Articles | Volume 14, issue 12
https://doi.org/10.5194/amt-14-7749-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-7749-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2021
Research article |
| 09 Dec 2021
| 09 Dec 2021
Improved cloud detection for the Aura Microwave Limb Sounder (MLS): training an artificial neural network on colocated MLS and Aqua MODIS data
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109, USA
Nathaniel J. Livesey
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109, USA
Michael J. Schwartz
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109, USA
William G. Read
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109, USA
Michelle L. Santee
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, California 91109, USA
Galina Wind
NASA Goddard Space Flight Center, Greenbelt, Maryland 20771, USA
SSAI Inc., Lanham, Maryland 20706, USA
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Hong Chen, Sebastian Schmidt, Michael D. King, Galina Wind, Anthony Bucholtz, Elizabeth A. Reid, Michal Segal-Rozenhaimer, William L. Smith, Patrick C. Taylor, Seiji Kato, and Peter Pilewskie
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In this paper, we accessed the shortwave irradiance derived from MODIS cloud optical properties by using aircraft measurements. We developed a data aggregation technique to parameterize spectral surface albedo by snow fraction in the Arctic. We found that undetected clouds have the most significant impact on the imagery-derived irradiance. This study suggests that passive imagery cloud detection could be improved through a multi-pixel approach that would make it more dependable in the Arctic.
Seidai Nara, Tomohiro O. Sato, Takayoshi Yamada, Tamaki Fujinawa, Kota Kuribayashi, Takeshi Manabe, Lucien Froidevaux, Nathaniel J. Livesey, Kaley A. Walker, Jian Xu, Franz Schreier, Yvan J. Orsolini, Varavut Limpasuvan, Nario Kuno, and Yasuko Kasai
Atmos. Meas. Tech., 13, 6837–6852, https://doi.org/10.5194/amt-13-6837-2020, https://doi.org/10.5194/amt-13-6837-2020, 2020
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In the atmosphere, more than 80 % of chlorine compounds are anthropogenic. Hydrogen chloride (HCl), the main stratospheric chlorine reservoir, is useful to estimate the total budget of the atmospheric chlorine compounds. We report, for the first time, the HCl vertical distribution from the middle troposphere to the lower thermosphere using a high-sensitivity SMILES measurement; the data quality is quantified by comparisons with other measurements and via theoretical error analysis.
Kazuyuki Miyazaki, Kevin Bowman, Takashi Sekiya, Henk Eskes, Folkert Boersma, Helen Worden, Nathaniel Livesey, Vivienne H. Payne, Kengo Sudo, Yugo Kanaya, Masayuki Takigawa, and Koji Ogochi
Earth Syst. Sci. Data, 12, 2223–2259, https://doi.org/10.5194/essd-12-2223-2020, https://doi.org/10.5194/essd-12-2223-2020, 2020
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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.
Benjamin Marchant, Steven Platnick, Kerry Meyer, and Galina Wind
Atmos. Meas. Tech., 13, 3263–3275, https://doi.org/10.5194/amt-13-3263-2020, https://doi.org/10.5194/amt-13-3263-2020, 2020
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Multilayer cloud scenes (such as an ice cloud overlapping a liquid cloud) are common in the Earth's atmosphere and are quite difficult to detect from space. The detection of multilayer clouds is important to better understand how they interact with the light and their impact on the climate. So, for the instrument MODIS an algorithm has been developed to detect those clouds, and this paper presents an evaluation of this algorithm by comparing it with
other instruments.
Frank Werner and Hartwig Deneke
Atmos. Meas. Tech., 13, 1089–1111, https://doi.org/10.5194/amt-13-1089-2020, https://doi.org/10.5194/amt-13-1089-2020, 2020
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The reliability of remotely sensed cloud variables from space depends on the horizontal resolution of the instrument. This study presents and evaluates several candidate approaches for increasing the spatial resolution of observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) from the native 3 km scale to a horizontal resolution of 1 km. It is shown that uncertainties in the derived cloud products can be significantly mitigated by applying an appropriate downscaling scheme.
Piao Rong, Christian von Savigny, Chunmin Zhang, Christoph G. Hoffmann, and Michael J. Schwartz
Atmos. Chem. Phys., 20, 1737–1755, https://doi.org/10.5194/acp-20-1737-2020, https://doi.org/10.5194/acp-20-1737-2020, 2020
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We study the presence and characteristics of 27 d solar signatures in middle atmospheric temperature observed by the microwave limb sounder on NASA's Aura spacecraft. This is a highly interesting and significant subject because the physical and chemical mechanisms leading to these 27 d solar-driven signatures are, in many cases, not well understood. The analysis shows that highly significant 27 d solar signatures in middle atmospheric temperature are present at many altitudes and latitudes.
Quentin Errera, Simon Chabrillat, Yves Christophe, Jonas Debosscher, Daan Hubert, William Lahoz, Michelle L. Santee, Masato Shiotani, Sergey Skachko, Thomas von Clarmann, and Kaley Walker
Atmos. Chem. Phys., 19, 13647–13679, https://doi.org/10.5194/acp-19-13647-2019, https://doi.org/10.5194/acp-19-13647-2019, 2019
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BRAM2 is a 13-year reanalysis of the chemical composition from the upper troposphere to the lower mesosphere based on the assimilation of the Microwave Limb Sounder observations where eight species are assimilated: O3, H2O, N2O, HNO3, HCl, ClO, CH3Cl and CO. BRAM2 agrees generally well with independent observations in the middle stratosphere, the polar vortex and the upper troposphere–lower stratosphere but also shows several issues in the model and in the observations.
Dan Weaver, Kimberly Strong, Kaley A. Walker, Chris Sioris, Matthias Schneider, C. Thomas McElroy, Holger Vömel, Michael Sommer, Katja Weigel, Alexei Rozanov, John P. Burrows, William G. Read, Evan Fishbein, and Gabriele Stiller
Atmos. Meas. Tech., 12, 4039–4063, https://doi.org/10.5194/amt-12-4039-2019, https://doi.org/10.5194/amt-12-4039-2019, 2019
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This work assesses water vapour profiles acquired by Atmospheric Chemistry Experiment (ACE) satellite instruments in the upper troposphere and lower stratosphere (UTLS) using comparisons to radiosondes and ground-based Fourier transform infrared spectrometer measurements acquired at a Canadian high Arctic measurement site in Eureka, Nunavut. Additional comparisons are made between these Eureka measurements and other water vapour satellite datasets for context, including AIRS, MLS, and others.
Sören Johansson, Michelle L. Santee, Jens-Uwe Grooß, Michael Höpfner, Marleen Braun, Felix Friedl-Vallon, Farahnaz Khosrawi, Oliver Kirner, Erik Kretschmer, Hermann Oelhaf, Johannes Orphal, Björn-Martin Sinnhuber, Ines Tritscher, Jörn Ungermann, Kaley A. Walker, and Wolfgang Woiwode
Atmos. Chem. Phys., 19, 8311–8338, https://doi.org/10.5194/acp-19-8311-2019, https://doi.org/10.5194/acp-19-8311-2019, 2019
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We present a study based on GLORIA aircraft and MLS/ACE-FTS/CALIOP satellite measurements during the Arctic winter 2015/16, which demonstrate (for the Arctic) unusual chlorine deactivation into HCl instead of ClONO2 due to low ozone abundances in the lowermost stratosphere, with a focus at 380 K potential temperature. The atmospheric models CLaMS and EMAC are evaluated, and measured ClONO2 is linked with transport and in situ deactivation in the lowermost stratosphere.
Stefan Lossow, Farahnaz Khosrawi, Michael Kiefer, Kaley A. Walker, Jean-Loup Bertaux, Laurent Blanot, James M. Russell, Ellis E. Remsberg, John C. Gille, Takafumi Sugita, Christopher E. Sioris, Bianca M. Dinelli, Enzo Papandrea, Piera Raspollini, Maya García-Comas, Gabriele P. Stiller, Thomas von Clarmann, Anu Dudhia, William G. Read, Gerald E. Nedoluha, Robert P. Damadeo, Joseph M. Zawodny, Katja Weigel, Alexei Rozanov, Faiza Azam, Klaus Bramstedt, Stefan Noël, John P. Burrows, Hideo Sagawa, Yasuko Kasai, Joachim Urban, Patrick Eriksson, Donal P. Murtagh, Mark E. Hervig, Charlotta Högberg, Dale F. Hurst, and Karen H. Rosenlof
Atmos. Meas. Tech., 12, 2693–2732, https://doi.org/10.5194/amt-12-2693-2019, https://doi.org/10.5194/amt-12-2693-2019, 2019
Mohamadou Diallo, Paul Konopka, Michelle L. Santee, Rolf Müller, Mengchu Tao, Kaley A. Walker, Bernard Legras, Martin Riese, Manfred Ern, and Felix Ploeger
Atmos. Chem. Phys., 19, 425–446, https://doi.org/10.5194/acp-19-425-2019, https://doi.org/10.5194/acp-19-425-2019, 2019
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This paper assesses the structural changes in the shallow and transition branches of the BDC induced by El Nino using the Lagrangian model simulations driven by ERAi and JRA-55 combined with MLS observations. We found a clear evidence of a weakening of the transition branch due to an upward shift in the dissipation height of the planetary and gravity waves and a strengthening of the shallow branch due to enhanced GW breaking in the tropics–subtropics and PW breaking at high latitudes.
Dejian Fu, Susan S. Kulawik, Kazuyuki Miyazaki, Kevin W. Bowman, John R. Worden, Annmarie Eldering, Nathaniel J. Livesey, Joao Teixeira, Fredrick W. Irion, Robert L. Herman, Gregory B. Osterman, Xiong Liu, Pieternel F. Levelt, Anne M. Thompson, and Ming Luo
Atmos. Meas. Tech., 11, 5587–5605, https://doi.org/10.5194/amt-11-5587-2018, https://doi.org/10.5194/amt-11-5587-2018, 2018
Mohamadou Diallo, Martin Riese, Thomas Birner, Paul Konopka, Rolf Müller, Michaela I. Hegglin, Michelle L. Santee, Mark Baldwin, Bernard Legras, and Felix Ploeger
Atmos. Chem. Phys., 18, 13055–13073, https://doi.org/10.5194/acp-18-13055-2018, https://doi.org/10.5194/acp-18-13055-2018, 2018
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The unprecedented timing of an El Niño event aligned with the disrupted QBO in 2015–2016 caused a perturbation to the stratospheric circulation, affecting trace gases. This paper resolves the puzzling response of the lower stratospheric water vapor by showing that the QBO disruption reversed the lower stratosphere moistening triggered by the alignment of the El Niño event with a westerly QBO in early boreal winter.
Sören Johansson, Wolfgang Woiwode, Michael Höpfner, Felix Friedl-Vallon, Anne Kleinert, Erik Kretschmer, Thomas Latzko, Johannes Orphal, Peter Preusse, Jörn Ungermann, Michelle L. Santee, Tina Jurkat-Witschas, Andreas Marsing, Christiane Voigt, Andreas Giez, Martina Krämer, Christian Rolf, Andreas Zahn, Andreas Engel, Björn-Martin Sinnhuber, and Hermann Oelhaf
Atmos. Meas. Tech., 11, 4737–4756, https://doi.org/10.5194/amt-11-4737-2018, https://doi.org/10.5194/amt-11-4737-2018, 2018
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We present two-dimensional cross sections of temperature, HNO3, O3, ClONO2, H2O and CFC-12 from measurements of the GLORIA infrared limb imager during the POLSTRACC/GW-LCYCLE/SALSA aircraft campaigns in the Arctic winter 2015/2016. GLORIA sounded the atmosphere between 5 and 14 km with vertical resolutions of 0.4–1 km. Estimated errors are in the range of 1–2 K (temperature) and 10 %–20 % (trace gases). Comparisons to in situ instruments onboard the aircraft and to Aura/MLS are shown.
Farahnaz Khosrawi, Stefan Lossow, Gabriele P. Stiller, Karen H. Rosenlof, Joachim Urban, John P. Burrows, Robert P. Damadeo, Patrick Eriksson, Maya García-Comas, John C. Gille, Yasuko Kasai, Michael Kiefer, Gerald E. Nedoluha, Stefan Noël, Piera Raspollini, William G. Read, Alexei Rozanov, Christopher E. Sioris, Kaley A. Walker, and Katja Weigel
Atmos. Meas. Tech., 11, 4435–4463, https://doi.org/10.5194/amt-11-4435-2018, https://doi.org/10.5194/amt-11-4435-2018, 2018
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Time series of stratospheric and lower mesospheric water vapour using 33 data sets from 15 satellite instruments were compared in the framework of the second SPARC water vapour assessment. We find that most data sets can be considered in observational and modelling studies addressing, e.g. stratospheric and lower mesospheric water vapour variability and trends if data-set-specific characteristics (e.g. a drift) and restrictions (e.g. temporal and spatial coverage) are taken into account.
Farahnaz Khosrawi, Oliver Kirner, Gabriele Stiller, Michael Höpfner, Michelle L. Santee, Sylvia Kellmann, and Peter Braesicke
Atmos. Chem. Phys., 18, 8873–8892, https://doi.org/10.5194/acp-18-8873-2018, https://doi.org/10.5194/acp-18-8873-2018, 2018
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An extensive assessment of the performance of the chemistry–climate model EMAC is given for Arctic winters 2009/2010 and 2010/2011. The EMAC simulations are compared to satellite observations. The comparisons between EMAC simulations and satellite observations show that model and measurements compare well for these two Arctic winters. However, differences between model and observations are found that need improvements in the model in the future.
Stefan Lossow, Dale F. Hurst, Karen H. Rosenlof, Gabriele P. Stiller, Thomas von Clarmann, Sabine Brinkop, Martin Dameris, Patrick Jöckel, Doug E. Kinnison, Johannes Plieninger, David A. Plummer, Felix Ploeger, William G. Read, Ellis E. Remsberg, James M. Russell, and Mengchu Tao
Atmos. Chem. Phys., 18, 8331–8351, https://doi.org/10.5194/acp-18-8331-2018, https://doi.org/10.5194/acp-18-8331-2018, 2018
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Trend estimates of lower stratospheric H2O derived from the FPH observations at Boulder and a merged zonal mean satellite data set clearly differ for the time period from the late 1980s to 2010. We investigate if a sampling bias between Boulder and the zonal mean around the Boulder latitude can explain these trend discrepancies. Typically they are small and not sufficient to explain the trend discrepancies in the observational database.
Xiaolu Yan, Paul Konopka, Felix Ploeger, Mengchu Tao, Rolf Müller, Michelle L. Santee, Jianchun Bian, and Martin Riese
Atmos. Chem. Phys., 18, 8079–8096, https://doi.org/10.5194/acp-18-8079-2018, https://doi.org/10.5194/acp-18-8079-2018, 2018
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Many works investigate the impact of ENSO on the troposphere. However, only a few works check the impact of ENSO at higher altitudes.
Here, we analyse the impact of ENSO on the vicinity of the tropopause using reanalysis, satellite, in situ and model data. We find that ENSO shows the strongest signal in winter, but its impact can last until early the next summer. The ENSO anomaly is insignificant in late summer. Our study can help to understand the atmosphere propagation after ENSO.
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).
Luis F. Millán, Nathaniel J. Livesey, Michelle L. Santee, and Thomas von Clarmann
Atmos. Chem. Phys., 18, 4187–4199, https://doi.org/10.5194/acp-18-4187-2018, https://doi.org/10.5194/acp-18-4187-2018, 2018
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This study investigates orbital sampling biases and evaluates the additional impact caused by data quality screening for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and the Aura Microwave Limb Sounder (MLS).
Alyn Lambert and Michelle L. Santee
Atmos. Chem. Phys., 18, 1945–1975, https://doi.org/10.5194/acp-18-1945-2018, https://doi.org/10.5194/acp-18-1945-2018, 2018
Hugh C. Pumphrey, Norbert Glatthor, Peter F. Bernath, Christopher D. Boone, James W. Hannigan, Ivan Ortega, Nathaniel J. Livesey, and William G. Read
Atmos. Chem. Phys., 18, 691–703, https://doi.org/10.5194/acp-18-691-2018, https://doi.org/10.5194/acp-18-691-2018, 2018
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The Microwave Limb Sounder (MLS) is a satellite instrument that has been measuring the amount of various gases in the atmosphere since 2004. In late 2015 and 2016 it observed unusual amounts of hydrogen cyanide (HCN), a gas produced when vegetation is burned. We compare the MLS observations to similar observations from other instruments. The excess HCN is shown to come from fires in Indonesia. There are more fires than usual in 2015–16 due to a drought caused by an El Niño event.
Gerald E. Nedoluha, Michael Kiefer, Stefan Lossow, R. Michael Gomez, Niklaus Kämpfer, Martin Lainer, Peter Forkman, Ole Martin Christensen, Jung Jin Oh, Paul Hartogh, John Anderson, Klaus Bramstedt, Bianca M. Dinelli, Maya Garcia-Comas, Mark Hervig, Donal Murtagh, Piera Raspollini, William G. Read, Karen Rosenlof, Gabriele P. Stiller, and Kaley A. Walker
Atmos. Chem. Phys., 17, 14543–14558, https://doi.org/10.5194/acp-17-14543-2017, https://doi.org/10.5194/acp-17-14543-2017, 2017
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As part of the second SPARC (Stratosphere–troposphere Processes And their Role in Climate) water vapor assessment (WAVAS-II), we present measurements taken from or coincident with seven sites from which ground-based microwave instruments measure water vapor in the middle atmosphere. In the lower mesosphere, we quantify instrumental differences in the observed trends and annual variations at six sites. We then present a range of observed trends in water vapor over the past 20 years.
Farahnaz Khosrawi, Oliver Kirner, Björn-Martin Sinnhuber, Sören Johansson, Michael Höpfner, Michelle L. Santee, Lucien Froidevaux, Jörn Ungermann, Roland Ruhnke, Wolfgang Woiwode, Hermann Oelhaf, and Peter Braesicke
Atmos. Chem. Phys., 17, 12893–12910, https://doi.org/10.5194/acp-17-12893-2017, https://doi.org/10.5194/acp-17-12893-2017, 2017
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The 2015/2016 Arctic winter was one of the coldest winters in recent years, allowing extensive PSC formation and chlorine activation. Model simulations of the 2015/2016 Arctic winter were performed with the atmospheric chemistry–climate model ECHAM5/MESSy Atmospheric Chemistry (EMAC). We find that ozone loss was quite strong but not as strong as in 2010/2011; denitrification and dehydration were so far the strongest observed in the Arctic stratosphere in at least the past 10 years.
Gloria L. Manney, Michaela I. Hegglin, Zachary D. Lawrence, Krzysztof Wargan, Luis F. Millán, Michael J. Schwartz, Michelle L. Santee, Alyn Lambert, Steven Pawson, Brian W. Knosp, Ryan A. Fuller, and William H. Daffer
Atmos. Chem. Phys., 17, 11541–11566, https://doi.org/10.5194/acp-17-11541-2017, https://doi.org/10.5194/acp-17-11541-2017, 2017
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The upper tropospheric–lower stratospheric (UTLS) jet stream and multiple tropopause distributions are compared among five state-of-the-art reanalyses. The reanalyses show very similar global distributions of UTLS jets, reflecting their overall high quality; slightly larger differences are seen in tropopause characteristics. Regional and seasonal differences, albeit small, may have implications for using these reanalyses for quantitative dynamical and transport studies focusing on the UTLS.
Robert L. Herman, Eric A. Ray, Karen H. Rosenlof, Kristopher M. Bedka, Michael J. Schwartz, William G. Read, Robert F. Troy, Keith Chin, Lance E. Christensen, Dejian Fu, Robert A. Stachnik, T. Paul Bui, and Jonathan M. Dean-Day
Atmos. Chem. Phys., 17, 6113–6124, https://doi.org/10.5194/acp-17-6113-2017, https://doi.org/10.5194/acp-17-6113-2017, 2017
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This study reports new aircraft field observations of elevated water vapor greater than 10 ppmv in the overworld stratosphere over the summertime continental US. Back trajectories from the flight track intersect overshooting convective tops within the previous 1 to 7 days, suggesting that ice is convectively and irreversibly transported to the stratosphere in the most energetic overshooting convective events. Satellite measurements (Aura MLS) indicate that such events are uncommon (< 1 %).
Stefan Lossow, Farahnaz Khosrawi, Gerald E. Nedoluha, Faiza Azam, Klaus Bramstedt, John. P. Burrows, Bianca M. Dinelli, Patrick Eriksson, Patrick J. Espy, Maya García-Comas, John C. Gille, Michael Kiefer, Stefan Noël, Piera Raspollini, William G. Read, Karen H. Rosenlof, Alexei Rozanov, Christopher E. Sioris, Gabriele P. Stiller, Kaley A. Walker, and Katja Weigel
Atmos. Meas. Tech., 10, 1111–1137, https://doi.org/10.5194/amt-10-1111-2017, https://doi.org/10.5194/amt-10-1111-2017, 2017
Masatomo Fujiwara, Jonathon S. Wright, Gloria L. Manney, Lesley J. Gray, James Anstey, Thomas Birner, Sean Davis, Edwin P. Gerber, V. Lynn Harvey, Michaela I. Hegglin, Cameron R. Homeyer, John A. Knox, Kirstin Krüger, Alyn Lambert, Craig S. Long, Patrick Martineau, Andrea Molod, Beatriz M. Monge-Sanz, Michelle L. Santee, Susann Tegtmeier, Simon Chabrillat, David G. H. Tan, David R. Jackson, Saroja Polavarapu, Gilbert P. Compo, Rossana Dragani, Wesley Ebisuzaki, Yayoi Harada, Chiaki Kobayashi, Will McCarty, Kazutoshi Onogi, Steven Pawson, Adrian Simmons, Krzysztof Wargan, Jeffrey S. Whitaker, and Cheng-Zhi Zou
Atmos. Chem. Phys., 17, 1417–1452, https://doi.org/10.5194/acp-17-1417-2017, https://doi.org/10.5194/acp-17-1417-2017, 2017
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We introduce the SPARC Reanalysis Intercomparison Project (S-RIP), review key concepts and elements of atmospheric reanalysis systems, and summarize the technical details of and differences among 11 of these systems. This work supports scientific studies and intercomparisons of reanalysis products by collecting these background materials and technical details into a single reference. We also address several common misunderstandings and points of confusion regarding reanalyses.
Alyn Lambert, Michelle L. Santee, and Nathaniel J. Livesey
Atmos. Chem. Phys., 16, 15219–15246, https://doi.org/10.5194/acp-16-15219-2016, https://doi.org/10.5194/acp-16-15219-2016, 2016
Frank Werner, Galina Wind, Zhibo Zhang, Steven Platnick, Larry Di Girolamo, Guangyu Zhao, Nandana Amarasinghe, and Kerry Meyer
Atmos. Meas. Tech., 9, 5869–5894, https://doi.org/10.5194/amt-9-5869-2016, https://doi.org/10.5194/amt-9-5869-2016, 2016
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A research–level retrieval algorithm for cloud optical and microphysical properties is developed for the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) aboard the Terra satellite. This yields reliable estimates of important cloud variables at a horizontal resolution of 30 m. Comparisons of the ASTER retrieval results with the operational cloud products from the Moderate Resolution Imaging Spectroradiometer (MODIS) show a high agreement for 48 example cloud fields.
Sean M. Davis, Karen H. Rosenlof, Birgit Hassler, Dale F. Hurst, William G. Read, Holger Vömel, Henry Selkirk, Masatomo Fujiwara, and Robert Damadeo
Earth Syst. Sci. Data, 8, 461–490, https://doi.org/10.5194/essd-8-461-2016, https://doi.org/10.5194/essd-8-461-2016, 2016
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This paper describes the construction of the Stratospheric Water and Ozone Satellite Homogenized (SWOOSH) database, whose main feature is a combined data product created by homogenizing multiple satellite records. This motivation for SWOOSH is that in order to study multiyear to decadal variability in ozone and water vapor concentrations, it is necessary to have a continuous and smooth record without artificial jumps in the data.
Luis F. Millán, Nathaniel J. Livesey, Michelle L. Santee, Jessica L. Neu, Gloria L. Manney, and Ryan A. Fuller
Atmos. Chem. Phys., 16, 11521–11534, https://doi.org/10.5194/acp-16-11521-2016, https://doi.org/10.5194/acp-16-11521-2016, 2016
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This paper describes the impact of orbital sampling applied to stratospheric temperature and trace gas fields. Model fields are sampled using real sampling patterns from different satellites. We find that coarse nonuniform sampling patterns may introduce non-negligible errors into the inferred magnitude of temperature and trace gas trends and necessitate considerably longer records for their definitive detection.
Dale F. Hurst, William G. Read, Holger Vömel, Henry B. Selkirk, Karen H. Rosenlof, Sean M. Davis, Emrys G. Hall, Allen F. Jordan, and Samuel J. Oltmans
Atmos. Meas. Tech., 9, 4447–4457, https://doi.org/10.5194/amt-9-4447-2016, https://doi.org/10.5194/amt-9-4447-2016, 2016
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This study compares stratospheric water vapor measurements by the Aura Microwave Limb Sounder (MLS) and balloon-borne frost point hygrometers (FPs) at five sites that launch two different types of FPs. The results demonstrate that FP and MLS measurements have been diverging at statistically significant rates of 0.6 to 1.5 % per year since approximately 2010. Similarities in the divergences at different sites suggest a positive drift in MLS retrievals since approximately 2010.
Gerald E. Nedoluha, Brian J. Connor, Thomas Mooney, James W. Barrett, Alan Parrish, R. Michael Gomez, Ian Boyd, Douglas R. Allen, Michael Kotkamp, Stefanie Kremser, Terry Deshler, Paul Newman, and Michelle L. Santee
Atmos. Chem. Phys., 16, 10725–10734, https://doi.org/10.5194/acp-16-10725-2016, https://doi.org/10.5194/acp-16-10725-2016, 2016
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Chlorine monoxide (ClO) is central to the formation of the springtime Antarctic ozone hole since it is the catalytic agent in the most important ozone-depleting chemical cycle. We present 20 years of measurements of ClO from the Chlorine monOxide Experiment at Scott Base, Antarctica, and 12 years of measurements from the Aura Microwave Limb Sounder to show that the trends in ClO during the ozone hole season are consistent with changes in stratospheric chlorine observed elsewhere.
Manfred Ern, Quang Thai Trinh, Martin Kaufmann, Isabell Krisch, Peter Preusse, Jörn Ungermann, Yajun Zhu, John C. Gille, Martin G. Mlynczak, James M. Russell III, Michael J. Schwartz, and Martin Riese
Atmos. Chem. Phys., 16, 9983–10019, https://doi.org/10.5194/acp-16-9983-2016, https://doi.org/10.5194/acp-16-9983-2016, 2016
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Sudden stratospheric warmings (SSWs) influence the atmospheric circulation over a large range of altitudes and latitudes. We investigate the global distribution of small-scale gravity waves (GWs) during SSWs as derived from 13 years of satellite observations.
We find that GWs may play an important role for triggering SSWs by preconditioning the polar vortex, as well as during long-lasting vortex recovery phases after SSWs. The GW distribution during SSWs displays strong day-to-day variability.
Xiaolu Yan, Jonathon S. Wright, Xiangdong Zheng, Nathaniel J. Livesey, Holger Vömel, and Xiuji Zhou
Atmos. Meas. Tech., 9, 3547–3566, https://doi.org/10.5194/amt-9-3547-2016, https://doi.org/10.5194/amt-9-3547-2016, 2016
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We evaluate Aura Microwave Limb Sounder retrievals of temperature, water vapour and ozone over the eastern Tibetan Plateau against measurements from balloon-borne instruments. The newest version of the retrievals (v4) represents a slight improvement over the previous version, particularly with respect to data yields and upper tropospheric ozone. We identify several biases that did not appear in evaluations conducted elsewhere, highlighting the unique challenges of remote sensing in this region.
Galina Wind, Arlindo M. da Silva, Peter M. Norris, Steven Platnick, Shana Mattoo, and Robert C. Levy
Geosci. Model Dev., 9, 2377–2389, https://doi.org/10.5194/gmd-9-2377-2016, https://doi.org/10.5194/gmd-9-2377-2016, 2016
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The MCARS code creates sensor radiances using model-generated atmospheric columns and actual sensor and solar geometry. MCARS output looks like real data, so it is usable by any code that reads MODIS data. MCARS output can be used to test remote-sensing retrieval algorithms. Users know what went into creating the radiance: atmosphere, surface, clouds, and aerosols. Models can use MCARS output to create new parameterizations of relations of atmospheric physical quantities and measured radiances.
Luis Millán, Matthew Lebsock, Nathaniel Livesey, and Simone Tanelli
Atmos. Meas. Tech., 9, 2633–2646, https://doi.org/10.5194/amt-9-2633-2016, https://doi.org/10.5194/amt-9-2633-2016, 2016
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We discuss the theoretical capabilities of a radar technique to measure profiles of water vapor in cloudy/precipitating areas. The method uses two radar pulses at different frequencies near the 183 GHz H2O absorption line to determine water vapor profiles by measuring the differential absorption on and off the line. Results of inverting synthetic data assuming a satellite radar are presented.
Lei Huang, Jonathan H. Jiang, Lee T. Murray, Megan R. Damon, Hui Su, and Nathaniel J. Livesey
Atmos. Chem. Phys., 16, 5641–5663, https://doi.org/10.5194/acp-16-5641-2016, https://doi.org/10.5194/acp-16-5641-2016, 2016
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This study evaluates the distribution and variation of carbon monoxide (CO) in the upper troposphere and lower stratosphere (UTLS) during 2004–2012 on global and regional scales as simulated by two chemical transport models (GMI and GEOS-Chem), using the latest version (V4) of Aura Microwave Limb Sounder (MLS) observations. The impacts of surface emissions and convection on CO concentrations in the UTLS over different regions are investigated, using both model simulations and MLS observations.
Hideaki Nakajima, Ingo Wohltmann, Tobias Wegner, Masanori Takeda, Michael C. Pitts, Lamont R. Poole, Ralph Lehmann, Michelle L. Santee, and Markus Rex
Atmos. Chem. Phys., 16, 3311–3325, https://doi.org/10.5194/acp-16-3311-2016, https://doi.org/10.5194/acp-16-3311-2016, 2016
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This paper presents the first trial of analyzing amount of chlorine activation on different PSC compositions by using match analysis on trajectories initiated from PSC locations identified by CALIPSO/CALIOP measurements. The measured minor species such as HCl and ClO by MLS are compared with ATLAS chemistry-transport model (CTM) results. PSC growth to NAT, NAT/STS mixture, and ice were identified by different temperature decrease histories on trajectories.
K. Weigel, A. Rozanov, F. Azam, K. Bramstedt, R. Damadeo, K.-U. Eichmann, C. Gebhardt, D. Hurst, M. Kraemer, S. Lossow, W. Read, N. Spelten, G. P. Stiller, K. A. Walker, M. Weber, H. Bovensmann, and J. P. Burrows
Atmos. Meas. Tech., 9, 133–158, https://doi.org/10.5194/amt-9-133-2016, https://doi.org/10.5194/amt-9-133-2016, 2016
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The SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) aboard the Envisat satellite provided measurements between 2002 and 2012 with different viewing geometries. The limb viewing geometry allows the retrieval of water vapour profiles in the UTLS (upper troposphere and lower stratosphere) from the near-infrared spectral range (1353–1410 nm). Here, we present data version 3.01 and compare it to other water vapour data.
L. Froidevaux, J. Anderson, H.-J. Wang, R. A. Fuller, M. J. Schwartz, M. L. Santee, N. J. Livesey, H. C. Pumphrey, P. F. Bernath, J. M. Russell III, and M. P. McCormick
Atmos. Chem. Phys., 15, 10471–10507, https://doi.org/10.5194/acp-15-10471-2015, https://doi.org/10.5194/acp-15-10471-2015, 2015
J. Kuttippurath, S. Godin-Beekmann, F. Lefèvre, M. L. Santee, L. Froidevaux, and A. Hauchecorne
Atmos. Chem. Phys., 15, 10385–10397, https://doi.org/10.5194/acp-15-10385-2015, https://doi.org/10.5194/acp-15-10385-2015, 2015
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Our study finds large interannual variability in Antarctic ozone loss in the recent decade, with a number of winters showing shallow ozone holes but also with the year of the largest ozone hole in the last decades. These smaller ozone holes or ozone losses are mainly related to the year-to-year changes in dynamical processes rather than the variations in anthropogenic ozone-depleting substances (ODSs), as the change in ODS levels during the study period was very small.
N. J. Livesey, M. L. Santee, and G. L. Manney
Atmos. Chem. Phys., 15, 9945–9963, https://doi.org/10.5194/acp-15-9945-2015, https://doi.org/10.5194/acp-15-9945-2015, 2015
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Employing the well-established "Match" technique, we quantify polar
stratospheric ozone loss during multiple Arctic and Antarctic winters,
based on observations from the spaceborne Aura Microwave Limb Sounder
(MLS) instrument. The dense MLS spatial coverage enables many more
matches than is possible for balloon-based observations. Applying the
same technique to MLS observations of the long-lived N2O molecule gives
an measure of the impact of transport errors on our ozone loss
estimates.
M. Höpfner, C. D. Boone, B. Funke, N. Glatthor, U. Grabowski, A. Günther, S. Kellmann, M. Kiefer, A. Linden, S. Lossow, H. C. Pumphrey, W. G. Read, A. Roiger, G. Stiller, H. Schlager, T. von Clarmann, and K. Wissmüller
Atmos. Chem. Phys., 15, 7017–7037, https://doi.org/10.5194/acp-15-7017-2015, https://doi.org/10.5194/acp-15-7017-2015, 2015
G. L. Manney, Z. D. Lawrence, M. L. Santee, N. J. Livesey, A. Lambert, and M. C. Pitts
Atmos. Chem. Phys., 15, 5381–5403, https://doi.org/10.5194/acp-15-5381-2015, https://doi.org/10.5194/acp-15-5381-2015, 2015
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Sudden stratospheric warmings (SSWs) cause a rapid rise in lower stratospheric temperatures, terminating conditions favorable to chemical ozone loss. We show that although temperatures rose precipitously during the vortex split SSW in early Jan 2013, because the offspring vortices each remained isolated and in regions that received sunlight, chemical ozone loss continued for over 1 month after the SSW. Dec/Jan Arctic ozone loss was larger than any previously observed during that period.
Z. D. Lawrence, G. L. Manney, K. Minschwaner, M. L. Santee, and A. Lambert
Atmos. Chem. Phys., 15, 3873–3892, https://doi.org/10.5194/acp-15-3873-2015, https://doi.org/10.5194/acp-15-3873-2015, 2015
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We use a comprehensive set of diagnostics to investigate how two widely used modern reanalysis data sets might affect studies of lower stratospheric polar processing and ozone loss. Our results show that the agreement in temperature diagnostics between the two reanalyses improves over time in both hemispheres with increasing assimilation model inputs. This suggests that both data sets are appropriate choices for studies of polar processing in recent winters.
L. Millán, S. Wang, N. Livesey, D. Kinnison, H. Sagawa, and Y. Kasai
Atmos. Chem. Phys., 15, 2889–2902, https://doi.org/10.5194/acp-15-2889-2015, https://doi.org/10.5194/acp-15-2889-2015, 2015
H. C. Pumphrey, W. G. Read, N. J. Livesey, and K. Yang
Atmos. Meas. Tech., 8, 195–209, https://doi.org/10.5194/amt-8-195-2015, https://doi.org/10.5194/amt-8-195-2015, 2015
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Volcanic eruptions can be violent enough to inject sulfur dioxide into the stratosphere: the layer of the atmosphere which contains the ozone layer. Sulfur dioxide is a gas, but once it is in the stratosphere various chemical reactions convert it into tiny particles. These particles can alter the Earth's climate by reflecting sunlight. In this paper we describe how we used a satellite instrument called the Microwave Limb Sounder to observe volcanic sulfur dioxide in the stratosphere.
L. Millán, M. Lebsock, N. Livesey, S. Tanelli, and G. Stephens
Atmos. Meas. Tech., 7, 3959–3970, https://doi.org/10.5194/amt-7-3959-2014, https://doi.org/10.5194/amt-7-3959-2014, 2014
M. García-Comas, B. Funke, A. Gardini, M. López-Puertas, A. Jurado-Navarro, T. von Clarmann, G. Stiller, M. Kiefer, C. D. Boone, T. Leblanc, B. T. Marshall, M. J. Schwartz, and P. E. Sheese
Atmos. Meas. Tech., 7, 3633–3651, https://doi.org/10.5194/amt-7-3633-2014, https://doi.org/10.5194/amt-7-3633-2014, 2014
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We present the new vM21 MIPAS temperatures from 20 to 102km for all of its 2005-2012 MA, UA and NLC measurements. The main upgrades are the update of ESA L1b spectra, spectroscopic database and O and CO2 climatologies, and improvement in Tk-gradient and offset regularizations and apodization accuracy. The vM21 Tk's correct the main systematic errors of previous versions and lead to remarkable improvement in their comparisons with ACE-FTS, MLS, OSIRIS, SABER and SOFIE and the MLO and TMF lidars.
U. Hamann, A. Walther, B. Baum, R. Bennartz, L. Bugliaro, M. Derrien, P. N. Francis, A. Heidinger, S. Joro, A. Kniffka, H. Le Gléau, M. Lockhoff, H.-J. Lutz, J. F. Meirink, P. Minnis, R. Palikonda, R. Roebeling, A. Thoss, S. Platnick, P. Watts, and G. Wind
Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, https://doi.org/10.5194/amt-7-2839-2014, 2014
M. Rex, S. Kremser, P. Huck, G. Bodeker, I. Wohltmann, M. L. Santee, and P. Bernath
Atmos. Chem. Phys., 14, 6545–6555, https://doi.org/10.5194/acp-14-6545-2014, https://doi.org/10.5194/acp-14-6545-2014, 2014
S. M. Khaykin, I. Engel, H. Vömel, I. M. Formanyuk, R. Kivi, L. I. Korshunov, M. Krämer, A. D. Lykov, S. Meier, T. Naebert, M. C. Pitts, M. L. Santee, N. Spelten, F. G. Wienhold, V. A. Yushkov, and T. Peter
Atmos. Chem. Phys., 13, 11503–11517, https://doi.org/10.5194/acp-13-11503-2013, https://doi.org/10.5194/acp-13-11503-2013, 2013
G. Wind, A. M. da Silva, P. M. Norris, and S. Platnick
Geosci. Model Dev., 6, 2049–2062, https://doi.org/10.5194/gmd-6-2049-2013, https://doi.org/10.5194/gmd-6-2049-2013, 2013
I. Fiorucci, G. Muscari, L. Froidevaux, and M. L. Santee
Atmos. Meas. Tech., 6, 2441–2453, https://doi.org/10.5194/amt-6-2441-2013, https://doi.org/10.5194/amt-6-2441-2013, 2013
M. von Hobe, S. Bekki, S. Borrmann, F. Cairo, F. D'Amato, G. Di Donfrancesco, A. Dörnbrack, A. Ebersoldt, M. Ebert, C. Emde, I. Engel, M. Ern, W. Frey, S. Genco, S. Griessbach, J.-U. Grooß, T. Gulde, G. Günther, E. Hösen, L. Hoffmann, V. Homonnai, C. R. Hoyle, I. S. A. Isaksen, D. R. Jackson, I. M. Jánosi, R. L. Jones, K. Kandler, C. Kalicinsky, A. Keil, S. M. Khaykin, F. Khosrawi, R. Kivi, J. Kuttippurath, J. C. Laube, F. Lefèvre, R. Lehmann, S. Ludmann, B. P. Luo, M. Marchand, J. Meyer, V. Mitev, S. Molleker, R. Müller, H. Oelhaf, F. Olschewski, Y. Orsolini, T. Peter, K. Pfeilsticker, C. Piesch, M. C. Pitts, L. R. Poole, F. D. Pope, F. Ravegnani, M. Rex, M. Riese, T. Röckmann, B. Rognerud, A. Roiger, C. Rolf, M. L. Santee, M. Scheibe, C. Schiller, H. Schlager, M. Siciliani de Cumis, N. Sitnikov, O. A. Søvde, R. Spang, N. Spelten, F. Stordal, O. Sumińska-Ebersoldt, A. Ulanovski, J. Ungermann, S. Viciani, C. M. Volk, M. vom Scheidt, P. von der Gathen, K. Walker, T. Wegner, R. Weigel, S. Weinbruch, G. Wetzel, F. G. Wienhold, I. Wohltmann, W. Woiwode, I. A. K. Young, V. Yushkov, B. Zobrist, and F. Stroh
Atmos. Chem. Phys., 13, 9233–9268, https://doi.org/10.5194/acp-13-9233-2013, https://doi.org/10.5194/acp-13-9233-2013, 2013
B. J. Connor, T. Mooney, G. E. Nedoluha, J. W. Barrett, A. Parrish, J. Koda, M. L. Santee, and R. M. Gomez
Atmos. Chem. Phys., 13, 8643–8650, https://doi.org/10.5194/acp-13-8643-2013, https://doi.org/10.5194/acp-13-8643-2013, 2013
M. Khosravi, P. Baron, J. Urban, L. Froidevaux, A. I. Jonsson, Y. Kasai, K. Kuribayashi, C. Mitsuda, D. P. Murtagh, H. Sagawa, M. L. Santee, T. O. Sato, M. Shiotani, M. Suzuki, T. von Clarmann, K. A. Walker, and S. Wang
Atmos. Chem. Phys., 13, 7587–7606, https://doi.org/10.5194/acp-13-7587-2013, https://doi.org/10.5194/acp-13-7587-2013, 2013
N. J. Livesey, J. A. Logan, M. L. Santee, J. W. Waters, R. M. Doherty, W. G. Read, L. Froidevaux, and J. H. Jiang
Atmos. Chem. Phys., 13, 579–598, https://doi.org/10.5194/acp-13-579-2013, https://doi.org/10.5194/acp-13-579-2013, 2013
J. Liu, J. A. Logan, L. T. Murray, H. C. Pumphrey, M. J. Schwartz, and I. A. Megretskaia
Atmos. Chem. Phys., 13, 129–146, https://doi.org/10.5194/acp-13-129-2013, https://doi.org/10.5194/acp-13-129-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Retrieval of cloud fraction using machine learning algorithms based on FY-4A AGRI observations
PEAKO and peakTree: tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
An advanced spatial coregistration of cloud properties for the atmospheric Sentinel missions: application to TROPOMI
Contrail altitude estimation using GOES-16 ABI data and deep learning
The Ice Cloud Imager: retrieval of frozen water column properties
Supercooled liquid water cloud classification using lidar backscatter peak properties
Marine cloud base height retrieval from MODIS cloud properties using machine learning
How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Simulation and detection efficiency analysis for measurements of polar mesospheric clouds using a spaceborne wide-field-of-view ultraviolet imager
The Chalmers Cloud Ice Climatology: retrieval implementation and validation
The algorithm of microphysical-parameter profiles of aerosol and small cloud droplets based on the dual-wavelength lidar data
Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Severe hail detection with C-band dual-polarisation radars using convolutional neural networks
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
Retrieval of cloud fraction and optical thickness from multi-angle polarization observations
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
3-D Cloud Masking Across a Broad Swath using Multi-angle Polarimetry and Deep Learning
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2024-2090, https://doi.org/10.5194/egusphere-2024-2090, 2024
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This article presents a novel technique to estimate the liquid water content (LWC) in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows to retrieve the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of the LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
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This study demonstrates the potential for enhancing severe hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-87, https://doi.org/10.5194/amt-2024-87, 2024
Revised manuscript accepted for AMT
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1180, https://doi.org/10.5194/egusphere-2024-1180, 2024
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We introduce an innovative method to retrieve cloud fraction and optical thickness based on polarimetry, well-suited for satellite observations providing multi-angle polarization measurements. The cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and a second in the sun-glint region or at a scattering angle of approximately 90°.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40, https://doi.org/10.5194/amt-2024-40, 2024
Revised manuscript accepted for AMT
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we measured that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles, and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
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Short summary
In this study we present an improved cloud detection scheme for the Microwave Limb Sounder, which is based on a feedforward artificial neural network. This new algorithm is shown not only to reliably detect high and mid-level convection containing even small amounts of cloud water but also to distinguish between high-reaching and mid-level to low convection.
In this study we present an improved cloud detection scheme for the Microwave Limb Sounder,...
