Articles | Volume 8, issue 11
https://doi.org/10.5194/amt-8-4931-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-4931-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Impact of cloud horizontal inhomogeneity and directional sampling on the retrieval of cloud droplet size by the POLDER instrument
H. Shang
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
College of Resources and Environment, University of the Chinese Academy of Sciences, Beijing, China
L. Chen
CORRESPONDING AUTHOR
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
F. M. Bréon
Laboratoire des Sciences du Climat et de l'Environnement, UMR CEA-CNRS-UVSQ, Gif-Sur-Yvette, France
H. Letu
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Research and Information Center, Tokai University, Tokyo, Japan
S. Li
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
Z. Wang
Satellite Environment Center, Ministry of Environmental Protection, Beijing, China
L. Su
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China
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Ziming Wang, Husi Letu, Huazhe Shang, and Luca Bugliaro
Atmos. Chem. Phys., 24, 7559–7574, https://doi.org/10.5194/acp-24-7559-2024, https://doi.org/10.5194/acp-24-7559-2024, 2024
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The supercooled liquid fraction (SLF) in mixed-phase clouds is retrieved for the first time using passive geostationary satellite observations based on differences in liquid droplet and ice particle radiative properties. The retrieved results are comparable to global distributions observed by active instruments, and the feasibility of the retrieval method to analyze the observed trends of the SLF has been validated.
Huazhe Shang, Souichiro Hioki, Guillaume Penide, Céline Cornet, Husi Letu, and Jérôme Riedi
Atmos. Chem. Phys., 23, 2729–2746, https://doi.org/10.5194/acp-23-2729-2023, https://doi.org/10.5194/acp-23-2729-2023, 2023
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We find that cloud profiles can be divided into four prominent patterns, and the frequency of these four patterns is related to intensities of cloud-top entrainment and precipitation. Based on these analyses, we further propose a cloud profile parameterization scheme allowing us to represent these patterns. Our results shed light on how to facilitate the representation of cloud profiles and how to link them to cloud entrainment or precipitating status in future remote-sensing applications.
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.
H. Shang, H. Letu, Z. Peng, and Z. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W5, 61–66, https://doi.org/10.5194/isprs-archives-XLII-3-W5-61-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W5-61-2018, 2018
Q. Gao, D. Dong, X. Yang, L. Husi, and H. Shang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 363–370, https://doi.org/10.5194/isprs-archives-XLII-3-363-2018, https://doi.org/10.5194/isprs-archives-XLII-3-363-2018, 2018
Jinghui Lian, Olivier Laurent, Mali Chariot, Luc Lienhardt, Michel Ramonet, Hervé Utard, Thomas Lauvaux, François-Marie Bréon, Grégoire Broquet, Karina Cucchi, Laurent Millair, and Philippe Ciais
Atmos. Meas. Tech., 17, 5821–5839, https://doi.org/10.5194/amt-17-5821-2024, https://doi.org/10.5194/amt-17-5821-2024, 2024
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We have designed and deployed a mid-cost medium-precision CO2 sensor monitoring network in Paris since July 2020. The data are automatically calibrated by a newly implemented data processing system. The accuracies of the mid-cost instruments vary from 1.0 to 2.4 ppm for hourly afternoon measurements. Our model–data analyses highlight prospects for integrating mid-cost instrument data with high-precision measurements to improve fine-scale CO2 emission quantification in urban areas.
Josselin Doc, Michel Ramonet, François-Marie Bréon, Delphine Combaz, Mali Chariot, Morgan Lopez, Marc Delmotte, Cristelle Cailteau-Fischbach, Guillaume Nief, Nathanaël Laporte, Thomas Lauvaux, and Philippe Ciais
EGUsphere, https://doi.org/10.5194/egusphere-2024-2826, https://doi.org/10.5194/egusphere-2024-2826, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Description of the network for measuring greenhouse gas concentrations in the Paris region and analysis of eight years of continuous monitoring.
Ziming Wang, Husi Letu, Huazhe Shang, and Luca Bugliaro
Atmos. Chem. Phys., 24, 7559–7574, https://doi.org/10.5194/acp-24-7559-2024, https://doi.org/10.5194/acp-24-7559-2024, 2024
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The supercooled liquid fraction (SLF) in mixed-phase clouds is retrieved for the first time using passive geostationary satellite observations based on differences in liquid droplet and ice particle radiative properties. The retrieved results are comparable to global distributions observed by active instruments, and the feasibility of the retrieval method to analyze the observed trends of the SLF has been validated.
Alexandre Danjou, Grégoire Broquet, Andrew Schuh, François-Marie Bréon, and Thomas Lauvaux
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2023-199, https://doi.org/10.5194/amt-2023-199, 2023
Revised manuscript has not been submitted
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We study the capacity of XCO2 space-borne imagery to estimate urban CO2 emissions with synthetic data. We define automatic and standard methods, and objective criteria for image selection. Wind variability and urban emission budget guide the emission estimation error. Images with low wind variability and high urban emissions account for 47 % of images and give a bias on the emission estimation of -7 % of the emissions and a spread of 56 %. Other images give a bias of -31 % and a spread of 99 %.
Jinghui Lian, Thomas Lauvaux, Hervé Utard, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, Olivier Laurent, Ivonne Albarus, Mali Chariot, Simone Kotthaus, Martial Haeffelin, Olivier Sanchez, Olivier Perrussel, Hugo Anne Denier van der Gon, Stijn Nicolaas Camiel Dellaert, and Philippe Ciais
Atmos. Chem. Phys., 23, 8823–8835, https://doi.org/10.5194/acp-23-8823-2023, https://doi.org/10.5194/acp-23-8823-2023, 2023
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Huazhe Shang, Souichiro Hioki, Guillaume Penide, Céline Cornet, Husi Letu, and Jérôme Riedi
Atmos. Chem. Phys., 23, 2729–2746, https://doi.org/10.5194/acp-23-2729-2023, https://doi.org/10.5194/acp-23-2729-2023, 2023
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We find that cloud profiles can be divided into four prominent patterns, and the frequency of these four patterns is related to intensities of cloud-top entrainment and precipitation. Based on these analyses, we further propose a cloud profile parameterization scheme allowing us to represent these patterns. Our results shed light on how to facilitate the representation of cloud profiles and how to link them to cloud entrainment or precipitating status in future remote-sensing applications.
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.
Elise Potier, Grégoire Broquet, Yilong Wang, Diego Santaren, Antoine Berchet, Isabelle Pison, Julia Marshall, Philippe Ciais, François-Marie Bréon, and Frédéric Chevallier
Atmos. Meas. Tech., 15, 5261–5288, https://doi.org/10.5194/amt-15-5261-2022, https://doi.org/10.5194/amt-15-5261-2022, 2022
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Atmospheric inversion at local–regional scales over Europe and pseudo-data assimilation are used to evaluate how CO2 and 14CO2 ground-based measurement networks could complement satellite CO2 imagers to monitor fossil fuel (FF) CO2 emissions. This combination significantly improves precision in the FF emission estimates in areas with a dense network but does not strongly support the separation of the FF from the biogenic signals or the spatio-temporal extrapolation of the satellite information.
François-Marie Bréon, Leslie David, Pierre Chatelanaz, and Frédéric Chevallier
Atmos. Meas. Tech., 15, 5219–5234, https://doi.org/10.5194/amt-15-5219-2022, https://doi.org/10.5194/amt-15-5219-2022, 2022
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The estimate of atmospheric CO2 from space measurement is difficult. Current methods are based on a detailed description of the atmospheric radiative transfer. These are affected by significant biases and errors and are very computer intensive. Instead we have proposed using a neural network approach. A first attempt led to confusing results. Here we provide an interpretation for these results and describe a new version that leads to high-quality estimates.
Runmei Ma, Jie Ban, Qing Wang, Yayi Zhang, Yang Yang, Shenshen Li, Wenjiao Shi, Zhen Zhou, Jiawei Zang, and Tiantian Li
Earth Syst. Sci. Data, 14, 943–954, https://doi.org/10.5194/essd-14-943-2022, https://doi.org/10.5194/essd-14-943-2022, 2022
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We constructed multi-variable random forest models based on 10-fold cross-validation and estimated daily PM2.5 and O3 concentration of China in 2005–2017 at a resolution of 1 km. The daily R2 values of PM2.5 and O3 were 0.85 and 0.77. The meteorological variables can significantly affect both PM2.5 and O3 modeling. During 2005–2017, PM2.5 exhibited an overall downward trend, while O3 experienced the opposite. The temporal trend of PM2.5 and O3 had spatial characteristics during the study period.
Jinghui Lian, François-Marie Bréon, Grégoire Broquet, Thomas Lauvaux, Bo Zheng, Michel Ramonet, Irène Xueref-Remy, Simone Kotthaus, Martial Haeffelin, and Philippe Ciais
Atmos. Chem. Phys., 21, 10707–10726, https://doi.org/10.5194/acp-21-10707-2021, https://doi.org/10.5194/acp-21-10707-2021, 2021
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Currently there is growing interest in monitoring city-scale CO2 emissions based on atmospheric CO2 measurements, atmospheric transport modeling, and inversion technique. We analyze the various sources of uncertainty that impact the atmospheric CO2 modeling and that may compromise the potential of this method for the monitoring of CO2 emission over Paris. Results suggest selection criteria for the assimilation of CO2 measurements into the inversion system that aims at retrieving city emissions.
Diego Santaren, Grégoire Broquet, François-Marie Bréon, Frédéric Chevallier, Denis Siméoni, Bo Zheng, and Philippe Ciais
Atmos. Meas. Tech., 14, 403–433, https://doi.org/10.5194/amt-14-403-2021, https://doi.org/10.5194/amt-14-403-2021, 2021
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Atmospheric transport inversions with synthetic data are used to assess the potential of new satellite observations of atmospheric CO2 to monitor anthropogenic emissions from regions, cities and large industrial plants. The analysis, applied to a large ensemble of sources in western Europe, shows a strong dependence of the results on different characteristics of the spaceborne instrument, on the source emission budgets and spreads, and on the wind conditions.
Leslie David, François-Marie Bréon, and Frédéric Chevallier
Atmos. Meas. Tech., 14, 117–132, https://doi.org/10.5194/amt-14-117-2021, https://doi.org/10.5194/amt-14-117-2021, 2021
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This paper shows that a neural network (NN) approach can be used to process spaceborne observations from the OCO-2 satellite and retrieve both surface pressure and atmospheric CO2 content. The accuracy evaluation indicates that the retrievals have an accuracy that is at least as good as those of the operational approach, which relies on complex algorithms and is computer intensive. The NN approach is therefore a promising alternative for the processing of CO2-monitoring missions.
Yilong Wang, Grégoire Broquet, François-Marie Bréon, Franck Lespinas, Michael Buchwitz, Maximilian Reuter, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, and Philippe Ciais
Geosci. Model Dev., 13, 5813–5831, https://doi.org/10.5194/gmd-13-5813-2020, https://doi.org/10.5194/gmd-13-5813-2020, 2020
Jean-Loup Bertaux, Alain Hauchecorne, Franck Lefèvre, François-Marie Bréon, Laurent Blanot, Denis Jouglet, Pierre Lafrique, and Pavel Akaev
Atmos. Meas. Tech., 13, 3329–3374, https://doi.org/10.5194/amt-13-3329-2020, https://doi.org/10.5194/amt-13-3329-2020, 2020
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Monitoring of greenhouse gases from space is usually done by measuring the quantity of CO2 and O2 in the atmosphere from their spectral absorption imprinted on the solar spectrum backscattered upwards. We show that the use of the near-infrared band of O2 at 1.27 µm, instead of the O2 band at 0.76 nm used up to now, may be more appropriate to better account for aerosols, in spite of a known airglow emission from ozone. The climate space mission MicroCarb (launched in 2021) includes this new band.
Jinghui Lian, François-Marie Bréon, Grégoire Broquet, T. Scott Zaccheo, Jeremy Dobler, Michel Ramonet, Johannes Staufer, Diego Santaren, Irène Xueref-Remy, and Philippe Ciais
Atmos. Chem. Phys., 19, 13809–13825, https://doi.org/10.5194/acp-19-13809-2019, https://doi.org/10.5194/acp-19-13809-2019, 2019
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CO2 emissions within urban areas impact nearby and downwind concentrations. A different system, based on bi-wavelength laser measurements, has been deployed over Paris. It samples CO2 concentrations along horizontal lines, between a transceiver and a reflector. In this paper, we analyze the measurements provided by this system, together with the more classical in situ sampling and high-resolution modeling. We focus on the temporal and spatial variability of atmospheric CO2 concentrations.
Yilong Wang, Philippe Ciais, Grégoire Broquet, François-Marie Bréon, Tomohiro Oda, Franck Lespinas, Yasjka Meijer, Armin Loescher, Greet Janssens-Maenhout, Bo Zheng, Haoran Xu, Shu Tao, Kevin R. Gurney, Geoffrey Roest, Diego Santaren, and Yongxian Su
Earth Syst. Sci. Data, 11, 687–703, https://doi.org/10.5194/essd-11-687-2019, https://doi.org/10.5194/essd-11-687-2019, 2019
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We address the question of the global characterization of fossil fuel CO2 emission hotspots that may cause coherent XCO2 plumes in space-borne CO2 images, based on the ODIAC global high-resolution 1 km fossil fuel emission data product. For space imagery with 0.5 ppm precision for a single XCO2 measurement, a total of 11 314 hotspots are identified, covering 72 % of the global emissions. These hotspots define the targets for the purpose of monitoring fossil fuel CO2 emissions from space.
H. Shang, H. Letu, Z. Peng, and Z. Wang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W5, 61–66, https://doi.org/10.5194/isprs-archives-XLII-3-W5-61-2018, https://doi.org/10.5194/isprs-archives-XLII-3-W5-61-2018, 2018
Q. Gao, D. Dong, X. Yang, L. Husi, and H. Shang
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 363–370, https://doi.org/10.5194/isprs-archives-XLII-3-363-2018, https://doi.org/10.5194/isprs-archives-XLII-3-363-2018, 2018
Y. Si, S. Li, L. Chen, C. Yu, and W. Zhu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 1553–1564, https://doi.org/10.5194/isprs-archives-XLII-3-1553-2018, https://doi.org/10.5194/isprs-archives-XLII-3-1553-2018, 2018
Irène Xueref-Remy, Elsa Dieudonné, Cyrille Vuillemin, Morgan Lopez, Christine Lac, Martina Schmidt, Marc Delmotte, Frédéric Chevallier, François Ravetta, Olivier Perrussel, Philippe Ciais, François-Marie Bréon, Grégoire Broquet, Michel Ramonet, T. Gerard Spain, and Christophe Ampe
Atmos. Chem. Phys., 18, 3335–3362, https://doi.org/10.5194/acp-18-3335-2018, https://doi.org/10.5194/acp-18-3335-2018, 2018
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Urbanized and industrialized areas are the largest source of fossil CO2. This work analyses the atmospheric CO2 variability observed from the first in situ network deployed in the Paris megacity area. Gradients of several ppm are found between the rural, peri-urban and urban sites at the diurnal to the seasonal scales. Wind direction and speed as well as boundary layer dynamics, correlated to highly variable urban emissions, are shown to be key regulator factors of the observed CO2 records.
Grégoire Broquet, François-Marie Bréon, Emmanuel Renault, Michael Buchwitz, Maximilian Reuter, Heinrich Bovensmann, Frédéric Chevallier, Lin Wu, and Philippe Ciais
Atmos. Meas. Tech., 11, 681–708, https://doi.org/10.5194/amt-11-681-2018, https://doi.org/10.5194/amt-11-681-2018, 2018
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This study assesses the potential of space-borne imagery of CO2 atmospheric concentrations for monitoring the emissions from the Paris area. Such imagery could be provided by European and American missions in the next decade. It highlights the difficulty to improve current knowledge on CO2 emissions for urban areas with CO2 observations from satellites, and calls for more technological innovations in the remote sensing of CO2 and in the models that exploit it.
Francois-Marie Breon and Fabienne Maignan
Earth Syst. Sci. Data, 9, 31–45, https://doi.org/10.5194/essd-9-31-2017, https://doi.org/10.5194/essd-9-31-2017, 2017
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We have used a large database of multidirectional land surface reflectance measured from space, including polarization properties, to build a database of representative BRDFs and BPDFs. This database can be used to assess the variability in land surface reflectances, in particular their directional and polarization signatures, and to evaluate models. We have also built an interactive tool for an easy analysis of the database contents.
Johannes Staufer, Grégoire Broquet, François-Marie Bréon, Vincent Puygrenier, Frédéric Chevallier, Irène Xueref-Rémy, Elsa Dieudonné, Morgan Lopez, Martina Schmidt, Michel Ramonet, Olivier Perrussel, Christine Lac, Lin Wu, and Philippe Ciais
Atmos. Chem. Phys., 16, 14703–14726, https://doi.org/10.5194/acp-16-14703-2016, https://doi.org/10.5194/acp-16-14703-2016, 2016
Mingmin Zou, Xiaozhen Xiong, Naoko Saitoh, Juying Warner, Ying Zhang, Liangfu Chen, Fuzhong Weng, and Meng Fan
Atmos. Meas. Tech., 9, 3567–3576, https://doi.org/10.5194/amt-9-3567-2016, https://doi.org/10.5194/amt-9-3567-2016, 2016
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Both AIRS and GOSAT TANSO-FTS TIR have been used to retrieve atmosphere CH4. The purpose of an intercomparison is to provide useful information to users on the characteristics of these two different products when they investigate the spatial and temporal variation of CH4. On the other hand, AIRS V6 CH4 data were already validated by comparing with aircraft data. This intercomparison is also a kind of indirect validation to GOSAT data.
Q. Xiao, H. Zhang, M. Choi, S. Li, S. Kondragunta, J. Kim, B. Holben, R. C. Levy, and Y. Liu
Atmos. Chem. Phys., 16, 1255–1269, https://doi.org/10.5194/acp-16-1255-2016, https://doi.org/10.5194/acp-16-1255-2016, 2016
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Using ground AOD measurements from AERONET, DRAGON-Asia Campaign, and handheld sunphotometers, we evaluated emerging aerosol products from VIIRS, GOCI, and Terra and Aqua MODIS (Collection 6) in East Asia in 2012–2013. We found that satellite aerosol products performed better in tracking the day-to-day variability than the high-resolution spatial variability. VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3 km products had positive biases.
N. MacBean, F. Maignan, P. Peylin, C. Bacour, F.-M. Bréon, and P. Ciais
Biogeosciences, 12, 7185–7208, https://doi.org/10.5194/bg-12-7185-2015, https://doi.org/10.5194/bg-12-7185-2015, 2015
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Previous model evaluation studies have shown that terrestrial biosphere models (TBMs) need a better representation of the leaf phenology, but the model deficiency could be related to incorrect model parameters or inaccurate model structure. This paper presents a framework for optimising the parameters of phenology models that are commonly used in TBMs. It further demonstrates that the optimisation can result in changes to trends in vegetation productivity and an improvement in gross C fluxes.
S. Li, R. Kahn, M. Chin, M. J. Garay, and Y. Liu
Atmos. Meas. Tech., 8, 1157–1171, https://doi.org/10.5194/amt-8-1157-2015, https://doi.org/10.5194/amt-8-1157-2015, 2015
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We demonstrate a post-processing technique to improve MISR-retrieved aerosol optical properties when information content is low. By filtering the list of aerosol mixtures that pass the MISR retrieval acceptance criteria using pre-defined discrepancy thresholds between MISR and GOCART model simulations, the adjusted MISR Angstrom exponent (ANG) and absorbing AOD (AAOD) agree significantly better with sun-photometer validation data, especially when AOD<0.2 for ANG and AOD<0.5 for AAOD.
F. M. Bréon, G. Broquet, V. Puygrenier, F. Chevallier, I. Xueref-Remy, M. Ramonet, E. Dieudonné, M. Lopez, M. Schmidt, O. Perrussel, and P. Ciais
Atmos. Chem. Phys., 15, 1707–1724, https://doi.org/10.5194/acp-15-1707-2015, https://doi.org/10.5194/acp-15-1707-2015, 2015
N. V. Rokotyan, V. I. Zakharov, K. G. Gribanov, M. Schneider, F.-M. Bréon, J. Jouzel, R. Imasu, M. Werner, M. Butzin, C. Petri, T. Warneke, and J. Notholt
Atmos. Meas. Tech., 7, 2567–2580, https://doi.org/10.5194/amt-7-2567-2014, https://doi.org/10.5194/amt-7-2567-2014, 2014
P. Ciais, A. J. Dolman, A. Bombelli, R. Duren, A. Peregon, P. J. Rayner, C. Miller, N. Gobron, G. Kinderman, G. Marland, N. Gruber, F. Chevallier, R. J. Andres, G. Balsamo, L. Bopp, F.-M. Bréon, G. Broquet, R. Dargaville, T. J. Battin, A. Borges, H. Bovensmann, M. Buchwitz, J. Butler, J. G. Canadell, R. B. Cook, R. DeFries, R. Engelen, K. R. Gurney, C. Heinze, M. Heimann, A. Held, M. Henry, B. Law, S. Luyssaert, J. Miller, T. Moriyama, C. Moulin, R. B. Myneni, C. Nussli, M. Obersteiner, D. Ojima, Y. Pan, J.-D. Paris, S. L. Piao, B. Poulter, S. Plummer, S. Quegan, P. Raymond, M. Reichstein, L. Rivier, C. Sabine, D. Schimel, O. Tarasova, R. Valentini, R. Wang, G. van der Werf, D. Wickland, M. Williams, and C. Zehner
Biogeosciences, 11, 3547–3602, https://doi.org/10.5194/bg-11-3547-2014, https://doi.org/10.5194/bg-11-3547-2014, 2014
M. Pommier, J.-L. Lacour, C. Risi, F. M. Bréon, C. Clerbaux, P.-F. Coheur, K. Gribanov, D. Hurtmans, J. Jouzel, and V. Zakharov
Atmos. Meas. Tech., 7, 1581–1595, https://doi.org/10.5194/amt-7-1581-2014, https://doi.org/10.5194/amt-7-1581-2014, 2014
J. Otto, D. Berveiller, F.-M. Bréon, N. Delpierre, G. Geppert, A. Granier, W. Jans, A. Knohl, A. Kuusk, B. Longdoz, E. Moors, M. Mund, B. Pinty, M.-J. Schelhaas, and S. Luyssaert
Biogeosciences, 11, 2411–2427, https://doi.org/10.5194/bg-11-2411-2014, https://doi.org/10.5194/bg-11-2411-2014, 2014
G. Broquet, F. Chevallier, F.-M. Bréon, N. Kadygrov, M. Alemanno, F. Apadula, S. Hammer, L. Haszpra, F. Meinhardt, J. A. Morguí, J. Necki, S. Piacentino, M. Ramonet, M. Schmidt, R. L. Thompson, A. T. Vermeulen, C. Yver, and P. Ciais
Atmos. Chem. Phys., 13, 9039–9056, https://doi.org/10.5194/acp-13-9039-2013, https://doi.org/10.5194/acp-13-9039-2013, 2013
L. Costantino and F.-M. Bréon
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-23295-2013, https://doi.org/10.5194/acpd-13-23295-2013, 2013
Revised manuscript not accepted
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Dual-frequency (Ka-band and G-band) radar estimates of liquid water content profiles in shallow clouds
Bayesian cloud-top phase determination for Meteosat Second Generation
Lidar–radar synergistic method to retrieve ice, supercooled water and mixed-phase cloud properties
Deriving cloud droplet number concentration from surface-based remote sensors with an emphasis on lidar measurements
A random forest algorithm for the prediction of cloud liquid water content from combined CloudSat–CALIPSO observations
Using machine learning algorithm to retrieve cloud fraction based on FY-4A AGRI observations
Identification of ice-over-water multilayer clouds using multispectral satellite data in an artificial neural network
A new approach to crystal habit retrieval from far-infrared spectral radiance measurements
Severe hail detection with C-band dual-polarisation radars using convolutional neural networks
Multiple-scattering effects on single-wavelength lidar sounding of multi-layered clouds
Optimal estimation of cloud properties from thermal infrared observations with a combination of deep learning and radiative transfer simulation
Retrieval of cloud fraction and optical thickness from multi-angle polarization observations
Cancellation of cloud shadow effects in the absorbing aerosol index retrieval algorithm of TROPOMI
PEAKO and peakTree: Tools for detecting and interpreting peaks in cloud radar Doppler spectra – capabilities and limitations
A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations
Infrared Radiometric Image Classification and Segmentation of Cloud Structure Using Deep-learning Framework for Ground-based Infrared Thermal Camera Observations
Geometrical and optical properties of cirrus clouds in Barcelona, Spain: analysis with the two-way transmittance method of 4 years of lidar measurements
Determination of the vertical distribution of in-cloud particle shape using SLDR-mode 35 GHz scanning cloud radar
Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data
The EarthCARE mission: science data processing chain overview
3-D Cloud Masking Across a Broad Swath using Multi-angle Polarimetry and Deep Learning
Cloud optical and physical properties retrieval from EarthCARE multi-spectral imager: the M-COP products
Cloud top heights and aerosol columnar properties from combined EarthCARE lidar and imager observations: the AM-CTH and AM-ACD products
Raman lidar-derived optical and microphysical properties of ice crystals within thin Arctic clouds during PARCS campaign
Evaluation of four ground-based retrievals of cloud droplet number concentration in marine stratocumulus with aircraft in situ measurements
Deep convective cloud system size and structure across the global tropics and subtropics
A neural-network-based method for generating synthetic 1.6 µm near-infrared satellite images
Numerical model generation of test frames for pre-launch studies of EarthCARE's retrieval algorithms and data management system
Segmentation of polarimetric radar imagery using statistical texture
Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives
Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: Local optimization
Particle inertial effects on radar Doppler spectra simulation
Detection of aerosol and cloud features for the EarthCARE atmospheric lidar (ATLID): the ATLID FeatureMask (A-FM) product
A unified synergistic retrieval of clouds, aerosols, and precipitation from EarthCARE: the ACM-CAP product
Incorporating EarthCARE observations into a multi-lidar cloud climate record: the ATLID (Atmospheric Lidar) cloud climate product
Introduction to EarthCARE synthetic data using a global storm-resolving simulation
Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data
Liquid cloud optical property retrieval and associated uncertainties using multi-angular and bispectral measurements of the airborne radiometer OSIRIS
Global evaluation of Doppler velocity errors of EarthCARE cloud-profiling radar using a global storm-resolving simulation
Cloud and precipitation microphysical retrievals from the EarthCARE Cloud Profiling Radar: the C-CLD product
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2024-2090, https://doi.org/10.5194/egusphere-2024-2090, 2024
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This article presents a novel technique to estimate the liquid water content (LWC) in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows to retrieve the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of the LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Jinyi Xia and Li Guan
EGUsphere, https://doi.org/10.5194/egusphere-2024-977, https://doi.org/10.5194/egusphere-2024-977, 2024
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This study presents a method for estimating cloud cover from FY4A AGRI observations using LSTM neural networks. The results demonstrate excellent performance in distinguishing clear sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloe David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
EGUsphere, https://doi.org/10.5194/egusphere-2024-1336, https://doi.org/10.5194/egusphere-2024-1336, 2024
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This study demonstrates the potential for enhancing severe hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-87, https://doi.org/10.5194/amt-2024-87, 2024
Revised manuscript accepted for AMT
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This study introduces a cloud property retrieval method which integrates traditional radiative transfer simulations with a machine-learning method. Retrievals from a machine learning algorithm are used to provide initial guesses, and a radiative transfer model is used to create radiance lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and is applicable both daytime and nighttime conditions.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
EGUsphere, https://doi.org/10.5194/egusphere-2024-1180, https://doi.org/10.5194/egusphere-2024-1180, 2024
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We introduce an innovative method to retrieve cloud fraction and optical thickness based on polarimetry, well-suited for satellite observations providing multi-angle polarization measurements. The cloud fraction and the cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and a second in the sun-glint region or at a scattering angle of approximately 90°.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-40, https://doi.org/10.5194/amt-2024-40, 2024
Revised manuscript accepted for AMT
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Our study investigates the impact of cloud shadows on satellite-based aerosol index measurements over Europe by TROPOMI. Using a cloud shadow detection algorithm and simulations, we found that the overall effect on the aerosol index is minimal. Interestingly, we measured that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
EGUsphere, https://doi.org/10.5194/egusphere-2024-837, https://doi.org/10.5194/egusphere-2024-837, 2024
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In this study, we present a toolkit of two Python algorithms to extract information about the cloud and precipitation particles present in clouds from data measured by ground-based radar instruments. The data consist of Doppler spectra, in which several peaks are formed by hydrometeor populations with different fall velocities. The detection of the specific peaks makes it possible to assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
EGUsphere, https://doi.org/10.5194/egusphere-2023-2392, https://doi.org/10.5194/egusphere-2023-2392, 2024
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Measuring the shape of clouds helps scientists understand how the Earth will continue to respond to climate change. Satellites measure clouds in different ways. One way is to take pictures of clouds from multiple angles, and to use the differences between the pictures to measure cloud structure. However, doing this accurately can be challenging. We propose a way to use machine learning to recover the shape of clouds from multi-angle satellite data.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Hadrien Verbois, Yves-Marie Saint-Drenan, Vadim Becquet, Benoit Gschwind, and Philippe Blanc
Atmos. Meas. Tech., 16, 4165–4181, https://doi.org/10.5194/amt-16-4165-2023, https://doi.org/10.5194/amt-16-4165-2023, 2023
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Solar surface irradiance (SSI) estimations inferred from satellite images are essential to gain a comprehensive understanding of the solar resource, which is crucial in many fields. This study examines the recent data-driven methods for inferring SSI from satellite images and explores their strengths and weaknesses. The results suggest that while these methods show great promise, they sometimes dramatically underperform and should probably be used in conjunction with physical approaches.
Jesse Loveridge, Aviad Levis, Larry Di Girolamo, Vadim Holodovsky, Linda Forster, Anthony B. Davis, and Yoav Y. Schechner
Atmos. Meas. Tech., 16, 3931–3957, https://doi.org/10.5194/amt-16-3931-2023, https://doi.org/10.5194/amt-16-3931-2023, 2023
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We test a new method for measuring the 3D spatial variations of water within clouds, using measurements of reflections of the Sun's light observed at multiple angles by satellites. This is a great improvement on older methods, which typically assume that clouds occur in a slab shape. Our study used computer modeling to show that our 3D method will work well in cumulus clouds, where older slab methods do not. Our method will inform us about these clouds and their role in our climate.
Zeen Zhu, Pavlos Kollias, and Fan Yang
Atmos. Meas. Tech., 16, 3727–3737, https://doi.org/10.5194/amt-16-3727-2023, https://doi.org/10.5194/amt-16-3727-2023, 2023
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We show that large rain droplets, with large inertia, are unable to follow the rapid change of velocity field in a turbulent environment. A lack of consideration for this inertial effect leads to an artificial broadening of the Doppler spectrum from the conventional simulator. Based on the physics-based simulation, we propose a new approach to generate the radar Doppler spectra. This simulator provides a valuable tool to decode cloud microphysical and dynamical properties from radar observation.
Gerd-Jan van Zadelhoff, David P. Donovan, and Ping Wang
Atmos. Meas. Tech., 16, 3631–3651, https://doi.org/10.5194/amt-16-3631-2023, https://doi.org/10.5194/amt-16-3631-2023, 2023
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The Earth Clouds, Aerosols and Radiation (EarthCARE) satellite mission features the UV lidar ATLID. The ATLID FeatureMask algorithm provides a high-resolution detection probability mask which is used to guide smoothing strategies within the ATLID profile retrieval algorithm, one step further in the EarthCARE level-2 processing chain, in which the microphysical retrievals and target classification are performed.
Shannon L. Mason, Robin J. Hogan, Alessio Bozzo, and Nicola L. Pounder
Atmos. Meas. Tech., 16, 3459–3486, https://doi.org/10.5194/amt-16-3459-2023, https://doi.org/10.5194/amt-16-3459-2023, 2023
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We present a method for accurately estimating the contents and properties of clouds, snow, rain, and aerosols through the atmosphere, using the combined measurements of the radar, lidar, and radiometer instruments aboard the upcoming EarthCARE satellite, and evaluate the performance of the retrieval, using test scenes simulated from a numerical forecast model. When EarthCARE is in operation, these quantities and their estimated uncertainties will be distributed in a data product called ACM-CAP.
Artem G. Feofilov, Hélène Chepfer, Vincent Noël, and Frederic Szczap
Atmos. Meas. Tech., 16, 3363–3390, https://doi.org/10.5194/amt-16-3363-2023, https://doi.org/10.5194/amt-16-3363-2023, 2023
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The response of clouds to human-induced climate warming remains the largest source of uncertainty in model predictions of climate. We consider cloud retrievals from spaceborne observations, the existing CALIOP lidar and future ATLID lidar; show how they compare for the same scenes; and discuss the advantage of adding a new lidar for detecting cloud changes in the long run. We show that ATLID's advanced technology should allow for better detecting thinner clouds during daytime than before.
Woosub Roh, Masaki Satoh, Tempei Hashino, Shuhei Matsugishi, Tomoe Nasuno, and Takuji Kubota
Atmos. Meas. Tech., 16, 3331–3344, https://doi.org/10.5194/amt-16-3331-2023, https://doi.org/10.5194/amt-16-3331-2023, 2023
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JAXA EarthCARE synthetic data (JAXA L1 data) were compiled using the global storm-resolving model (GSRM) NICAM (Nonhydrostatic ICosahedral
Atmospheric Model) simulation with 3.5 km horizontal resolution and the Joint-Simulator. JAXA L1 data are intended to support the development of JAXA retrieval algorithms for the EarthCARE sensor before launch of the satellite. The expected orbit of EarthCARE and horizontal sampling of each sensor were used to simulate the signals.
Philipp Gregor, Tobias Zinner, Fabian Jakub, and Bernhard Mayer
Atmos. Meas. Tech., 16, 3257–3271, https://doi.org/10.5194/amt-16-3257-2023, https://doi.org/10.5194/amt-16-3257-2023, 2023
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This work introduces MACIN, a model for short-term forecasting of direct irradiance for solar energy applications. MACIN exploits cloud images of multiple cameras to predict irradiance. The model is applied to artificial images of clouds from a weather model. The artificial cloud data allow for a more in-depth evaluation and attribution of errors compared with real data. Good performance of derived cloud information and significant forecast improvements over a baseline forecast were found.
Christian Matar, Céline Cornet, Frédéric Parol, Laurent C.-Labonnote, Frédérique Auriol, and Marc Nicolas
Atmos. Meas. Tech., 16, 3221–3243, https://doi.org/10.5194/amt-16-3221-2023, https://doi.org/10.5194/amt-16-3221-2023, 2023
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The optimal estimation formalism is applied to OSIRIS airborne high-resolution multi-angular measurements to retrieve COT and Reff. The corresponding uncertainties related to measurement errors, which are up to 6 and 12 %, the non-retrieved parameters, which are less than 0.5 %, and the cloud model assumptions show that the heterogeneous vertical profiles and the 3D radiative transfer effects lead to average uncertainties of 5 and 4 % for COT and 13 and 9 % for Reff.
Yuichiro Hagihara, Yuichi Ohno, Hiroaki Horie, Woosub Roh, Masaki Satoh, and Takuji Kubota
Atmos. Meas. Tech., 16, 3211–3219, https://doi.org/10.5194/amt-16-3211-2023, https://doi.org/10.5194/amt-16-3211-2023, 2023
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The CPR on the EarthCARE satellite is the first satellite-borne Doppler radar. We evaluated the effectiveness of horizontal integration and the unfolding method for the reduction of the Doppler error (the standard deviation of the random error) in the CPR_ECO product. The error was higher in the tropics than in the other latitudes due to frequent rain echo occurrence and limitation of its unfolding correction. If we use low-mode operation (high PRF), the errors become small enough.
Kamil Mroz, Bernat Puidgomènech Treserras, Alessandro Battaglia, Pavlos Kollias, Aleksandra Tatarevic, and Frederic Tridon
Atmos. Meas. Tech., 16, 2865–2888, https://doi.org/10.5194/amt-16-2865-2023, https://doi.org/10.5194/amt-16-2865-2023, 2023
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We present the theoretical basis of the algorithm that estimates the amount of water and size of particles in clouds and precipitation. The algorithm uses data collected by the Cloud Profiling Radar that was developed for the upcoming Earth Clouds, Aerosols and Radiation Explorer (EarthCARE) satellite mission. After the satellite launch, the vertical distribution of cloud and precipitation properties will be delivered as the C-CLD product.
Cited articles
Alexandrov, M. D. and Lacis, A. A.: A new three-parameter cloud/aerosol particle size distribution based on the generalized inverse Gaussian density function, Appl. Math. Comput., 116, 153–165, 2000.
Alexandrov, M. D., Cairns, B., Emde, C., Ackerman, A. S., and van Diedenhoven, B.: Accuracy assessments of cloud droplet size retrievals from polarized reflectance measurements by the research scanning polarimeter, Remote Sens. Environ., 125, 92–111, https://doi.org/10.1016/j.rse.2012.07.012, 2012a.
Alexandrov, M. D., Cairns, B., and Mishchenko, M. I.: Rainbow Fourier transform, J. Quant. Spectrosc. Ra., 113, 2521–2535, 2012b.
Alexandrov, M. D., Cairns, B., Wasilewski, A. P., Ackerman, A. S., McGill, M. J., Yorks, J. E., Hlavka, D. L., Platnick, S. E., Thomas Arnold, G., van Diedenhoven, B., Chowdhary, J., Ottaviani, M., and Knobelspiesse, K. D.: Liquid water cloud properties during the Polarimeter Definition Experiment (PODEX), Remote Sens. Environ., 169, 20–36, https://doi.org/10.1016/j.rse.2015.07.029, 2015.
Baum, B. A., Kratz, D. P., Yang, P., Ou, S. C., Hu, Y. X., Soulen, P. F., and Tsay, S. C.: Remote sensing of cloud properties using MODIS airborne simulator imagery during SUCCESS 1. Data and models, J. Geophys. Res.-Atmos., 105, 11767–11780, https://doi.org/10.1029/1999jd901089, 2000.
Bevington, P. and Robinson, D.: Data Reduction and Error Analysis for the Physical Sciences, McGraw–Hill, New York, 98–104, 1969.
Bodhaine, B. A., Wood, N. B., Dutton, E. G., and Slusser, J. R.: On Rayleigh optical depth calculations, J. Atmos. Ocean. Tech., 16, 1854–1861, https://doi.org/10.1175/1520-0426(1999)016<1854:orodc>2.0.co;2, 1999.
Bohren, C. F. and Huffman, D. R.: Absorption and scattering by a sphere, Absorption and Scattering of Light by Small Particles, 82–129, 1983.
Bréon, F. M.: Climate. How do aerosols affect cloudiness and climate?, Science, 313, 623–624, https://doi.org/10.1126/science.1131668, 2006.
Bréon, F. M. and Colzy, S.: Global distribution of cloud droplet effective radius from POLDER polarization measurements, Geophys. Res. Lett., 27, 4065–4068, https://doi.org/10.1029/2000gl011691, 2000.
Bréon, F. M. and Doutriaux-Boucher, M.: A comparison of cloud droplet radii measured from space, IEEE T. Geosci. Remote, 43, 1796–1805, https://doi.org/10.1109/TGRS.2005.852838, 2005.
Bréon, F. M. and Goloub, P.: Cloud droplet effective radius from spaceborne polarization measurements, Geophys. Res. Lett., 25, 1879–1882, https://doi.org/10.1029/98gl01221, 1998.
Cairns, B., Russell, E. E., LaVeigne, J. D., and Tennant, P. M. W.: Research scanning polarimeter and airborne usage for remote sensing of aerosols, in: Polarization Science and Remote Sensing, edited by: Shaw, J. A. and Tyo, J. S., Proceedings of SPIE – The International Society for Optical Engineering, 33–44, 2003.
Cheng, T. H., Gu, X. F., Chen, L. F., Yu, T., and Tian, G. L.: Multi-angular polarized characteristics of cirrus clouds, Acta Phys. Sin., 57, 5323–5332, 2008.
Coddington, O. M., Pilewskie, P., Redemann, J., Platnick, S., Russell, P. B., Schmidt, K. S., Gore, W. J., Livingston, J., Wind, G., and Vukicevic, T.: Examining the impact of overlying aerosols on the retrieval of cloud optical properties from passive remote sensing, J. Geophys. Res., 115, D10211, https://doi.org/10.1029/2009jd012829, 2010.
Dandin, P., Pontikis, C., and Hicks, E.: Sensitivity of a GCM to changes in the droplet effective radius parameterization, Geophys. Res. Lett., 24, 437–440, https://doi.org/10.1029/97gl00214, 1997.
Di Girolamo, L., Liang, L., and Platnick, S.: A global view of one-dimensional solar radiative transfer through oceanic water clouds, Geophys. Res. Lett., 37, L18809, https://doi.org/10.1029/2010gl044094, 2010.
Evans, K. F. and Stephens, G. L.: A new polarized atmospheric radiative-transfer model, J. Quant. Spectrosc. Ra., 46, 413–423, https://doi.org/10.1016/0022-4073(91)90043-p, 1991.
Evans, K. F. and Stephens, G. L.: Many polarized radiative transfer models, J. Quant. Spectrosc. Ra., 111, 1686–1688, https://doi.org/10.1016/j.jqsrt.2010.01.029, 2010.
Fougnie, B., Bracco, G., Lafrance, B., Ruffel, C., Hagolle, O., and Tinell, C.: PARASOL in-flight calibration and performance, Appl. Optics, 46, 5435–5451, https://doi.org/10.1364/ao.46.005435, 2007.
Goloub, P., Herman, M., Chepfer, H., Riedi, J., Brogniez, G., Couvert, P., and Séze, G.: Cloud thermodynamical phase classification from the POLDER spaceborne instrument, J. Geophys. Res., 105, 14747, https://doi.org/10.1029/1999jd901183, 2000.
Gryspeerdt, E., Stier, P., and Partridge, D. G.: Satellite observations of cloud regime development: the role of aerosol processes, Atmos. Chem. Phys., 14, 1141–1158, https://doi.org/10.5194/acp-14-1141-2014, 2014.
Hansen, J. and Travis, L.: Light scattering in planetary atmospheres, Space Sci. Rev., 16, 527–610, https://doi.org/10.1007/BF00168069, 1974.
Hansen, J. E. and Hovenier, J. W.: Doubling method applied to multiple scattering of polarized light, J. Quant. Spectrosc. Ra., 11, 809–812, https://doi.org/10.1016/0022-4073(71)90057-4, 1971.
Haywood, J. M., Osborne, S. R., and Abel, S. J.: The effect of overlying absorbing aerosol layers on remote sensing retrievals of cloud effective radius and cloud optical depth, Q. J. Roy. Meteor. Soc., 130, 779–800, https://doi.org/10.1256/qj.03.100, 2004.
Horváth, Á.: Anisotropy of water cloud reflectance: A comparison of measurements and 1D theory, Geophys. Res. Lett., 31, L01102, https://doi.org/10.1029/2003gl018386, 2004.
Hovenier, J. and Van der Mee, C.: Fundamental relationships relevant to the transfer of polarized light in a scattering atmosphere, Astron. Astrophys., 128, 1–16, 1983.
Hovenier, J. W.: The polarization of light scattered by small particles: A personal review, J. Quant. Spectrosc. Ra., 113, 2280–2291, https://doi.org/10.1016/j.jqsrt.2012.03.029, 2012.
Hovenier, J. W., van der Mee, C. V., and Domke, H.: Transfer of polarized light in planetary atmospheres: basic concepts and practical methods, Springer Science & Business Media, 44–47, 2014.
Ishimaru, A., Lesselier, D., and Yeh, C.: Multiple scattering calculations for nonspherical particles based on the vector radiative transfer theory, Radio Sci., 19, 1356–1366, 1984.
Kawamoto, K., Nakajima, T., and Nakajima, T. Y.: A global determination of cloud microphysics with AVHRR remote sensing, J. Climate, 14, 2054–2068, https://doi.org/10.1175/1520-0442(2001)014<2054:agdocm>2.0.co;2, 2001.
Kotchenova, S. Y., Vermote, E. F., Matarrese, R., and Klemm, F. J., Jr.: Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: Path radiance, Appl. Optics, 45, 6762–6774, https://doi.org/10.1364/ao.45.006762, 2006.
Kotchenova, S. Y. and Vermote, E. F.: Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part II. Homogeneous Lambertian and anisotropic surfaces, Appl. Optics, 46, 4455–4464, https://doi.org/10.1364/ao.46.004455, 2007.
Lafrance, B., Hagolle, O., Bonnel, B., Fouquart, Y., and Brogniez, G.: Interband calibration over clouds for POLDER space sensor, IEEE T. Geosci. Remote, 40, 131–142, https://doi.org/10.1109/36.981355, 2002.
Leroy, M., Deuzé, J. L., Bréon, F. M., Hautecoeur, O., Herman, M., Buriez, J. C., Tanré, D., Bouffiès, S., Chazette, P., and Roujean, J. L.: Retrieval of atmospheric properties and surface bidirectional reflectances over land from POLDER/ADEOS, J. Geophys. Res., 102, 17023, https://doi.org/10.1029/96jd02662, 1997.
Liu, Y., Wu, W., Jensen, M. P., and Toto, T.: Relationship between cloud radiative forcing, cloud fraction and cloud albedo, and new surface-based approach for determining cloud albedo, Atmos. Chem. Phys., 11, 7155–7170, https://doi.org/10.5194/acp-11-7155-2011, 2011.
Liu, Y. G., Daum, P. H., and Hallett, J.: A generalized systems theory for the effect of varying fluctuations on cloud droplet size distributions, J. Atmos. Sci., 59, 2279–2290, https://doi.org/10.1175/1520-0469(2002)059<2279:agstft>2.0.co;2, 2002.
Marshak, A., Platnick, S., Várnai, T., Wen, G., and Cahalan, R. F.: Impact of three-dimensional radiative effects on satellite retrievals of cloud droplet sizes, J. Geophys. Res., 111, D09207, https://doi.org/10.1029/2005jd006686, 2006.
Nagao, T. M., Suzuki, K., and Nakajima, T. Y.: Interpretation of Multiwavelength-Retrieved Droplet Effective Radii for Warm Water Clouds in Terms of In-Cloud Vertical Inhomogeneity by Using a Spectral Bin Microphysics Cloud Model, J. Atmos. Sci., 70, 2376–2392, https://doi.org/10.1175/jas-d-12-0225.1, 2013.
Nakajima, T. and King, M. D.: Determination of the optical-thickness and effective particle radius of clouds from reflected solar-radiation measurements, J. Atmos. Sci., 47, 1878–1893, https://doi.org/10.1175/1520-0469(1990)047<1878:dotota>2.0.co;2, 1990.
Nakajima, T. Y. and Nakajima, T.: Wide-area determination of cloud microphysical properties from NOAA AVHRR measurements for FIRE and ASTEX regions, J. Atmos. Sci., 52, 4043–4059, https://doi.org/10.1175/1520-0469(1995)052<4043:wadocm>2.0.co;2, 1995.
Nakajima, T. Y., Suzuki, K., and Stephens, G. L.: Droplet Growth in Warm Water Clouds Observed by the A-Train. Part II: A Multisensor View, J. Atmos. Sci., 67, 1897–1907, https://doi.org/10.1175/2010jas3276.1, 2010a.
Nakajima, T. Y., Suzuki, K., and Stephens, G. L.: Droplet Growth in Warm Water Clouds Observed by the A-Train. Part I: Sensitivity Analysis of the MODIS-Derived Cloud Droplet Sizes, J. Atmos. Sci., 67, 1884–1896, https://doi.org/10.1175/2009jas3280.1, 2010b.
Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and optical thickness retrievals over the Southeast Pacific with VOCALS-REx in situ measurements, J. Geophys. Res., 116, D24206, https://doi.org/10.1029/2011jd016155, 2011.
Painemal, D., Minnis, P., and Sun-Mack, S.: The impact of horizontal heterogeneities, cloud fraction, and liquid water path on warm cloud effective radii from CERES-like Aqua MODIS retrievals, Atmos. Chem. Phys., 13, 9997–10003, https://doi.org/10.5194/acp-13-9997-2013, 2013.
Parol, F., Buriez, J. C., Vanbauce, C., Riedi, J., C.-Labonnote, L., Doutriaux-Boucher, M., Vesperini, M., Sèze, G., Couvert, P., Viollier, M., and Bréon, F. M.: Review of capabilities of multi-angle and polarization cloud measurements from POLDER, Adv. Space Res., 33, 1080–1088, https://doi.org/10.1016/s0273-1177(03)00734-8, 2004.
Penner, J. E., Dong, X. Q., and Chen, Y.: Observational evidence of a change in radiative forcing due to the indirect aerosol effect, Nature, 427, 231–234, https://doi.org/10.1038/nature02234, 2004.
Platnick, S., King, M. D., Ackerman, S. A., Menzel, W. P., Baum, B. A., Riedi, J. C., and Frey, R. A.: The MODIS cloud products: algorithms and examples from Terra, IEEE T. Geosci. Remote, 41, 459–473, https://doi.org/10.1109/TGRS.2002.808301, 2003.
Sayer, A. M., Poulsen, C. A., Arnold, C., Campmany, E., Dean, S., Ewen, G. B. L., Grainger, R. G., Lawrence, B. N., Siddans, R., Thomas, G. E., and Watts, P. D.: Global retrieval of ATSR cloud parameters and evaluation (GRAPE): dataset assessment, Atmos. Chem. Phys., 11, 3913–3936, https://doi.org/10.5194/acp-11-3913-2011, 2011.
Schutgens, N. A. J. and Roebeling, R. A.: Validating the Validation: The Influence of Liquid Water Distribution in Clouds on the Intercomparison of Satellite and Surface Observations, J. Atmos. Ocean. Tech., 26, 1457–1474, https://doi.org/10.1175/2009jtecha1226.1, 2009.
Sekiguchi, M.: A study of the direct and indirect effects of aerosols using global satellite data sets of aerosol and cloud parameters, J. Geophys. Res., 108, 4699, https://doi.org/10.1029/2002jd003359, 2003.
Shang, H., Chen, L., Tao, J., Su, L., and Jia, S.: Synergetic Use of MODIS Cloud Parameters for Distinguishing High Aerosol Loadings From Clouds Over the North China Plain, IEEE J. Sel. Top. Appl., 7, 4879–4886, https://doi.org/10.1109/jstars.2014.2332427, 2014.
Stephens, G. L., Vane, D. G., Boain, R. J., Mace, G. G., Sassen, K., Wang, Z., Illingworth, A. J., O'Connor, E. J., Rossow, W. B., Durden, S. L., Miller, S. D., Austin, R. T., Benedetti, A., Mitrescu, C., and CloudSat Science Team, T.: The Cloudsat Mission and the a-Train, B. Am. Meteorol. Soc., 83, 1771–1790, https://doi.org/10.1175/bams-83-12-1771, 2002.
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G., Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A., Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen, C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.: Assessment of Global Cloud Datasets from Satellites: Project and Database Initiated by the GEWEX Radiation Panel, B. Am. Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/bams-d-12-00117.1, 2013.
Tanré, D., Bréon, F. M., Deuzé, J. L., Dubovik, O., Ducos, F., François, P., Goloub, P., Herman, M., Lifermann, A., and Waquet, F.: Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: the PARASOL mission, Atmos. Meas. Tech., 4, 1383–1395, https://doi.org/10.5194/amt-4-1383-2011, 2011.
van Diedenhoven, B., Cairns, B., Geogdzhayev, I. V., Fridlind, A. M., Ackerman, A. S., Yang, P., and Baum, B. A.: Remote sensing of ice crystal asymmetry parameter using multi-directional polarization measurements – Part 1: Methodology and evaluation with simulated measurements, Atmos. Meas. Tech., 5, 2361–2374, https://doi.org/10.5194/amt-5-2361-2012, 2012.
Vant-Hull, B., Marshak, A., Remer, L. A., and Zhanqing, L.: The Effects of Scattering Angle and Cumulus Cloud Geometry on Satellite Retrievals of Cloud Droplet Effective Radius, IEEE T. Geosci. Remote, 45, 1039–1045, https://doi.org/10.1109/TGRS.2006.890416, 2007.
Walther, A. and Heidinger, A. K.: Implementation of the Daytime Cloud Optical and Microphysical Properties Algorithm (DCOMP) in PATMOS-x, J. Appl. Meteorol. Clim., 51, 1371–1390, https://doi.org/10.1175/jamc-d-11-0108.1, 2012.
Walther, A., Heidinger, A. K., and Miller, S.: The expected performance of cloud optical and microphysical properties derived from Suomi NPP VIIRS day/night band lunar reflectance, J. Geophys. Res.-Atmos., 118, 13230–13240, https://doi.org/10.1002/2013jd020478, 2013.
Wolters, E. L. A., Deneke, H. M., van den Hurk, B. J. J. M., Meirink, J. F., and Roebeling, R. A.: Broken and inhomogeneous cloud impact on satellite cloud particle effective radius and cloud-phase retrievals, J. Geophys. Res., 115, D10214, https://doi.org/10.1029/2009jd012205, 2010.
Zeng, S., Parol, F., Riedi, J., Cornet, C., and Thieuleux, F.: Examination of POLDER/PARASOL and MODIS/Aqua cloud fractions and properties representativeness, J. Climate, 24, 4435–4450, https://doi.org/10.1175/2011jcli3857.1, 2011.
Zhang, Z.: On the sensitivity of cloud effective radius retrieval based on spectral method to bi-modal droplet size distribution: A semi-analytical model, J. Quant. Spectrosc. Ra., 129, 79–88, https://doi.org/10.1016/j.jqsrt.2013.05.033, 2013.
Zhang, Z. and Platnick, S.: An assessment of differences between cloud effective particle radius retrievals for marine water clouds from three MODIS spectral bands, J. Geophys. Res., 116, D20215, https://doi.org/10.1029/2011jd016216, 2011.
Zhang, Z., Ackerman, A. S., Feingold, G., Platnick, S., Pincus, R., and Xue, H.: Effects of cloud horizontal inhomogeneity and drizzle on remote sensing of cloud droplet effective radius: Case studies based on large-eddy simulations, J. Geophys. Res.-Atmos., 117, D19208, https://doi.org/10.1029/2012jd017655, 2012.
Zinner, T., Wind, G., Platnick, S., and Ackerman, A. S.: Testing remote sensing on artificial observations: impact of drizzle and 3-D cloud structure on effective radius retrievals, Atmos. Chem. Phys., 10, 9535–9549, https://doi.org/10.5194/acp-10-9535-2010, 2010.
Short summary
The cloud droplet size retrieval of POLDER is accurate even when the measurements are limited. The algorithm can be improved by (1) including the measurements in the primary rainbow region to provide accurate large droplet (>15 µm) retrievals; (2) performing higher-resolution retrieval (42 km × 42 km) to ensure more successful retrievals and reduce the bias introduced by cloud horizontal inhomogeneity.
The cloud droplet size retrieval of POLDER is accurate even when the measurements are limited....