Articles | Volume 15, issue 14
https://doi.org/10.5194/amt-15-4257-2022
© Author(s) 2022. 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-15-4257-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Jul 2022
Research article |
| 27 Jul 2022
| 27 Jul 2022
High-resolution satellite-based cloud detection for the analysis of land surface effects on boundary layer clouds
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Hendrik Andersen
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Jan Cermak
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Eva Pauli
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
Karlsruhe Institute of Technology (KIT), Institute of Photogrammetry and Remote Sensing, Karlsruhe, Germany
Rob Roebeling
European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), Darmstadt, Germany
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This study separates the influence of aerosol on cloud properties in the southeast Atlantic region from meteorological conditions in the biomass-burning season. Machine learning is used to link 8-day-averaged satellite and reanalysis data sets. Distinct regimes of aerosol–cloud interactions are identified in the subregions of the southeast Atlantic based on the obtained sensitivities.
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Aerosol-cloud interactions continue to contribute large uncertainties to our climate system understanding. In this study, we use near-global satellite and reanalysis data sets to predict marine liquid-water clouds by means of artificial neural networks. We show that on the system scale, lower-tropospheric stability and boundary layer height are the main determinants of liquid-water clouds. Aerosols show the expected impact on clouds but are less relevant than some meteorological factors.
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EGUsphere, https://doi.org/10.5194/egusphere-2023-1667, https://doi.org/10.5194/egusphere-2023-1667, 2023
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Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez
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Air pollution endangers human health and poses a problem particularly in densely populated areas. Here, an explainable machine learning approach is used to analyse periods of high particle concentrations for a suburban site southwest of Paris to better understand its atmospheric drivers. Air pollution is particularly excaberated by low temperatures and low mixed layer heights, but processes vary substantially between and within seasons.
Hendrik Andersen, Jan Cermak, Julia Fuchs, Peter Knippertz, Marco Gaetani, Julian Quinting, Sebastian Sippel, and Roland Vogt
Atmos. Chem. Phys., 20, 3415–3438, https://doi.org/10.5194/acp-20-3415-2020, https://doi.org/10.5194/acp-20-3415-2020, 2020
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Fog and low clouds (FLCs) are an essential but poorly understood element of Namib regional climate. Here, a satellite-based data set of FLCs in central Namib, reanalysis data, and back trajectories are used to systematically analyze conditions when FLCs occur. Synoptic-scale mechanisms are identified that influence the formation of FLCs and the onshore advection of marine boundary-layer air masses. The findings lead to a new conceptual model of mechanisms that drive FLC variability in the Namib.
Hendrik Andersen, Jan Cermak, Irina Solodovnik, Luca Lelli, and Roland Vogt
Atmos. Chem. Phys., 19, 4383–4392, https://doi.org/10.5194/acp-19-4383-2019, https://doi.org/10.5194/acp-19-4383-2019, 2019
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Fog and low clouds (FLCs) are an essential but poorly understood component of Namib-region climate. This study uses observations from multiple satellite platforms and ground-based measurements to coherently characterize Namib-region FLC patterns. Findings concerning the seasonal cycle of the vertical structure and the diurnal cycle of FLCs lead to a new conceptual model of the spatiotemporal dynamics of FLCs in the Namib and help to improve the understanding of underlying processes.
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Matthias Wiegner, Ina Mattis, Margit Pattantyús-Ábrahám, Juan Antonio Bravo-Aranda, Yann Poltera, Alexander Haefele, Maxime Hervo, Ulrich Görsdorf, Ronny Leinweber, Josef Gasteiger, Martial Haeffelin, Frank Wagner, Jan Cermak, Katerina Komínková, Mike Brettle, Christoph Münkel, and Kornelia Pönitz
Atmos. Meas. Tech., 12, 471–490, https://doi.org/10.5194/amt-12-471-2019, https://doi.org/10.5194/amt-12-471-2019, 2019
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Many ceilometers are influenced by water vapor absorption in the spectral range around 910 nm. Thus, a correction is required to retrieve aerosol optical properties. Validation of this correction scheme was performed in the framework of CeiLinEx2015 for several ceilometers with good agreement for Vaisala's CL51 ceilometer. For future applications we recommend monitoring the emitted wavelength and providing
darkmeasurements on a regular basis to be able to correct for signal artifacts.
Bianca Adler, Karmen Babić, Norbert Kalthoff, Fabienne Lohou, Marie Lothon, Cheikh Dione, Xabier Pedruzo-Bagazgoitia, and Hendrik Andersen
Atmos. Chem. Phys., 19, 663–681, https://doi.org/10.5194/acp-19-663-2019, https://doi.org/10.5194/acp-19-663-2019, 2019
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This study deals with nocturnal stratiform low-level clouds that frequently form in the atmospheric boundary layer over southern West Africa. We use observational data from 11 nights to characterize the clouds and intranight variability of boundary layer conditions as well as to assess the physical processes relevant for cloud formation. We find that cooling is crucial to reach saturation and a large part of the cooling is related to horizontal advection of cool air from the Gulf of Guinea.
Julia Fuchs, Jan Cermak, and Hendrik Andersen
Atmos. Chem. Phys., 18, 16537–16552, https://doi.org/10.5194/acp-18-16537-2018, https://doi.org/10.5194/acp-18-16537-2018, 2018
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This study separates the influence of aerosol on cloud properties in the southeast Atlantic region from meteorological conditions in the biomass-burning season. Machine learning is used to link 8-day-averaged satellite and reanalysis data sets. Distinct regimes of aerosol–cloud interactions are identified in the subregions of the southeast Atlantic based on the obtained sensitivities.
Hendrik Andersen and Jan Cermak
Atmos. Meas. Tech., 11, 5461–5470, https://doi.org/10.5194/amt-11-5461-2018, https://doi.org/10.5194/amt-11-5461-2018, 2018
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Fog and low clouds (FLCs) are a valuable source of water for many ecosystems in the Namib. This study presents the first fully diurnal satellite detection of FLCs, revealing the spatial and temporal patterns in the Namib. A validation is conducted against station measurements in the central Namib and shows a high overall accuracy. The average timing and persistence of FLCs seem to depend on the distance to the coast, suggesting that the region is dominated by advection-driven FLCs.
Chellappan Seethala, Jan Fokke Meirink, Ákos Horváth, Ralf Bennartz, and Rob Roebeling
Atmos. Chem. Phys., 18, 13283–13304, https://doi.org/10.5194/acp-18-13283-2018, https://doi.org/10.5194/acp-18-13283-2018, 2018
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We compared the microphysical properties of South Atlantic stratocumulus (Sc) from three different satellite instruments (SEVIRI, TMI, MODIS). The liquid water path (LWP) and its diurnal cycle from the three datasets agreed very well in overcast, smoke-free scenes. LWP showed a decrease from an early morning peak to a late afternoon minimum, after which it increased until morning. The presence of smoke aloft Sc, however, negatively biased the LWP retrieved by the visible/near-infrared technique.
Hendrik Andersen, Jan Cermak, Julia Fuchs, Reto Knutti, and Ulrike Lohmann
Atmos. Chem. Phys., 17, 9535–9546, https://doi.org/10.5194/acp-17-9535-2017, https://doi.org/10.5194/acp-17-9535-2017, 2017
Short summary
Short summary
Aerosol-cloud interactions continue to contribute large uncertainties to our climate system understanding. In this study, we use near-global satellite and reanalysis data sets to predict marine liquid-water clouds by means of artificial neural networks. We show that on the system scale, lower-tropospheric stability and boundary layer height are the main determinants of liquid-water clouds. Aerosols show the expected impact on clouds but are less relevant than some meteorological factors.
U. Hamann, A. Walther, B. Baum, R. Bennartz, L. Bugliaro, M. Derrien, P. N. Francis, A. Heidinger, S. Joro, A. Kniffka, H. Le Gléau, M. Lockhoff, H.-J. Lutz, J. F. Meirink, P. Minnis, R. Palikonda, R. Roebeling, A. Thoss, S. Platnick, P. Watts, and G. Wind
Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, https://doi.org/10.5194/amt-7-2839-2014, 2014
J. F. Meirink, R. A. Roebeling, and P. Stammes
Atmos. Meas. Tech., 6, 2495–2508, https://doi.org/10.5194/amt-6-2495-2013, https://doi.org/10.5194/amt-6-2495-2013, 2013
N. Schaller, J. Cermak, M. Wild, and R. Knutti
Earth Syst. Dynam., 4, 253–266, https://doi.org/10.5194/esd-4-253-2013, https://doi.org/10.5194/esd-4-253-2013, 2013
Related subject area
Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
EGUsphere, https://doi.org/10.5194/egusphere-2024-2090, https://doi.org/10.5194/egusphere-2024-2090, 2024
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This article presents a novel technique to estimate the liquid water content (LWC) in shallow warm clouds using a pair of collocated Ka-band (35 GHz) and G-band (239 GHz) radars. We demonstrate that the use of a G-band radar allows to retrieve the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of the LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
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.
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
Short summary
Short summary
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
Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C. C., and Gumley, L. E.:
Discriminating clear sky from clouds with MODIS, J. Geophys. Res.-Atmos., 103, 32141–32157, https://doi.org/10.1029/1998JD200032, 1998. a
Andersen, H. and Cermak, J.:
First fully diurnal fog and low cloud satellite detection reveals life cycle in the Namib, Atmos. Meas. Tech., 11, 5461–5470, https://doi.org/10.5194/amt-11-5461-2018, 2018. a
Bendix, J.:
A satellite-based climatology of fog and low-level stratus in Germany and adjacent areas, Atmos. Res., 64, 3–18, https://doi.org/10.1016/S0169-8095(02)00075-3, 2002. a
Bley, S. and Deneke, H.:
A threshold-based cloud mask for the high-resolution visible channel of Meteosat Second Generation SEVIRI, Atmos. Meas. Tech., 6, 2713–2723, https://doi.org/10.5194/amt-6-2713-2013, 2013. a, b, c, d
Cermak, J. and Bendix, J.:
A novel approach to fog/low stratus detection using Meteosat 8 data, Atmos. Res., 87, 279–292, https://doi.org/10.1016/j.atmosres.2007.11.009, 2008. a
Chen, P. Y., Srinivasan, R., and Fedosejevs, G.:
An automated cloud detection method for daily NOAA 16 advanced very high resolution radiometer data over Texas and Mexico, J. Geophys. Res.-Atmos., 108, 1–9, https://doi.org/10.1029/2003jd003554, 2003. a
Collier, C. G.:
The impact of urban areas on weather, Q. J. Roy. Meteor. Soc., 132, 1–25, https://doi.org/10.1256/qj.05.199, 2006. a, b
Deneke, H., Feijt, A., van Lammeren, A., and Simmer, C.:
Validation of a physical retrieval scheme of solar surface irradiances from narrowband satellite radiances, J. Appl. Meteorol., 44, 1453–1466, https://doi.org/10.1175/JAM2290.1, 2005. a
Deneke, H. M. and Roebeling, R. A.:
Downscaling of METEOSAT SEVIRI 0.6 and 0.8 µm channel radiances utilizing the high-resolution visible channel, Atmos. Chem. Phys., 10, 9761–9772, https://doi.org/10.5194/acp-10-9761-2010, 2010. a, b
Deneke, H. M., Knap, W. H., and Simmer, C.:
Multiresolution analysis of the temporal variance and correlation of transmittance and reflectance of an atmospheric column, J. Geophys. Res.-Atmos., 114, D17206, https://doi.org/10.1029/2008JD011680, 2009. a
Derrien, M., Gléau, H. L., and Raoul, M.:
The use of the high resolution visible in SAFNWC/MSG cloud mask, Proceedings of the 2010 EUMETSAT Meteorological Satellite Conference, 20–24 September 2010, Córdoba, Spain, 57 pp., http://hal.archives-ouvertes.fr/meteo-00604325/ (last access: 5 July 2021), 2010. a
Di Vittorio, A. V. and Emery, W. J.:
An automated, dynamic threshold cloud-masking algorithm for daytime AVHRR images over land, IEEE T. Geosci. Remote, 40, 1682–1694, https://doi.org/10.1109/TGRS.2002.802455, 2002. a
Dozier, J. and Painter, T. H.:
Multispectral and hyperspectral remote sensing of alpine snow properties, Annu. Rev. Earth Pl. Sc., 32, 465–494, https://doi.org/10.1146/annurev.earth.32.101802.120404, 2004. a
EUMETSAT: EUMETSAT CM SAF Algorithm Theoretical Basis Document (ATBD) Meteosat Solar Surface Radiation and effective Cloud Albedo Climate Data Records - Heliosat (SARAH-2), Satellite Application Facility on Climate Monitoring (CM SAF), 31 pp., https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002_01, 2019 (data available at: https://www.eumetsat.int/access-our-data, last access: 22 April 2020). a, b, c, d
Fan, J., Wang, Y., Rosenfeld, D., Liu, X., Fan, J., Wang, Y., Rosenfeld, D., and Liu, X.:
Review of Aerosol-Cloud Interactions: Mechanisms, Significance, and Challenges, J. Atmos. Sci., 73, 4221–4252, https://doi.org/10.1175/JAS-D-16-0037.1, 2016. a
Greuell, W. and Roebeling, R. A.:
Toward a standard procedure for validation of satellite-derived cloud liquid water path: A study with SEVIRI data, J. Appl. Meteorol. Clim., 48, 1575–1590, https://doi.org/10.1175/2009JAMC2112.1, 2009. a, b
Haeffelin, M., Barthès, L., Bock, O., Boitel, C., Bony, S., Bouniol, D., Chepfer, H., Chiriaco, M., Cuesta, J., Delanoë, J., Drobinski, P., Dufresne, J.-L., Flamant, C., Grall, M., Hodzic, A., Hourdin, F., Lapouge, F., Lemaître, Y., Mathieu, A., Morille, Y., Naud, C., Noël, V., O'Hirok, W., Pelon, J., Pietras, C., Protat, A., Romand, B., Scialom, G., and Vautard, R.:
SIRTA, a ground-based atmospheric observatory for cloud and aerosol research, Ann. Geophys., 23, 253–275, https://doi.org/10.5194/angeo-23-253-2005, 2005. a, b
Hall, D. K., Riggs, G. A., and Salomonson, V. V.:
Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice-mapping algorithms, Tech. rep., NASA (National Aeronautics and Space Administration), Goddard Space Flight Center, Greenbelt, MD, 2001. a
Han, J.-Y., Baik, J.-J., and Lee, H.:
Urban impacts on precipitation, Asia-Pac. J. Atmos. Sci., 50, 17–30, https://doi.org/10.1007/s13143-014-0016-7, 2014. a
Henken, C. C., Schmeits, M. J., Deneke, H., and Roebeling, R. A.:
Using MSG-SEVIRI cloud physical properties and weather radar observations for the detection of Cb/TCu clouds, J. Appl. Meteorol. Clim., 50, 1587–1600, https://doi.org/10.1175/2011JAMC2601.1, 2011. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.:
ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.adbb2d47, 2018 (data available at: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview, last access: 2 November 2021). a, b, c
Hutchison, K. D., Roskovensky, J. K., Jackson, J. M., Heidinger, A. K., Kopp, T. J., Pavolonis, M. J., and Frey, R.:
Automated cloud detection and classification of data collected by the Visible Infrared Imager Radiometer Suite (VIIRS), Int. J. Remote Sens., 26, 4681–4706, https://doi.org/10.1080/01431160500196786, 2005. a
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoë, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W., Haeffelin, M., Klein Baltinik, H., Krasnov, O. A., Pelon, J., Piriou, J. M., Protat, A., Russchenberg, H. W., Seifert, A., Tompkins, A. M., van Zadelhoff, G. J., Vinit, F., Willen, U., Wilson, D. R., and Wrench, C. L.:
Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations, B. Am. Meteorol. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007 (data available at: https://actris.nilu.no/, last access: 2 February 2020). a, b
Ipe, A., Clerbaux, N., Bertrand, C., Dewitte, S., and Gonzalez, L.:
Pixel-scale composite top-of-the-atmosphere clear-sky reflectances for Meteosat-7 visible data, J. Geophys. Res.-Atmos., 108, 1–7, https://doi.org/10.1029/2002jd002771, 2003. a, b
Jain, A., Duin, R. P. W., and Mao, J.:
Statistical pattern recognition: a review, IEEE T. Pattern Anal., 22, 4–37, https://doi.org/10.1109/34.824819, 2000. a
Kim, M., Cermak, J., Andersen, H., Fuchs, J., and Stirnberg, R.:
A new satellite-based retrieval of low-cloud liquid-water path using machine learning and meteosat seviri data, Remote Sens.-Basel, 12, 1–16, https://doi.org/10.3390/rs12213475, 2020. a
Klüser, L., Rosenfeld, D., Macke, A., and Holzer-Popp, T.:
Observations of shallow convective clouds generated by solar heating of dark smoke plumes, Atmos. Chem. Phys., 8, 2833–2840, https://doi.org/10.5194/acp-8-2833-2008, 2008. a
Liang, X., Miao, S., Li, J., Bornstein, R., Zhang, X., Gao, Y., Chen, F., Cao, X., Cheng, Z., Clements, C., Dabberdt, W., Ding, A., Ding, D., Dou, J. J., Dou, J. X., Dou, Y., Grimmond, C. S., Gonzál, J. E., He, J., Huang, M., Huang, X., Ju, S., Li, Q., NiYogi, D., Quan, J., Sun, J., Sun, J. Z., Yu, M., Zhang, J., Zhang, Y., Zhao, X., Zheng, Z., and Zhou, M.:
Surf: Understanding and predicting urban convection and haze, B. Am. Meteorol. Soc., 99, 1391–1413, https://doi.org/10.1175/BAMS-D-16-0178.1, 2018. a
Matthews, E. and Rossow, W. B.:
Regional and Seasonal Variations of Surface Reflectance from Satellite Observations at 0.6 µm, J. Clim. Appl. Meteorol., 26, 170–202, https://doi.org/10.1175/1520-0450(1987)026<0170:RASVOS>2.0.CO;2, 1987. a
Minnis, P. and Harrison, E. F.:
Diurnal variability of regional cloud and clear-sky radiative parameters derived from GOES data. Part III: November 1978 radiative parameters., J. Clim. Appl. Meteorol., 23, 1032–1051, https://doi.org/10.1175/1520-0450(1984)023<1032:DVORCA>2.0.CO;2, 1984. a
Nilo, S. T., Romano, F., Cermak, J., Cimini, D., Ricciardelli, E., Cersosimo, A., Di Paola, F., Gallucci, D., Gentile, S., Geraldi, E., Larosa, S., Ripepi, E., and Viggiano, M.:
Fog detection based on Meteosat Second Generation-Spinning enhanced visible and infrared imager high resolution visible channel, Remote Sens.-Basel, 10, 1–20, https://doi.org/10.3390/rs10040541, 2018. a
Pauli, E., Cermak, J., and Teuling, A. J.: Enhanced nighttime fog and low stratus occurrence over the Landes forest, France, Geophys. Res. Lett., 49, e2021GL097058, https://doi.org/10.1029/2021GL097058, 2022. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Louppe, G., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, https://doi.org/10.1007/s13398-014-0173-7.2, 2011. a
Richter, R., Louis, J., and Berthelot, B.:
Sentinel-2 MSI – Level 2A Products Algorithm Theoretical Basis Document, European Space Agency, ESA SP Publ., 49, 1–72, 2012. a
Rossow, W. B. and Garder, L. C.:
Cloud Detection Using Satellite Measurements of Infrared and Visible Radiances for ISCCP, J. Climate, 6, 2341–2369, https://doi.org/10.1175/1520-0442(1993)006<2341:CDUSMO>2.0.CO;2, 1993. a
Saunders, R. W. and Kriebel, K. T.:
An improved method for detecting clear sky and cloudy radiances from AVHRR data, Int. J. Remote Sens., 9, 123–150, https://doi.org/10.1080/01431168808954841, 1988. a
Schmetz, J., Pili, P., Tjemkes, S., Just, D., Kerkmann, J., Rota, S., and Ratier, A.: AN INTRODUCTION TO METEOSAT SECOND GENERATION (MSG), B. Am. Meteorol. Soc., 83, 977–992, https://doi.org/10.1175/1520-0477(2002)083<0977:AITMSG>2.3.CO;2, 2002. a
Schulz, H. M., Thies, B., Cermak, J., and Bendix, J.:
1 km fog and low stratus detection using pan-sharpened MSG SEVIRI data, Atmos. Meas. Tech., 5, 2469–2480, https://doi.org/10.5194/amt-5-2469-2012, 2012. a
Schutgens, N. A. 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. a
Shepherd, J. M.:
A Review of Current Investigations of Urban-Induced Rainfall and Recommendations for the Future, Earth Interact., 9, 1–27, https://doi.org/10.1175/EI156.1, 2005. a, b
Stowe, L. L., Davis, P. A., and Mcclain, E. P.:
Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer, J. Atmos. Ocean. Tech., 16, 656–681, https://doi.org/10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2, 1999. a
Strabala, K. I., Ackerman, S. A., and Menzel, W. P.: Cloud Properties inferred from 8–12- µm Data, J. Appl. Meteorol., 33, 212–229, https://doi.org/10.1175/1520-0450(1994)033<0212:CPIFD>2.0.CO;2, 1994. a
Teuling, A. J., Taylor, C. M., Meirink, J. F., Melsen, L. A., Miralles, D. G., van Heerwaarden, C. C., Vautard, R., Stegehuis, A. I., Nabuurs, G.-J., and de Arellano, J. V.-G.:
Observational evidence for cloud cover enhancement over western European forests, Nat. Commun., 8, 14065, https://doi.org/10.1038/ncomms14065, 2017. a, b, c, d
Tøttrup, C.:
EU-DEM Statistical Validation Report August 2014, European Environment Agency (EEA), Copenhagen, Denmark, p. 27, 2014. a
Underwood, S. J. and Hansen, C.:
Investigating urban clear islands in fog and low stratus clouds in the San Joaquin Valley of California, Phys. Geogr., 29, 442–454, https://doi.org/10.2747/0272-3646.29.5.442, 2008. a
Varentsov, M., Wouters, H., Platonov, V., and Konstantinov, P.:
Megacity-Induced Mesoclimatic Effects in the Lower Atmosphere: A Modeling Study for Multiple Summers over Moscow, Russia, Atmosphere, 9, 50, https://doi.org/10.3390/atmos9020050, 2018. a
Veefkind, J. P., de Haan, J. F., Sneep, M., and Levelt, P. F.:
Improvements to the OMI
Wærsted, E. G., Haeffelin, M., Steeneveld, G., and Dupont, J.:
Understanding the dissipation of continental fog by analysing the LWP budget using idealized LES and in situ observations, Q. J. Roy. Meteor. Soc., 145, 784–804, https://doi.org/10.1002/qj.3465, 2019.
a
Westerhuis, S., Fuhrer, O., Cermak, J., and Eugster, W.:
Identifying the key challenges for fog and low stratus forecasting in complex terrain, Q. J. Roy. Meteor. Soc., 146, 3347–3367, https://doi.org/10.1002/qj.3849, 2020. a, b
Williams, A. P., Schwartz, R. E., Iacobellis, S., Seager, R., Cook, B. I., Still, C. J., Husak, G., and Michaelsen, J.:
Urbanization causes increased cloud base height and decreased fog in coastal Southern California, Geophys. Res. Lett., 42, 1527–1536, https://doi.org/10.1002/2015GL063266, 2015. a
Yang, Y., Di Girolamo, L., and Mazzoni, D.:
Selection of the automated thresholding algorithm for the Multi-angle Imaging SpectroRadiometer Radiometric Camera-by-Camera Cloud Mask over land, Remote Sen. Environ., 107, 159–171, https://doi.org/10.1016/j.rse.2006.05.020, 2007. a, b, c
Zhong, S., Qian, Y., Zhao, C., Leung, R., Wang, H., Yang, B., Fan, J., Yan, H., Yang, X.-Q., and Liu, D.:
Urbanization-induced urban heat island and aerosol effects on climate extremes in the Yangtze River Delta region of China, Atmos. Chem. Phys., 17, 5439–5457, https://doi.org/10.5194/acp-17-5439-2017, 2017. a
Short summary
Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite data are developed and validated for the region of Paris to improve applicability for analyses of urban effects on low clouds. We found that cloud masks obtained from the regional approach are more appropriate for the high-resolution analysis of locally induced cloud processes. Its applicability is tested for the analysis of typical fog conditions over different surface types.
Two cloud-masking approaches, a local and a regional approach, using high-resolution satellite...
