Articles | Volume 14, issue 7
https://doi.org/10.5194/amt-14-5199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-5199-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jul 2021
Research article |
| 30 Jul 2021
| 30 Jul 2021
Cloud height measurement by a network of all-sky imagers
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Bijan Nouri
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Stefan Wilbert
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Paseo de Almería, 73, 2, 04001 Almeria, Spain
Thomas Schmidt
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Ontje Lünsdorf
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Jonas Stührenberg
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Detlev Heinemann
Institut für Vernetzte Energiesysteme, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Carl-von-Ossietzky-Straße 15, 26129 Oldenburg, Germany
Andreas Kazantzidis
Laboratory of Atmospheric Physics, Department of Physics, University of Patras, 26500 Patras, Greece
Robert Pitz-Paal
Institut für Solarforschung, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Linder Höhe, 51147 Cologne, Germany
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Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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This study demonstrates the potential of enhancing severe-hail detection through the application of convolutional neural networks (CNNs) to dual-polarization radar data. It is shown that current methods can be calibrated to significantly enhance their performance for severe-hail detection. This study establishes the foundation for the solution of a more complex problem: the estimation of the maximum size of hailstones on the ground using deep learning applied to radar data.
Jinyi Xia and Li Guan
Atmos. Meas. Tech., 17, 6697–6706, https://doi.org/10.5194/amt-17-6697-2024, https://doi.org/10.5194/amt-17-6697-2024, 2024
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This study presents a method for estimating cloud cover from FY-4A AGRI observations using random forest (RF) and multilayer perceptron (MLP) algorithms. The results demonstrate excellent performance in distinguishing clear-sky scenes and reducing errors in cloud cover estimation. It shows significant improvements compared to existing methods.
Yuli Liu and Ian S. Adams
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Revised manuscript accepted for AMT
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This paper presents our latest development in tomographic cloud reconstruction algorithms that use multi-angle TB observations to reconstruct the spatial distribution of ice clouds. Compared to nadir-only retrievals, the tomographic technique provides a detailed reconstruction of ice clouds’ inner structure with high spatial resolution and significantly improves retrieval accuracy. Also, the tomography technique effectively increases detection sensitivity for small ice cloud particles.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
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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.
Min Deng, Scott E. Giangrande, Michael P. Jensen, Karen Johnson, Christopher R. Williams, Jennifer M. Comstock, Ya-Chien Feng, Alyssa Matthews, Iosif A. Lindenmaier, Timothy G. Wendler, Marquette Rocque, Aifang Zhou, Zeen Zhu, Edward Luke, and Die Wang
EGUsphere, https://doi.org/10.5194/egusphere-2024-2615, https://doi.org/10.5194/egusphere-2024-2615, 2024
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A relative calibration technique is developed for the cloud radar by monitoring the intercept of the wet-radome attenuation (WRA) logarithmic behavior as a function of rainfall rates in light and moderate rain conditions. This WRA technique is applied to the measurements during the ARM TRACER campaign and reports Ze offsets that compare favorably with results from other traditional calibration methods.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
Howard W. Barker, Jason N. S. Cole, Najda Villefranque, Zhipeng Qu, Almudena Velázquez Blázquez, Carlos Domenech, Shannon L. Mason, and Robin J. Hogan
EGUsphere, https://doi.org/10.5194/egusphere-2024-1651, https://doi.org/10.5194/egusphere-2024-1651, 2024
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Measurements made by three instruments aboard EarthCARE are used to retrieve estimates of cloud and aerosol properties. A radiative closure assessment of these retrievals is performed by the ACMB-DF processor. Radiative transfer models acting on retrieved information produce broadband radiances commensurate with measurements made by EarthCARE’s broadband radiometer. Measured and modelled radiances for small domains are compared and the likelihood of them differing by 10 W/m2 defines the closure.
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, https://doi.org/10.5194/amt-17-4891-2024, 2024
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ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
Ke Ren, Haiyang Gao, Shuqi Niu, Shaoyang Sun, Leilei Kou, Yanqing Xie, Liguo Zhang, and Lingbing Bu
Atmos. Meas. Tech., 17, 4825–4842, https://doi.org/10.5194/amt-17-4825-2024, https://doi.org/10.5194/amt-17-4825-2024, 2024
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Ultraviolet imaging technology has significantly advanced the research and development of polar mesospheric clouds (PMCs). In this study, we proposed the wide-field-of-view ultraviolet imager (WFUI) and built a forward model to evaluate the detection capability and efficiency. The results demonstrate that the WFUI performs well in PMC detection and has high detection efficiency. The relationship between ice water content and detection efficiency follows an exponential function distribution.
Adrià Amell, Simon Pfreundschuh, and Patrick Eriksson
Atmos. Meas. Tech., 17, 4337–4368, https://doi.org/10.5194/amt-17-4337-2024, https://doi.org/10.5194/amt-17-4337-2024, 2024
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The representation of clouds in numerical weather and climate models remains a major challenge that is difficult to address because of the limitations of currently available data records of cloud properties. In this work, we address this issue by using machine learning to extract novel information on ice clouds from a long record of satellite observations. Through extensive validation, we show that this novel approach provides surprisingly accurate estimates of clouds and their properties.
Huige Di, Xinhong Wang, Ning Chen, Jing Guo, Wenhui Xin, Shichun Li, Yan Guo, Qing Yan, Yufeng Wang, and Dengxin Hua
Atmos. Meas. Tech., 17, 4183–4196, https://doi.org/10.5194/amt-17-4183-2024, https://doi.org/10.5194/amt-17-4183-2024, 2024
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This study proposes an inversion method for atmospheric-aerosol or cloud microphysical parameters based on dual-wavelength lidar data. It is suitable for the inversion of uniformly mixed and single-property aerosol layers or small cloud droplets. For aerosol particles, the inversion range that this algorithm can achieve is 0.3–1.7 μm. For cloud droplets, it is 1.0–10 μm. This algorithm can quickly obtain the microphysical parameters of atmospheric particles and has better robustness.
Johanna Mayer, Luca Bugliaro, Bernhard Mayer, Dennis Piontek, and Christiane Voigt
Atmos. Meas. Tech., 17, 4015–4039, https://doi.org/10.5194/amt-17-4015-2024, https://doi.org/10.5194/amt-17-4015-2024, 2024
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ProPS (PRObabilistic cloud top Phase retrieval for SEVIRI) is a method to detect clouds and their thermodynamic phase with a geostationary satellite, distinguishing between clear sky and ice, mixed-phase, supercooled and warm liquid clouds. It uses a Bayesian approach based on the lidar–radar product DARDAR. The method allows studying cloud phases, especially mixed-phase and supercooled clouds, rarely observed from geostationary satellites. This can be used for comparison with climate models.
Yu-Wen Chen, K. Sebastian Schmidt, Hong Chen, Steven T. Massie, Susan S. Kulawik, and Hironobu Iwabuchi
EGUsphere, https://doi.org/10.5194/egusphere-2024-1936, https://doi.org/10.5194/egusphere-2024-1936, 2024
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Retrievals of CO2 column-averaged dry air mole fractions from space can be done with spaceborne spectrometers such as OCO-2. Clouds in the vicinity of a footprint lead to spectral perturbations that bias those retrievals well beyond the required accuracy for global assessments of CO2 sources and sinks. This paper presents two physics-based mitigation techniques for these biases based on accompanying imagery, which can be used operationally.
Clémantyne Aubry, Julien Delanoë, Silke Groß, Florian Ewald, Frédéric Tridon, Olivier Jourdan, and Guillaume Mioche
Atmos. Meas. Tech., 17, 3863–3881, https://doi.org/10.5194/amt-17-3863-2024, https://doi.org/10.5194/amt-17-3863-2024, 2024
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Radar–lidar synergy is used to retrieve ice, supercooled water and mixed-phase cloud properties, making the most of the radar sensitivity to ice crystals and the lidar sensitivity to supercooled droplets. A first analysis of the output of the algorithm run on the satellite data is compared with in situ data during an airborne Arctic field campaign, giving a mean percent error of 49 % for liquid water content and 75 % for ice water content.
Gerald G. Mace
Atmos. Meas. Tech., 17, 3679–3695, https://doi.org/10.5194/amt-17-3679-2024, https://doi.org/10.5194/amt-17-3679-2024, 2024
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The number of cloud droplets per unit volume, Nd, in a cloud is important for understanding aerosol–cloud interaction. In this study, we develop techniques to derive cloud droplet number concentration from lidar measurements combined with other remote sensing measurements such as cloud radar and microwave radiometers. We show that deriving Nd is very uncertain, although a synergistic algorithm seems to produce useful characterizations of Nd and effective particle size.
Richard M. Schulte, Matthew D. Lebsock, John M. Haynes, and Yongxiang Hu
Atmos. Meas. Tech., 17, 3583–3596, https://doi.org/10.5194/amt-17-3583-2024, https://doi.org/10.5194/amt-17-3583-2024, 2024
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This paper describes a method to improve the detection of liquid clouds that are easily missed by the CloudSat satellite radar. To address this, we use machine learning techniques to estimate cloud properties (optical depth and droplet size) based on other satellite measurements. The results are compared with data from the MODIS instrument on the Aqua satellite, showing good correlations.
Sunny Sun-Mack, Patrick Minnis, Yan Chen, Gang Hong, and William L. Smith Jr.
Atmos. Meas. Tech., 17, 3323–3346, https://doi.org/10.5194/amt-17-3323-2024, https://doi.org/10.5194/amt-17-3323-2024, 2024
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Multilayer clouds (MCs) affect the radiation budget differently than single-layer clouds (SCs) and need to be identified in satellite images. A neural network was trained to identify MCs by matching imagery with lidar/radar data. This method correctly identifies ~87 % SCs and MCs with a net accuracy gain of 7.5 % over snow-free surfaces. It is more accurate than most available methods and constitutes a first step in providing a reasonable 3-D characterization of the cloudy atmosphere.
Gianluca Di Natale, Marco Ridolfi, and Luca Palchetti
Atmos. Meas. Tech., 17, 3171–3186, https://doi.org/10.5194/amt-17-3171-2024, https://doi.org/10.5194/amt-17-3171-2024, 2024
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This work aims to define a new approach to retrieve the distribution of the main ice crystal shapes occurring inside ice and cirrus clouds from infrared spectral measurements. The capability of retrieving these shapes of the ice crystals from satellites will allow us to extend the currently available climatologies to be used as physical constraints in general circulation models. This could could allow us to improve their accuracy and prediction performance.
Valery Shcherbakov, Frédéric Szczap, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 17, 3011–3028, https://doi.org/10.5194/amt-17-3011-2024, https://doi.org/10.5194/amt-17-3011-2024, 2024
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We performed Monte Carlo simulations of single-wavelength lidar signals from multi-layered clouds with special attention focused on the multiple-scattering (MS) effect in regions of the cloud-free molecular atmosphere. The MS effect on lidar signals always decreases with the increasing distance from the cloud far edge. The decrease is the direct consequence of the fact that the forward peak of particle phase functions is much larger than the receiver field of view.
Babak Jahani, Steffen Karalus, Julia Fuchs, Tobias Zech, Marina Zara, and Jan Cermak
EGUsphere, https://doi.org/10.5194/egusphere-2023-2885, https://doi.org/10.5194/egusphere-2023-2885, 2024
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Fog and low stratus (FLS) are both persistent clouds close to the Earth’s surface. In the context of photovoltaic power production, FLS is particularly important, as FLS, impact large regions simultaneously, making regional power grid balancing hard. This study introduces a new machine leanring based algorithm developed for the MSG geostationary satellites that can provide a coherent and detailed view of FLS development over large areas over the 24 H day cycle.
Fani Alexandri, Felix Müller, Goutam Choudhury, Peggy Achtert, Torsten Seelig, and Matthias Tesche
Atmos. Meas. Tech., 17, 1739–1757, https://doi.org/10.5194/amt-17-1739-2024, https://doi.org/10.5194/amt-17-1739-2024, 2024
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We present a novel method for studying aerosol–cloud interactions. It combines cloud-relevant aerosol concentrations from polar-orbiting lidar observations with the development of individual clouds from geostationary observations. Application to 1 year of data gives first results on the impact of aerosols on the concentration and size of cloud droplets and on cloud phase in the regime of heterogeneous ice formation. The method could enable the systematic investigation of warm and cold clouds.
Kélian Sommer, Wassim Kabalan, and Romain Brunet
EGUsphere, https://doi.org/10.5194/egusphere-2024-101, https://doi.org/10.5194/egusphere-2024-101, 2024
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Our research introduces a novel deep-learning approach for classifying and segmenting ground-based infrared thermal images, a crucial step in cloud monitoring. Tests on self-captured data showcase its excellent accuracy in distinguishing image types and in structure segmentation. With potential applications in astronomical observations, our work pioneers a robust solution for ground-based sky quality assessment, promising advancements in the photometric observations experiments.
Cristina Gil-Díaz, Michäel Sicard, Adolfo Comerón, Daniel Camilo Fortunato dos Santos Oliveira, Constantino Muñoz-Porcar, Alejandro Rodríguez-Gómez, Jasper R. Lewis, Ellsworth J. Welton, and Simone Lolli
Atmos. Meas. Tech., 17, 1197–1216, https://doi.org/10.5194/amt-17-1197-2024, https://doi.org/10.5194/amt-17-1197-2024, 2024
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In this paper, a statistical study of cirrus geometrical and optical properties based on 4 years of continuous ground-based lidar measurements with the Barcelona (Spain) Micro Pulse Lidar (MPL) is analysed. The cloud optical depth, effective column lidar ratio and linear cloud depolarisation ratio have been calculated by a new approach to the two-way transmittance method, which is valid for both ground-based and spaceborne lidar systems. Their associated errors are also provided.
Audrey Teisseire, Patric Seifert, Alexander Myagkov, Johannes Bühl, and Martin Radenz
Atmos. Meas. Tech., 17, 999–1016, https://doi.org/10.5194/amt-17-999-2024, https://doi.org/10.5194/amt-17-999-2024, 2024
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The vertical distribution of particle shape (VDPS) method, introduced in this study, aids in characterizing the density-weighted shape of cloud particles from scanning slanted linear depolarization ratio (SLDR)-mode cloud radar observations. The VDPS approach represents a new, versatile way to study microphysical processes by combining a spheroidal scattering model with real measurements of SLDR.
Sarah Brüning, Stefan Niebler, and Holger Tost
Atmos. Meas. Tech., 17, 961–978, https://doi.org/10.5194/amt-17-961-2024, https://doi.org/10.5194/amt-17-961-2024, 2024
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We apply the Res-UNet to derive a comprehensive 3D cloud tomography from 2D satellite data over heterogeneous landscapes. We combine observational data from passive and active remote sensing sensors by an automated matching algorithm. These data are fed into a neural network to predict cloud reflectivities on the whole satellite domain between 2.4 and 24 km height. With an average RMSE of 2.99 dBZ, we contribute to closing data gaps in the representation of clouds in observational data.
Michael Eisinger, Fabien Marnas, Kotska Wallace, Takuji Kubota, Nobuhiro Tomiyama, Yuichi Ohno, Toshiyuki Tanaka, Eichi Tomita, Tobias Wehr, and Dirk Bernaerts
Atmos. Meas. Tech., 17, 839–862, https://doi.org/10.5194/amt-17-839-2024, https://doi.org/10.5194/amt-17-839-2024, 2024
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The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is an ESA–JAXA satellite mission to be launched in 2024. We presented an overview of the EarthCARE processors' development, with processors developed by teams in Europe, Japan, and Canada. EarthCARE will allow scientists to evaluate the representation of cloud, aerosol, precipitation, and radiative flux in weather forecast and climate models, with the objective to better understand cloud processes and improve weather and climate models.
Anja Hünerbein, Sebastian Bley, Hartwig Deneke, Jan Fokke Meirink, Gerd-Jan van Zadelhoff, and Andi Walther
Atmos. Meas. Tech., 17, 261–276, https://doi.org/10.5194/amt-17-261-2024, https://doi.org/10.5194/amt-17-261-2024, 2024
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The ESA cloud, aerosol and radiation mission EarthCARE will provide active profiling and passive imaging measurements from a single satellite platform. The passive multi-spectral imager (MSI) will add information in the across-track direction. We present the cloud optical and physical properties algorithm, which combines the visible to infrared MSI channels to determine the cloud top pressure, optical thickness, particle size and water path.
Moritz Haarig, Anja Hünerbein, Ulla Wandinger, Nicole Docter, Sebastian Bley, David Donovan, and Gerd-Jan van Zadelhoff
Atmos. Meas. Tech., 16, 5953–5975, https://doi.org/10.5194/amt-16-5953-2023, https://doi.org/10.5194/amt-16-5953-2023, 2023
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The atmospheric lidar (ATLID) and Multi-Spectral Imager (MSI) will be carried by the EarthCARE satellite. The synergistic ATLID–MSI Column Products (AM-COL) algorithm described in the paper combines the strengths of ATLID in vertically resolved profiles of aerosol and clouds (e.g., cloud top height) with the strengths of MSI in observing the complete scene beside the satellite track and in extending the lidar information to the swath. The algorithm is validated against simulated test scenes.
Patrick Chazette and Jean-Christophe Raut
Atmos. Meas. Tech., 16, 5847–5861, https://doi.org/10.5194/amt-16-5847-2023, https://doi.org/10.5194/amt-16-5847-2023, 2023
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The vertical profiles of the effective radii of ice crystals and ice water content in Arctic semi-transparent stratiform clouds were assessed using quantitative ground-based lidar measurements. The field campaign was part of the Pollution in the ARCtic System (PARCS) project which took place from 13 to 26 May 2016 in Hammerfest (70° 39′ 48″ N, 23° 41′ 00″ E). We show that under certain cloud conditions, lidar measurement combined with a dedicated algorithmic approach is an efficient tool.
Damao Zhang, Andrew M. Vogelmann, Fan Yang, Edward Luke, Pavlos Kollias, Zhien Wang, Peng Wu, William I. Gustafson Jr., Fan Mei, Susanne Glienke, Jason Tomlinson, and Neel Desai
Atmos. Meas. Tech., 16, 5827–5846, https://doi.org/10.5194/amt-16-5827-2023, https://doi.org/10.5194/amt-16-5827-2023, 2023
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Cloud droplet number concentration can be retrieved from remote sensing measurements. Aircraft measurements are used to validate four ground-based retrievals of cloud droplet number concentration. We demonstrate that retrieved cloud droplet number concentrations align well with aircraft measurements for overcast clouds, but they may substantially differ for broken clouds. The ensemble of various retrievals can help quantify retrieval uncertainties and identify reliable retrieval scenarios.
Eric M. Wilcox, Tianle Yuan, and Hua Song
Atmos. Meas. Tech., 16, 5387–5401, https://doi.org/10.5194/amt-16-5387-2023, https://doi.org/10.5194/amt-16-5387-2023, 2023
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A new database is constructed from over 20 years of satellite records that comprises millions of deep convective clouds and spans the global tropics and subtropics. The database is a collection of clouds ranging from isolated cells to giant cloud systems. The cloud database provides a means of empirically studying the factors that determine the spatial structure and coverage of convective cloud systems, which are strongly related to the overall radiative forcing by cloud systems.
Florian Baur, Leonhard Scheck, Christina Stumpf, Christina Köpken-Watts, and Roland Potthast
Atmos. Meas. Tech., 16, 5305–5326, https://doi.org/10.5194/amt-16-5305-2023, https://doi.org/10.5194/amt-16-5305-2023, 2023
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Near-infrared satellite images have information on clouds that is complementary to what is available from the visible and infrared parts of the spectrum. Using this information for data assimilation and model evaluation requires a fast, accurate forward operator to compute synthetic images from numerical weather prediction model output. We discuss a novel, neural-network-based approach for the 1.6 µm near-infrared channel that is suitable for this purpose and also works for other solar channels.
Zhipeng Qu, David P. Donovan, Howard W. Barker, Jason N. S. Cole, Mark W. Shephard, and Vincent Huijnen
Atmos. Meas. Tech., 16, 4927–4946, https://doi.org/10.5194/amt-16-4927-2023, https://doi.org/10.5194/amt-16-4927-2023, 2023
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The EarthCARE satellite mission Level 2 algorithm development requires realistic 3D cloud and aerosol scenes along the satellite orbits. One of the best ways to produce these scenes is to use a high-resolution numerical weather prediction model to simulate atmospheric conditions at 250 m horizontal resolution. This paper describes the production and validation of three EarthCARE test scenes.
Adrien Guyot, Jordan P. Brook, Alain Protat, Kathryn Turner, Joshua Soderholm, Nicholas F. McCarthy, and Hamish McGowan
Atmos. Meas. Tech., 16, 4571–4588, https://doi.org/10.5194/amt-16-4571-2023, https://doi.org/10.5194/amt-16-4571-2023, 2023
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We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
Cited articles
Aides, A., Levis, A., Holodovsky, V., Schechner, Y. Y., Althausen, D., and
Vainiger, A.: Distributed Sky Imaging Radiometry and Tomography, in: IEEE
Xplore/ 2020 IEEE International Conference on Computational Photography
(ICCP), Saint Louis, MO, USA, 24–26 April 2020, pp. 1–12, 2020. a
Allmen, M. C. and Kegelmeyer Jr., W. P.: The Computation of Cloud-Base Height
from Paired Whole-Sky Imaging Cameras, J. Atmos. Ocean.
Tech., 13, 97–113,
https://doi.org/10.1175/1520-0426(1996)013<0097:TCOCBH>2.0.CO;2, 1996. a, b
Beekmans, C., Schneider, J., Läbe, T., Lennefer, M., Stachniss, C., and Simmer, C.: Cloud photogrammetry with dense stereo for fisheye cameras, Atmos. Chem. Phys., 16, 14231–14248, https://doi.org/10.5194/acp-16-14231-2016, 2016. a, b
Bieliński, T.: A parallax shift effect correction based on cloud height for
geostationary satellites and radar observations, Remote Sens., 12, 365,
https://doi.org/10.3390/rs12030365, 2020. a
Blanc, P., Massip, P., Kazantzidis, A., Tzoumanikas, P., Kuhn, P., Wilbert, S.,
Schüler, D., and Prahl, C.: Short-term forecasting of high resolution
local DNI maps with multiple fish-eye cameras in stereoscopic mode, AIP
Conf. Proc., 1850, 140004, https://doi.org/10.1063/1.4984512, 2017. a, b, c
Blum, N., Schmidt, T., Nouri, B., Wilbert, S., Heinemann, D., Schmidt, T.,
Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Optimierte Gruppierung
verschiedener Wolkenkameras im Oldenburger Nowcasting Netzwerk, in:
Tagungsunterlagen/ 34. PV-Symposium Bad Staffelstein, Bad
Staffelstein, Germany, 19–21 March 2019, pp. 552–562, 2019a. a
Blum, N., Schmidt, T., Nouri, B., Wilbert, S., Peerlings, E., Heinemann, D.,
Schmidt, T., Kuhn, P., Kazantzidis, A., Zarzalejo, L. F., and Pitz-Paal, R.:
Nowcasting of Irradiance Using a Network of All-Sky-Imagers, in: EU PVSEC
2019 Proceedings/ 36th European Photovoltaic Solar Energy Conference and
Exhibition, Marseille, France, 9–13 September 2019, pp. 1403–1409,
https://doi.org/10.4229/EUPVSEC20192019-5DO.2.1, 2019b. a
Chan, K. L., Wiegner, M., Flentje, H., Mattis, I., Wagner, F., Gasteiger, J., and Geiß, A.: Evaluation of ECMWF-IFS (version 41R1) operational model forecasts of aerosol transport by using ceilometer network measurements, Geosci. Model Dev., 11, 3807–3831, https://doi.org/10.5194/gmd-11-3807-2018, 2018. a
Cirés, E., Marcos, J., de la Parra, I., García, M., and Marroyo, L.:
The potential of forecasting in reducing the LCOE in PV plants under
ramp-rate restrictions, Energy, 188, 116053,
https://doi.org/10.1016/j.energy.2019.116053, 2019. a
Costa-Surós, M., Calbó, J., González, J., and Martin-Vide, J.:
Behavior of cloud base height from ceilometer measurements, Atmos.
Res., 127, 64–76, https://doi.org/10.1016/j.atmosres.2013.02.005, 2013. a
de Haij, M., Apituley, A., Koetse, W., and Bloemink, H.: Transition towards a
new ceilometer network in the Netherlands: challenges and experiences, in:
Instruments and Observing Methods Report No. 125/ WMO Technical Conference on
Meteorological and Environmental Instruments and Methods of Observation (CIMO
TECO 2016), Madrid, Spain, 27–30 September 2016,
available at: https://library.wmo.int/index.php?lvl=notice_display&id=19676#.XirnGzJKiUk (last access: 28 May 2021),
2016. a, b, c, d, e
Fabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Applying self-supervised learning for semantic cloud segmentation of all-sky images, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2021-1, in review, 2021. a
Ghosh, S., Rahman, S., and Pipattanasomporn, M.: Distribution voltage
regulation through active power curtailment with PV inverters and solar
generation forecasts, IEEE T. Sustain. Energ., 8, 13–22,
https://doi.org/10.1109/TSTE.2016.2577559, 2016. a
Görsdorf, U., Mattis, I., Pittke, G., Bravo-Aranda, J. A., Brettle, M.,
Cermak, J., Drouin, M.-A., Geiß, A., Haefele, A., and Hervo, M.: The
ceilometer inter-comparison campaign CeiLinEx2015 — Cloud detection and
cloud base height, in: Instruments and Observing Methods Report No. 125/ WMO
Technical Conference on Meteorological and Environmental Instruments and
Methods of Observation (CIMO TECO 2016), Madrid, Spain, 27–30 September 2016, pp. 27–30, available at:
https://library.wmo.int/index.php?lvl=notice_display&id=19676#.XirnGzJKiUk (last access: 28 May 2021),
2016. a, b, c
Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P. N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J. F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., and Wind, G.: Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms, Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, 2014. a
Heese, B., Flentje, H., Althausen, D., Ansmann, A., and Frey, S.: Ceilometer lidar comparison: backscatter coefficient retrieval and signal-to-noise ratio determination, Atmos. Meas. Tech., 3, 1763–1770, https://doi.org/10.5194/amt-3-1763-2010, 2010. a
Hogan, R. J., O'Connor, E. J., and Illingworth, A. J.: Verification of
cloud-fraction forecasts, Q. J. Roy. Meteor.
Soc., 135, 1494–1511, https://doi.org/10.1002/qj.481, 2009. a
Howie, R. M., Paxman, J., Bland, P. A., Towner, M. C., Cupak, M., Sansom,
E. K., and Devillepoix, H. A.: How to build a continental scale fireball
camera network, Exp. Astron., 43, 237–266,
https://doi.org/10.1007/s10686-017-9532-7, 2017. a
Isaac, G. A., Bailey, M., Boudala, F. S., Burrows, W. R., Cober, S. G.,
Crawford, R. W., Donaldson, N., Gultepe, I., Hansen, B., Heckman, I., Huang,
L. X., Ling, A., Mailhot, J., Milbrandt, J. A., Reid, J., and Fournier, M.:
The Canadian Airport Nowcasting System (CAN-Now), Meteorol.
Appl., 21, 30–49, https://doi.org/10.1002/met.1342, 2014. a
Kaur, A., Nonnenmacher, L., Pedro, H. T., and Coimbra, C. F.: Benefits of solar
forecasting for energy imbalance markets, Renew. Energ., 86, 819–830,
https://doi.org/10.1016/j.renene.2015.09.011, 2016. a
Khlopenkov, K., Spangenberg, D., and Smith Jr., W. L.: Fusion of Surface
Ceilometer Data and Satellite Cloud Retrievals in 2D Mesh Interpolating Model
with Clustering, in: Proc. SPIE 11152, Remote Sensing of Clouds and the
Atmosphere XXIV/ SPIE Remote Sensing 2019, Strasbourg, France, 9 October 2019, p. 111521F, https://doi.org/10.1117/12.2533370, 2019. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World map of the
Köppen-Geiger climate classification updated, Meteorol.
Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a, b
Kuhn, P., Nouri, B., Wilbert, S., Prahl, C., Kozonek, N., Schmidt, T., Yasser,
Z., Ramirez, L., Zarzalejo, L., and Meyer, A.: Validation of an all‐sky
imager–based nowcasting system for industrial PV plants, Prog.
Photovoltaics, 26, 608–621,
https://doi.org/10.1002/pip.2968, 2018a. a
Kuhn, P., Wirtz, M., Killius, N., Wilbert, S., Bosch, J. L., Hanrieder, N.,
Nouri, B., Kleissl, J., Ramirez, L., Schroedter-Homscheidt, M., Heinemann,
D., Kazantzidis, A., Blanc, P., and Pitz-Paal, R.: Benchmarking three
low-cost, low-maintenance cloud height measurement systems and ECMWF cloud
heights against a ceilometer, Sol. Energy, 168, 140–152,
https://doi.org/10.1016/j.solener.2018.02.050, 2018b. a, b, c, d, e, f
Kuhn, P., Nouri, B., Wilbert, S., Hanrieder, N., Prahl, C., Ramirez, L.,
Zarzalejo, L., Schmidt, T., Yasser, Z., Heinemann, D., Tzoumanikas, P.,
Kazantzidis, A., Kleissl, J., Blanc, P., and Pitz-Paal, R.: Determination of
the optimal camera distance for cloud height measurements with two all-sky
imagers, Sol. Energy, 179, 74–88, https://doi.org/10.1016/j.solener.2018.12.038,
2019. a, b, c, d, e, f, g, h, i, j, k
Law, E. W., Prasad, A. A., Kay, M., and Taylor, R. A.: Direct normal irradiance
forecasting and its application to concentrated solar thermal output
forecasting–A review, Sol. Energy, 108, 287–307,
https://doi.org/10.1016/j.solener.2014.07.008, 2014. a
Luhmann, T.: Nahbereichsphotogrammetrie: Grundlagen, Methoden und Anwendungen,
Wichmann Verlag, Heidelberg, Germany, 2000. a
Macke, A., Seifert, P., Baars, H., Barthlott, C., Beekmans, C., Behrendt, A., Bohn, B., Brueck, M., Bühl, J., Crewell, S., Damian, T., Deneke, H., Düsing, S., Foth, A., Di Girolamo, P., Hammann, E., Heinze, R., Hirsikko, A., Kalisch, J., Kalthoff, N., Kinne, S., Kohler, M., Löhnert, U., Madhavan, B. L., Maurer, V., Muppa, S. K., Schween, J., Serikov, I., Siebert, H., Simmer, C., Späth, F., Steinke, S., Träumner, K., Trömel, S., Wehner, B., Wieser, A., Wulfmeyer, V., and Xie, X.: The HD(CP)2 Observational Prototype Experiment (HOPE) – an overview, Atmos. Chem. Phys., 17, 4887–4914, https://doi.org/10.5194/acp-17-4887-2017, 2017. a
Martucci, G., Milroy, C., and O'Dowd, C. D.: Detection of cloud-base height
using Jenoptik CHM15K and Vaisala CL31 ceilometers, J. Atmos.
Ocean. Tech., 27, 305–318, https://doi.org/10.1175/2009JTECHA1326.1, 2010. a
Mejia, F. A., Kurtz, B., Levis, A., de la Parra, Í., and Kleissl, J.: Cloud
tomography applied to sky images: A virtual testbed, Sol. Energy, 176,
287–300, https://doi.org/10.1016/j.solener.2018.10.023, 2018. a, b
Mobotix: Technical Specifications MOBOTIX Q25 Hemispheric, Data sheet, Mobotix
AG, Langmeil, Germany, available at:
https://www.mobotix.com/sites/default/files/2017-10/Mx_TS_Q25_en_20170515.pdf (last access: 28 May 2021),
2017. a
Nguyen, D. and Kleissl, J.: Stereographic methods for cloud base height
determination using two sky imagers, Sol. Energy, 107, 495–509,
https://doi.org/10.1016/j.solener.2014.05.005, 2014. a, b, c
Noh, Y.-J., Forsythe, J. M., Miller, S. D., Seaman, C. J., Li, Y., Heidinger,
A. K., Lindsey, D. T., Rogers, M. A., and Partain, P. T.: Cloud-Base Height
Estimation from VIIRS. Part II: A Statistical Algorithm Based on A-Train
Satellite Data, J. Atmos. Ocean. Tech., 34, 585–598,
https://doi.org/10.1175/JTECH-D-16-0110.1, 2017. a
Nouri, B., Kuhn, P., Wilbert, S., Prahl, C., Pitz-Paal, R., Blanc, P., Schmidt,
T., Yasser, Z., Santigosa, L. R., and Heineman, D.: Nowcasting of DNI maps
for the solar field based on voxel carving and individual 3D cloud objects
from all sky images, AIP Conf. Proc., 2033, 190011,
https://doi.org/10.1063/1.5067196, 2018. a
Nouri, B., Kuhn, P., Wilbert, S., Hanrieder, N., Prahl, C., Zarzalejo, L.,
Kazantzidis, A., Blanc, P., and Pitz-Paal, R.: Cloud height and tracking
accuracy of three all sky imager systems for individual clouds, Sol. Energy,
177, 213–228, https://doi.org/10.1016/j.solener.2018.10.079, 2019a. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q
Nouri, B., Wilbert, S., Kuhn, P., Hanrieder, N., Schroedter-Homscheidt, M.,
Kazantzidis, A., Zarzalejo, L., Blanc, P., Kumar, S., and Goswami, N.:
Real-Time Uncertainty Specification of All Sky Imager Derived Irradiance
Nowcasts, Remote Sens., 11, 1059, https://doi.org/10.3390/rs11091059,
2019b. a, b
Nouri, B., Wilbert, S., Segura, L., Kuhn, P., Hanrieder, N., Kazantzidis, A.,
Schmidt, T., Zarzalejo, L., Blanc, P., and Pitz-Paal, R.: Determination of
cloud transmittance for all sky imager based solar nowcasting, Sol. Energy,
181, 251–263, https://doi.org/10.1016/j.solener.2019.02.004, 2019c. a, b
Nouri, B., Noureldin, K., Schlichting, T., Wilbert, S., Hirsch, T.,
Schroedter-Homscheidt, M., Kuhn, P., Kazantzidis, A., Zarzalejo, L. F.,
Blanc, P., Yasser, Z., Fernández, J., and Pitz-Paal, R.: Optimization of
parabolic trough power plant operations in variable irradiance conditions
using all sky imagers, Sol. Energy, 198, 434–453,
https://doi.org/10.1016/j.solener.2020.01.045, 2020a. a, b
Nouri, B., Wilbert, S., Blum, N., Kuhn, P., Schmidt, T., Yasser, Z., Schmidt,
T., Zarzalejo, L. F., Lopes, F. M., Silva, H. G., Schroedter-Homscheidt, M.,
Kazantzidis, A., Raeder, C., Blanc, P., and Pitz-Paal, R.: Evaluation of an
All Sky Imager Based Nowcasting System for Distinct Conditions and Five
sites, AIP Conf. Proc., 2303, 180006, https://doi.org/10.1063/5.0028670,
2020b. a, b
Peng, Z., Yu, D., Huang, D., Heiser, J., Yoo, S., and Kalb, P.: 3D cloud
detection and tracking system for solar forecast using multiple sky imagers,
Sol. Energy, 118, 496–519, https://doi.org/10.1016/j.solener.2015.05.037, 2015. a, b
Reynolds, D. W., Clark, D. A., Wilson, F. W., and Cook, L.: Forecast-Based
Decision Support for San Francisco International Airport: A NextGen Prototype
System That Improves Operations during Summer Stratus Season, B.
Am. Meteorol. Soc., 93, 1503–1518,
https://doi.org/10.1175/BAMS-D-11-00038.1, 2012. a
Scaramuzza, D., Martinelli, A., and Siegwart, R.: A Toolbox for Easily
Calibrating Omnidirectional Cameras, in: 2006 IEEE/ RSJ International
Conference on Intelligent Robots and Systems, Beijing, China,
9–15 October 2006, pp. 5695–5701, https://doi.org/10.1109/IROS.2006.282372, 2006. a
Schmidt, T., Kalisch, J., Lorenz, E., and Heinemann, D.: Evaluating the spatio-temporal performance of sky-imager-based solar irradiance analysis and forecasts, Atmos. Chem. Phys., 16, 3399–3412, https://doi.org/10.5194/acp-16-3399-2016, 2016. a
Schmidt, T., Heinemann, D., Vogt, T., Blum, N., Nouri, B., Wilbert, S., and
Kuhn, P.: Energiemeteorologisches Wolkenkameranetzwerk für die
hochaufgelöste Kurzfristprognose der solaren Einstrahlung, in:
DACH-Tagung, Garmisch-Partenkirchen, Deutschland, 18–22 March 2019, 2019. a
Sky cameras: Homepage, https://www.solar-repository.sg/sky-cameras, last
access: 8 July 2020. a
Wang, G., Kurtz, B., and Kleissl, J.: Cloud base height from sky imager and
cloud speed sensor, Sol. Energy, 131, 208–221,
https://doi.org/10.1016/j.solener.2016.02.027, 2016. a
Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., and Roth, A.:
Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS
data, ISPRS J. Photogramm., 139, 171–182,
https://doi.org/10.1016/j.isprsjprs.2018.02.017, 2018. a
World Meteorological Organization: Guide to meteorological instruments and
methods of observation, vol. I – Measurement of Meteorological Variables of
WMO – No. 8, WMO, Geneva, Switzerland, 29 edn., 2018. a
World Meteorological Organisation: Manual on Codes – International Codes, Volume I.1, Annex II to the WMO Technical Regulations: part A – Alphanumeric Code, 2019 edn., WMO, Geneva, Switzerland, 2019. a
Short summary
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it, photovoltaic production. All-sky imagers (ASIs), cameras monitoring the sky above their point of installation, can provide such forecasts and also measure CBH. We present a network of ASIs to measure CBH. The network provides numerous readings of CBH simultaneously. We combine these with a statistical procedure. Validation attests to significantly higher accuracy of the combination compared to two ASIs alone.
Cloud base height (CBH) is important, e.g., to forecast solar irradiance and, with it,...
