Articles | Volume 6, issue 1
https://doi.org/10.5194/amt-6-105-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-6-105-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
22 Jan 2013
Research article |
| 22 Jan 2013
Retrieval of subvisual cirrus cloud optical thickness from limb-scatter measurements
J. T. Wiensz
Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, Canada
SRON Netherlands Institute for Space Research, Utrecht, The Netherlands
D. A. Degenstein
Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, Canada
N. D. Lloyd
Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, Canada
A. E. Bourassa
Institute of Space and Atmospheric Studies, University of Saskatchewan, Saskatoon, Canada
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Daniel Letros, Liam Graham, Adam Bourassa, Doug Degenstein, Paul Loewen, Landon Rieger, and Nick Lloyd
EGUsphere, https://doi.org/10.5194/egusphere-2025-67, https://doi.org/10.5194/egusphere-2025-67, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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The Aerosol Limb Imager (ALI) is an optical instrument which measures stratospheric aerosols. These aerosols are of interest to atmospheric science as they have a significant impact on the Earth's climate. ALI has the ability to measure the polarization of atmospheric light over a wide spectral range, which is a novel ability for the measurement ALI uses. We demonstrate and discuss ALI capability, and how the polarized information may improve aerosol information for this type measurement.
Björn Linder, Jörg Gumbel, Donal P. Murtagh, Linda Megner, Lukas Krasauskas, Doug Degenstein, Ole Martin Christensen, and Nickolay Ivchenko
EGUsphere, https://doi.org/10.5194/egusphere-2025-493, https://doi.org/10.5194/egusphere-2025-493, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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In this study, the primary instrument carried by the satellite MATS is compared to the OSIRIS instrument onboard the Odin satellite. A total of 36 close approaches between December 2022 and February 2023 were identified and analysed. The comparison reveals that the two instruments have good structural agreement and that MATS detects a signal that is ~20 % stronger than what is measured by OSIRIS.
Alexei Rozanov, Christine Pohl, Carlo Arosio, Adam Bourassa, Klaus Bramstedt, Elizaveta Malinina, Landon Rieger, and John P. Burrows
Atmos. Meas. Tech., 17, 6677–6695, https://doi.org/10.5194/amt-17-6677-2024, https://doi.org/10.5194/amt-17-6677-2024, 2024
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We developed a new algorithm to retrieve vertical distributions of aerosol extinction coefficients in the stratosphere. The algorithm is applied to measurements of scattered solar light from the spaceborne OMPS-LP (Ozone Mapper and Profiler Suite Limb Profiler) instrument. The retrieval results are compared to data from other spaceborne instruments and used to investigate the evolution of the aerosol plume following the eruption of the Hunga Tonga–Hunga Ha'apai volcano in January 2022.
Kimberlee Dubé, Susann Tegtmeier, Adam Bourassa, Daniel Zawada, Douglas Degenstein, William Randel, Sean Davis, Michael Schwartz, Nathaniel Livesey, and Anne Smith
Atmos. Chem. Phys., 24, 12925–12941, https://doi.org/10.5194/acp-24-12925-2024, https://doi.org/10.5194/acp-24-12925-2024, 2024
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Greenhouse gas emissions that warm the troposphere also result in stratospheric cooling. The cooling rate is difficult to quantify above 35 km due to a deficit of long-term observational data with high vertical resolution in this region. We use satellite observations from several instruments, including a new temperature product from OSIRIS, to show that the upper stratosphere, from 35–60 km, cooled by 0.5 to 1 K per decade over 2005–2021 and by 0.6 K per decade over 1979–2021.
Viktoria F. Sofieva, Alexei Rozanov, Monika Szelag, John P. Burrows, Christian Retscher, Robert Damadeo, Doug Degenstein, Landon A. Rieger, and Adam Bourassa
Earth Syst. Sci. Data, 16, 5227–5241, https://doi.org/10.5194/essd-16-5227-2024, https://doi.org/10.5194/essd-16-5227-2024, 2024
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Climate-related studies need information about the distribution of stratospheric aerosols, which influence the energy balance of the Earth’s atmosphere. In this work, we present a merged dataset of vertically resolved stratospheric aerosol extinction coefficients, which is derived from data of six limb and occultation satellite instruments. The created aerosol climate record covers the period from October 1984 to December 2023. It can be used in various climate-related studies.
Sujan Khanal, Matthew Toohey, Adam Bourassa, C. Thomas McElroy, Christopher Sioris, and Kaley A. Walker
EGUsphere, https://doi.org/10.5194/egusphere-2024-3286, https://doi.org/10.5194/egusphere-2024-3286, 2024
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Measurements of stratospheric aerosol from the MAESTRO instrument are compared to other measurements to assess their scientific value. We find that medians of MAESTRO measurements binned by month and latitude show reasonable correlation with other data sets, with notable increases after volcanic eruptions, and that biases in the data can be alleviated through a simple correction technique. Used with care, MAESTRO aerosol measurements provide information that can complement other data sets.
Christine Pohl, Felix Wrana, Alexei Rozanov, Terry Deshler, Elizaveta Malinina, Christian von Savigny, Landon A. Rieger, Adam E. Bourassa, and John P. Burrows
Atmos. Meas. Tech., 17, 4153–4181, https://doi.org/10.5194/amt-17-4153-2024, https://doi.org/10.5194/amt-17-4153-2024, 2024
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Knowledge of stratospheric aerosol characteristics is important for understanding chemical and climate aerosol feedbacks. Two particle size distribution parameters, the aerosol extinction coefficient and the effective radius, are obtained from SCIAMACHY limb observations. The aerosol characteristics show good agreement with independent data sets from balloon-borne and satellite observations. This data set expands the limited knowledge of stratospheric aerosol characteristics.
Daniel Zawada, Kimberlee Dubé, Taran Warnock, Adam Bourassa, Susann Tegtmeier, and Douglas Degenstein
Atmos. Meas. Tech., 17, 1995–2010, https://doi.org/10.5194/amt-17-1995-2024, https://doi.org/10.5194/amt-17-1995-2024, 2024
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There remain large uncertainties in long-term changes of stratospheric–atmospheric temperatures. We have produced a time series of more than 20 years of satellite-based temperature measurements from the OSIRIS instrument in the upper–middle stratosphere. The dataset is publicly available and intended to be used for a better understanding of changes in stratospheric temperatures.
Lukas Fehr, Chris McLinden, Debora Griffin, Daniel Zawada, Doug Degenstein, and Adam Bourassa
Geosci. Model Dev., 16, 7491–7507, https://doi.org/10.5194/gmd-16-7491-2023, https://doi.org/10.5194/gmd-16-7491-2023, 2023
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This work highlights upgrades to SASKTRAN, a model that simulates sunlight interacting with the atmosphere to help measure trace gases. The upgrades were verified by detailed comparisons between different numerical methods. A case study was performed using SASKTRAN’s multidimensional capabilities, which found that ignoring horizontal variation in the atmosphere (a common practice in the field) can introduce non-negligible errors where there is snow or high pollution.
Kimberlee Dubé, Susann Tegtmeier, Adam Bourassa, Daniel Zawada, Douglas Degenstein, Patrick E. Sheese, Kaley A. Walker, and William Randel
Atmos. Chem. Phys., 23, 13283–13300, https://doi.org/10.5194/acp-23-13283-2023, https://doi.org/10.5194/acp-23-13283-2023, 2023
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This paper presents a technique for understanding the causes of long-term changes in stratospheric composition. By using N2O as a proxy for stratospheric circulation in the model used to calculated trends, it is possible to separate the effects of dynamics and chemistry on observed trace gas trends. We find that observed HCl increases are due to changes in the stratospheric circulation, as are O3 decreases above 30 hPa in the Northern Hemisphere.
Ethan Runge, Jeff Langille, Daniel Zawada, Adam Bourassa, and Doug Degenstein
Atmos. Meas. Tech., 16, 3123–3139, https://doi.org/10.5194/amt-16-3123-2023, https://doi.org/10.5194/amt-16-3123-2023, 2023
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The Limb Imaging Fourier Transform Spectrometer Experiment (LIFE) instrument takes vertical images of limb radiance across a wide mid-infrared spectral band from a stratospheric balloon. Measurements are used to infer vertical-trace-gas-profile retrievals of H2O, O3, HNO3, CH4, and N2O. Nearly time-/space-coincident observations from the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) and Microwave Limb Sounder (MLS) instruments are compared to the LIFE results.
Viktoria F. Sofieva, Monika Szelag, Johanna Tamminen, Carlo Arosio, Alexei Rozanov, Mark Weber, Doug Degenstein, Adam Bourassa, Daniel Zawada, Michael Kiefer, Alexandra Laeng, Kaley A. Walker, Patrick Sheese, Daan Hubert, Michel van Roozendael, Christian Retscher, Robert Damadeo, and Jerry D. Lumpe
Atmos. Meas. Tech., 16, 1881–1899, https://doi.org/10.5194/amt-16-1881-2023, https://doi.org/10.5194/amt-16-1881-2023, 2023
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The paper presents the updated SAGE-CCI-OMPS+ climate data record of monthly zonal mean ozone profiles. This dataset covers the stratosphere and combines measurements by nine limb and occultation satellite instruments (SAGE II, OSIRIS, MIPAS, SCIAMACHY, GOMOS, ACE-FTS, OMPS-LP, POAM III, and SAGE III/ISS). The update includes new versions of MIPAS, ACE-FTS, and OSIRIS datasets and introduces data from additional sensors (POAM III and SAGE III/ISS) and retrieval processors (OMPS-LP).
Yi Wang, Mark Schoeberl, Ghassan Taha, Daniel Zawada, and Adam Bourassa
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-36, https://doi.org/10.5194/amt-2023-36, 2023
Revised manuscript not accepted
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The OMPS-LP satellite instrument measures aerosol scattering properties across the atmospheric limb. Adopting an algorithm that uses extinction at two wavelengths, we retrieve vertical profiles of particle size and concentration. We demonstrate that these profiles are consistent with in-situ balloon and SAGE-III/ISS satellite measurements. We also show how aerosol size and concentration evolve during Reikoke and Hunga Tonga-Hunga Ha'apai eruptions.
Paul S. Jeffery, Kaley A. Walker, Chris E. Sioris, Chris D. Boone, Doug Degenstein, Gloria L. Manney, C. Thomas McElroy, Luis Millán, David A. Plummer, Niall J. Ryan, Patrick E. Sheese, and Jiansheng Zou
Atmos. Chem. Phys., 22, 14709–14734, https://doi.org/10.5194/acp-22-14709-2022, https://doi.org/10.5194/acp-22-14709-2022, 2022
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The upper troposphere–lower stratosphere is one of the most variable regions in the atmosphere. To improve our understanding of water vapour and ozone concentrations in this region, climatologies have been developed from 14 years of measurements from three Canadian satellite instruments. Horizontal and vertical coordinates have been chosen to minimize the effects of variability. To aid in analysis, model simulations have been used to characterize differences between instrument climatologies.
Kimberlee Dubé, Daniel Zawada, Adam Bourassa, Doug Degenstein, William Randel, David Flittner, Patrick Sheese, and Kaley Walker
Atmos. Meas. Tech., 15, 6163–6180, https://doi.org/10.5194/amt-15-6163-2022, https://doi.org/10.5194/amt-15-6163-2022, 2022
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Satellite observations are important for monitoring changes in atmospheric composition. Here we describe an improved version of the NO2 retrieval for the Optical Spectrograph and InfraRed Imager System. The resulting NO2 profiles are compared to those from the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer and the Stratospheric Aerosol and Gas Experiment III on the International Space Station. All datasets agree within 20 % throughout the stratosphere.
Sophie Godin-Beekmann, Niramson Azouz, Viktoria F. Sofieva, Daan Hubert, Irina Petropavlovskikh, Peter Effertz, Gérard Ancellet, Doug A. Degenstein, Daniel Zawada, Lucien Froidevaux, Stacey Frith, Jeannette Wild, Sean Davis, Wolfgang Steinbrecht, Thierry Leblanc, Richard Querel, Kleareti Tourpali, Robert Damadeo, Eliane Maillard Barras, René Stübi, Corinne Vigouroux, Carlo Arosio, Gerald Nedoluha, Ian Boyd, Roeland Van Malderen, Emmanuel Mahieu, Dan Smale, and Ralf Sussmann
Atmos. Chem. Phys., 22, 11657–11673, https://doi.org/10.5194/acp-22-11657-2022, https://doi.org/10.5194/acp-22-11657-2022, 2022
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An updated evaluation up to 2020 of stratospheric ozone profile long-term trends at extrapolar latitudes based on satellite and ground-based records is presented. Ozone increase in the upper stratosphere is confirmed, with significant trends at most latitudes. In this altitude region, a very good agreement is found with trends derived from chemistry–climate model simulations. Observed and modelled trends diverge in the lower stratosphere, but the differences are non-significant.
Kristof Bognar, Susann Tegtmeier, Adam Bourassa, Chris Roth, Taran Warnock, Daniel Zawada, and Doug Degenstein
Atmos. Chem. Phys., 22, 9553–9569, https://doi.org/10.5194/acp-22-9553-2022, https://doi.org/10.5194/acp-22-9553-2022, 2022
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We quantify recent changes in stratospheric ozone (outside the polar regions) using a combination of three satellite datasets. We find that upper stratospheric ozone have increased significantly since 2000, although the recovery shows an unexpected pause in the Northern Hemisphere. Combined with the likely decrease in ozone in the lower stratosphere, this presents an interesting challenge for predicting the future of the ozone layer.
Julia Koch, Adam Bourassa, Nick Lloyd, Chris Roth, and Christian von Savigny
Atmos. Chem. Phys., 22, 3191–3202, https://doi.org/10.5194/acp-22-3191-2022, https://doi.org/10.5194/acp-22-3191-2022, 2022
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The mesopause, the region of the earth's atmosphere between 85 and 100 km, is hard to access by direct measurements. Therefore we look for parameters that can be measured using satellite or ground-based measurements. In this study we researched sodium airglow, a phenomenon that occurs when sodium atoms are excited by chemical reactions. We compared satellite measurements of the airglow and resulting sodium concentration profiles to gain a better understanding of the sodium in that region.
Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, Adam E. Bourassa, Doug A. Degenstein, Lucien Froidevaux, C. Thomas McElroy, Donal Murtagh, James M. Russell III, and Jiansheng Zou
Atmos. Meas. Tech., 15, 1233–1249, https://doi.org/10.5194/amt-15-1233-2022, https://doi.org/10.5194/amt-15-1233-2022, 2022
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This study analyzes the quality of two versions (v3.6 and v4.1) of ozone concentration measurements from the ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer), by comparing with data from five satellite instruments between 2004 and 2020. It was found that although the v3.6 data exhibit a better agreement than v4.1 with respect to the other instruments, v4.1 exhibits much better stability over time than v3.6. The stability of v4.1 makes it suitable for ozone trend studies.
Debora Griffin, Chris A. McLinden, Enrico Dammers, Cristen Adams, Chelsea E. Stockwell, Carsten Warneke, Ilann Bourgeois, Jeff Peischl, Thomas B. Ryerson, Kyle J. Zarzana, Jake P. Rowe, Rainer Volkamer, Christoph Knote, Natalie Kille, Theodore K. Koenig, Christopher F. Lee, Drew Rollins, Pamela S. Rickly, Jack Chen, Lukas Fehr, Adam Bourassa, Doug Degenstein, Katherine Hayden, Cristian Mihele, Sumi N. Wren, John Liggio, Ayodeji Akingunola, and Paul Makar
Atmos. Meas. Tech., 14, 7929–7957, https://doi.org/10.5194/amt-14-7929-2021, https://doi.org/10.5194/amt-14-7929-2021, 2021
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Satellite-derived NOx emissions from biomass burning are estimated with TROPOMI observations. Two common emission estimation methods are applied, and sensitivity tests with model output were performed to determine the accuracy of these methods. The effect of smoke aerosols on TROPOMI NO2 columns is estimated and compared to aircraft observations from four different aircraft campaigns measuring biomass burning plumes in 2018 and 2019 in North America.
Anqi Li, Chris Z. Roth, Adam E. Bourassa, Douglas A. Degenstein, Kristell Pérot, Ole Martin Christensen, and Donal P. Murtagh
Earth Syst. Sci. Data, 13, 5115–5126, https://doi.org/10.5194/essd-13-5115-2021, https://doi.org/10.5194/essd-13-5115-2021, 2021
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The nightglow emission originating from the vibrationally excited hydroxyl layer (about 85 km altitude) has been measured by the infrared imager (IRI) on the Odin satellite for more than 15 years. In this study, we document the retrieval steps, the resulting volume emission rates and the layer characteristics. Finally, we use the monthly zonal averages to demonstrate the fidelity of the data set. This unique, long-term data set will be valuable for studying various topics near the mesopause.
Daniel Zawada, Ghislain Franssens, Robert Loughman, Antti Mikkonen, Alexei Rozanov, Claudia Emde, Adam Bourassa, Seth Dueck, Hannakaisa Lindqvist, Didier Ramon, Vladimir Rozanov, Emmanuel Dekemper, Erkki Kyrölä, John P. Burrows, Didier Fussen, and Doug Degenstein
Atmos. Meas. Tech., 14, 3953–3972, https://doi.org/10.5194/amt-14-3953-2021, https://doi.org/10.5194/amt-14-3953-2021, 2021
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Satellite measurements of atmospheric composition often rely on computer tools known as radiative transfer models to model the propagation of sunlight within the atmosphere. Here we have performed a detailed inter-comparison of seven different radiative transfer models in a variety of conditions. We have found that the models agree remarkably well, at a level better than previously reported. This result provides confidence in our understanding of atmospheric radiative transfer.
Michaela I. Hegglin, Susann Tegtmeier, John Anderson, Adam E. Bourassa, Samuel Brohede, Doug Degenstein, Lucien Froidevaux, Bernd Funke, John Gille, Yasuko Kasai, Erkki T. Kyrölä, Jerry Lumpe, Donal Murtagh, Jessica L. Neu, Kristell Pérot, Ellis E. Remsberg, Alexei Rozanov, Matthew Toohey, Joachim Urban, Thomas von Clarmann, Kaley A. Walker, Hsiang-Jui Wang, Carlo Arosio, Robert Damadeo, Ryan A. Fuller, Gretchen Lingenfelser, Christopher McLinden, Diane Pendlebury, Chris Roth, Niall J. Ryan, Christopher Sioris, Lesley Smith, and Katja Weigel
Earth Syst. Sci. Data, 13, 1855–1903, https://doi.org/10.5194/essd-13-1855-2021, https://doi.org/10.5194/essd-13-1855-2021, 2021
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An overview of the SPARC Data Initiative is presented, to date the most comprehensive assessment of stratospheric composition measurements spanning 1979–2018. Measurements of 26 chemical constituents obtained from an international suite of space-based limb sounders were compiled into vertically resolved, zonal monthly mean time series. The quality and consistency of these gridded datasets are then evaluated using a climatological validation approach and a range of diagnostics.
Viktoria F. Sofieva, Monika Szeląg, Johanna Tamminen, Erkki Kyrölä, Doug Degenstein, Chris Roth, Daniel Zawada, Alexei Rozanov, Carlo Arosio, John P. Burrows, Mark Weber, Alexandra Laeng, Gabriele P. Stiller, Thomas von Clarmann, Lucien Froidevaux, Nathaniel Livesey, Michel van Roozendael, and Christian Retscher
Atmos. Chem. Phys., 21, 6707–6720, https://doi.org/10.5194/acp-21-6707-2021, https://doi.org/10.5194/acp-21-6707-2021, 2021
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The MErged GRIdded Dataset of Ozone Profiles is a long-term (2001–2018) stratospheric ozone profile climate data record with resolved longitudinal structure that combines the data from six limb satellite instruments. The dataset can be used for various analyses, some of which are discussed in the paper. In particular, regionally and vertically resolved ozone trends are evaluated, including trends in the polar regions.
Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, Doug A. Degenstein, Felicia Kolonjari, David Plummer, Douglas E. Kinnison, Patrick Jöckel, and Thomas von Clarmann
Atmos. Meas. Tech., 14, 1425–1438, https://doi.org/10.5194/amt-14-1425-2021, https://doi.org/10.5194/amt-14-1425-2021, 2021
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Output from climate chemistry models (CMAM, EMAC, and WACCM) is used to estimate the expected geophysical variability of ozone concentrations between coincident satellite instrument measurement times and geolocations. We use the Canadian ACE-FTS and OSIRIS instruments as a case study. Ensemble mean estimates are used to optimize coincidence criteria between the two instruments, allowing for the use of more coincident profiles while providing an estimate of the geophysical variation.
Ghassan Taha, Robert Loughman, Tong Zhu, Larry Thomason, Jayanta Kar, Landon Rieger, and Adam Bourassa
Atmos. Meas. Tech., 14, 1015–1036, https://doi.org/10.5194/amt-14-1015-2021, https://doi.org/10.5194/amt-14-1015-2021, 2021
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This work describes the newly released OMPS LP aerosol extinction profile multi-wavelength Version 2.0 algorithm and dataset. It is shown that the V2.0 aerosols exhibit significant improvements in OMPS LP retrieval performance in the Southern Hemisphere and at lower altitudes. The new product is compared to the SAGE III/ISS, OSIRIS and CALIPSO missions and shown to be of good quality and suitable for scientific studies.
Kimberlee Dubé, Adam Bourassa, Daniel Zawada, Douglas Degenstein, Robert Damadeo, David Flittner, and William Randel
Atmos. Meas. Tech., 14, 557–566, https://doi.org/10.5194/amt-14-557-2021, https://doi.org/10.5194/amt-14-557-2021, 2021
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SAGE III/ISS measures profiles of NO2; however the algorithm to convert raw measurements to NO2 concentration neglects variations caused by changes in chemistry over the course of a day. We devised a procedure to account for these diurnal variations and assess their impact on NO2 measurements from SAGE III/ISS. We find that the new NO2 concentration is more than 10 % lower than NO2 from the standard algorithm below 30 km, showing that this effect is important to consider at lower altitudes.
Anqi Li, Chris Z. Roth, Kristell Pérot, Ole Martin Christensen, Adam Bourassa, Doug A. Degenstein, and Donal P. Murtagh
Atmos. Meas. Tech., 13, 6215–6236, https://doi.org/10.5194/amt-13-6215-2020, https://doi.org/10.5194/amt-13-6215-2020, 2020
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The OSIRIS IR imager, one of the instruments on the Odin satellite, routinely measures the oxygen airglow at 1.27 μm. In this study, we primarily focus on the steps done for retrieving the calibrated IRA band limb radiance, the volume emission rate of O2(a1∆g) and finally the ozone number density. Specifically, we use a novel approach to address the issue of the measurements that were made close to the local sunrise, where the O2(a1∆g) diverges from the equilibrium state.
Mahesh Kovilakam, Larry W. Thomason, Nicholas Ernest, Landon Rieger, Adam Bourassa, and Luis Millán
Earth Syst. Sci. Data, 12, 2607–2634, https://doi.org/10.5194/essd-12-2607-2020, https://doi.org/10.5194/essd-12-2607-2020, 2020
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A robust stratospheric aerosol climatology is important as many global climate models (GCMs) make use of observed aerosol properties to prescribe aerosols in the stratosphere. Here, we present version 2.0 of the GloSSAC data set in which a new methodology is used for the post-2005 data that improves the quality of data in the lower stratosphere, which includes an improved 1020 nm extinction. Additionally, size information from multiwavelength measurements of SAGE III/ISS is provided.
Thomas von Clarmann, Douglas A. Degenstein, Nathaniel J. Livesey, Stefan Bender, Amy Braverman, André Butz, Steven Compernolle, Robert Damadeo, Seth Dueck, Patrick Eriksson, Bernd Funke, Margaret C. Johnson, Yasuko Kasai, Arno Keppens, Anne Kleinert, Natalya A. Kramarova, Alexandra Laeng, Bavo Langerock, Vivienne H. Payne, Alexei Rozanov, Tomohiro O. Sato, Matthias Schneider, Patrick Sheese, Viktoria Sofieva, Gabriele P. Stiller, Christian von Savigny, and Daniel Zawada
Atmos. Meas. Tech., 13, 4393–4436, https://doi.org/10.5194/amt-13-4393-2020, https://doi.org/10.5194/amt-13-4393-2020, 2020
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Remote sensing of atmospheric state variables typically relies on the inverse solution of the radiative transfer equation. An adequately characterized retrieval provides information on the uncertainties of the estimated state variables as well as on how any constraint or a priori assumption affects the estimate. This paper summarizes related techniques and provides recommendations for unified error reporting.
Monika E. Szeląg, Viktoria F. Sofieva, Doug Degenstein, Chris Roth, Sean Davis, and Lucien Froidevaux
Atmos. Chem. Phys., 20, 7035–7047, https://doi.org/10.5194/acp-20-7035-2020, https://doi.org/10.5194/acp-20-7035-2020, 2020
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We analyze seasonal dependence of stratospheric ozone trends over 2000–2018. We demonstrate that the mid-latitude upper stratospheric ozone recovery maximizes during local winters and equinoxes. In the tropics, a very strong seasonal dependence of ozone trends is observed at all altitudes. We found hemispheric asymmetry of summertime ozone trend patterns below 35 km. The seasonal dependence of ozone trends and stratospheric temperature trends shows a clear inter-relation of the trend patterns.
Jeffery Langille, Adam Bourassa, Laura L. Pan, Daniel Letros, Brian Solheim, Daniel Zawada, and Doug Degenstein
Atmos. Chem. Phys., 20, 5477–5486, https://doi.org/10.5194/acp-20-5477-2020, https://doi.org/10.5194/acp-20-5477-2020, 2020
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Water vapour (WV) is a highly variable and extremely important trace gas in Earth’s atmosphere. Due to its radiative and chemical properties, it is coupled to the climate in an extremely complex manner. This is especially true in the lowermost stratosphere (LMS). Despite its importance, the physical processes that control mixing and the distribution of WV in the LMS are poorly understood. This study provides observational evidence of moistening the LMS via mixing across the subtropical jet.
Jacob Zalach, Christian von Savigny, Arvid Langenbach, Gerd Baumgarten, Franz-Josef Lübken, and Adam Bourassa
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2019-267, https://doi.org/10.5194/amt-2019-267, 2019
Revised manuscript not accepted
Elizaveta Malinina, Alexei Rozanov, Landon Rieger, Adam Bourassa, Heinrich Bovensmann, John P. Burrows, and Doug Degenstein
Atmos. Meas. Tech., 12, 3485–3502, https://doi.org/10.5194/amt-12-3485-2019, https://doi.org/10.5194/amt-12-3485-2019, 2019
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This paper covers the problems related to the derivation of aerosol extinction coefficients and Ångström exponents from space-borne instruments working in limb and occultation viewing geometries. Aerosol extinction coefficients and Ångström exponents were calculated from the SCIAMACHY aerosol particle size data set. The results were compared with the data from SAGE II and OSIRIS. The Ångström exponent in the tropical regions and its dependency on particle size parameters are discussed.
Jeffery Langille, Daniel Letros, Adam Bourassa, Brian Solheim, Doug Degenstein, Fabien Dupont, Daniel Zawada, and Nick D. Lloyd
Atmos. Meas. Tech., 12, 431–455, https://doi.org/10.5194/amt-12-431-2019, https://doi.org/10.5194/amt-12-431-2019, 2019
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The SHOW instrument is a prototype satellite concept that is being developed through collaboration between the University of Saskatchewan, the Canadian Space Agency, and ABB Inc. to provide high vertical resolution (< 200 m) measurements of UTLS water vapour with < 1 ppm accuracy. This paper presents suborbital measurements obtained during a demonstration flight aboard NASA's ER-2 aircraft. These measurements are validated through a comparison with coincident radiosonde measurements.
Landon A. Rieger, Elizaveta P. Malinina, Alexei V. Rozanov, John P. Burrows, Adam E. Bourassa, and Doug A. Degenstein
Atmos. Meas. Tech., 11, 3433–3445, https://doi.org/10.5194/amt-11-3433-2018, https://doi.org/10.5194/amt-11-3433-2018, 2018
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This paper compares aerosol extinction records from two limb scattering instruments, OSIRIS and SCIAMACHY, to that from the occultation instrument SAGE II. Differences are investigated through modelling and retrieval studies and important sources of systematic errors are quantified. It is found that the largest biases come from uncertainties in the aerosol size distribution and the aerosol particle concentration at altitudes above 30 km.
Natalya A. Kramarova, Pawan K. Bhartia, Glen Jaross, Leslie Moy, Philippe Xu, Zhong Chen, Matthew DeLand, Lucien Froidevaux, Nathaniel Livesey, Douglas Degenstein, Adam Bourassa, Kaley A. Walker, and Patrick Sheese
Atmos. Meas. Tech., 11, 2837–2861, https://doi.org/10.5194/amt-11-2837-2018, https://doi.org/10.5194/amt-11-2837-2018, 2018
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The Ozone Mapping and Profiler Suite (OMPS) Limb Profiler (LP) is a newly designed research sensor aiming to continue high vertical resolution ozone records from space-borne sensors. In summer 2017 all LP measurements were processed with the new version 2.5 algorithm. In this paper we provide a description of the key changes implemented in the new algorithm and evaluate the quality of ozone retrievals by comparing with independent satellite profile measurements (MLS, ACE-FTS and OSIRIS).
Daniel J. Zawada, Landon A. Rieger, Adam E. Bourassa, and Douglas A. Degenstein
Atmos. Meas. Tech., 11, 2375–2393, https://doi.org/10.5194/amt-11-2375-2018, https://doi.org/10.5194/amt-11-2375-2018, 2018
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The Ozone Mapping and Profiler Suite Limb Profiler measures scattered sunlight, which is then inverted to obtain vertical profiles of ozone in the atmosphere. We have developed a new algorithm for inverting the data which is better suited for areas with large horizontal ozone gradients, such as the polar vortex. Data from the full currently 5-year mission have been processed and are publicly available.
Larry W. Thomason, Nicholas Ernest, Luis Millán, Landon Rieger, Adam Bourassa, Jean-Paul Vernier, Gloria Manney, Beiping Luo, Florian Arfeuille, and Thomas Peter
Earth Syst. Sci. Data, 10, 469–492, https://doi.org/10.5194/essd-10-469-2018, https://doi.org/10.5194/essd-10-469-2018, 2018
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We describe the construction of a continuous 38-year record of stratospheric aerosol optical properties. The Global Space-based Stratospheric Aerosol Climatology, or GloSSAC, provided the input data to the construction of the Climate Model Intercomparison Project stratospheric aerosol forcing data set (1979 to 2014) and is now extended through 2016. GloSSAC focuses on the the SAGE series of instruments through mid-2005 and on OSIRIS and CALIPSO after that time.
William T. Ball, Justin Alsing, Daniel J. Mortlock, Johannes Staehelin, Joanna D. Haigh, Thomas Peter, Fiona Tummon, Rene Stübi, Andrea Stenke, John Anderson, Adam Bourassa, Sean M. Davis, Doug Degenstein, Stacey Frith, Lucien Froidevaux, Chris Roth, Viktoria Sofieva, Ray Wang, Jeannette Wild, Pengfei Yu, Jerald R. Ziemke, and Eugene V. Rozanov
Atmos. Chem. Phys., 18, 1379–1394, https://doi.org/10.5194/acp-18-1379-2018, https://doi.org/10.5194/acp-18-1379-2018, 2018
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Using a robust analysis, with artefact-corrected ozone data, we confirm upper stratospheric ozone is recovering following the Montreal Protocol, but that lower stratospheric ozone (50° S–50° N) has continued to decrease since 1998, and the ozone layer as a whole (60° S–60° N) may be lower today than in 1998. No change in total column ozone may be due to increasing tropospheric ozone. State-of-the-art models do not reproduce lower stratospheric ozone decreases.
Adam E. Bourassa, Chris Z. Roth, Daniel J. Zawada, Landon A. Rieger, Chris A. McLinden, and Douglas A. Degenstein
Atmos. Meas. Tech., 11, 489–498, https://doi.org/10.5194/amt-11-489-2018, https://doi.org/10.5194/amt-11-489-2018, 2018
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OSIRIS satellite measurements of ozone in the stratosphere are corrected for slowly varying errors. These changes make the OSIRIS data compare better with other satellite measurements over the long term and make an impact on our understanding of the recovery of the ozone layer.
Viktoria F. Sofieva, Erkki Kyrölä, Marko Laine, Johanna Tamminen, Doug Degenstein, Adam Bourassa, Chris Roth, Daniel Zawada, Mark Weber, Alexei Rozanov, Nabiz Rahpoe, Gabriele Stiller, Alexandra Laeng, Thomas von Clarmann, Kaley A. Walker, Patrick Sheese, Daan Hubert, Michel van Roozendael, Claus Zehner, Robert Damadeo, Joseph Zawodny, Natalya Kramarova, and Pawan K. Bhartia
Atmos. Chem. Phys., 17, 12533–12552, https://doi.org/10.5194/acp-17-12533-2017, https://doi.org/10.5194/acp-17-12533-2017, 2017
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We present a merged dataset of ozone profiles from several satellite instruments: SAGE II, GOMOS, SCIAMACHY, MIPAS, OSIRIS, ACE-FTS and OMPS. For merging, we used the latest versions of the original ozone datasets.
The merged SAGE–CCI–OMPS dataset is used for evaluating ozone trends in the stratosphere through multiple linear regression. Negative ozone trends in the upper stratosphere are observed before 1997 and positive trends are found after 1997.
Wolfgang Steinbrecht, Lucien Froidevaux, Ryan Fuller, Ray Wang, John Anderson, Chris Roth, Adam Bourassa, Doug Degenstein, Robert Damadeo, Joe Zawodny, Stacey Frith, Richard McPeters, Pawan Bhartia, Jeannette Wild, Craig Long, Sean Davis, Karen Rosenlof, Viktoria Sofieva, Kaley Walker, Nabiz Rahpoe, Alexei Rozanov, Mark Weber, Alexandra Laeng, Thomas von Clarmann, Gabriele Stiller, Natalya Kramarova, Sophie Godin-Beekmann, Thierry Leblanc, Richard Querel, Daan Swart, Ian Boyd, Klemens Hocke, Niklaus Kämpfer, Eliane Maillard Barras, Lorena Moreira, Gerald Nedoluha, Corinne Vigouroux, Thomas Blumenstock, Matthias Schneider, Omaira García, Nicholas Jones, Emmanuel Mahieu, Dan Smale, Michael Kotkamp, John Robinson, Irina Petropavlovskikh, Neil Harris, Birgit Hassler, Daan Hubert, and Fiona Tummon
Atmos. Chem. Phys., 17, 10675–10690, https://doi.org/10.5194/acp-17-10675-2017, https://doi.org/10.5194/acp-17-10675-2017, 2017
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Thanks to the 1987 Montreal Protocol and its amendments, ozone-depleting chlorine (and bromine) in the stratosphere has declined slowly since the late 1990s. Improved and extended long-term ozone profile observations from satellites and ground-based stations confirm that ozone is responding as expected and has increased by about 2 % per decade since 2000 in the upper stratosphere, around 40 km altitude. At lower altitudes, however, ozone has not changed significantly since 2000.
Cristen Adams, Adam E. Bourassa, Chris A. McLinden, Chris E. Sioris, Thomas von Clarmann, Bernd Funke, Landon A. Rieger, and Douglas A. Degenstein
Atmos. Chem. Phys., 17, 8063–8080, https://doi.org/10.5194/acp-17-8063-2017, https://doi.org/10.5194/acp-17-8063-2017, 2017
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We measured the relationship between volcanic aerosol and trace gases in the stratosphere using the OSIRIS and MIPAS satellite instruments between 2002 and 2014. We found that levels of stratospheric NO2 and N2O5 both decreased significantly in the presence of volcanic aerosol. These decreases were consistent with the modeling results.
Christopher E. Sioris, Landon A. Rieger, Nicholas D. Lloyd, Adam E. Bourassa, Chris Z. Roth, Douglas A. Degenstein, Claude Camy-Peyret, Klaus Pfeilsticker, Gwenaël Berthet, Valéry Catoire, Florence Goutail, Jean-Pierre Pommereau, and Chris A. McLinden
Atmos. Meas. Tech., 10, 1155–1168, https://doi.org/10.5194/amt-10-1155-2017, https://doi.org/10.5194/amt-10-1155-2017, 2017
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A new OSIRIS NO2 retrieval algorithm is described and validated using > 40 balloon-based profile measurements. The validation results indicate a slight improvement relative to the existing operational algorithm in terms of the bias versus the balloon data, particularly in the lower stratosphere. The implication is that this new algorithm should replace the operational one. The motivation was to combine spectral fitting and the SaskTRAN radiative transfer model to achieve an improved product.
Gwenaël Berthet, Fabrice Jégou, Valéry Catoire, Gisèle Krysztofiak, Jean-Baptiste Renard, Adam E. Bourassa, Doug A. Degenstein, Colette Brogniez, Marcel Dorf, Sebastian Kreycy, Klaus Pfeilsticker, Bodo Werner, Franck Lefèvre, Tjarda J. Roberts, Thibaut Lurton, Damien Vignelles, Nelson Bègue, Quentin Bourgeois, Daniel Daugeron, Michel Chartier, Claude Robert, Bertrand Gaubicher, and Christophe Guimbaud
Atmos. Chem. Phys., 17, 2229–2253, https://doi.org/10.5194/acp-17-2229-2017, https://doi.org/10.5194/acp-17-2229-2017, 2017
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Since the last major volcanic event, i.e. the Pinatubo eruption in 1991, only
moderateeruptions have regularly injected sulfur into the stratosphere, typically enhancing the aerosol loading for several months. We investigate here for the first time the chemical perturbation associated with the Sarychev eruption in June 2009, using balloon-borne instruments and model calculations. Some chemical compounds are significantly affected by the aerosols, but the impact on stratospheric ozone is weak.
Sergey M. Khaykin, Sophie Godin-Beekmann, Philippe Keckhut, Alain Hauchecorne, Julien Jumelet, Jean-Paul Vernier, Adam Bourassa, Doug A. Degenstein, Landon A. Rieger, Christine Bingen, Filip Vanhellemont, Charles Robert, Matthew DeLand, and Pawan K. Bhartia
Atmos. Chem. Phys., 17, 1829–1845, https://doi.org/10.5194/acp-17-1829-2017, https://doi.org/10.5194/acp-17-1829-2017, 2017
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The article is devoted to the long-term evolution and variability of stratospheric aerosol, which plays an important role in climate change and the ozone layer. We use 22-year long continuous observations using laser radar soundings in southern France and satellite-based observations to distinguish between natural aerosol variability (caused by volcanic eruptions) and human-induced change in aerosol concentration. An influence of growing pollution above Asia on stratospheric aerosol is found.
Patrick E. Sheese, Kaley A. Walker, Chris D. Boone, Chris A. McLinden, Peter F. Bernath, Adam E. Bourassa, John P. Burrows, Doug A. Degenstein, Bernd Funke, Didier Fussen, Gloria L. Manney, C. Thomas McElroy, Donal Murtagh, Cora E. Randall, Piera Raspollini, Alexei Rozanov, James M. Russell III, Makoto Suzuki, Masato Shiotani, Joachim Urban, Thomas von Clarmann, and Joseph M. Zawodny
Atmos. Meas. Tech., 9, 5781–5810, https://doi.org/10.5194/amt-9-5781-2016, https://doi.org/10.5194/amt-9-5781-2016, 2016
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This study validates version 3.5 of the ACE-FTS NOy species data sets by comparing diurnally scaled ACE-FTS data to correlative data from 11 other satellite limb sounders. For all five species examined (NO, NO2, HNO3, N2O5, and ClONO2), there is good agreement between ACE-FTS and the other data sets in various regions of the atmosphere. In these validated regions, these NOy data products can be used for further investigation into the composition, dynamics, and climate of the stratosphere.
Charles Étienne Robert, Christine Bingen, Filip Vanhellemont, Nina Mateshvili, Emmanuel Dekemper, Cédric Tétard, Didier Fussen, Adam Bourassa, and Claus Zehner
Atmos. Meas. Tech., 9, 4701–4718, https://doi.org/10.5194/amt-9-4701-2016, https://doi.org/10.5194/amt-9-4701-2016, 2016
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We compare stratospheric aerosol loading computed with a new computer algorithm with various established datasets to determine the overall agreement. Since the new results are based on observation of starlight through the Earth's atmosphere, various aspects of these measurements can influence the final results. A systematic analysis of these aspects, such as the star brightness and temperature, is carried out to see if, and how, they influence the agreement of the results with other datasets.
Cristen Adams, Elise N. Normand, Chris A. McLinden, Adam E. Bourassa, Nicholas D. Lloyd, Douglas A. Degenstein, Nickolay A. Krotkov, Maria Belmonte Rivas, K. Folkert Boersma, and Henk Eskes
Atmos. Meas. Tech., 9, 4103–4122, https://doi.org/10.5194/amt-9-4103-2016, https://doi.org/10.5194/amt-9-4103-2016, 2016
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A new "OMI-minus-OSIRIS" (OmO) prototype dataset for tropospheric NO2 was created by combining information from the OMI satellite instrument, which is sensitive to NO2 in both the troposphere and stratosphere, with information from the OSIRIS satellite instrument, which measures NO2 in the stratosphere. This paper demonstrates that this approach is feasible and could be applied to future geostationary missions.
Daan Hubert, Jean-Christopher Lambert, Tijl Verhoelst, José Granville, Arno Keppens, Jean-Luc Baray, Adam E. Bourassa, Ugo Cortesi, Doug A. Degenstein, Lucien Froidevaux, Sophie Godin-Beekmann, Karl W. Hoppel, Bryan J. Johnson, Erkki Kyrölä, Thierry Leblanc, Günter Lichtenberg, Marion Marchand, C. Thomas McElroy, Donal Murtagh, Hideaki Nakane, Thierry Portafaix, Richard Querel, James M. Russell III, Jacobo Salvador, Herman G. J. Smit, Kerstin Stebel, Wolfgang Steinbrecht, Kevin B. Strawbridge, René Stübi, Daan P. J. Swart, Ghassan Taha, David W. Tarasick, Anne M. Thompson, Joachim Urban, Joanna A. E. van Gijsel, Roeland Van Malderen, Peter von der Gathen, Kaley A. Walker, Elian Wolfram, and Joseph M. Zawodny
Atmos. Meas. Tech., 9, 2497–2534, https://doi.org/10.5194/amt-9-2497-2016, https://doi.org/10.5194/amt-9-2497-2016, 2016
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A more detailed understanding of satellite O3 profile data records is vital for further progress in O3 research. To this end, we made a comprehensive assessment of 14 limb/occultation profilers using ground-based reference data. The mutual consistency of satellite O3 in terms of bias, short-term variability and decadal stability is generally good over most of the stratosphere. However, we identified some exceptions that impact the quality of recently merged data sets and ozone trend assessments.
B. J. Elash, A. E. Bourassa, P. R. Loewen, N. D. Lloyd, and D. A. Degenstein
Atmos. Meas. Tech., 9, 1261–1277, https://doi.org/10.5194/amt-9-1261-2016, https://doi.org/10.5194/amt-9-1261-2016, 2016
D. Pendlebury, D. Plummer, J. Scinocca, P. Sheese, K. Strong, K. Walker, and D. Degenstein
Atmos. Chem. Phys., 15, 12465–12485, https://doi.org/10.5194/acp-15-12465-2015, https://doi.org/10.5194/acp-15-12465-2015, 2015
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The CMAM30 data set takes a chemistry-climate model and relaxes the dynamics to reanalysis, which can then provide chemistry fields not available from the reanalysis data set. This paper addresses this gap by comparing temperature, water vapour, ozone and methane to satellite data to determine and document any biases in the model fields. The lack of ozone destruction and dehydration in the SH polar vortex is shown to be due to the treatment of polar stratosphere clouds in the model.
F. Deng, D. B. A. Jones, T. W. Walker, M. Keller, K. W. Bowman, D. K. Henze, R. Nassar, E. A. Kort, S. C. Wofsy, K. A. Walker, A. E. Bourassa, and D. A. Degenstein
Atmos. Chem. Phys., 15, 11773–11788, https://doi.org/10.5194/acp-15-11773-2015, https://doi.org/10.5194/acp-15-11773-2015, 2015
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The upper troposphere and lower stratosphere (UTLS) is characterized by strong gradients in the distribution of long-lived tracers, which are sensitive to discrepancies in transport in models. We found that our model overestimates CO2 in the polar UTLS through comparison of modeled CO2 with aircraft observations. We then corrected the modeled CO2 and quantified the impact of the correction on the flux estimates using an atmospheric model together with atmospheric CO2 measured from a satellite.
N. Rahpoe, M. Weber, A. V. Rozanov, K. Weigel, H. Bovensmann, J. P. Burrows, A. Laeng, G. Stiller, T. von Clarmann, E. Kyrölä, V. F. Sofieva, J. Tamminen, K. Walker, D. Degenstein, A. E. Bourassa, R. Hargreaves, P. Bernath, J. Urban, and D. P. Murtagh
Atmos. Meas. Tech., 8, 4369–4381, https://doi.org/10.5194/amt-8-4369-2015, https://doi.org/10.5194/amt-8-4369-2015, 2015
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The analyses among six satellite instruments measuring ozone reveals that the relative drift between the sensors is not significant in the stratosphere and we conclude that merging of data from these instruments is possible. The merged ozone profiles can then be ingested in global climate models for long-term forecasts of ozone and climate change in the atmosphere. The added drift uncertainty is estimated at about 3% per decade (1 sigma) and should be applied in the calculation of ozone trends.
N. R. P. Harris, B. Hassler, F. Tummon, G. E. Bodeker, D. Hubert, I. Petropavlovskikh, W. Steinbrecht, J. Anderson, P. K. Bhartia, C. D. Boone, A. Bourassa, S. M. Davis, D. Degenstein, A. Delcloo, S. M. Frith, L. Froidevaux, S. Godin-Beekmann, N. Jones, M. J. Kurylo, E. Kyrölä, M. Laine, S. T. Leblanc, J.-C. Lambert, B. Liley, E. Mahieu, A. Maycock, M. de Mazière, A. Parrish, R. Querel, K. H. Rosenlof, C. Roth, C. Sioris, J. Staehelin, R. S. Stolarski, R. Stübi, J. Tamminen, C. Vigouroux, K. A. Walker, H. J. Wang, J. Wild, and J. M. Zawodny
Atmos. Chem. Phys., 15, 9965–9982, https://doi.org/10.5194/acp-15-9965-2015, https://doi.org/10.5194/acp-15-9965-2015, 2015
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Trends in the vertical distribution of ozone are reported for new and recently revised data sets. The amount of ozone-depleting compounds in the stratosphere peaked in the second half of the 1990s. We examine the trends before and after that peak to see if any change in trend is discernible. The previously reported decreases are confirmed. Furthermore, the downward trend in upper stratospheric ozone has not continued. The possible significance of any increase is discussed in detail.
D. J. Zawada, S. R. Dueck, L. A. Rieger, A. E. Bourassa, N. D. Lloyd, and D. A. Degenstein
Atmos. Meas. Tech., 8, 2609–2623, https://doi.org/10.5194/amt-8-2609-2015, https://doi.org/10.5194/amt-8-2609-2015, 2015
F. Tummon, B. Hassler, N. R. P. Harris, J. Staehelin, W. Steinbrecht, J. Anderson, G. E. Bodeker, A. Bourassa, S. M. Davis, D. Degenstein, S. M. Frith, L. Froidevaux, E. Kyrölä, M. Laine, C. Long, A. A. Penckwitt, C. E. Sioris, K. H. Rosenlof, C. Roth, H.-J. Wang, and J. Wild
Atmos. Chem. Phys., 15, 3021–3043, https://doi.org/10.5194/acp-15-3021-2015, https://doi.org/10.5194/acp-15-3021-2015, 2015
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Understanding ozone trends in the vertical is vital in terms of assessing the success of the Montreal Protocol. This paper compares and analyses the long-term trends in stratospheric ozone from seven new merged satellite data sets. The data sets largely agree well with each other, particularly for the negative trends seen in the early period 1984-1997. For the 1998-2011 period there is less agreement, but a clear shift from negative to mostly positive trends.
A. Laeng, U. Grabowski, T. von Clarmann, G. Stiller, N. Glatthor, M. Höpfner, S. Kellmann, M. Kiefer, A. Linden, S. Lossow, V. Sofieva, I. Petropavlovskikh, D. Hubert, T. Bathgate, P. Bernath, C. D. Boone, C. Clerbaux, P. Coheur, R. Damadeo, D. Degenstein, S. Frith, L. Froidevaux, J. Gille, K. Hoppel, M. McHugh, Y. Kasai, J. Lumpe, N. Rahpoe, G. Toon, T. Sano, M. Suzuki, J. Tamminen, J. Urban, K. Walker, M. Weber, and J. Zawodny
Atmos. Meas. Tech., 7, 3971–3987, https://doi.org/10.5194/amt-7-3971-2014, https://doi.org/10.5194/amt-7-3971-2014, 2014
A. E. Bourassa, D. A. Degenstein, W. J. Randel, J. M. Zawodny, E. Kyrölä, C. A. McLinden, C. E. Sioris, and C. Z. Roth
Atmos. Chem. Phys., 14, 6983–6994, https://doi.org/10.5194/acp-14-6983-2014, https://doi.org/10.5194/acp-14-6983-2014, 2014
B. Hassler, I. Petropavlovskikh, J. Staehelin, T. August, P. K. Bhartia, C. Clerbaux, D. Degenstein, M. De Mazière, B. M. Dinelli, A. Dudhia, G. Dufour, S. M. Frith, L. Froidevaux, S. Godin-Beekmann, J. Granville, N. R. P. Harris, K. Hoppel, D. Hubert, Y. Kasai, M. J. Kurylo, E. Kyrölä, J.-C. Lambert, P. F. Levelt, C. T. McElroy, R. D. McPeters, R. Munro, H. Nakajima, A. Parrish, P. Raspollini, E. E. Remsberg, K. H. Rosenlof, A. Rozanov, T. Sano, Y. Sasano, M. Shiotani, H. G. J. Smit, G. Stiller, J. Tamminen, D. W. Tarasick, J. Urban, R. J. van der A, J. P. Veefkind, C. Vigouroux, T. von Clarmann, C. von Savigny, K. A. Walker, M. Weber, J. Wild, and J. M. Zawodny
Atmos. Meas. Tech., 7, 1395–1427, https://doi.org/10.5194/amt-7-1395-2014, https://doi.org/10.5194/amt-7-1395-2014, 2014
C. E. Sioris, C. A. McLinden, V. E. Fioletov, C. Adams, J. M. Zawodny, A. E. Bourassa, C. Z. Roth, and D. A. Degenstein
Atmos. Chem. Phys., 14, 3479–3496, https://doi.org/10.5194/acp-14-3479-2014, https://doi.org/10.5194/acp-14-3479-2014, 2014
L. A. Rieger, A. E. Bourassa, and D. A. Degenstein
Atmos. Meas. Tech., 7, 777–780, https://doi.org/10.5194/amt-7-777-2014, https://doi.org/10.5194/amt-7-777-2014, 2014
E. Eckert, T. von Clarmann, M. Kiefer, G. P. Stiller, S. Lossow, N. Glatthor, D. A. Degenstein, L. Froidevaux, S. Godin-Beekmann, T. Leblanc, S. McDermid, M. Pastel, W. Steinbrecht, D. P. J. Swart, K. A. Walker, and P. F. Bernath
Atmos. Chem. Phys., 14, 2571–2589, https://doi.org/10.5194/acp-14-2571-2014, https://doi.org/10.5194/acp-14-2571-2014, 2014
L. A. Rieger, A. E. Bourassa, and D. A. Degenstein
Atmos. Meas. Tech., 7, 507–522, https://doi.org/10.5194/amt-7-507-2014, https://doi.org/10.5194/amt-7-507-2014, 2014
C. Gebhardt, A. Rozanov, R. Hommel, M. Weber, H. Bovensmann, J. P. Burrows, D. Degenstein, L. Froidevaux, and A. M. Thompson
Atmos. Chem. Phys., 14, 831–846, https://doi.org/10.5194/acp-14-831-2014, https://doi.org/10.5194/acp-14-831-2014, 2014
C. Adams, A. E. Bourassa, V. Sofieva, L. Froidevaux, C. A. McLinden, D. Hubert, J.-C. Lambert, C. E. Sioris, and D. A. Degenstein
Atmos. Meas. Tech., 7, 49–64, https://doi.org/10.5194/amt-7-49-2014, https://doi.org/10.5194/amt-7-49-2014, 2014
E. N. Normand, J. T. Wiensz, A. E. Bourassa, and D. A. Degenstein
Atmos. Meas. Tech., 6, 3359–3368, https://doi.org/10.5194/amt-6-3359-2013, https://doi.org/10.5194/amt-6-3359-2013, 2013
V. F. Sofieva, N. Rahpoe, J. Tamminen, E. Kyrölä, N. Kalakoski, M. Weber, A. Rozanov, C. von Savigny, A. Laeng, T. von Clarmann, G. Stiller, S. Lossow, D. Degenstein, A. Bourassa, C. Adams, C. Roth, N. Lloyd, P. Bernath, R. J. Hargreaves, J. Urban, D. Murtagh, A. Hauchecorne, F. Dalaudier, M. van Roozendael, N. Kalb, and C. Zehner
Earth Syst. Sci. Data, 5, 349–363, https://doi.org/10.5194/essd-5-349-2013, https://doi.org/10.5194/essd-5-349-2013, 2013
A. Wiacek, R. V. Martin, A. E. Bourassa, N. D. Lloyd, and D. A. Degenstein
Atmos. Meas. Tech., 6, 2761–2776, https://doi.org/10.5194/amt-6-2761-2013, https://doi.org/10.5194/amt-6-2761-2013, 2013
Y. Kasai, H. Sagawa, D. Kreyling, E. Dupuy, P. Baron, J. Mendrok, K. Suzuki, T. O. Sato, T. Nishibori, S. Mizobuchi, K. Kikuchi, T. Manabe, H. Ozeki, T. Sugita, M. Fujiwara, Y. Irimajiri, K. A. Walker, P. F. Bernath, C. Boone, G. Stiller, T. von Clarmann, J. Orphal, J. Urban, D. Murtagh, E. J. Llewellyn, D. Degenstein, A. E. Bourassa, N. D. Lloyd, L. Froidevaux, M. Birk, G. Wagner, F. Schreier, J. Xu, P. Vogt, T. Trautmann, and M. Yasui
Atmos. Meas. Tech., 6, 2311–2338, https://doi.org/10.5194/amt-6-2311-2013, https://doi.org/10.5194/amt-6-2311-2013, 2013
C. Adams, A. E. Bourassa, A. F. Bathgate, C. A. McLinden, N. D. Lloyd, C. Z. Roth, E. J. Llewellyn, J. M. Zawodny, D. E. Flittner, G. L. Manney, W. H. Daffer, and D. A. Degenstein
Atmos. Meas. Tech., 6, 1447–1459, https://doi.org/10.5194/amt-6-1447-2013, https://doi.org/10.5194/amt-6-1447-2013, 2013
C. Adams, K. Strong, X. Zhao, A. E. Bourassa, W. H. Daffer, D. Degenstein, J. R. Drummond, E. E. Farahani, A. Fraser, N. D. Lloyd, G. L. Manney, C. A. McLinden, M. Rex, C. Roth, S. E. Strahan, K. A. Walker, and I. Wohltmann
Atmos. Chem. Phys., 13, 611–624, https://doi.org/10.5194/acp-13-611-2013, https://doi.org/10.5194/acp-13-611-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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
Retrieving cloud-base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
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
Tomographic reconstruction algorithms for retrieving two-dimensional ice cloud microphysical parameters using along-track (sub)millimeter-wave radiometer 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
Empirical model for backscattering polarimetric variables in rain at W-band: motivation and implications
Wet-Radome Attenuation in ARM Cloud Radars and Its Utilization in Radar Calibration Using Disdrometer Measurements
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Bayesian cloud-top phase determination for Meteosat Second Generation
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)
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
Algorithm for continual monitoring of fog life cycles based on geostationary satellite imagery as a basis for solar energy forecasting
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Kaori Sato, Hajime Okamoto, Tomoaki Nishizawa, Yoshitaka Jin, Takashi Y. Nakajima, Minrui Wang, Masaki Satoh, Woosub Roh, Hiroshi Ishimoto, and Rei Kudo
Atmos. Meas. Tech., 18, 1325–1338, https://doi.org/10.5194/amt-18-1325-2025, https://doi.org/10.5194/amt-18-1325-2025, 2025
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This study introduces the JAXA EarthCARE Level 2 (L2) cloud product using satellite observations and simulated EarthCARE data. The outputs from the product feature a 3D global view of the dominant ice habit categories and corresponding microphysics. Habit and size distribution transitions from cloud to precipitation are quantified by the L2 cloud algorithms. With Doppler data, the products can be beneficial for further understanding of the coupling of cloud microphysics, radiation, and dynamics.
Ho Yi Lydia Mak and Christine Unal
Atmos. Meas. Tech., 18, 1209–1242, https://doi.org/10.5194/amt-18-1209-2025, https://doi.org/10.5194/amt-18-1209-2025, 2025
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The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
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In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine learning method. Retrievals from a machine learning algorithm are used to provide a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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This article presents a novel technique to estimate liquid water content (LWC) profiles in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
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We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Yuli Liu and Ian S. Adams
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-188, https://doi.org/10.5194/amt-2024-188, 2024
Revised manuscript accepted for AMT
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This paper presents our latest development in tomographic cloud reconstruction algorithms that use multi-angle TB observations to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomographic technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the tomography technique effectively increases detection sensitivity for small ice cloud particles.
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.
Alexander Myagkov, Tatiana Nomokonova, and Michael Frech
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-143, https://doi.org/10.5194/amt-2024-143, 2024
Revised manuscript accepted for AMT
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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 (EMS) 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.
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
EGUsphere, https://doi.org/10.5194/egusphere-2024-2615, https://doi.org/10.5194/egusphere-2024-2615, 2024
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation (WRA) logarithmic behavior as a function of rainfall rates in light and moderate rain conditions. This WRA technique is applied to the measurements during the ARM TRACER campaign and reports Ze offsets that compare favorably with results from other traditional calibration methods.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1936, https://doi.org/10.5194/egusphere-2024-1936, 2024
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Retrievals of CO2 column-averaged dry air mole fractions from space can be done with spaceborne spectrometers such as OCO-2. Clouds in the vicinity of a footprint lead to spectral perturbations that bias those retrievals well beyond the required accuracy for global assessments of CO2 sources and sinks. This paper presents two physics-based mitigation techniques for these biases based on accompanying imagery, which can be used operationally.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
EGUsphere, https://doi.org/10.5194/egusphere-2023-2885, https://doi.org/10.5194/egusphere-2023-2885, 2024
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Fog and low stratus (FLS) are both persistent clouds close to the Earth’s surface. In the context of photovoltaic power production, FLS is particularly important, as FLS, impact large regions simultaneously, making regional power grid balancing hard. This study introduces a new machine leanring based algorithm developed for the MSG geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 H day cycle.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
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