Articles | Volume 8, issue 9
https://doi.org/10.5194/amt-8-3893-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/amt-8-3893-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
23 Sep 2015
Research article |
| 23 Sep 2015
A novel approach for the extraction of cloud motion vectors using airglow imager measurements
S. Satheesh Kumar
National Atmospheric Research Laboratory (NARL), Gadanki, India
National Atmospheric Research Laboratory (NARL), Gadanki, India
National Atmospheric Research Laboratory (NARL), Gadanki, India
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This preprint is open for discussion and under review for Geoscientific Instrumentation, Methods and Data Systems (GI).
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The effect of lightning strikes on the data with increasing distance, intensity and ground conductivity shows that higher intensity strikes has had an impact of the data and instruments. It is crucial to determine the location and configuration where the installation can avoid the effects groundwater fluctuations as well as lightning strikes. The susceptibility model along with conductivity information are used to make a selection of a new site for trial measurements.
Kumar Abhijeet, Thota Narayana Rao, Nidamanuri Rama Rao, and Kasimahanthi Amar Jyothi
Atmos. Meas. Tech., 16, 871–888, https://doi.org/10.5194/amt-16-871-2023, https://doi.org/10.5194/amt-16-871-2023, 2023
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The present study focuses on retrieving and validating raindrop size distribution (DSD) relations for monsoonal rainfall, which are required for retrieving DSDs with polarimetric radar measurements. The seasonal variation in DSD is quite large and significant, and as a result the coefficients also vary considerably between the seasons and from those existing elsewhere. Among the existing DSD methods, the N-gamma method performs better than the other methods.
Kadiri Saikranthi, Basivi Radhakrishna, Thota Narayana Rao, and Sreedharan Krishnakumari Satheesh
Atmos. Chem. Phys., 19, 10423–10432, https://doi.org/10.5194/acp-19-10423-2019, https://doi.org/10.5194/acp-19-10423-2019, 2019
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Recent studies have shown that simulation of monsoons can be improved with an exact representation of SST–precipitation relationship. The vertical structure of precipitation with SST is distinctly different over the Arabian Sea than over the Bay of Bengal. The reflectivity profiles show variation with SST over the Arabian Sea and do not show considerable variation with SST over the Bay of Bengal. The variations in reflectivity profiles seem to originate at the cloud formation stage itself.
K. Sunilkumar, T. Narayana Rao, and S. Satheeshkumar
Hydrol. Earth Syst. Sci., 20, 1719–1735, https://doi.org/10.5194/hess-20-1719-2016, https://doi.org/10.5194/hess-20-1719-2016, 2016
Rupesh N. Ghodpage, Michael P. Hickey, Alok K. Taori, Devendraa Siingh, and Parashram T. Patil
Atmos. Chem. Phys., 16, 5611–5621, https://doi.org/10.5194/acp-16-5611-2016, https://doi.org/10.5194/acp-16-5611-2016, 2016
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Gravity-wave-induced oscillations have been characterized over Kolhapur (16.8°N and 74.2°E), India, using the adiabatic variations in OH airglow intensity and temperature data. The results show that there exist large deviations from one investigation to the other. We also use a full-wave model to simulate the response of OH emission to the wave motion and compare the results with observed values. This report discusses the observed wave characteristics and cause of the noted difference.
M. Sivakandan, I. Paulino, A. Taori, and K. Niranjan
Atmos. Meas. Tech., 9, 93–102, https://doi.org/10.5194/amt-9-93-2016, https://doi.org/10.5194/amt-9-93-2016, 2016
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The small-scale gravity waves are the least understood processes in the middle-atmospheric variability. Using airglow imaging, we provide new measurements of gravity wave propagation over equatorial latitudes in India. We find that propagation of waves is often in the opposite direction to the tropospheric convective regions. These waves are found to have horizontal wavelengths ranging from 12 to 42 km, with the phase velocities in the 20 to 90 km range.
A. Sandeep, T. N. Rao, and S. V. B. Rao
Atmos. Chem. Phys., 15, 7605–7617, https://doi.org/10.5194/acp-15-7605-2015, https://doi.org/10.5194/acp-15-7605-2015, 2015
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The afternoon-evening transition (AET) in the atmospheric boundary layer has been studied in an integrated approach using 3 years of tower, sodar and wind profiler measurements. Such a long-term data set has been used for the first time to understand the behavior of AET. It allowed us to study the seasonal variation. In contrast to the common belief that the transition evolves from bottom to top, the present study clearly showed that the start time of transition follows top-to-bottom evolution.
N. Parihar and A. Taori
Ann. Geophys., 33, 547–560, https://doi.org/10.5194/angeo-33-547-2015, https://doi.org/10.5194/angeo-33-547-2015, 2015
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This study investigates the long-distance propagation (~ 1200–2000km) of gravity waves in the Indian subcontinent using coordinated nightglow measurements at Allahabad and Gadanki (separated by ~ 12º latitude). On few occasions, an identical wave (period in range ~ 2.2–4.5h) was seen at both sites that shared a common source. Waves had large horizontal wavelength (~ 1194–2746km) and phase speed (77–331m/s). The m2 profile analysis suggests the ducted propagation of the common waves.
M. Pramitha, M. Venkat Ratnam, A. Taori, B. V. Krishna Murthy, D. Pallamraju, and S. Vijaya Bhaskar Rao
Atmos. Chem. Phys., 15, 2709–2721, https://doi.org/10.5194/acp-15-2709-2015, https://doi.org/10.5194/acp-15-2709-2015, 2015
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Sources and propagation characteristics of high-frequency gravity waves observed in the mesosphere using airglow emissions from Gadanki and Hyderabad, India, are investigated using reverse ray tracing. Wave amplitudes are also traced back, including both radiative and diffusive damping. Interestingly, large vertical shears in the horizontal wind are noticed near the ray terminal points (at 10-12km altitude) and are thus identified to be the source for generating the observed gravity waves.
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N. Parihar, A. Taori, S. Gurubaran, and G. K. Mukherjee
Ann. Geophys., 31, 197–208, https://doi.org/10.5194/angeo-31-197-2013, https://doi.org/10.5194/angeo-31-197-2013, 2013
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Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
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The study examines the use of the spheroidal shape approximation for calculating cloud radar observables in rain and identifies some limitations. To address these, it introduces the empirical scattering model (ESM) based on high-quality Doppler spectra from a 94 GHz radar. The ESM offers improved accuracy and directly incorporates natural rain's microphysical effects. This new model can enhance retrieval and calibration methods, benefiting cloud radar polarimetry experts and scattering modelers.
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The dynamics of thunderclouds are studied using cloud radar. Supercooled liquid water and conical graupel are likely present, while chain-like ice crystals may occur at cloud top. Ice crystals are vertically aligned seconds before lightning and resume their usual horizontal alignment afterwards in some cases. Updrafts and downdrafts are found near cloud core and edges respectively. Turbulence is strong. Radar measurement modes that are more suited for investigating thunderstorms are recommended.
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Simulations of storm characteristics and associated radar signatures were improved, especially under the freezing level, using an advanced cloud scheme. Discrepancies between observations and forecasts at and above the melting layer highlighted issues in both the radar forward operator and the microphysics. To overcome part of these issues, different parametrizations of the operator were suggested. This work aligns with the future integration of polarimetric data into assimilation systems.
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech., 18, 773–792, https://doi.org/10.5194/amt-18-773-2025, https://doi.org/10.5194/amt-18-773-2025, 2025
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In satellite remote sensing, estimating cloud-base height (CBH) is more challenging than estimating cloud-top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI on JAXA’s GCOM-C satellite to estimate CBHs together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
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The topic is finding methods to extend climate data records from single-instrument satellite observations, in this case the Advanced Very High Resolution Radiometer (AVHRR). Several modern instruments include AVHRR-heritage channels but some corrections are necessary to account for some differences. We have simulated AVHRR data from the VIIIRS sensor on NOAA polar satellites. We find that methods based on machine learning are capable of performing these corrections.
Johanna Roschke, Jonas Witthuhn, Marcus Klingebiel, Moritz Haarig, Andreas Foth, Anton Kötsche, and Heike Kalesse-Los
Atmos. Meas. Tech., 18, 487–508, https://doi.org/10.5194/amt-18-487-2025, https://doi.org/10.5194/amt-18-487-2025, 2025
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We present a technique to discriminate between the Cloudnet target classification of "drizzle or rain" and sea salt aerosols that is applicable to marine Cloudnet sites. The method is crucial for investigating the occurrence of precipitation and significantly improves the Cloudnet target classification scheme for measurements over the Barbados Cloud Observatory (BCO). A first-ever analysis of the Cloudnet product including the new "haze echo" target over 2 years at the BCO is presented.
Zhaolong Wu, Patric Seifert, Yun He, Holger Baars, Haoran Li, Cristofer Jimenez, Chengcai Li, and Albert Ansmann
EGUsphere, https://doi.org/10.5194/egusphere-2024-3841, https://doi.org/10.5194/egusphere-2024-3841, 2025
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This study introduces a novel method to detect horizontally oriented ice crystals (HOICs) using two ground-based polarization lidars at different zenith angles, based on a year-long dataset collected in Beijing. Combined with cloud radar and reanalysis data, the fine categorization results reveal HOICs occur in calm winds and moderately cold temperatures and are influenced by turbulence near cloud bases. The results enhance our understanding of cloud processes and improve the atmospheric model.
Andrzej Zbigniew Kotarba and Izabela Wojciechowska
EGUsphere, https://doi.org/10.5194/egusphere-2024-3693, https://doi.org/10.5194/egusphere-2024-3693, 2025
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The research investigates methods for detecting deep convective clouds (DCCs) using satellite infrared data, essential for understanding long-term climate trends. By validating three popular detection methods against lidar-radar data, it found moderate accuracy (below 75 %), emphasizing the importance of fine-tuning thresholds regionally. The study discovers how small threshold changes significantly affect climatology of severe storms.
Aaron Sarna, Vincent Meijer, Rémi Chevallier, Allie Duncan, Kyle McConnaughay, Scott Geraedts, and Kevin McCloskey
EGUsphere, https://doi.org/10.5194/egusphere-2024-3664, https://doi.org/10.5194/egusphere-2024-3664, 2025
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Contrails, the linear clouds formed by aircraft, are have a substantial climate impact. Flight deviations to avoid forming contrails should decrease this impact. We introduce a method for matching contrails seen by satellites to the flights that made them. This can determine if avoidance was successful and improve contrail forecasts. We also introduce a synthetic contrail dataset to evaluate the accuracy of the matches. We show that our attributions are much more accurate than previous methods.
Victor J. H. Trees, Ping Wang, Piet Stammes, Lieuwe G. Tilstra, David P. Donovan, and A. Pier Siebesma
Atmos. Meas. Tech., 18, 73–91, https://doi.org/10.5194/amt-18-73-2025, https://doi.org/10.5194/amt-18-73-2025, 2025
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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 found that cloud shadows are significantly bluer than their shadow-free surroundings, but the traditional algorithm already (partly) automatically corrects for this increased blueness.
Nina Maherndl, Alessandro Battaglia, Anton Kötsche, and Maximilian Maahn
EGUsphere, https://doi.org/10.5194/egusphere-2024-3916, https://doi.org/10.5194/egusphere-2024-3916, 2025
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Accurate measurements of cloud water content IWC and snowfall rate SR are challenging due to high spatial variability and limitations of our measurement techniques. Here we present a novel method to derive IWC and SR from W-band cloud radar observations, considering the degree of riming. We also investigate the use of the liquid water path as a proxy for the occurrence of riming, which is easier to measure, so that the method can be applied to both ground-based and space-based instruments.
He Huang, Quan Wang, Chao Liu, and Chen Zhou
Atmos. Meas. Tech., 17, 7129–7141, https://doi.org/10.5194/amt-17-7129-2024, https://doi.org/10.5194/amt-17-7129-2024, 2024
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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 a priori states, and a radiative transfer model is used to create lookup tables for later iteration processes. The new method combines the advantages of traditional and machine learning algorithms, and it is applicable to both daytime and nighttime conditions.
Sean R. Foley, Kirk D. Knobelspiesse, Andrew M. Sayer, Meng Gao, James Hays, and Judy Hoffman
Atmos. Meas. Tech., 17, 7027–7047, https://doi.org/10.5194/amt-17-7027-2024, https://doi.org/10.5194/amt-17-7027-2024, 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.
Juan M. Socuellamos, Raquel Rodriguez Monje, Matthew D. Lebsock, Ken B. Cooper, and Pavlos Kollias
Atmos. Meas. Tech., 17, 6965–6981, https://doi.org/10.5194/amt-17-6965-2024, https://doi.org/10.5194/amt-17-6965-2024, 2024
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This article presents a novel technique to estimate liquid water content (LWC) profiles 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 retrieving the LWC with 3 times better accuracy than previous works reported in the literature, providing improved ability to understand the vertical profile of LWC and characterize microphysical and dynamical processes more precisely in shallow clouds.
Claudia Emde, Veronika Pörtge, Mihail Manev, and Bernhard Mayer
Atmos. Meas. Tech., 17, 6769–6789, https://doi.org/10.5194/amt-17-6769-2024, https://doi.org/10.5194/amt-17-6769-2024, 2024
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We introduce an innovative method to retrieve the cloud fraction and optical thickness of liquid water clouds over the ocean based on polarimetry. This is well suited for satellite observations providing multi-angle polarization measurements. Cloud fraction and cloud optical thickness can be derived from measurements at two viewing angles: one within the cloudbow and one in the sun glint region.
Vincent Forcadell, Clotilde Augros, Olivier Caumont, Kévin Dedieu, Maxandre Ouradou, Cloé David, Jordi Figueras i Ventura, Olivier Laurantin, and Hassan Al-Sakka
Atmos. Meas. Tech., 17, 6707–6734, https://doi.org/10.5194/amt-17-6707-2024, https://doi.org/10.5194/amt-17-6707-2024, 2024
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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.
Teresa Vogl, Martin Radenz, Fabiola Ramelli, Rosa Gierens, and Heike Kalesse-Los
Atmos. Meas. Tech., 17, 6547–6568, https://doi.org/10.5194/amt-17-6547-2024, https://doi.org/10.5194/amt-17-6547-2024, 2024
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In this study, we present a toolkit of two Python algorithms to extract information from Doppler spectra measured by ground-based cloud radars. In these Doppler spectra, several peaks can be formed due to populations of droplets/ice particles with different fall velocities coexisting in the same measurement time and height. The two algorithms can detect peaks and assign them to certain particle types, such as small cloud droplets or fast-falling ice particles like graupel.
Athina Argyrouli, Diego Loyola, Fabian Romahn, Ronny Lutz, Víctor Molina García, Pascal Hedelt, Klaus-Peter Heue, and Richard Siddans
Atmos. Meas. Tech., 17, 6345–6367, https://doi.org/10.5194/amt-17-6345-2024, https://doi.org/10.5194/amt-17-6345-2024, 2024
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This paper describes a new treatment of the spatial misregistration of cloud properties for Sentinel-5 Precursor, when the footprints of different spectral bands are not perfectly aligned. The methodology exploits synergies between spectrometers and imagers, like TROPOMI and VIIRS. The largest improvements have been identified for heterogeneous scenes at cloud edges. This approach is generic and can also be applied to future Sentinel-4 and Sentinel-5 instruments.
Vincent R. Meijer, Sebastian D. Eastham, Ian A. Waitz, and Steven R. H. Barrett
Atmos. Meas. Tech., 17, 6145–6162, https://doi.org/10.5194/amt-17-6145-2024, https://doi.org/10.5194/amt-17-6145-2024, 2024
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Aviation's climate impact is partly due to contrails: the clouds that form behind aircraft and which can linger for hours under certain atmospheric conditions. Accurately forecasting these conditions could allow aircraft to avoid forming these contrails and thus reduce their environmental footprint. Our research uses deep learning to identify three-dimensional contrail locations in two-dimensional satellite imagery, which can be used to assess and improve these forecasts.
Eleanor May, Bengt Rydberg, Inderpreet Kaur, Vinia Mattioli, Hanna Hallborn, and Patrick Eriksson
Atmos. Meas. Tech., 17, 5957–5987, https://doi.org/10.5194/amt-17-5957-2024, https://doi.org/10.5194/amt-17-5957-2024, 2024
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The upcoming Ice Cloud Imager (ICI) mission is set to improve measurements of atmospheric ice through passive microwave and sub-millimetre wave observations. In this study, we perform detailed simulations of ICI observations. Machine learning is used to characterise the atmospheric ice present for a given simulated observation. This study acts as a final pre-launch assessment of ICI's capability to measure atmospheric ice, providing valuable information to climate and weather applications.
Luke Edgar Whitehead, Adrian James McDonald, and Adrien Guyot
Atmos. Meas. Tech., 17, 5765–5784, https://doi.org/10.5194/amt-17-5765-2024, https://doi.org/10.5194/amt-17-5765-2024, 2024
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Supercooled liquid water cloud is important to represent in weather and climate models, particularly in the Southern Hemisphere. Previous work has developed a new machine learning method for measuring supercooled liquid water in Antarctic clouds using simple lidar observations. We evaluate this technique using a lidar dataset from Christchurch, New Zealand, and develop an updated algorithm for accurate supercooled liquid water detection at mid-latitudes.
Julien Lenhardt, Johannes Quaas, and Dino Sejdinovic
Atmos. Meas. Tech., 17, 5655–5677, https://doi.org/10.5194/amt-17-5655-2024, https://doi.org/10.5194/amt-17-5655-2024, 2024
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Clouds play a key role in the regulation of the Earth's climate. Aspects like the height of their base are of essential interest to quantify their radiative effects but remain difficult to derive from satellite data. In this study, we combine observations from the surface and satellite retrievals of cloud properties to build a robust and accurate method to retrieve the cloud base height, based on a computer vision model and ordinal regression.
Johanna Mayer, Bernhard Mayer, Luca Bugliaro, Ralf Meerkötter, and Christiane Voigt
Atmos. Meas. Tech., 17, 5161–5185, https://doi.org/10.5194/amt-17-5161-2024, https://doi.org/10.5194/amt-17-5161-2024, 2024
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This study uses radiative transfer calculations to characterize the relation of two satellite channel combinations (namely infrared window brightness temperature differences – BTDs – of SEVIRI) to the thermodynamic cloud phase. A sensitivity analysis reveals the complex interplay of cloud parameters and their contribution to the observed phase dependence of BTDs. This knowledge helps to design optimal cloud-phase retrievals and to understand their potential and limitations.
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.
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.
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.
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.
Cited articles
Argyle, E.: Techniques for edge detection, Proc. IEEE, 59, 285–286, 1971.
Batista, P. P., Takahashi, H., Gobbi, D., and Medeiros, A. F.: First airglow all sky images at 23° S, Adv. Space. Res., 26, 925–928, 2000.
Bedka, K. and Mecikalski, J. R.: Application of satellite derived atmospheric motion vectors for estimating Mesoscale flows, J. Appl. Meteorol., 44, 1761–1772, 2005.
Bhavanikumar, Y.: Portable lidar system for atmospheric boundary layer measurements, Opt. Eng., 45, 076201, https://doi.org/10.1117/1.2221555, 2006.
Borde, R. and García-Pereda, J.: Impact of wind guess on the tracking of atmospheric motion vectors, J. Atmos. Ocean. Tech., 31, 458–467, 2014.
Bresky, W. C., Daniels, J. M., Bailey, A. A., and Wanzong, S. T.: New methods toward minimizing the slow speed bias associated with atmospheric motion vectors, J. Appl. Meteorol. Clim., 51, 2137–2151, 2012.
Canny, J. F.: A computational approach to edge detection, IEEE T. Pattern Anal., PAMI-8, 679–697, 1986.
Das, S. K., Chiang, C.-W., and Nee, J.-B: Influence of tropical easterly jet on upper tropical cirrus: An observational study from CALIPSO, Aura-MLS, and NCEP/NCAR data, J. Geophys. Res., 116, D12204, https://doi.org/10.1029/2011JD015923, 2011.
Deb, S. K., Kishtawal, C. M., Kaur, I., Pal, P. K., and Kiran Kumar, A. S.: Assessment of a new quality control technique in the retrieval of atmospheric motion vectors, Meteorol. Appl., 22, 178–186, 2015.
Escrig, H., Batlles F. J., Alonso J., Baena, F. M., Bosch, J. L., Salbidegoitia, I. B., and Burgaleta, J. I.: Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast, Energy, 55, 853–859, https://doi.org/10.1016/j.energy.2013.01.054, 2013.
Garcia-Pereda, J. and Borde, R.: Impact of the tracer size and the temporal gap between images in the extraction of atmospheric motion vectors, J. Atmos. Ocean. Tech., 30, 1171–1179, https://doi.org/10.1175/JTECH-D-13-00235.1, 2014.
Glantz, P.: Satellite retrieved cloud optical thickness sensitive to surface wind speed in the subarctic marine boundary layer, Environ. Res. Lett., 5, 1–5, https://doi.org/10.1088/1748-9326/5/3/034002, 2010.
Grimson, W. E. and Hildreth, E. C.: Comments on Digital step edges from zero crossings of second directional derivatives, IEEE T.. Pattern Anal., PAMI-7, 121–129, 1985.
Hubert, L. and Whitney Jr., L. F.: Wind estimation from geostationary-satellite pictures, Mon. Wea. Rev., 99, 665–672, 1971.
Hutchison, K. D., Hardy, K. R., and Gao, B. C.: Improved detection of optically thin cirrus clouds in nighttime multispectral meteorological satellite using total integrated water vapor information, J. Appl. Meteorol., 34, 1161–1168, 1995.
Izawa, T. and Fujita, T.: Relationship between observed winds and cloud velocities determined from pictures obtained by the ESSA 3, ESSA 5 and ATS-I satellites, Space Research IX, Amsterdam, North-Holland Publ. Co., 571–579, 1969.
Jolivet, D. and Feijt, A. J.: Cloud thermodynamic phase and particle size estimation using the 0.67 and 1.6 μm channels from meteorological satellites, Atmos. Chem. Phys. Discuss., 3, 4461–4488, https://doi.org/10.5194/acpd-3-4461-2003, 2003.
Kaur, I., Deb, S. K., Kishtawal, C. M., Pal, P. K., and Kumar, R.: Atmospheric motion vector retrieval using improved tracer selection algorithm, Theor. Appl. Climatol., 119, 299–312, https://doi.org/10.1007/s00704-014-1115-1, 2014.
Kazantzidis, A., Tzoumanikas, P., Bais, A. F., Fotopoulos, S., and Economou, G.: Cloud detection and classification with the use of whole-sky ground-based images, Atmos. Res., 113, 80–88, https://doi.org/10.1016/j.atmosres.2012.05.005, 2012.
Kishtawal, C. M., Deb, S. K., Pal, P. K., and Joshi, P. C.: Estimation of atmospheric motion vectors from Kalpana-1 imagers, J. Appl. Meteorol. Clim., 48, 2410–2421, 2009.
Klebe, D. I., Blatherwick, R. D., and Morris, V. R.: Ground-based all-sky mid-infrared and visible imagery for purposes of characterizing cloud properties, Atmos. Meas. Tech., 7, 637–645, https://doi.org/10.5194/amt-7-637-2014, 2014.
Kubota, M., Fukunishi, H., and Okano, S.: Characteristics of medium- and large-scale TIDs over Japan derived from O1630 nm nightglow observation, Earth Planets Space, 53, 741–751, 2001.
Leese, J. A., Novak, C. S., and Clark, B. B.: An automated technique for obtaining cloud motion from Geosynchronous satellite data using cross correlation, J. Appl. Meteorol., 10, 118–132, 1971.
Liu, L., Sun, X.-J., Gao, T.-C., and Zhao, S.-J.: Comparison of cloud properties from ground-based infrared cloud measurement and visual observations, J. Atmos. Ocean. Tech., 30, 1171–1179, https://doi.org/10.1175/JTECH-D-12-00157.1, 2013.
Long, J. M., Sabburg, J., Calbo, and Pages, D.: Retrieving cloud characteristics from ground-based daytime color all-sky images, J. Atmos. Ocean. Tech., 23, 633–652, 2006.
Menzel, W. P.: Cloud tracking with satellite imagery: from the pioneering work of Ted Fujita to the present, B. Am. Meteorol. Soc., 82, 33–47, 2001.
Otsu, N.: A Threshold selection method from gray-level histograms, IEEE T. Syst. Man Cyb., 9, 62–66, 1979.
Park, J.-H., Ou, M.-L., Kim, S., and Cho, H.: Sensitivity of satellite-derived wind retrieval over cloudy scenes, IEEE T. Geosci. Remote., 50, 2063–2073, 2012.
Pfister, G., McKenzie, R. L., Liley, J. B., Thomas, A., Forgan, B. W., and Long, C. N.: Cloud coverage based on all-sky imaging and its impact on surface solar irradiance, J. Appl. Meteorol., 42, 1421–1434, 2003.
Raghunath, K., Bhavanikumar, Y., Sivakumar, V., Rao, P. B., Mizutani, K., Aoki, T., Yasui, M., and Itabe, T.: Indo-Japanese lidar observations of aerosols over a tropical latitude, Indian J. Radio Space Phys., 25, 239–244, 2000.
Rao, P. B., Jain, A. R., Kishore, P., Balamuralidhar, P., Damle, S. H., and Viswanathan, G.: Indian MST radar 1. System description and sample vector wind measurements in ST mode, Radio Sci., 30, 1125–1138, https://doi.org/10.1029/95RS00787, 1995.
Rao, T. N., Radhakrishna, B., Nakamura, K., and Prabhakara Rao, N.: Differences in raindrop size distribution from southwest monsoon to northeast monsoon at Gadanki, Q. J. R. Meteorol. Soc., 135, 1630–1637, 2009.
Rassow, W. B. and Schiffer, R. A.: Advances in understanding clouds from ISCCP, B. Am. Meteorol. Soc., 80, 2261–2288, 1999.
Schmetz, J., Holmlund, K., Hoffman, J., Strauss, B., Mason,B., Gaertner, V., Koch, A., and van de Berg, L.: Operational cloud-motion winds from Meteosat infrared images, J. Appl. Meteorol., 32, 1206–1225, 1993.
Seiz, G., Baltsavias, E. P., and Gruen, A.: Cloud mapping from the ground: Use of Photogrammetric methods, Photogramm. Eng. Remote., 68, 941–951, 2002.
Sivakandan, M., Taori, A., Satishkumar, S., and Jayaraman, A.: Multi-instrument investigation of a mesospheric gravity wave event absorbed into background, J. Geophy. Res., 33, 3150–3159, https://doi.org/10.1002/2014JA020896, 2015.
Siva Kumar, V., Bhavani Kumar, Y., Raghunath, K., Rao, P. B., Krishnaiah, M., Mizutani, K., Aoki, T., Yasui, M., and Itabe, T.: Lidar measurements of mesospheric temperature inversion at a low latitude, Ann. Geophys., 19, 1039–1044, https://doi.org/10.5194/angeo-19-1039-2001, 2001.
Smith, S. and Toumi, R.: Measuring cloud cover and brightness temperature with a ground-based thermal infrared camera, J. Appl. Meteorol. Clim., 47, 683–693, 2008.
Souza-Echer, M. P., Pereira, E. B., Bins, L. S., and Andrade, M. A. R.: A simple method for the assessment of the cloud cover state in high-latitude regions by a ground-based digital camera, J. Atmos. Ocean. Tech., 23, 437–447, 2006.
Taori, A., Jayaraman, A., and Kamalakar, V.: Imaging of mesosphere–thermosphere airglow emissions over Gadanki (13.51° N, 79.21° E) – first results, J. Atmos. Sol.-Terr. Phy., 93, 21–28, 2013.
Taylor, M. J., Bishop, M. B., and Taylor, V.: All-sky measurements of short period waves imaged in the OI (556.7nm), Na (589.2mn) and near IR OH and O2 (0, l) nightglow emission during the ALOHA-93 Campaign, Geophys. Res. Lett., 22, 2833–2836, 1995.
Thies, B. and Bendix, J.: Satellite based remote sensing of weather and climate: recent achievements and future perspectives, Meteorol. Appl., 18, 262–295, 2011.
Torre, V. and Poggio, T. A.: On edge detection, IEEE T. Pattern Anal., PAMI-8, 187–163, 1986.
Tsuda, T., Venkatratnam, M., Kozu, T., and Mori, S.: Characteristics of 10-day Kelvin wave observed with radiosondes and CHAMP/GPS occultation during CPEA campaign (April–May 2004), J. Meteorol. Soc. Jpn., 84A, 277–293, https://doi.org/10.2151/jmsj.84A.277, 2006.
Velden, C. S., Hayden, C. M., Nieman, S. J., Menzel, W. P., Wanzong, S., and Goerss, J. S.: Upper-tropospheric winds derived from geostationary satellite water vapor observations, B. Am. Meteorol. Soc., 78, 173–195, 1997.
Yang, J., Weitao, L., Ying, M., and Wen, Y.: An automated Cirrus cloud detection method for a ground-based cloud image, J. Atmos. Ocean. Tech., 29, 527–537, https://doi.org/10.1175/JTECH-D-11-00002.1, 2012.
