Articles | Volume 18, issue 8
https://doi.org/10.5194/amt-18-1981-2025
© Author(s) 2025. 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-18-1981-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Apr 2025
Research article |
| 30 Apr 2025
| 30 Apr 2025
An analysis of cloud microphysical features over United Arab Emirates using multiple data sources
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Vesta Afzali Gorooh
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Duncan Axisa
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
Chandrasekar Radhakrishnan
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Eun Yeol Kim
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Venkatachalam Chandrasekar
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO, USA
Luca Delle Monache
Center for Western Weather and Water Extremes, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA
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Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
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Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Yuan Yang, Ming Pan, Dapeng Feng, Mu Xiao, Taylor Dixon, Robert Hartman, Chaopeng Shen, Yalan Song, Agniv Sengupta, Luca Delle Monache, and F. Martin Ralph
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We explore a machine learning-based data integration method that integrates streamflow (Q) and snow water equivalent (SWE) to improve streamflow estimates at various lag times (1–10 days, 1–6 months) and timescales (daily and monthly) over Western U.S. basins. Benefits rank as: integrating Q at the daily scale > Q at the monthly scale > SWE at the monthly scale > SWE at the daily scale. Results highlight the method’s potential for short- and long-term streamflow forecasting in the Western U.S.
Marc Schneebeli, Andreas Leuenberger, Philipp J. Schmid, Jacopo Grazioli, Heather Corden, Alexis Berne, Patrick Kennedy, Jim George, Francesc Junyent, and V. Chandrasekar
EGUsphere, https://doi.org/10.5194/egusphere-2025-1702, https://doi.org/10.5194/egusphere-2025-1702, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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A new technique for the end-to-end calibration of weather radars is introduced. Highly precise artificial radar targets are generated with a radar target simulator and serve as a calibration reference for weather radar observables like reflectivity and Doppler velocity. The system allows to investigate and correct any biases associated with weather radar observations.
Zhenhai Zhang, F. Martin Ralph, Xun Zou, Brian Kawzenuk, Minghua Zheng, Irina V. Gorodetskaya, Penny M. Rowe, and David H. Bromwich
The Cryosphere, 18, 5239–5258, https://doi.org/10.5194/tc-18-5239-2024, https://doi.org/10.5194/tc-18-5239-2024, 2024
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Atmospheric rivers (ARs) are long, narrow corridors of strong water vapor transport in the atmosphere. ARs play an important role in extreme weather in polar regions, including heavy rain and/or snow, heat waves, and surface melt. The standard AR scale is developed based on the midlatitude climate and is insufficient for polar regions. This paper introduces an extended version of the AR scale tuned to polar regions, aiming to quantify polar ARs objectively based on their strength and impact.
Mahen Konwar, Benjamin Werden, Edward C. Fortner, Sudarsan Bera, Mercy Varghese, Subharthi Chowdhuri, Kurt Hibert, Philip Croteau, John Jayne, Manjula Canagaratna, Neelam Malap, Sandeep Jayakumar, Shivsai A. Dixit, Palani Murugavel, Duncan Axisa, Darrel Baumgardner, Peter F. DeCarlo, Doug R. Worsnop, and Thara Prabhakaran
Atmos. Meas. Tech., 17, 2387–2400, https://doi.org/10.5194/amt-17-2387-2024, https://doi.org/10.5194/amt-17-2387-2024, 2024
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In a warm cloud seeding experiment hygroscopic particles are released to alter cloud processes to induce early raindrops. During the Cloud–Aerosol Interaction and Precipitation Enhancement Experiment, airborne mini aerosol mass spectrometers analyse the particles on which clouds form. The seeded clouds showed higher concentrations of chlorine and potassium, the oxidizing agents of flares. Small cloud droplet concentrations increased, and seeding particles were detected in deep cloud depths.
Sagar P. Parajuli, Georgiy L. Stenchikov, Alexander Ukhov, Suleiman Mostamandi, Paul A. Kucera, Duncan Axisa, William I. Gustafson Jr., and Yannian Zhu
Atmos. Chem. Phys., 22, 8659–8682, https://doi.org/10.5194/acp-22-8659-2022, https://doi.org/10.5194/acp-22-8659-2022, 2022
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Rainfall affects the distribution of surface- and groundwater resources, which are constantly declining over the Middle East and North Africa (MENA) due to overexploitation. Here, we explored the effects of dust on rainfall using WRF-Chem model simulations. Although dust is considered a nuisance from an air quality perspective, our results highlight the positive fundamental role of dust particles in modulating rainfall formation and distribution, which has implications for cloud seeding.
Nicholas J. Kedzuf, J. Christine Chiu, V. Chandrasekar, Sounak Biswas, Shashank S. Joshil, Yinghui Lu, Peter Jan van Leeuwen, Christopher Westbrook, Yann Blanchard, and Sebastian O'Shea
Atmos. Meas. Tech., 14, 6885–6904, https://doi.org/10.5194/amt-14-6885-2021, https://doi.org/10.5194/amt-14-6885-2021, 2021
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Ice clouds play a key role in our climate system due to their strong controls on precipitation and the radiation budget. However, it is difficult to characterize co-existing ice species using radar observations. We present a new method that separates the radar signals of pristine ice embedded in snow aggregates and retrieves their respective abundances and sizes for the first time. The ability to provide their quantitative microphysical properties will open up many research opportunities.
Yu Ma, Guangheng Ni, Chandrasekar V. Chandra, Fuqiang Tian, and Haonan Chen
Hydrol. Earth Syst. Sci., 23, 4153–4170, https://doi.org/10.5194/hess-23-4153-2019, https://doi.org/10.5194/hess-23-4153-2019, 2019
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Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation. This study extensively investigates the DSD characteristics during rainy seasons in the Beijing urban area using 5-year DSD observations from a Parsivel2 disdrometer. The statistical distributions of DSD parameters are examined and the polarimetric radar rainfall algorithms are derived to support the ongoing development of an X-band radar network.
Luca Delle Monache, Stefano Alessandrini, Irina Djalalova, James Wilczak, and Jason C. Knievel
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2017-1214, https://doi.org/10.5194/acp-2017-1214, 2018
Preprint withdrawn
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The authors demonstrate how the analog ensemble (AnEn) can efficiently generate deterministic and probabilistic forecasts of air quality. The method avoids the complexity and real-time computational expense of dynamical (i.e., model-based) ensembles. AnEn deterministic predictions have lower errors and are better correlated with observations. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.
Zhao Shi, Fangqiang Wei, and Venkatachalam Chandrasekar
Nat. Hazards Earth Syst. Sci., 18, 765–780, https://doi.org/10.5194/nhess-18-765-2018, https://doi.org/10.5194/nhess-18-765-2018, 2018
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The aim of this paper is to evaluate the debris flow occurrence thresholds of the rainfall intensity–duration in the earthquake-affected areas of Sichuan province over the rainy seasons from 2012 to 2014. it is clear that radar-based rainfall estimate and threshold supplement the monitoring gap of EWS where rain gauge is scarce. A better understanding of relationship between rainfall and debris flow initiation can be enhanced by the radar with highly spatiotemporal resolution.
Youhua Tang, Mariusz Pagowski, Tianfeng Chai, Li Pan, Pius Lee, Barry Baker, Rajesh Kumar, Luca Delle Monache, Daniel Tong, and Hyun-Cheol Kim
Geosci. Model Dev., 10, 4743–4758, https://doi.org/10.5194/gmd-10-4743-2017, https://doi.org/10.5194/gmd-10-4743-2017, 2017
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In order to evaluate the data assimilation tools for regional real-time PM2.5 forecasts, we applied a 3D-Var assimilation tool to adjust the aerosol initial condition by assimilating satellite-retrieved aerosol optical depth and surface PM2.5 observations for a regional air quality model, which is compared to another assimilation method, optimal interpolation. We discuss the pros and cons of these two assimilation methods based on the comparison of their 1-month four-cycles-per-day runs.
Mattia Vaccarono, Renzo Bechini, Chandra V. Chandrasekar, Roberto Cremonini, and Claudio Cassardo
Atmos. Meas. Tech., 9, 5367–5383, https://doi.org/10.5194/amt-9-5367-2016, https://doi.org/10.5194/amt-9-5367-2016, 2016
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The data quality of radars must be ensured and continuously monitored. The aim of this paper is to provide an integrated approach able to monitor the calibration of operational dual-polarization radars. The set of methods considered appears suitable to establish an online tool to monitor the stability of the radar calibration with an accuracy of about 2 dB. This is considered adequate to automatically detect any unexpected change in the radar system requiring further investigations.
Roberto Cremonini, Dmitri Moisseev, and Venkatachalam Chandrasekar
Atmos. Meas. Tech., 9, 5063–5075, https://doi.org/10.5194/amt-9-5063-2016, https://doi.org/10.5194/amt-9-5063-2016, 2016
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Although high-spatial-resolution weather radar observations are of primary relevance for urban hydrology, weather radar siting and characterization are challenging in an urban environment. Buildings, masts and trees cause partial beam blockages and clutter that seriously affect the observations. For the first time, this paper investigates the benefits of using airborne laser scanner (ALS) data for quantitative estimations of partial beam blockages in an urban environment.
S. Alessandrini, F. Davò, S. Sperati, M. Benini, and L. Delle Monache
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A. Alqudah, V. Chandrasekar, and M. Le
Nat. Hazards Earth Syst. Sci., 13, 535–544, https://doi.org/10.5194/nhess-13-535-2013, https://doi.org/10.5194/nhess-13-535-2013, 2013
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
IceDetectNet: a rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme
Distribution characteristics of the summer precipitation raindrop spectrum on the Qinghai–Tibet Plateau
Exploring the effect of training set size and number of categories on ice crystal classification through a contrastive semi-supervised learning algorithm
Ice crystal images from optical array probes. Compatibility of morphology specific size distributions, retrieved with specific and global Convolutional Neural Networks for HVPS, PIP, CIP, and 2DS
In situ observations of supercooled liquid water clouds over Dome C, Antarctica, by balloon-borne sondes
Partition between supercooled liquid droplets and ice crystals in mixed-phase clouds based on airborne in situ observations
Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging
Revealing halos concealed by cirrus clouds
Quantifying riming from airborne data during the HALO-(AC)3 campaign
Estimation of 24 h continuous cloud cover using a ground-based imager with a convolutional neural network
Neural network processing of holographic images
Ice crystal images from optical array probes: classification with convolutional neural networks
Detection and analysis of cloud boundary in Xi'an, China, employing 35 GHz cloud radar aided by 1064 nm lidar
The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning
Twenty-four-hour cloud cover calculation using a ground-based imager with machine learning
Application of cloud particle sensor sondes for estimating the number concentration of cloud water droplets and liquid water content: case studies in the Arctic region
Clouds over Hyytiälä, Finland: an algorithm to classify clouds based on solar radiation and cloud base height measurements
A convolutional neural network for classifying cloud particles recorded by imaging probes
Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice
Analysis algorithm for sky type and ice halo recognition in all-sky images
Study of the diffraction pattern of cloud particles and the respective responses of optical array probes
A method for computing the three-dimensional radial distribution function of cloud particles from holographic images
A new method for calculating number concentrations of cloud condensation nuclei based on measurements of a three-wavelength humidified nephelometer system
Cloud radiative effect, cloud fraction and cloud type at two stations in Switzerland using hemispherical sky cameras
Evaluation of radar reflectivity factor simulations of ice crystal populations from in situ observations for the retrieval of condensed water content in tropical mesoscale convective systems
Cloud detection in all-sky images via multi-scale neighborhood features and multiple supervised learning techniques
From pixels to patches: a cloud classification method based on a bag of micro-structures
Evaluation of cloud base height measurements from Ceilometer CL31 and MODIS satellite over Ahmedabad, India
Software to analyze the relationship between aerosol, clouds, and precipitation: SAMAC
Block-based cloud classification with statistical features and distribution of local texture features
Assessment of the performance of the inter-arrival time algorithm to identify ice shattering artifacts in cloud particle probe measurements
Assessment of cloud supersaturation by size-resolved aerosol particle and cloud condensation nuclei (CCN) measurements
Inversion of droplet aerosol analyzer data for long-term aerosol–cloud interaction measurements
Response of the Nevzorov hot wire probe in clouds dominated by droplet conditions in the drizzle size range
Huiying Zhang, Xia Li, Fabiola Ramelli, Robert O. David, Julie Pasquier, and Jan Henneberger
Atmos. Meas. Tech., 17, 7109–7128, https://doi.org/10.5194/amt-17-7109-2024, https://doi.org/10.5194/amt-17-7109-2024, 2024
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Our innovative IceDetectNet algorithm classifies each part of aggregated ice crystals, considering both their basic shape and physical processes. Trained on ice crystal images from the Arctic taken by a holographic camera, it correctly classifies over 92 % of the ice crystals. These more detailed insights into the components of aggregated ice crystals have the potential to improve our estimates of microphysical properties such as riming rate, aggregation rate, and ice water content.
Fuzeng Wang, Yuanyu Duan, Yao Huo, Yaxi Cao, Qiusong Wang, Tong Zhang, Junqing Liu, and Guangmin Cao
Atmos. Meas. Tech., 17, 6933–6944, https://doi.org/10.5194/amt-17-6933-2024, https://doi.org/10.5194/amt-17-6933-2024, 2024
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This study analyzes precipitation features over the Qinghai–Tibet Plateau, focusing on drop size distribution (DSD), terminal velocity, number density, radar reflectivity (Z), and rainfall intensity (I) at four sites. Terminal velocity is fastest at Nyalam and slowest at Lhasa. Five microphysical characteristics exhibit different correlations with altitude. Z–I relationships differ by site, and both exponential and gamma distributions fit DSD, with gamma parameters varying with altitude.
Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger
EGUsphere, https://doi.org/10.5194/egusphere-2024-3160, https://doi.org/10.5194/egusphere-2024-3160, 2024
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Our study improves ice crystal shape classification, key for understanding weather and climate. By adding unsupervised pre-training before supervised classification, our algorithm reduces manual labeling effort while maintaining high accuracy. It outperforms fully supervised models across datasets of varying sizes and categories, showing strong generalization ability. This method improves ice crystal classification techniques, making it adaptable to different environmental datasets.
Louis Jaffeux, Jan Breiner, Pierre Coutris, and Alfons Schwarzenböck
EGUsphere, https://doi.org/10.5194/egusphere-2024-1910, https://doi.org/10.5194/egusphere-2024-1910, 2024
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Airborne cloud observation relies on high frequency black and white image information. The study presents automatic shape recognition tools developed with machine learning techniques and adapted for this type of images. Applied on a recent field campaign, these tools are compared across four instruments that cover different size ranges. The analysis show that the tools are performing well and are consistent across the different instruments.
Philippe Ricaud, Pierre Durand, Paolo Grigioni, Massimo Del Guasta, Giuseppe Camporeale, Axel Roy, Jean-Luc Attié, and John Bognar
Atmos. Meas. Tech., 17, 5071–5089, https://doi.org/10.5194/amt-17-5071-2024, https://doi.org/10.5194/amt-17-5071-2024, 2024
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Clouds in Antarctica are key elements affecting climate evolution. Some clouds are composed of supercooled liquid water (SLW; water held in liquid form below 0 °C) and are difficult to forecast by models. We performed in situ observations of SLW clouds at Concordia Station using SLW sondes attached to meteorological balloons in summer 2021–2022. The SLW clouds were observed in a saturated layer at the top of the planetary boundary layer in agreement with ground-based lidar observations.
Flor Vanessa Maciel, Minghui Diao, and Ching An Yang
Atmos. Meas. Tech., 17, 4843–4861, https://doi.org/10.5194/amt-17-4843-2024, https://doi.org/10.5194/amt-17-4843-2024, 2024
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The partition between supercooled liquid water and ice crystals in mixed-phase clouds is investigated using aircraft-based in situ observations over the Southern Ocean. A novel method is developed to define four phases of mixed-phase clouds. Relationships between cloud macrophysical and microphysical properties are quantified. Effects of aerosols and thermodynamic and dynamical conditions on ice nucleation and phase partitioning are examined.
Jingxuan Luo, Yubing Pan, Debin Su, Jinhua Zhong, Lingxiao Wu, Wei Zhao, Xiaoru Hu, Zhengchao Qi, Daren Lu, and Yinan Wang
Atmos. Meas. Tech., 17, 3765–3781, https://doi.org/10.5194/amt-17-3765-2024, https://doi.org/10.5194/amt-17-3765-2024, 2024
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Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.
Yuji Ayatsuka
Atmos. Meas. Tech., 17, 3739–3750, https://doi.org/10.5194/amt-17-3739-2024, https://doi.org/10.5194/amt-17-3739-2024, 2024
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Many types of halos appear in the sky. Each type of halo reflects the state of the atmosphere; therefore observing them from the ground greatly helps in understanding the state of the atmosphere. However, halos are easily obscured by the contrast of the cloud itself, making it difficult to observe them. This study describes the construction of a sky-color model for halos and a new effective algorithm to reveal halos in images.
Nina Maherndl, Manuel Moser, Johannes Lucke, Mario Mech, Nils Risse, Imke Schirmacher, and Maximilian Maahn
Atmos. Meas. Tech., 17, 1475–1495, https://doi.org/10.5194/amt-17-1475-2024, https://doi.org/10.5194/amt-17-1475-2024, 2024
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In some clouds, liquid water droplets can freeze onto ice crystals (riming). Riming leads to the formation of snowflakes. We show two ways to quantify riming using aircraft data collected in the Arctic. One aircraft had a cloud radar, while the other aircraft was measuring directly in cloud. The first method compares radar and direct observations. The second looks at snowflake shape. Both methods agree, except when there were gaps in the cloud. This improves our ability to understand riming.
Bu-Yo Kim, Joo Wan Cha, and Yong Hee Lee
Atmos. Meas. Tech., 16, 5403–5413, https://doi.org/10.5194/amt-16-5403-2023, https://doi.org/10.5194/amt-16-5403-2023, 2023
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A camera-based imager and convolutional neural network (CNN) were used to estimate ground cloud cover. Image data from 2019 were used for training and validation, and those from 2020 were used for testing. The CNN model exhibited high performance, with an accuracy of 0.92, RMSE of 1.40 tenths, and 93% agreement with observed cloud cover within ±2 tenths' difference. It also outperformed satellites and ceilometers and proved to be the most suitable for ground-based cloud cover estimation.
John S. Schreck, Gabrielle Gantos, Matthew Hayman, Aaron Bansemer, and David John Gagne
Atmos. Meas. Tech., 15, 5793–5819, https://doi.org/10.5194/amt-15-5793-2022, https://doi.org/10.5194/amt-15-5793-2022, 2022
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We show promising results for a new machine-learning based paradigm for processing field-acquired cloud droplet holograms. The approach is fast, scalable, and leverages GPUs and other heterogeneous computing platforms. It combines applications of transfer and active learning by using synthetic data for training and a small set of hand-labeled data for refinement and validation. Artificial noise applied during synthetic training enables optimized models for real-world situations.
Louis Jaffeux, Alfons Schwarzenböck, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 15, 5141–5157, https://doi.org/10.5194/amt-15-5141-2022, https://doi.org/10.5194/amt-15-5141-2022, 2022
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Optical array probes are instruments used aboard research aircraft to capture 2D images of ice or water particles in clouds. This study presents a new tool using innovative machine learning, called convolutional neural networks, designed to identify the shape of imaged ice particles for two of these imagers, namely 2DS and PIP. Such a tool will be a very strong asset for understanding cloud microphysics. Beyond traditional evaluation metrics, human inspections were performed of unknown data.
Yun Yuan, Huige Di, Yuanyuan Liu, Tao Yang, Qimeng Li, Qing Yan, Wenhui Xin, Shichun Li, and Dengxin Hua
Atmos. Meas. Tech., 15, 4989–5006, https://doi.org/10.5194/amt-15-4989-2022, https://doi.org/10.5194/amt-15-4989-2022, 2022
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We put forward a new algorithm for joint observation of the cloud boundary by lidar and Ka-band millimetre-wave cloud radar. Cloud cover and boundary distribution characteristics are analysed from December 2020 to November 2021 in Xi'an. More than 34 % of clouds appear as a single layer every month. The maximum and minimum normalized cloud cover occurs in summer and winter, respectively. The study can provide more information on aerosol–cloud interactions and forecasting numerical models.
Rachel Atlas, Johannes Mohrmann, Joseph Finlon, Jeremy Lu, Ian Hsiao, Robert Wood, and Minghui Diao
Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021, https://doi.org/10.5194/amt-14-7079-2021, 2021
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Many clouds with temperatures between 0 °C and −40 °C contain both liquid and ice particles, and the ratio of liquid to ice particles influences how the clouds interact with radiation and moderate Earth's climate. We use a machine learning method called random forest to classify images of individual cloud particles as either liquid or ice. We apply our algorithm to images captured by aircraft within clouds overlying the Southern Ocean, and we find that it outperforms two existing algorithms.
Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
Atmos. Meas. Tech., 14, 6695–6710, https://doi.org/10.5194/amt-14-6695-2021, https://doi.org/10.5194/amt-14-6695-2021, 2021
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This study investigates a method for 24 h cloud cover calculation using a camera-based imager and supervised machine learning methods. The cloud cover is calculated by learning the statistical characteristics of the ratio, difference, and luminance using RGB channels of the image with a machine learning model. The proposed approach is suitable for nowcasting because it has higher learning and prediction speed than the method in which the many pixels of a 2D image are learned.
Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
Atmos. Meas. Tech., 14, 4971–4987, https://doi.org/10.5194/amt-14-4971-2021, https://doi.org/10.5194/amt-14-4971-2021, 2021
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A cloud particle sensor (CPS) sonde is an observing system to obtain the signals of the phase, size, and the number of cloud particles. Based on the field experiments in the Arctic regions and numerical experiments, we proposed a method to correct the CPS sonde data and found that the CPS sonde system can appropriately observe the liquid cloud if our correction method is applied.
Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
Atmos. Meas. Tech., 13, 5595–5619, https://doi.org/10.5194/amt-13-5595-2020, https://doi.org/10.5194/amt-13-5595-2020, 2020
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In this study, we developed a new algorithm for cloud classification using solar radiation and cloud base height measurements. Our objective was to develop a simple and inexpensive but effective algorithm for the needs of studies related to ecosystem and atmosphere interactions. In the present study, we used the algorithm for obtaining cloud statistics at a measurement station in southern Finland, and we discuss the advantages and shortcomings of the algorithm.
Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
Atmos. Meas. Tech., 13, 2219–2239, https://doi.org/10.5194/amt-13-2219-2020, https://doi.org/10.5194/amt-13-2219-2020, 2020
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Images of cloud particles give important information for improving our understanding of microphysical cloud processes. For phase-resolved measurements, a large number of water droplets and ice crystals need to be classified by an automated approach. In this study, a convolutional neural network was designed, which exceeds the classification ability of traditional methods and therefore shortens the analysis procedure of cloud particle images.
Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
Atmos. Meas. Tech., 13, 1757–1775, https://doi.org/10.5194/amt-13-1757-2020, https://doi.org/10.5194/amt-13-1757-2020, 2020
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In the changing Arctic, quantifying the resulting variability of incoming solar radiation is important to better elucidate the net radiative effect of clouds. As part of a multidisciplinary expedition in the central Arctic held in early summer 2017, a novel network of pyranometers was deployed over an ice floe to investigate the spatiotemporal variability of solar radiation under different sky conditions. This study presents the collected data and an analysis of the spatiotemporal variability.
Sylke Boyd, Stephen Sorenson, Shelby Richard, Michelle King, and Morton Greenslit
Atmos. Meas. Tech., 12, 4241–4259, https://doi.org/10.5194/amt-12-4241-2019, https://doi.org/10.5194/amt-12-4241-2019, 2019
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How cirroform clouds affect the radiation balance of the atmosphere depends on their properties, including ice particle types such as crystals, pellets, and fragments. Ice halos form if ice particles in these clouds are in a smooth hexagonal crystalline form. This paper introduces a method to search long-term records of sky images for ice halos, as gathered by total sky imagers (TSIs). Such an analysis will allow one to explore geographical and seasonal variations in cirrus cloud particle types.
Thibault Vaillant de Guélis, Alfons Schwarzenböck, Valery Shcherbakov, Christophe Gourbeyre, Bastien Laurent, Régis Dupuy, Pierre Coutris, and Christophe Duroure
Atmos. Meas. Tech., 12, 2513–2529, https://doi.org/10.5194/amt-12-2513-2019, https://doi.org/10.5194/amt-12-2513-2019, 2019
Michael L. Larsen and Raymond A. Shaw
Atmos. Meas. Tech., 11, 4261–4272, https://doi.org/10.5194/amt-11-4261-2018, https://doi.org/10.5194/amt-11-4261-2018, 2018
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A statistical tool frequently utilized to measure scale-dependent departures from perfect randomness is the radial distribution function. This tool has many strengths, but it is not easy to calculate for particle detections within a three-dimensional sample volume. In this manuscript, we introduce and test a new method to estimate the three-dimensional radial distribution function in realistic measurement volumes.
Jiangchuan Tao, Chunsheng Zhao, Ye Kuang, Gang Zhao, Chuanyang Shen, Yingli Yu, Yuxuan Bian, and Wanyun Xu
Atmos. Meas. Tech., 11, 895–906, https://doi.org/10.5194/amt-11-895-2018, https://doi.org/10.5194/amt-11-895-2018, 2018
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Existing chamber technologies for direct measurements of number concentration of cloud condensation nuclei (NCCN) are sophisticated and expensive. In this paper, a new method is proposed to calculate NCCN based only on measurements of a humidified nephelometer system which have accounted for influences of both aerosol size and aerosol hygroscopicity on NCCN calculation. This new method makes NCCN measurements more convenient and is capable of obtaining NCCN at lower supersaturations.
Christine Aebi, Julian Gröbner, Niklaus Kämpfer, and Laurent Vuilleumier
Atmos. Meas. Tech., 10, 4587–4600, https://doi.org/10.5194/amt-10-4587-2017, https://doi.org/10.5194/amt-10-4587-2017, 2017
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The current study analyses the cloud radiative effect during the daytime depending on cloud fraction and cloud type at two stations in Switzerland over a time period of 3–5 years. Information about fractional cloud coverage and cloud type is retrieved from images taken by visible all-sky cameras. Cloud cover, cloud type and other atmospheric parameters have an influence on the magnitude of the longwave cloud effect as well as on the shortwave.
Emmanuel Fontaine, Delphine Leroy, Alfons Schwarzenboeck, Julien Delanoë, Alain Protat, Fabien Dezitter, Alice Grandin, John Walter Strapp, and Lyle Edward Lilie
Atmos. Meas. Tech., 10, 2239–2252, https://doi.org/10.5194/amt-10-2239-2017, https://doi.org/10.5194/amt-10-2239-2017, 2017
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In this study we evaluate a method to estimate cloud water content (CWC) knowing cloud reflectivity. Ice hydrometeors are replace by ice oblate spheroids to simulate their reflectivity. There is no assumption on the relation between mass and their size. Then, a broad range of CWCs are compared with direct measurements of CWC. The accuracy of the method is ~ ±32 %. This study is performed in areas of convective clouds where reflectivity and CWC are especially high, what makes it unique.
Hsu-Yung Cheng and Chih-Lung Lin
Atmos. Meas. Tech., 10, 199–208, https://doi.org/10.5194/amt-10-199-2017, https://doi.org/10.5194/amt-10-199-2017, 2017
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A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.
Qingyong Li, Zhen Zhang, Weitao Lu, Jun Yang, Ying Ma, and Wen Yao
Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, https://doi.org/10.5194/amt-9-753-2016, 2016
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This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.
Som Sharma, Rajesh Vaishnav, Munn V. Shukla, Prashant Kumar, Prateek Kumar, Pradeep K. Thapliyal, Shyam Lal, and Yashwant B. Acharya
Atmos. Meas. Tech., 9, 711–719, https://doi.org/10.5194/amt-9-711-2016, https://doi.org/10.5194/amt-9-711-2016, 2016
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Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.
S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce
Atmos. Meas. Tech., 9, 619–630, https://doi.org/10.5194/amt-9-619-2016, https://doi.org/10.5194/amt-9-619-2016, 2016
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Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
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This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
A. Korolev and P. R. Field
Atmos. Meas. Tech., 8, 761–777, https://doi.org/10.5194/amt-8-761-2015, https://doi.org/10.5194/amt-8-761-2015, 2015
M. L. Krüger, S. Mertes, T. Klimach, Y. F. Cheng, H. Su, J. Schneider, M. O. Andreae, U. Pöschl, and D. Rose
Atmos. Meas. Tech., 7, 2615–2629, https://doi.org/10.5194/amt-7-2615-2014, https://doi.org/10.5194/amt-7-2615-2014, 2014
M. I. A. Berghof, G. P. Frank, S. Sjogren, and B. G. Martinsson
Atmos. Meas. Tech., 7, 877–886, https://doi.org/10.5194/amt-7-877-2014, https://doi.org/10.5194/amt-7-877-2014, 2014
A. Schwarzenboeck, G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet
Atmos. Meas. Tech., 2, 779–788, https://doi.org/10.5194/amt-2-779-2009, https://doi.org/10.5194/amt-2-779-2009, 2009
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Short summary
Water is a precious resource, and it is essential to monitor and predict the current and future occurrence of precipitation-producing clouds. We investigate the cloud characteristics related to precipitation using several cloud cases in the United Arab Emirates with data from aircraft measurements, satellite observations, and weather radar observations. This study provides scientific support for the development of an applicable framework to examine cloud precipitation processes.
Water is a precious resource, and it is essential to monitor and predict the current and future...
