Articles | Volume 13, issue 10
https://doi.org/10.5194/amt-13-5259-2020
© Author(s) 2020. 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-13-5259-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Oct 2020
Research article |
| 06 Oct 2020
| 06 Oct 2020
Cloud-top pressure retrieval with DSCOVR EPIC oxygen A- and B-band observations
Bangsheng Yin
Atmospheric Sciences Research Center, University at Albany, Albany,
NY, USA
Qilong Min
CORRESPONDING AUTHOR
Atmospheric Sciences Research Center, University at Albany, Albany,
NY, USA
Emily Morgan
Atmospheric Sciences Research Center, University at Albany, Albany,
NY, USA
Yuekui Yang
NASA Goddard Space Flight Center, Climate and Radiation Laboratory,
Greenbelt, MD, USA
Alexander Marshak
NASA Goddard Space Flight Center, Climate and Radiation Laboratory,
Greenbelt, MD, USA
Anthony B. Davis
Jet Propulsion Laboratory, California Institute of Technology,
Pasadena, CA, USA
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Siwei Li, Everette Joseph, Qilong Min, Bangsheng Yin, Ricardo Sakai, and Megan K. Payne
Atmos. Meas. Tech., 10, 2093–2104, https://doi.org/10.5194/amt-10-2093-2017, https://doi.org/10.5194/amt-10-2093-2017, 2017
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Monitoring fine aerosol concentration is important because of the adverse impacts of high fine-particle concentration on human health. However, monitoring fine aerosols is difficult during cloudy and nighttime periods. In this study, an empirical model using measurements from ceilometers was developed to measure fine aerosol mass concentration even under cloudy or nighttime conditions. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring.
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Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-18943-2014, https://doi.org/10.5194/acpd-14-18943-2014, 2014
Revised manuscript not accepted
Q. Min, B. Yin, S. Li, J. Berndt, L. Harrison, E. Joseph, M. Duan, and P. Kiedron
Atmos. Meas. Tech., 7, 1711–1722, https://doi.org/10.5194/amt-7-1711-2014, https://doi.org/10.5194/amt-7-1711-2014, 2014
Clark Jay Weaver, Jay Herman, Alexander Marshak, Steven R. Lorentz, Yinan Yu, Allan W. Smith, and Adam Szabo
EGUsphere, https://doi.org/10.5194/egusphere-2023-638, https://doi.org/10.5194/egusphere-2023-638, 2023
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We calculate the total amount of solar energy reflected by the earth from the EPIC camera onboard the DSCOVR satellite positioned 1.5 million km from earth. We compare it with another estimate of the reflected energy from the NISTAR instrument, that is also on the DSCOVR satellite. Both energy estimates agree within the uncertainties of each instrument. Finally, we compare with a third estimate of solar reflected energy from the CERES instruments that are on board low-earth orbit satellites.
Nick Gorkavyi, Nickolay Krotkov, and Alexander Marshak
Atmos. Meas. Tech., 16, 1527–1537, https://doi.org/10.5194/amt-16-1527-2023, https://doi.org/10.5194/amt-16-1527-2023, 2023
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The article discusses topical issues of the visible (libration) motion of the Earth in the sky of the Moon in a rectangle measuring 13.4° × 15.8°. On the one hand, the librations of the Moon make these observations difficult. On the other hand, they can be used as a natural scanning mechanism for cameras and spectroscopes mounted on a fixed platform on the surface of the Moon.
Ying-Chieh Chen, Sheng-Hsiang Wang, Qilong Min, Sarah Lu, Pay-Liam Lin, Neng-Huei Lin, Kao-Shan Chung, and Everette Joseph
Atmos. Chem. Phys., 21, 4487–4502, https://doi.org/10.5194/acp-21-4487-2021, https://doi.org/10.5194/acp-21-4487-2021, 2021
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In this study, we integrate satellite and surface observations to statistically quantify aerosol impacts on low-level warm-cloud microphysics and drizzle over northern Taiwan. Our result provides observational evidence for aerosol indirect effects. The frequency of drizzle is reduced under polluted conditions. For light-precipitation events (≤ 1 mm h-1), however, higher aerosol concentrations drive raindrops toward smaller sizes and thus increase the appearance of the drizzle drops.
Guoyong Wen, Alexander Marshak, Si-Chee Tsay, Jay Herman, Ukkyo Jeong, Nader Abuhassan, Robert Swap, and Dong Wu
Atmos. Chem. Phys., 20, 10477–10491, https://doi.org/10.5194/acp-20-10477-2020, https://doi.org/10.5194/acp-20-10477-2020, 2020
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We combine the ground-based observations and radiative transfer model to quantify the impact of the 2017 solar eclipse on surface shortwave irradiation reduction. We find that the eclipse caused local reductions of time-averaged surface flux of about 379 W m-2 (50 %) and 329 W m-2 (46 %) during the ~ 3 h course of the eclipse at the Casper and Columbia sites, respectively. We estimate that the Moon’s shadow caused a reduction of approximately 7 %–8 % in global average surface broadband SW radiation.
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Atmos. Meas. Tech., 13, 1575–1591, https://doi.org/10.5194/amt-13-1575-2020, https://doi.org/10.5194/amt-13-1575-2020, 2020
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Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and the bright and cold surfaces; the Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) has very limited channels. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios for cloud detection over snow and ice surfaces.
Jonathan K. P. Shonk, Jui-Yuan Christine Chiu, Alexander Marshak, David M. Giles, Chiung-Huei Huang, Gerald G. Mace, Sally Benson, Ilya Slutsker, and Brent N. Holben
Atmos. Meas. Tech., 12, 5087–5099, https://doi.org/10.5194/amt-12-5087-2019, https://doi.org/10.5194/amt-12-5087-2019, 2019
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Retrievals of cloud optical depth made using AERONET radiometers in “cloud mode” rely on the assumption that all cloud is liquid. The presence of ice cloud therefore introduces errors in the retrieved optical depth, which can be over 25 in optically thick ice clouds. However, such clouds are not frequent and the long-term mean optical depth error is about 3 for a sample of real clouds. A correction equation could improve the retrieval further, although this would require extra instrumentation.
Yuekui Yang, Kerry Meyer, Galina Wind, Yaping Zhou, Alexander Marshak, Steven Platnick, Qilong Min, Anthony B. Davis, Joanna Joiner, Alexander Vasilkov, David Duda, and Wenying Su
Atmos. Meas. Tech., 12, 2019–2031, https://doi.org/10.5194/amt-12-2019-2019, https://doi.org/10.5194/amt-12-2019-2019, 2019
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Matthew Gibbons, Qilong Min, and Jiwen Fan
Atmos. Chem. Phys., 18, 12161–12184, https://doi.org/10.5194/acp-18-12161-2018, https://doi.org/10.5194/acp-18-12161-2018, 2018
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The effects of dust aerosols on ice formation within a tropical Atlantic thunderstorm system were investigated using a 3-D weather model and compared with observations. Updated ice formation mechanisms directly connect available dust particles with ice particle formation. The resulting clouds were lower and narrower and produced less rain at the surface compared to cleaner conditions, due to ice formation occurring at warmer temperatures. These results agree well with observed changes.
Jay Herman, Guoyong Wen, Alexander Marshak, Karin Blank, Liang Huang, Alexander Cede, Nader Abuhassan, and Matthew Kowalewski
Atmos. Meas. Tech., 11, 4373–4388, https://doi.org/10.5194/amt-11-4373-2018, https://doi.org/10.5194/amt-11-4373-2018, 2018
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The DSCOVR/EPIC instrument located near the Lagrange 1 Earth–Sun gravitational balance point is able to view the entire sunlit disk of the Earth. This means that during the eclipse of 21 August 2017 EPIC was able to see the region of totality and the much larger region of partial eclipse. Because of this, EPIC is able to measure the global reduction of reflected solar flux. For the wavelength range 388 to 780 nm, we estimated a 10 % reduction in reflected radiation.
Xiaomei Lu, Yongxiang Hu, Yuekui Yang, Mark Vaughan, Zhaoyan Liu, Sharon Rodier, William Hunt, Kathy Powell, Patricia Lucker, and Charles Trepte
Atmos. Meas. Tech., 11, 3281–3296, https://doi.org/10.5194/amt-11-3281-2018, https://doi.org/10.5194/amt-11-3281-2018, 2018
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Igor V. Geogdzhayev and Alexander Marshak
Atmos. Meas. Tech., 11, 359–368, https://doi.org/10.5194/amt-11-359-2018, https://doi.org/10.5194/amt-11-359-2018, 2018
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Stephen P. Palm, Vinay Kayetha, Yuekui Yang, and Rebecca Pauly
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Siwei Li, Everette Joseph, Qilong Min, Bangsheng Yin, Ricardo Sakai, and Megan K. Payne
Atmos. Meas. Tech., 10, 2093–2104, https://doi.org/10.5194/amt-10-2093-2017, https://doi.org/10.5194/amt-10-2093-2017, 2017
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Monitoring fine aerosol concentration is important because of the adverse impacts of high fine-particle concentration on human health. However, monitoring fine aerosols is difficult during cloudy and nighttime periods. In this study, an empirical model using measurements from ceilometers was developed to measure fine aerosol mass concentration even under cloudy or nighttime conditions. The findings of this study illustrate the strong need for ceilometer data in air quality monitoring.
Jun Yang, Qilong Min, Weitao Lu, Ying Ma, Wen Yao, and Tianshu Lu
Atmos. Meas. Tech., 10, 1191–1201, https://doi.org/10.5194/amt-10-1191-2017, https://doi.org/10.5194/amt-10-1191-2017, 2017
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A big challenge for accurate cloud detection is the inhomogeneous brightness distribution of sky background, which mainly caused by the difference in atmospheric scattering angles. In this manuscript, we report a new RGB channel operation aiming to remove this inhomogeneous sky background in the total sky images, and then a cloud detection algorithm based on this new channel is proposed which combined the merits of the threshold and differencing methods.
Matthew Gibbons, Qilong Min, and Jiwen Fan
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2016-368, https://doi.org/10.5194/acp-2016-368, 2016
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Observations suggest cloud systems evolve differently under dusty conditions compared to other aerosols. We have used numerical modeling to study one such case. Dust increases the formation of small sized ice in the mid-troposphere. This enhanced convective intensity, shifted precipitation top height to higher altitudes, and glaciated clouds at lower altitudes. Consistent with observations, average cloud height was lowered due to a greater number of heavy particles forming near the cloud tops.
Kerry Meyer, Yuekui Yang, and Steven Platnick
Atmos. Meas. Tech., 9, 1785–1797, https://doi.org/10.5194/amt-9-1785-2016, https://doi.org/10.5194/amt-9-1785-2016, 2016
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This paper presents the expected uncertainties of a single-channel cloud opacity retrieval technique and a temperature-based cloud phase approach in support of the Deep Space Climate Observatory (DSCOVR) mission; DSCOVR cloud products will be derived from Earth Polychromatic Imaging Camera (EPIC) observations. Results show that, for ice clouds, retrieval errors are minimal (< 2 %), while for liquid clouds the error is limited to within 10 %, although for thin clouds the error can be higher.
Jun Yang, Qilong Min, Weitao Lu, Ying Ma, Wen Yao, Tianshu Lu, Juan Du, and Guangyi Liu
Atmos. Meas. Tech., 9, 587–597, https://doi.org/10.5194/amt-9-587-2016, https://doi.org/10.5194/amt-9-587-2016, 2016
J. Yang, Q. Min, W. Lu, W. Yao, Y. Ma, J. Du, T. Lu, and G. Liu
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K. Knobelspiesse, B. van Diedenhoven, A. Marshak, S. Dunagan, B. Holben, and I. Slutsker
Atmos. Meas. Tech., 8, 1537–1554, https://doi.org/10.5194/amt-8-1537-2015, https://doi.org/10.5194/amt-8-1537-2015, 2015
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We test if ground-based sun photometers/radiometers like those in the Aerosol Robotic Network (AERONET) can use the polarization sensitivity of some instruments for improved cloud optical property retrieval. Our radiative transfer simulations show that the direction of linear polarization indicates cloud thermodynamic phase for optically thin clouds. In practice, data analysis shows a weak response with AERONET instruments, most likely due to noise and orientation/calibration ambiguity.
S. Li, E. Joseph, Q. Min, and B. Yin
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-18943-2014, https://doi.org/10.5194/acpd-14-18943-2014, 2014
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Q. Min, B. Yin, S. Li, J. Berndt, L. Harrison, E. Joseph, M. Duan, and P. Kiedron
Atmos. Meas. Tech., 7, 1711–1722, https://doi.org/10.5194/amt-7-1711-2014, https://doi.org/10.5194/amt-7-1711-2014, 2014
J. Fan, L. R. Leung, P. J. DeMott, J. M. Comstock, B. Singh, D. Rosenfeld, J. M. Tomlinson, A. White, K. A. Prather, P. Minnis, J. K. Ayers, and Q. Min
Atmos. Chem. Phys., 14, 81–101, https://doi.org/10.5194/acp-14-81-2014, https://doi.org/10.5194/acp-14-81-2014, 2014
T. Várnai, A. Marshak, and W. Yang
Atmos. Chem. Phys., 13, 3899–3908, https://doi.org/10.5194/acp-13-3899-2013, https://doi.org/10.5194/acp-13-3899-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
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Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
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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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Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
EGUsphere, https://doi.org/10.5194/egusphere-2024-192, https://doi.org/10.5194/egusphere-2024-192, 2024
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This study proposed an inversion method of atmosphere 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. Compared with the previous study, this algorithm could quickly obtain the microphysical parameters of atmosphere particles and has good robustness.
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.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
EGUsphere, https://doi.org/10.5194/egusphere-2023-2345, https://doi.org/10.5194/egusphere-2023-2345, 2024
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We introduce ProPS – a new method to detect clouds and their thermodynamic phase using a geostationary satellite. It distinguishes between clear sky, ice, mixed-phase, supercooled and warm liquid clouds. ProPS uses a Bayesian approach with the lidar-radar product DARDAR as reference data. The new method allows studying different cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparisons with climate models.
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.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-252, https://doi.org/10.5194/amt-2023-252, 2024
Revised manuscript accepted for AMT
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The radar-lidar synergy is used to retrieve ice, supercooled water and mixed-phase clouds properties, making the most of the radar sensitivity to ice crystals and the lidar to the supercooled droplets. 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 Mace
EGUsphere, https://doi.org/10.5194/egusphere-2023-2606, https://doi.org/10.5194/egusphere-2023-2606, 2023
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The number of cloud droplets, 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 radiometer. We show that the deriving Nd is very uncertain although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
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.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
EGUsphere, https://doi.org/10.5194/egusphere-2023-1953, https://doi.org/10.5194/egusphere-2023-1953, 2023
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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 limited amount of observations of clouds that are currently available. Our work proposes to address this 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.
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.
Anja Hünerbein, Sebastian Bley, Stefan Horn, Hartwig Deneke, and Andi Walther
Atmos. Meas. Tech., 16, 2821–2836, https://doi.org/10.5194/amt-16-2821-2023, https://doi.org/10.5194/amt-16-2821-2023, 2023
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The Multi-Spectral Imager (MSI) on board the EarthCARE satellite will provide the information needed for describing the cloud and aerosol properties in the cross-track direction, complementing the measurements from the Cloud Profiling Radar, Atmospheric Lidar and Broad-Band Radiometer. The accurate discrimination between clear and cloudy pixels is an essential first step. Therefore, the cloud mask algorithm provides a cloud flag, cloud phase and cloud type product for the MSI observations.
Zhipeng Qu, Howard W. Barker, Jason N. S. Cole, and Mark W. Shephard
Atmos. Meas. Tech., 16, 2319–2331, https://doi.org/10.5194/amt-16-2319-2023, https://doi.org/10.5194/amt-16-2319-2023, 2023
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This paper describes EarthCARE’s L2 product ACM-3D. It includes the scene construction algorithm (SCA) used to produce the indexes for reconstructing 3D atmospheric scene based on satellite nadir retrievals. It also provides the information about the buffer zone sizes of 3D assessment domains and the ranking scores for selecting the best 3D assessment domains. These output variables are needed to run 3D radiative transfer models for the radiative closure assessment of EarthCARE’s L2 retrievals.
Steven T. Massie, Heather Cronk, Aronne Merrelli, Sebastian Schmidt, and Steffen Mauceri
Atmos. Meas. Tech., 16, 2145–2166, https://doi.org/10.5194/amt-16-2145-2023, https://doi.org/10.5194/amt-16-2145-2023, 2023
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This paper provides insights into the effects of clouds on Orbiting Carbon Observatory (OCO-2) measurements of CO2. Calculations are carried out that indicate the extent to which this satellite experiment underestimates CO2, due to these cloud effects, as a function of the distance between the surface observation footprint and the nearest cloud. The paper discusses how to lessen the influence of these cloud effects.
Armin Blanke, Andrew J. Heymsfield, Manuel Moser, and Silke Trömel
Atmos. Meas. Tech., 16, 2089–2106, https://doi.org/10.5194/amt-16-2089-2023, https://doi.org/10.5194/amt-16-2089-2023, 2023
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We present an evaluation of current retrieval techniques in the ice phase applied to polarimetric radar measurements with collocated in situ observations of aircraft conducted over the Olympic Mountains, Washington State, during winter 2015. Radar estimates of ice properties agreed most with aircraft observations in regions with pronounced radar signatures, but uncertainties were identified that indicate issues of some retrievals, particularly in warmer temperature regimes.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 1803–1847, https://doi.org/10.5194/amt-16-1803-2023, https://doi.org/10.5194/amt-16-1803-2023, 2023
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We describe a new method for measuring the 3D spatial variations in water within clouds using the reflected light of the Sun viewed at multiple different angles by satellites. This is a great improvement over 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.
Leilei Kou, Zhengjian Lin, Haiyang Gao, Shujun Liao, and Piman Ding
Atmos. Meas. Tech., 16, 1723–1744, https://doi.org/10.5194/amt-16-1723-2023, https://doi.org/10.5194/amt-16-1723-2023, 2023
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Forward modeling of spaceborne millimeter-wave radar composed of eight submodules is presented. We quantify the uncertainties in radar reflectivity that may be caused by the physical model parameters via a sensitivity analysis. The simulations with improved and conventional settings are compared with CloudSat data, and the simulation results are evaluated and analyzed. The results are instructive to the optimization of forward modeling and microphysical parameter retrieval.
Heike Kalesse-Los, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn
Atmos. Meas. Tech., 16, 1683–1704, https://doi.org/10.5194/amt-16-1683-2023, https://doi.org/10.5194/amt-16-1683-2023, 2023
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The Virga-Sniffer, a new modular open-source Python package tool to characterize full precipitation evaporation (so-called virga) from ceilometer cloud base height and vertically pointing cloud radar reflectivity time–height fields, is described. Results of its first application to RV Meteor observations during the EUREC4A field experiment in January–February 2020 are shown. About half of all detected clouds with bases below the trade inversion height were found to produce virga.
Yulan Hong, Stephen W. Nesbitt, Robert J. Trapp, and Larry Di Girolamo
Atmos. Meas. Tech., 16, 1391–1406, https://doi.org/10.5194/amt-16-1391-2023, https://doi.org/10.5194/amt-16-1391-2023, 2023
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Deep convective updrafts form overshooting tops (OTs) when they extend into the upper troposphere and lower stratosphere. An OT often indicates hazardous weather conditions. The global distribution of OTs is useful for understanding global severe weather conditions. The Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra satellites provides 2 decades of records on the Earth–atmosphere system with stable orbits, which are used in this study to derive 20-year OT climatology.
Pragya Vishwakarma, Julien Delanoë, Susana Jorquera, Pauline Martinet, Frederic Burnet, Alistair Bell, and Jean-Charles Dupont
Atmos. Meas. Tech., 16, 1211–1237, https://doi.org/10.5194/amt-16-1211-2023, https://doi.org/10.5194/amt-16-1211-2023, 2023
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Cloud observations are necessary to characterize the cloud properties at local and global scales. The observations must be translated to cloud geophysical parameters. This paper presents the estimation of liquid water content (LWC) using radar and microwave radiometer (MWR) measurements. Liquid water path from MWR scales LWC and retrieves the scaling factor (ln a). The retrievals are compared with in situ observations. A climatology of ln a is built to estimate LWC using only radar information.
Bhupendra A. Raut, Paytsar Muradyan, Rajesh Sankaran, Robert C. Jackson, Seongha Park, Sean A. Shahkarami, Dario Dematties, Yongho Kim, Joseph Swantek, Neal Conrad, Wolfgang Gerlach, Sergey Shemyakin, Pete Beckman, Nicola J. Ferrier, and Scott M. Collis
Atmos. Meas. Tech., 16, 1195–1209, https://doi.org/10.5194/amt-16-1195-2023, https://doi.org/10.5194/amt-16-1195-2023, 2023
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We studied the stability of a blockwise phase correlation (PC) method to estimate cloud motion using a total sky imager (TSI). Shorter frame intervals and larger block sizes improve stability, while image resolution and color channels have minor effects. Raindrop contamination can be identified by the rotational motion of the TSI mirror. The correlations of cloud motion vectors (CMVs) from the PC method with wind data vary from 0.38 to 0.59. Optical flow vectors are more stable than PC vectors.
William K. Jones, Matthew W. Christensen, and Philip Stier
Atmos. Meas. Tech., 16, 1043–1059, https://doi.org/10.5194/amt-16-1043-2023, https://doi.org/10.5194/amt-16-1043-2023, 2023
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Geostationary weather satellites have been used to detect storm clouds since their earliest applications. However, this task remains difficult as imaging satellites cannot observe the strong vertical winds that are characteristic of storm clouds. Here we introduce a new method that allows us to detect the early development of storms and continue to track them throughout their lifetime, allowing us to study how their early behaviour affects subsequent weather.
Andrew M. Sayer, Luca Lelli, Brian Cairns, Bastiaan van Diedenhoven, Amir Ibrahim, Kirk D. Knobelspiesse, Sergey Korkin, and P. Jeremy Werdell
Atmos. Meas. Tech., 16, 969–996, https://doi.org/10.5194/amt-16-969-2023, https://doi.org/10.5194/amt-16-969-2023, 2023
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This paper presents a method to estimate the height of the top of clouds above Earth's surface using satellite measurements. It is based on light absorption by oxygen in Earth's atmosphere, which darkens the signal that a satellite will see at certain wavelengths of light. Clouds "shield" the satellite from some of this darkening, dependent on cloud height (and other factors), because clouds scatter light at these wavelengths. The method will be applied to the future NASA PACE mission.
Veronika Pörtge, Tobias Kölling, Anna Weber, Lea Volkmer, Claudia Emde, Tobias Zinner, Linda Forster, and Bernhard Mayer
Atmos. Meas. Tech., 16, 645–667, https://doi.org/10.5194/amt-16-645-2023, https://doi.org/10.5194/amt-16-645-2023, 2023
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In this work, we analyze polarized cloudbow observations by the airborne camera system specMACS to retrieve the cloud droplet size distribution defined by the effective radius (reff) and the effective variance (veff). Two case studies of trade-wind cumulus clouds observed during the EUREC4A field campaign are presented. The results are combined into maps of reff and veff with a very high spatial resolution (100 m × 100 m) that allow new insights into cloud microphysics.
Minrui Wang, Takashi Y. Nakajima, Woosub Roh, Masaki Satoh, Kentaroh Suzuki, Takuji Kubota, and Mayumi Yoshida
Atmos. Meas. Tech., 16, 603–623, https://doi.org/10.5194/amt-16-603-2023, https://doi.org/10.5194/amt-16-603-2023, 2023
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SMILE (a spectral misalignment in which a shift in the center wavelength appears as a distortion in the spectral image) was detected during our recent work. To evaluate how it affects the cloud retrieval products, we did a simulation of EarthCARE-MSI forward radiation, evaluating the error in simulated scenes from a global cloud system-resolving model and a satellite simulator. Our results indicated that the error from SMILE was generally small and negligible for oceanic scenes.
Ming Li, Husi Letu, Hiroshi Ishimoto, Shulei Li, Lei Liu, Takashi Y. Nakajima, Dabin Ji, Huazhe Shang, and Chong Shi
Atmos. Meas. Tech., 16, 331–353, https://doi.org/10.5194/amt-16-331-2023, https://doi.org/10.5194/amt-16-331-2023, 2023
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Influenced by the representativeness of ice crystal scattering models, the existing terahertz ice cloud remote sensing inversion algorithms still have significant uncertainties. We developed an ice cloud remote sensing retrieval algorithm of the ice water path and particle size from aircraft-based terahertz radiation measurements based on the Voronoi model. Validation revealed that the Voronoi model performs better than the sphere and hexagonal column models.
Yoonjin Lee, Christian D. Kummerow, and Milija Zupanski
Atmos. Meas. Tech., 15, 7119–7136, https://doi.org/10.5194/amt-15-7119-2022, https://doi.org/10.5194/amt-15-7119-2022, 2022
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Vertical profiles of latent heating are derived from GOES-16 to be used in convective initialization. They are compared with other latent heating products derived from NEXRAD and GPM satellites, and the results show that their values are very similar to the radar-derived products. Finally, using latent heating derived from GOES-16 for convective initialization shows improvements in precipitation forecasts, which are comparable to the results using latent heating derived from NEXRAD.
Simon Whitburn, Lieven Clarisse, Marc Crapeau, Thomas August, Tim Hultberg, Pierre François Coheur, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 6653–6668, https://doi.org/10.5194/amt-15-6653-2022, https://doi.org/10.5194/amt-15-6653-2022, 2022
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With more than 15 years of measurements, the IASI radiance dataset is becoming a reference climate data record. Its exploitation for satellite applications requires an accurate and unbiased detection of cloud scenes. Here, we present a new cloud detection algorithm for IASI that is both sensitive and consistent over time. It is based on the use of a neural network, relying on IASI radiance information only and taking as a reference the last version of the operational IASI L2 cloud product.
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Short summary
Cloud-top pressure (CTP) is an important cloud property for climate and weather studies. Based on differential oxygen absorption, both oxygen A-band and B-band pairs can be used to retrieve CTP. However, it is currently very challenging to perform a CTP retrieval accurately due to the complicated in-cloud penetration effect. To address this issue, we propose an analytic transfer inverse model for DSCOVR EPIC observations to retrieve CTP considering in-cloud photon penetration.
Cloud-top pressure (CTP) is an important cloud property for climate and weather studies. Based...
