, https://doi.org/10.5194/acp-15-3077-2015, 2015
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Particulate emissions in the form of organic aerosol from boreal forest fires in Canada have been measured during an aircraft measurement campaign. Ratios of the amount of aerosol emitted relative to gas species such as CO were calculated and show high levels of variability throughout the campaign. This variability is affected by both changes in fire conditions, as fires tended to die down later in the measurement period, and by changes to the aerosol due to chemical reactions in the atmosphere.
G. Allen, S. M. Illingworth, S. J. O'Shea, S. Newman, A. Vance, S. J.-B. Bauguitte, F. Marenco, J. Kent, K. Bower, M. W. Gallagher, J. Muller, C. J. Percival, C. Harlow, J. Lee, and J. P. Taylor
Atmos. Meas. Tech., 7, 4401–4416, https://doi.org/10.5194/amt-7-4401-2014, https://doi.org/10.5194/amt-7-4401-2014, 2014
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This paper presents a validated method and data set for new retrievals of trace gas concentrations and temperature from the ARIES infrared spectrometer instrument on the UK Atmospheric Research Aircraft (www.faam.ac.uk). This new capability for the aircraft will allow new science to be done because of the way it can sense information about the atmosphere without having to physically pass through it (remote sensing). This will allow us to better understand the make-up of the lower atmosphere.
S. J. O'Shea, G. Allen, M. W. Gallagher, K. Bower, S. M. Illingworth, J. B. A. Muller, B. T. Jones, C. J. Percival, S. J-B. Bauguitte, M. Cain, N. Warwick, A. Quiquet, U. Skiba, J. Drewer, K. Dinsmore, E. G. Nisbet, D. Lowry, R. E. Fisher, J. L. France, M. Aurela, A. Lohila, G. Hayman, C. George, D. B. Clark, A. J. Manning, A. D. Friend, and J. Pyle
Atmos. Chem. Phys., 14, 13159–13174, https://doi.org/10.5194/acp-14-13159-2014, https://doi.org/10.5194/acp-14-13159-2014, 2014
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This paper presents airborne measurements of greenhouse gases collected in the European Arctic. Regional scale flux estimates for the northern Scandinavian wetlands are derived. These fluxes are found to be in excellent agreement with coincident surface measurements within the aircraft's sampling domain. This has allowed a significant low bias to be identified in two commonly used process-based land surface models.
J. C. Chiu, J. A. Holmes, R. J. Hogan, and E. J. O'Connor
Atmos. Chem. Phys., 14, 8389–8401, https://doi.org/10.5194/acp-14-8389-2014, https://doi.org/10.5194/acp-14-8389-2014, 2014
A. Battaglia, C. D. Westbrook, S. Kneifel, P. Kollias, N. Humpage, U. Löhnert, J. Tyynelä, and G. W. Petty
Atmos. Meas. Tech., 7, 1527–1546, https://doi.org/10.5194/amt-7-1527-2014, https://doi.org/10.5194/amt-7-1527-2014, 2014
S. J. O'Shea, G. Allen, M. W. Gallagher, S. J.-B. Bauguitte, S. M. Illingworth, M. Le Breton, J. B. A. Muller, C. J. Percival, A. T. Archibald, D. E. Oram, M. Parrington, P. I. Palmer, and A. C. Lewis
Atmos. Chem. Phys., 13, 12451–12467, https://doi.org/10.5194/acp-13-12451-2013, https://doi.org/10.5194/acp-13-12451-2013, 2013
M. Le Breton, A. Bacak, J. B. A. Muller, S. J. O'Shea, P. Xiao, M. N. R. Ashfold, M. C. Cooke, R. Batt, D. E. Shallcross, D. E. Oram, G. Forster, S. J.-B. Bauguitte, P. I. Palmer, M. Parrington, A. C. Lewis, J. D. Lee, and C. J. Percival
Atmos. Chem. Phys., 13, 9217–9232, https://doi.org/10.5194/acp-13-9217-2013, https://doi.org/10.5194/acp-13-9217-2013, 2013
P. I. Palmer, M. Parrington, J. D. Lee, A. C. Lewis, A. R. Rickard, P. F. Bernath, T. J. Duck, D. L. Waugh, D. W. Tarasick, S. Andrews, E. Aruffo, L. J. Bailey, E. Barrett, S. J.-B. Bauguitte, K. R. Curry, P. Di Carlo, L. Chisholm, L. Dan, G. Forster, J. E. Franklin, M. D. Gibson, D. Griffin, D. Helmig, J. R. Hopkins, J. T. Hopper, M. E. Jenkin, D. Kindred, J. Kliever, M. Le Breton, S. Matthiesen, M. Maurice, S. Moller, D. P. Moore, D. E. Oram, S. J. O'Shea, R. C. Owen, C. M. L. S. Pagniello, S. Pawson, C. J. Percival, J. R. Pierce, S. Punjabi, R. M. Purvis, J. J. Remedios, K. M. Rotermund, K. M. Sakamoto, A. M. da Silva, K. B. Strawbridge, K. Strong, J. Taylor, R. Trigwell, K. A. Tereszchuk, K. A. Walker, D. Weaver, C. Whaley, and J. C. Young
Atmos. Chem. Phys., 13, 6239–6261, https://doi.org/10.5194/acp-13-6239-2013, https://doi.org/10.5194/acp-13-6239-2013, 2013
S. J. O'Shea, S. J.-B. Bauguitte, M. W. Gallagher, D. Lowry, and C. J. Percival
Atmos. Meas. Tech., 6, 1095–1109, https://doi.org/10.5194/amt-6-1095-2013, https://doi.org/10.5194/amt-6-1095-2013, 2013
A. Alqudah, V. Chandrasekar, and M. Le
Nat. Hazards Earth Syst. Sci., 13, 535–544, https://doi.org/10.5194/nhess-13-535-2013, https://doi.org/10.5194/nhess-13-535-2013, 2013
M. Antón, L. Alados-Arboledas, J. L. Guerrero-Rascado, M. J. Costa, J. C Chiu, and F. J. Olmo
Atmos. Chem. Phys., 12, 11723–11732, https://doi.org/10.5194/acp-12-11723-2012, https://doi.org/10.5194/acp-12-11723-2012, 2012
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Algorithm for continual monitoring of fog based on geostationary satellite imagery
Mitigation of satellite OCO-2 CO2 biases in the vicinity of clouds with 3D calculations using the Education and Research 3D Radiative Transfer Toolbox (EaR3T)
Wet-radome attenuation in ARM cloud radars and its utilization in radar calibration using disdrometer measurements
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer observations
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products
Peering into the heart of thunderstorm clouds: insights from cloud radar and spectral polarimetry
Improved Simulation of Thunderstorm Characteristics and Polarimetric Signatures with LIMA 2-Moment Microphysics in AROME
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Extension of AVHRR-based climate data records: Exploring ways to simulate AVHRR radiances from Suomi-NPP VIIRS data
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Satellite-based detection of deep convective clouds: the sensitivity of infrared methods, and implications for cloud climatology
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
Riming-dependent Snowfall Rate and Ice Water Content Retrievals for W-band cloud radar
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
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
Radiative Closure Assessment of Retrieved Cloud and Aerosol Properties for the EarthCARE Mission: The ACMB-DF Product
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
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
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
Atmos. Meas. Tech., 18, 1927–1941, https://doi.org/10.5194/amt-18-1927-2025, https://doi.org/10.5194/amt-18-1927-2025, 2025
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Fog and low stratus (FLS) are both persistent clouds close to the Earth's surface. This study introduces a new machine-learning-based algorithm developed for the Meteosat Second Generation geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 h day cycle.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
Atmos. Meas. Tech., 18, 1859–1884, https://doi.org/10.5194/amt-18-1859-2025, https://doi.org/10.5194/amt-18-1859-2025, 2025
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CO2 column-averaged dry-air mole fractions can be retrieved from space using spectrometers like OCO-2. However, nearby clouds induce spectral distortions that bias these retrievals beyond the accuracy needed for global CO2 source and sink assessments. This study employs a physics-based linearization approach to represent 3D cloud effects and introduces radiance-level mitigation techniques for actual OCO-2 data, enabling the operational implementation of these corrections.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
Atmos. Meas. Tech., 18, 1641–1657, https://doi.org/10.5194/amt-18-1641-2025, https://doi.org/10.5194/amt-18-1641-2025, 2025
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation log-linear behavior as a function of rainfall rates in light and moderate rain conditions. This resulting reflectivity offset during the recent field campaign is compared favorably with the traditional disdrometer comparison near the rain onset, while it also demonstrates similar trends with respect to collocated and independently calibrated reference radars.
Yuli Liu and Ian Stuart Adams
Atmos. Meas. Tech., 18, 1659–1674, https://doi.org/10.5194/amt-18-1659-2025, https://doi.org/10.5194/amt-18-1659-2025, 2025
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This paper presents our latest development in tomographic reconstruction algorithms that use multi-angle (sub)millimeter-wave brightness temperature to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomography technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the technique effectively increases detection sensitivity for small ice cloud particles.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech., 18, 1621–1640, https://doi.org/10.5194/amt-18-1621-2025, https://doi.org/10.5194/amt-18-1621-2025, 2025
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The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
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This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
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The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Cloé David, Clotilde Augros, Benoît Vié, François Bouttier, and Tony Le Bastard
EGUsphere, https://doi.org/10.5194/egusphere-2025-685, https://doi.org/10.5194/egusphere-2025-685, 2025
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Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
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In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Karl-Göran Karlsson, Nina Håkansson, Salomon Eliasson, Erwin Wolters, and Ronald Scheirer
EGUsphere, https://doi.org/10.5194/egusphere-2025-379, https://doi.org/10.5194/egusphere-2025-379, 2025
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The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Andrzej Zbigniew Kotarba and Izabela Wojciechowska
EGUsphere, https://doi.org/10.5194/egusphere-2024-3693, https://doi.org/10.5194/egusphere-2024-3693, 2025
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The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar-radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study discovers how small threshold changes significantly affect climatology of severe storms.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3916, https://doi.org/10.5194/egusphere-2024-3916, 2025
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Accurate measurements of cloud water content IWC and snowfall rate SR are challenging due to high spatial variability and limitations of our measurement techniques. Here we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path as a proxy for the occurrence of riming, which is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
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We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1651, https://doi.org/10.5194/egusphere-2024-1651, 2024
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Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared and the likelihood of them differing by 10 W/m2 defines the closure.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Cited articles
Abel, S. J., Cotton, R. J., Barrett, P. A., and Vance, A. K.: A comparison of ice water content measurement techniques on the FAAM BAe-146 aircraft, Atmos. Meas. Tech., 7, 3007–3022, https://doi.org/10.5194/amt-7-3007-2014, 2014.
Aydin, K. and Seliga,T. A.: Radar Polarimetric Backscattering Properties of
Conical Graupel, J. Atmos. Sci., 41, 1887–1892,
https://doi.org/10.1175/1520-0469(1984)041<1887:rpbpoc>2.0.co;2,
1984.
Baran, A. J., Connolly, P., and Lee, C.: Testing an ensemble model of cirrus
ice crystals using midlatitude in situ estimates of ice water content,
volume extinction coefficient and the total solar optical depth, J. Quant.
Spectrosc. Ra., 110, 1579–1598, https://doi.org/10.1016/j.jqsrt.2009.02.021, 2009.
Barrett, A. I., Westbrook, C. D., Nicol, J. C., and Stein, T. H. M.: Rapid ice aggregation process revealed through triple-wavelength Doppler spectrum radar analysis, Atmos. Chem. Phys., 19, 5753–5769, https://doi.org/10.5194/acp-19-5753-2019, 2019.
Bennett, L.: NCAS mobile X-band radar scan data from 1st November 2016 to
4th June 2018 deployed on long-term observations at the Chilbolton Facility
for Atmospheric and Radio Research (CFARR), Hampshire, UK, Centre for
Environmental Data Analysis [data set], https://doi.org/10.5285/ffc9ed384aea471dab35901cf62f70be,
2020.
Botta, G., Aydin, K., Verlinde, J., Avramov, A. E., Ackerman, A. S.,
Fridlind, A. M., McFarquhar, G. M., and Wolde, M.: Millimeter wave
scattering from ice crystals and their aggregates: Comparing cloud model
simulations with X- and Ka-band radar measurements, J. Geophys. Res., 116,
D00T04, https://doi.org/10.1029/2011JD015909, 2011.
Brath, M., Ekelund, R., Eriksson, P., Lemke, O., and Buehler, S. A.: Microwave and submillimeter wave scattering of oriented ice particles, Atmos. Meas. Tech., 13, 2309–2333, https://doi.org/10.5194/amt-13-2309-2020, 2020.
Bringi, V. N. and Chandrasekar, V.: Polarimetric Doppler weather radar:
principles and applications, Cambridge University Press, Cambridge, UK, 2001.
Chiu, J. C.: cjchiurams/PICASSO-IceSnowProperties: v1.0.0 (v1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.5590209, 2021.
Craven, P. and Wahba, G.: Smoothing noisy data with spline functions:
Estimating the correct degree of smoothing by the method of generalized
cross-validation, Numer. Math., 31, 377–403, 1979.
Crosier, J., Bower, K. N., Choularton, T. W., Westbrook, C. D., Connolly, P. J., Cui, Z. Q., Crawford, I. P., Capes, G. L., Coe, H., Dorsey, J. R., Williams, P. I., Illingworth, A. J., Gallagher, M. W., and Blyth, A. M.: Observations of ice multiplication in a weakly convective cell embedded in supercooled mid-level stratus, Atmos. Chem. Phys., 11, 257–273, https://doi.org/10.5194/acp-11-257-2011, 2011.
Delanoë, J., Protat, A., Testud, J., Bouniol, D., Heymsfield, A. J.,
Bansemer, A., Brown, P. R. A., and Forbes, R. M.: Statistical properties of
the normalized ice particle size distribution, J. Geophys.
Res.-Atmos., 110, D10201, https://doi.org/10.1029/2004jd005405, 2005.
Delanoë, J. M. E., Heymsfield, A. J., Protat, A., Bansemer, A., and
Hogan, R. J.: Normalized particle size distribution for remote sensing
application, J. Geophys. Res.-Atmos., 119, 4204–4227,
https://doi.org/10.1002/2013jd020700, 2014.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D.,
Twohy, C. H., Richardson, M., Eidhammer, T., and Rogers, D.: Predicting
global atmospheric ice nuclei distributions and their impacts on climate,
Proc. Natl. Acad. Sci. USA, 107, 11217–11222, 2010.
DeMott, P. J., Möhler, O., Stetzer, O., Vali, G., Levin, Z., Petters, M. D., Murakami, M., Leisner, T., Bundke, U., Klein, H., and Kanji, Z. A.: Resurgence in ice nuclei measurement research, B. Am.
Meteorol. Soc., 92, 1623–1635, https://doi.org/10.1175/2011bams3119.1, 2011.
Erfani, E. and Mitchell, D. L.: Growth of ice particle mass and projected area during riming, Atmos. Chem. Phys., 17, 1241–1257, https://doi.org/10.5194/acp-17-1241-2017, 2017.
Eriksson, P., Ekelund, R., Mendrok, J., Brath, M., Lemke, O., and Buehler, S. A.: A general database of hydrometeor single scattering properties at microwave and sub-millimetre wavelengths, Earth Syst. Sci. Data, 10, 1301–1326, https://doi.org/10.5194/essd-10-1301-2018, 2018.
Evensen, G., Raanes, P. N., Styrodal, A. S., and Hove, J.: Efficient
implementation of an iterative Ensemble Smoother for data assimilation and
reservoir history matching, Front. Appl. Math. Stat., 5, 47, https://doi.org/10.3389/fams.2019.00047, 2019.
Facility for Airborne Atmospheric Measurements (FAAM), Natural Environment Research Council, and Met Office: FAAM C081 PICASSO flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft, Centre for Environmental Data Analysis, available at: https://catalogue.ceda.ac.uk/uuid/64c9279112bb4e0cadb6adaebf1141eb (last access: 25 February 2021), 2018.
Field, P. R. and Heymsfield, A. J.: Importance of snow to global
precipitation, Geophys. Res. Lett., 42, 9512–9520,
https://doi.org/10.1002/2015gl065497, 2015.
Field, P. R., Hogan, R. J., Brown, P. R. A., Illingworth, A. J., Choularton,
T. W., and Cotton, R. J.: Parametrization of ice-particle size distributions
for mid-latitude stratiform cloud, Q. J. Roy. Meteor. Soc., 131, 1997–2017,
https://doi.org/10.1256/qj.04.134, 2005.
Field, P. R., Lawson, R. P., Brown, P. R. A., Lloyd, G., Westbrook, C.,
Moisseev, D., Miltenberger, A., Nenes, A., Blyth, A., Choularton, T.,
Connolly, P., Buehl, J., Crosier, J., Cui, Z., Dearden, C., DeMott, P.,
Flossman, A., Heymsfield, A., Huang, Y., Kalesse, H., Kanji, Z. A., Korolev,
A., Kirchgaessner, A., Lasher-Trapp, S., Leisner, T., McFarquhar, G.,
Phillips, V., Stith, J., and Sullivan, S.: Secondary ice production: current
state of the science and recommendations for the future, Meteor. Mon., 58,
7.1–7.20, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0014.1, 2017.
Fielding, M. D., Chiu, J. C., Hogan, R. J., and Feingold, G.: A novel
ensemble method for retrieving properties of warm cloud in 3-D using
ground-based scanning radar and zenith radiances, J. Geophys. Res.-Atmos.,
119, 10912–10930, 2014.
Fielding, M. D., Chiu, J. C., Hogan, R. J., Feingold, G., Eloranta, E., O'Connor, E. J., and Cadeddu, M. P.: Joint retrievals of cloud and drizzle in marine boundary layer clouds using ground-based radar, lidar and zenith radiances, Atmos. Meas. Tech., 8, 2663–2683, https://doi.org/10.5194/amt-8-2663-2015, 2015.
Garrett, T. J., Yuter, S. E., Fallgatter, C., Shkurko, K., Rhodes, S. R.,
and Endries, J. L.: Orientations and aspect ratios of falling snow, Geophys.
Res. Lett., 42, 4617–4622, https://doi.org/10.1002/2015gl064040, 2015.
Grazioli, J., Lloyd, G., Panziera, L., Hoyle, C. R., Connolly, P. J., Henneberger, J., and Berne, A.: Polarimetric radar and in situ observations of riming and snowfall microphysics during CLACE 2014, Atmos. Chem. Phys., 15, 13787–13802, https://doi.org/10.5194/acp-15-13787-2015, 2015.
Gultepe, I., Heymsfield, A. J., Field, P. R., and Axisa, D.: Ice-phase
precipitation, Meteor. Mon., 58, 6.1–6.36, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0013.1, 2017.
Heymsfield, A. J., Schmitt, C., Bansemer, A., and Twohy, C. H.: Improved
representation of ice particle masses based on observations in natural
clouds, J. Atmos. Scie., 67, 3303–3318, https://doi.org/10.1175/2010jas3507.1, 2010.
Hogan, R. J., Field, P. R., Illingworth, A. J., Cotton, R. J., and
Choularton, T. W.: Properties of embedded convection in warm-frontal
mixed-phase cloud from aircraft and polarimetric radar, Q. J. Roy. Meteor.
Soc., 128, 451–476, https://doi.org/10.1256/003590002321042054, 2002.
Hogan, R. J., Tian, L., Brown, P. R. A., Westbrook, C., Heymsfield, A. J.,
and Eastment, J. D.: Radar scattering from ice aggregates using the
horizontally aligned oblate spheroid approximation. J. Appl. Meteorol.
Clim., 51, 655–671, https://doi.org/10.1175/JAMC-D-11-074.1, 2012.
Hong, G., Yang, P., Baum, B. A., Heymsfield, A. J., Weng, F., Liu, Q.,
Heygster, G., and Buehler, S. A.: Scattering database in the millimeter and
submillimeter wave range of 100–1000 GHz for nonspherical ice particles, J.
Geophys. Res., 114, D06201, https://doi.org/10.1029/2008jd010451, 2009.
Hubbert, J. C., Ellis, S. M., Chang, W.-Y., Rutledge, S., and Dixon, M.:
Modeling and interpretation of S-Band ice crystal depolarization signatures
from data obtained by simultaneously transmitting horizontally and
vertically polarized fields, J. Appl. Meteorol. Clim., 53, 1659–1677,
https://doi.org/10.1175/jamc-d-13-0158.1, 2014.
Jiang, Z., Oue, M., Verlinde, J., Clothiaux, E. E., Aydin, K., Botta, G.,
and Lu, Y.: What can we conclude about the real aspect ratios of ice
particle aggregates from two-dimensional images?, J. Appl. Meteorol.
Clim., 56, 725–734, https://doi.org/10.1175/jamc-d-16-0248.1, 2017.
Jung, Y., Xue, M., and Zhang, G.: Simulations of polarimetric radar
signatures of a supercell storm using a two-moment bulk microphysics
scheme, J. Appl. Meteorol. Clim., 49, 146–163, https://doi.org/10.1175/2009jamc2178.1,
2010.
Kajikawa, M.: Observation of the falling motion of early snow-flakes. Part
II: On the variation of falling velocity, J. Meteorol. Soc. Jpn., 67,
731–738, 1989.
Keat, W. J. and Westbrook, C. D.: Revealing layers of pristine oriented
crystals embedded within deep ice clouds using differential reflectivity and
the copolar correlation coefficient, J. Geophys. Res.-Atmos., 122, 11737–11759, https://doi.org/10.1002/2017jd026754, 2017.
Keat, W. J., Westbrook, C. D., and Illingworth, A. J.: High-precision
measurements of the copolar correlation coefficient: Non-Gaussian errors and
retrieval of the dispersion parameter μ in rainfall, J. Appl. Meteorol.
Clim., 55, 1615–1632, https://doi.org/10.1175/jamc-d-15-0272.1, 2016.
Kennedy, P. C. and Rutledge, S. A.: S-Band dual-polarization radar
observations of winter storms, J. Appl. Meteorol. Clim., 50, 844–858,
https://doi.org/10.1175/2010jamc2558.1, 2011.
Kneifel, S., vonLerber, A., Tiira, J., Moisseev, D., Kollias, P., and
Leinonen, J.: Observed relations between snowfall microphysics and
triple-frequency radar measurements, J. Geophys. Res.-Atmos., 120,
6034–6055, https://doi.org/10.1002/2015JD023156, 2015.
Korolev, A., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang,
Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal,
J., Kramer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch,
M.: Mixed-phase clouds: progress and challenges, Meteor. Mon., 58,
5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Korolev, A., Heckman, I., Wolde, M., Ackerman, A. S., Fridlind, A. M., Ladino, L. A., Lawson, R. P., Milbrandt, J., and Williams, E.: A new look at the environmental conditions favorable to secondary ice production, Atmos. Chem. Phys., 20, 1391–1429, https://doi.org/10.5194/acp-20-1391-2020, 2020.
Kumjian, M.: Principles and applications of dual-polarization weather radar.
Part I: Description of the polarimetric radar variables, J. Operational
Meteor., 1, 226–242, https://doi.org/10.15191/nwajom.2013.0119, 2013.
Kuo, K.-S., Olson, W. S., Johnson, B. T., Grecu, M., Tian, L., Clune, T. L.,
Aartsen, B. H. V., Heymsfield, A. J., Liao, L., and Meneghini, R.: The
Microwave radiative properties of falling snow derived from nonspherical ice
particle models. Part I: An extensive database of simulated pristine
crystals and aggregate particles, and their scattering properties, J. Appl.
Meteorol. Clim., 55, 691–708, https://doi.org/10.1175/jamc-d-15-0130.1, 2016.
Li, J.–L. F., Forbes, R. M., Waliser, D. E., Stephens, G., and Lee, S.:
Characterizing the radiative impacts of precipitating snow in the ECMWF
integrated forecast system global model, J. Geophys. Res.-Atmos., 119,
9626–9637, https://doi.org/10.1002/2014JD021450, 2014.
Liu, G.: A database of microwave single-scattering properties for
nonspherical ice particles, B. Am. Meteorol. Soc., 89, 1563–1570,
https://doi.org/10.1175/2008bams2486.1, 2008.
Lu, Y., Aydin, K., Clothiaux, E. E., and Verlinde, J.: Retrieving cloud ice
water content using millimeter- and centimeter-wavelength radar
polarimetric observables, J. Appl. Meteorol. Clim., 54, 596–604, https://doi.org/10.1175/JAMC-D-14-0169.1, 2015.
Lu, Y., Jiang, Z., Aydin, K., Verlinde, J., Clothiaux, E. E., and Botta, G.: A polarimetric scattering database for non-spherical ice particles at microwave wavelengths, Atmos. Meas. Tech., 9, 5119–5134, https://doi.org/10.5194/amt-9-5119-2016, 2016 (data available at: https://adc.arm.gov/discovery/#/results/instrument_class_code::icepart-mod, last access: 1 October 2018).
Mason, S. L., Chiu, C. J., Hogan, R. J., Moisseev, D., and Kneifel, S.:
Retrievals of riming and snow density from vertically pointing Doppler
radars, J. Geophys. Res.-Atmos., 123, 13807–13834, https://doi.org/10.1029/2018JD028603, 2018.
Matsui, T., Dolan, B., Rutledge, S. A., Tao, W. K., Iguchi, T., Barnum, J.,
and Lang, S. E.: POLARRIS: A POLArimetric Radar Retrieval and Instrument
Simulator, J. Geophys. Res.-Atmos, 124, 4634–4657,
https://doi.org/10.1029/2018jd028317, 2019.
Mitchell, D. L.: Use of mass- and area-dimensional power laws for
determining precipitation particle terminal velocities, J. Atmos. Sci., 53,
1710–1723, https://doi.org/10.1175/1520-0469(1996)053<1710:uomaad>2.0.co;2, 1996.
Mitchell, D. L. and Heymsfield, A. J.: Refinements in the treatment of ice
particle terminal velocities, highlighting aggregates, J. Atmos. Sci., 62,
1637–1644, https://doi.org/10.1175/JAS3413.1, 2005.
Moisseev, D. N., Lautaportti, S., Tyynela, J., and Lim, S.:
Dual-polarization radar signatures in snowstorms: Role of snowflake
aggregation, J. Geophys. Res.-Atmos., 120, 12644–12655,
https://doi.org/10.1002/2015jd023884, 2015.
Morrison, H., van Lier-Walqui, M., Fridlind, A. M., Grabowski, W. W.,
Harrington, J. Y., Hoose, C., Korolev, A., Kumjian, M. R., Milbrandt, J. A.,
Pawlowska, H., Posselt, D. J., Prat, O. P., Reimel, K. J., Shima, S., van
Diedenhoven, B., and Xue, L.: Confronting the challenge of modelling cloud
and precipitation microphysics, J. Adv. Model. Earth Sy., 12, e2019MS001689, https://doi.org/10.1029/2019MS001689, 2020.
Mulmenstadt, J., Sourdeval, O., Delanoe, J., and Quass, J.: Frequency of
occurrence of rain from liquid-, mixed-, and ice-phase clouds derived
from A-Train satellite retrievals, Geophys. Res. Lett., 42, 6502–6509, https://doi.org/10.1002/2015GL064604, 2015.
Murphy, A. M., Ryzhkov, A., and Zhang, P.: Columnar Vertical Profile (CVP)
Methodology for validating polarimetric radar retrievals in ice using in
situ aircraft measurements, J. Atmos. Ocean. Tech., 37, 1623–1642,
https://doi.org/10.1175/jtech-d-20-0011.1, 2020.
Neely III, R. R., Bennett, L., Blyth, A., Collier, C., Dufton, D., Groves, J., Walker, D., Walden, C., Bradford, J., Brooks, B., Addison, F. I., Nicol, J., and Pickering, B.: The NCAS mobile dual-polarisation Doppler X-band weather radar (NXPol), Atmos. Meas. Tech., 11, 6481–6494, https://doi.org/10.5194/amt-11-6481-2018, 2018.
O'Shea, S., Crosier, J., Dorsey, J., Gallagher, L., Schledewitz, W., Bower, K., Schlenczek, O., Borrmann, S., Cotton, R., Westbrook, C., and Ulanowski, Z.: Characterising optical array particle imaging probes: implications for small-ice-crystal observations, Atmos. Meas. Tech., 14, 1917–1939, https://doi.org/10.5194/amt-14-1917-2021, 2021.
Oue, M., Kollias, P., Ryzhkov, A., and Luke, E. P.: Toward exploring the
synergy between cloud radar polarimetry and Doppler spectral analysis in
deep cold precipitating systems in the Arctic, J. Geophys.
Res.-Atmos., 123, 2797–2815, https://doi.org/10.1002/2017jd027717, 2018.
Protat, A., Delanoë, J., Bouniol, D., Heymsfield, A. J., Bansemer, A.,
and Brown, P.: Evaluation of ice water content retrievals from cloud radar
reflectivity and temperature using a large airborne in situ microphysical
database, J. Appl. Meteorol. Clim., 46, 557–572, https://doi.org/10.1175/jam2488.1,
2007.
Ryzhkov, A., Pinsky, M., Pokrovsky, A., and Khain, A.: Polarimetric radar
observation operator for a cloud model with spectral microphysics, J. Appl.
Meteorol. Clim., 50, 873–894, https://doi.org/10.1175/2010jamc2363.1, 2011.
Ryzhkov, A. V. and Zrnic, D. S.: Polarimetric microphysical retrievals, in
Radar Polarimetry for Weather Observations, Springer, 435–464,
https://doi.org/10.1007/978-3-030-05093-1_11, 2019.
Ryzhkov, A. V., Zrnic, D. S., and Gordon, B. A.: Polarimetric method for ice
water content determination, J. Appl. Meteorol. Clim., 37, 125–134, https://doi.org/10.1175/1520-0450(1998)037<0125:PMFIWC>2.0.CO;2, 1998.
Schrom, R. S., Kumjian, M. R., and Lu, Y.: Polarimetric radar signatures of
dendritic growth zones within Colorado winter storms, J. Appl. Meteorol.
Clim., 54, 2365–2388, https://doi.org/10.1175/JAMC-D-15-0004.1, 2015.
Seliga, T. A., Bringi, V. N., and Al-Khatib, H. H.: A preliminary study of
comparative measurements of rainfall rate using the differential
reflectivity radar technique and a rain gage Network, J. Appl.
Meteorol., 20, 1362–1368, 1981.
Spek, A. L. J., Unal, C. M. H., Moisseev, D. N., Russchenberg, H. W. J.,
Chandrasekar, V., and Dufournet, Y.: A new technique to categorize and
retrieve the microphysical properties of ice particles above the melting
layer using radar dual-polarization spectral analysis, J. Atmos. Ocean.
Tech., 25, 482–497, https://doi.org/10.1175/2007jtecha944.1, 2008.
Szyrmer, W. and Zawadzki, I.: Snow studies. part II: average relationship
between mass of snowflakes and their terminal fall velocity, J. Atmos. Sci.,
67, 3319–3335, https://doi.org/10.1175/2010JAS3390.1, 2010.
Testud, J., Oury, S., Black, R. A., Amayenc, P., and Dou, X.: The concept of
“normalized” distribution to describe raindrop spectra: A tool for cloud
physics and cloud remote sensing, J. Appl. Meteorol., 40, 1118–1140,
https://doi.org/10.1175/1520-0450(2001)040<1118:tcondt>2.0.co;2,
2001.
Tiira, J., Moisseev, D. N., von Lerber, A., Ori, D., Tokay, A., Bliven, L. F., and Petersen, W.: Ensemble mean density and its connection to other microphysical properties of falling snow as observed in Southern Finland, Atmos. Meas. Tech., 9, 4825–4841, https://doi.org/10.5194/amt-9-4825-2016, 2016.
Van Leeuwen, P. J.: A consistent interpretation of the stochastic version of
the Ensemble Kalman Filter, Q. J. Royal Met. Soc., 146, 2815–2825, https://doi.org/10.1002/qj.3819, 2020.
Vivekanandan, J., Martner, B., Politovich, M., and Zhang, G.: Retrieval of
atmospheric liquid and ice characteristics using dual-wavelength radar
observations, IEEE T. Geosci. Remote, 37, 2325–2334, https://doi.org/10.1109/36.789629,
1999.
Wang, Y. and Chandrasekar, V.: Algorithm for estimation of the specific
differential phase, J. Atmos. Ocean. Tech., 26, 2565–2578,
https://doi.org/10.1175/2009jtecha1358.1, 2009.
Xu, Y.-L.: Electromagnetic scattering by an aggregate of spheres, Appl.
Optics, 34, 4573, https://doi.org/10.1364/ao.34.004573, 1995.
Yurkin, M. A. and Hoekstra, A. G.: The discrete-dipole-approximation code
ADDA: Capabilities and known limitations, J. Quant. Spectrosc. Ra., 112,
2234–2247, https://doi.org/10.1016/j.jqsrt.2011.01.031, 2011.
Short summary
Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Ice clouds play a key role in our climate system due to their strong controls on precipitation...
