Articles | Volume 11, issue 7
https://doi.org/10.5194/amt-11-4261-2018
© Author(s) 2018. 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-11-4261-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Jul 2018
Research article |
| 19 Jul 2018
| 19 Jul 2018
A method for computing the three-dimensional radial distribution function of cloud particles from holographic images
Department of Physics and Astronomy, College of Charleston, Charleston, SC, USA
Department of Physics, Michigan Technological University, Houghton, MI, USA
Raymond A. Shaw
Department of Physics, Michigan Technological University, Houghton, MI, USA
Related authors
No articles found.
Fan Yang, Hamed Fahandezh Sadi, Raymond A. Shaw, Fabian Hoffmann, Pei Hou, Aaron Wang, and Mikhail Ovchinnikov
EGUsphere, https://doi.org/10.5194/egusphere-2024-1693, https://doi.org/10.5194/egusphere-2024-1693, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Large-eddy simulations of a convection cloud chamber show two new microphysics regimes, cloud oscillation and cloud collapse, due to haze-cloud interactions. Our results suggest that haze particles and their interactions with cloud droplets should be considered especially in polluted conditions. To properly simulate haze-cloud interactions, we need to resolve droplet activation and deactivation processes, instead of using Twomey-type activation parameterization.
Aaron Wang, Steve Krueger, Sisi Chen, Mikhail Ovchinnikov, Will Cantrell, and Raymond A. Shaw
EGUsphere, https://doi.org/10.5194/egusphere-2024-1140, https://doi.org/10.5194/egusphere-2024-1140, 2024
Short summary
Short summary
This study employs two methods to examine a laboratory experiment on clouds with both ice and liquid phases. The first assumes well-mixed properties; the second resolves the spatial distribution of turbulence and cloud particles. Results show that while the trends in mean properties generally align, when turbulence is resolved, liquid droplets are not fully depleted by ice due to incomplete mixing. This underscores the threshold of ice mass fraction in distinguishing mixed-phase from ice clouds.
Zeen Zhu, Fan Yang, Pavlos Kollias, Raymond A. Shaw, Alex B. Kostinski, Steve Krueger, Katia Lamer, Nithin Allwayin, and Mariko Oue
Atmos. Meas. Tech., 17, 1133–1143, https://doi.org/10.5194/amt-17-1133-2024, https://doi.org/10.5194/amt-17-1133-2024, 2024
Short summary
Short summary
In this article, we demonstrate the feasibility of applying advanced radar technology to detect liquid droplets generated in the cloud chamber. Specifically, we show that using radar with centimeter-scale resolution, single drizzle drops with a diameter larger than 40 µm can be detected. This study demonstrates the applicability of remote sensing instruments in laboratory experiments and suggests new applications of ultrahigh-resolution radar for atmospheric sensing.
Elise Rosky, Will Cantrell, Tianshu Li, Issei Nakamura, and Raymond A. Shaw
Atmos. Chem. Phys., 23, 10625–10642, https://doi.org/10.5194/acp-23-10625-2023, https://doi.org/10.5194/acp-23-10625-2023, 2023
Short summary
Short summary
Using computer simulations of water, we find that water under tension freezes more easily than under normal conditions. A linear equation describes how freezing temperature increases with tension. Accordingly, simulations show that naturally occurring tension in water capillary bridges leads to higher freezing temperatures. This work is an early step in determining if atmospheric cloud droplets freeze due to naturally occurring tension, for example, during processes such as droplet collisions.
Jesse C. Anderson, Subin Thomas, Prasanth Prabhakaran, Raymond A. Shaw, and Will Cantrell
Atmos. Meas. Tech., 14, 5473–5485, https://doi.org/10.5194/amt-14-5473-2021, https://doi.org/10.5194/amt-14-5473-2021, 2021
Short summary
Short summary
Fluctuations due to turbulence in Earth's atmosphere can play a role in how many droplets a cloud has and, eventually, whether that cloud rains or evaporates. We study such processes in Michigan Tech's cloud chamber. Here, we characterize the turbulent and large-scale motions of air in the chamber, measuring the spatial and temporal distributions of temperature and water vapor, which we can combine to get the distribution of relative humidity, which governs cloud formation and dissipation.
Dennis Niedermeier, Jens Voigtländer, Silvio Schmalfuß, Daniel Busch, Jörg Schumacher, Raymond A. Shaw, and Frank Stratmann
Atmos. Meas. Tech., 13, 2015–2033, https://doi.org/10.5194/amt-13-2015-2020, https://doi.org/10.5194/amt-13-2015-2020, 2020
Short summary
Short summary
In this paper, we present the new moist-air wind tunnel LACIS-T (Turbulent Leipzig Aerosol Cloud Interaction Simulator). It is used to study cloud physical processes in general and interactions between turbulence and cloud microphysical processes in particular. The operating principle of LACIS-T is explained, and the first results are depicted from deliquescence and droplet formation experiments observing clear indications on the effect of turbulence on these microphysical processes.
Katarzyna Karpińska, Jonathan F. E. Bodenschatz, Szymon P. Malinowski, Jakub L. Nowak, Steffen Risius, Tina Schmeissner, Raymond A. Shaw, Holger Siebert, Hengdong Xi, Haitao Xu, and Eberhard Bodenschatz
Atmos. Chem. Phys., 19, 4991–5003, https://doi.org/10.5194/acp-19-4991-2019, https://doi.org/10.5194/acp-19-4991-2019, 2019
Short summary
Short summary
Observations of clouds at a mountain-top laboratory revealed for the first time the presence of “voids”, i.e., elongated volumes inside a cloud that are devoid of droplets. Theoretical and numerical analyses suggest that these voids are a result of strong and long-lasting vortex presence in turbulent air. If this is confirmed in further investigation, the effect may become an important part of models describing cloud evolution and rain formation.
Fan Yang, Pavlos Kollias, Raymond A. Shaw, and Andrew M. Vogelmann
Atmos. Chem. Phys., 18, 7313–7328, https://doi.org/10.5194/acp-18-7313-2018, https://doi.org/10.5194/acp-18-7313-2018, 2018
Short summary
Short summary
Cloud droplet size distribution (CDSD), which is related to cloud albedo and lifetime, is usually observed broader than predicted from adiabatic parcel calculations. Results in this study show that the CDSD can be broadened during condensational growth as a result of Ostwald ripening amplified by droplet deactivation and reactivation. Our results suggest that it is important to consider both curvature and solute effects before and after cloud droplet activation in a 3-D cloud model.
Fan Yang, Raymond Shaw, and Huiwen Xue
Atmos. Chem. Phys., 16, 9421–9433, https://doi.org/10.5194/acp-16-9421-2016, https://doi.org/10.5194/acp-16-9421-2016, 2016
Short summary
Short summary
When dry air is mixed into a cloud, droplets evaporate. If the diluted cloud mixture continues to rise, the remaining droplets will grow. In this work we show theoretically and computationally that a critical height exists, above which the droplets in a mixed, diluted cloud volume become larger than those in an undiluted volume. An environment that is humid and aerosol free is most favorable for producing such large droplets, which may contribute to the onset of precipitation formation.
B. Wehner, F. Werner, F. Ditas, R. A. Shaw, M. Kulmala, and H. Siebert
Atmos. Chem. Phys., 15, 11701–11711, https://doi.org/10.5194/acp-15-11701-2015, https://doi.org/10.5194/acp-15-11701-2015, 2015
Short summary
Short summary
During the CARRIBA campaign on Barbados, 91 cases with increased aerosol particle number concentrations near clouds were detected from helicopter-borne measurements. Most of these cases are correlated with enhanced irradiance in the ultraviolet range. The events have a mean length of 100m, corresponding to a lifetime of 300s, meaning a growth of several nm/h. Such high values cannot be explained by sulfuric acid alone; thus extremely low volatility organic compounds are probably involved here.
S. Risius, H. Xu, F. Di Lorenzo, H. Xi, H. Siebert, R. A. Shaw, and E. Bodenschatz
Atmos. Meas. Tech., 8, 3209–3218, https://doi.org/10.5194/amt-8-3209-2015, https://doi.org/10.5194/amt-8-3209-2015, 2015
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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
The Transition from Supercooled Liquid Water to Ice Crystals in Mixed-phase Clouds based on Airborne In-situ Observations
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 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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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.
Flor Vanessa Maciel and Minghui Diao
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-256, https://doi.org/10.5194/amt-2022-256, 2022
Revised manuscript accepted for AMT
Short summary
Short summary
The transition from supercooled liquid water to ice crystals in mixed-phase clouds is investigated using aircraft-based in-situ observations over the Southern Ocean. A novel method was developed to distinguish four transition phases for mixed-phase cloud evolution. Relationships between cloud macrophysical and microphysical properties are quantified. Effects of aerosols, thermodynamic and dynamical conditions on ice nucleation and phase partitioning are investigated.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Cited articles
Ayala, O., Rosa, B., Wang, L.-P., and Grabowski, W.: Effects of turbulence on the geometric collision rate of sedimenting droplets. Part I: Results from direct numerical simulation, New J. Phys., 10, 075015, https://doi.org/10.1088/1367-2630/10/7/075015, 2008.
Baker, B.: Turbulent entrainment and mixing in clouds: A new observational approach, J. Atmos. Sci., 49, 387–404, 1992.
Baker, B. and Lawson, R.: Analysis of tools used to quantify droplet clustering in clouds, J. Atmos. Sci., 67, 3355–3367, 2010.
Balkovsky, E., Falkovich, G., and Fouxon, A.: Intermittent distribution of inertial particles in turbulent flows, Phys. Rev. Lett., 86, 2790–2793, 2001.
Bateson, C. and Aliseda, A.: Wind tunnel measurements of the preferential concentration of inertial droplets in homogenous isotropic turbulence, Exp. Fluids, 52, 1373–1387, 2012.
Baumgardner, D., Baker, B., and Weaver, K.: A technique fo rthe measurements of cloud structure in centimeter scales, J. Atmos. Ocean. Technol., 10, 557–565, 1993.
Beals, M., Fugal, J., Shaw, R., Lu, J., Spuler, S., and Stith, J.: Holographic measurements of inhomogeneous cloud mixing at the centimeter scale, Science, 350, 87–90, 2015.
Borrmann, S., Jaenicke, R., and Neumann, P.: On spatial distributions and inter-droplet distances measured in stratus clouds with in-line holography, Atmos. Res., 29, 229–245, 1993.
Brenguier, J.-L.: Observations of cloud microstructure at the centimeter scale, J. Appl. Meteorol., 32, 783–793, 1993.
Brown, P.: Use of holography for airborne cloud physics measurements, J. Atmos. Ocean. Technol., 6, 293–306, 1989.
Chandrakar, K., Cantrell, W., Chang, K., Ciochetto, D., Niedermeier, D., Ovchinnikov, M., Shaw, R., and Yang, F.: Aerosol indirect effect from turbulence-induced broadening of cloud-droplet size distributions, P. Natl. Acad. Sci., 113, 14243–14248, 2016.
Chandrakar, K., Cantrell, W., Ciochetto, D., Karki, S., Kinney, G., and Shaw, R.: Aerosol removal and cloud collapse accelerated by supersaturation fluctuations in turbulence, Geophys. Res. Lett., 44, 4359–4367, 2017.
Chang, K., Bench, J., Brege, M., Cantrell, W., Chandrakar, K., Ciochetto, D., Mazzoleni, C., Mazzoleni, L., Niedermeier, D., and Shaw, R.: A laboratory facility to study gas-aerosol-cloud interactions in a turbulent enviornment: The Π chamber, B. Am. Meteor. Soc., 97, 2343–2358, 2016.
Chaumat, L. and Brenguier, J.: Droplet spectra broadening in cumulus clouds. Part II: Microscale droplet concentration inhomogeneities, J. Atmos. Sci., 58, 642–654, 2001.
Cherkas, N. and Cherkas, S.: Model of the radial distribution function of pores in a layer of porous aluminum oxide, Crystallogr. Rep., 61, 285–290, 2016.
Chun, J., Koch, D., Rani, S., Ahluwalia, A., and Collins, L.: Clustering of aerosol particles in isotropic turbulence, J. Fluid Mech., 536, 219–251, 2005.
Collins, L. and Keswani, A.: Reynolds number scaling of particle clustering in turbulent aerosols, New J. Phys., 6, 1–17, 2004.
Conway, B., Caughey, S., Bentley, A., and Turton, J.: Ground-based and airborne holography of ice and water clouds, Atmospheric Environment, 16, 1193–1207, 1982.
Davis, A., Marshak, A., Gerber, H., and Wiscombe, W.: Horizontal structure of marine boundary layer clouds from centimeter to kilometer scales, J. Geophys. Res., 104, 6123–6144, 1999.
Desai, N., Chandrakar, K., Chang, K., Cantrell, W., and Shaw, R.: Influence of microphysical variability on stochastic condensation in a turbulent laboratory cloud, J. Atmos. Sci., 75, 189–201, 2018.
Erimbetova, L., Davletov, A., Kudyshev, Z. A., and Mukhametkarimov, Y. S.: Influence of polarization phenomena on radial distribution function of dust particles, Contrib. Plasm. Phys., 53, 414–418, 2013.
Frankel, A., Iaccarino, G., and Mani, A.: Optical depth in particle-laden turbulent flows, J. Quant. Spesc. Ra., 201, 10–16, 2017.
Fugal, J. P. and Shaw, R. A.: Cloud particle size distributions measured with an airborne digital in-line holographic instrument, Atmos. Meas. Tech., 2, 259-271, https://doi.org/10.5194/amt-2-259-2009, 2009.
Fugal, J., Shaw, R., Saw, E.-W., and Sergeyev, A.: Airborne digital holographic system for cloud particle measurements, Appl. Optics, 43, 5987–5995, 2004.
Glienke, S., Kostinski, A., Fugal, J., Shaw, R., Borrmann, S., and Stith, J.: Cloud droplets to drizzle: Contribution of transition drops to microphysical and optical properties of marine stratocumulus clouds, Geophys. Res. Lett., 44, 8002–8010, https://doi.org/10.1002/2017GL074430, 2017.
Holtzer, G. and Collins, L.: Relationship between the intrinsic radial distribution function for an isotropic field of particles and lower-dimensional measurements, J. Fluid Mech., 459, 93–102, https://doi.org/10.1017/S0022112002008169, 2002.
Jackson, R., McFarquhar, G., Stith, J., Beals, M., Shaw, R., Jensen, J., Fugal, J., and Korolev, A.: An assessment of the impact of antishattering tips and artifact removal techniques on cloud ice size distributions measured by the 2D cloud probe, J. Atmos. Ocean. Technol., 31, 2567–2590, 2014.
Kostinski, A.: On the extinction of radiation by a homogeneous but spatially correlated random medium, J. Opt. Soc. Am. A, 18, 1929–1933, https://doi.org/10.1364/JOSAA.18.001929, 2001.
Kostinski, A.: Simple approximations for condensational growth, Environ. Res. Lett., 4, 015005, https://doi.org/10.1088/1748-9326/4/1/015005, 2009.
Kostinski, A. and Jameson, A.: On the spatial distribution of cloud particles, J. Atmos. Sci., 57, 901–915, 2000.
Kostinski, A. and Shaw, R.: Scale-dependent droplet clustering in turbulent clouds, J. Fluid Mech., 434, 389–398, 2001.
Kozikowsa, A., Haman, K., and Supronowicz, J.: Preliminary results of an investigation of the spatial distribution of fog droplets by a holographic method, Q. J. Roy. Meteorol. Soc., 110, 65–73, 1984.
Landau, L. and Lifshitz, E.: Statistical Physics, Butterworth Heinemann, Oxford, UK, 1980.
Larsen, M.: Studies of discrete fluctuations in atmospheric phenomena, Ph.D. thesis, Michigan Technological University, 2006.
Larsen, M.: Scale localization of cloud particle clustering statistics, J. Atmos. Sci., 69, 3277–3289, https://doi.org/10.1175/JAS-D-12-02.1, 2012.
Larsen, M., Briner, C., and Boehner, P.: On the recovery of 3D spatial statistics of particles from 1D measurements: Implications for airborne instruments, J. Atmos. Ocean. Technol., 31, 2078–2087, https://doi.org/10.1175/JTECH-D-14-00004.1, 2014.
Lee, K. and Seong, W.: Percus-Yevick radial distribution function calculation for a water-saturated granular medium, Ocean Eng., 116, 268–272, 2016.
Lehmann, K., Siebert, H., Wendisch, M., and Shaw, R.: Evidence for inertial droplet clustering in weakly turbulent clouds, Tellus, 59B, 57–65, 2007.
Marshak, A., Knyazikhin, Y., Larsen, M., and Wiscombe, W. J.: Small-scale drop size variability: Empirical models for drop-size-dependent clustering in clouds, J. Atmos. Sci., 62, 551–558, 2005.
Martinez, V. and Saar, E.: Statistics of the Galaxy Distribution, CRC Press, Boca Raton, 456 pp., 2001.
Monchaux, R., Bourgoin, M., and Cartellier, A.: Analyzing preferential concentration and clustering of inertial particles in turbulence, Int. J. Multiphas. Flow, 40, 1–18, 2012.
Onishi, R., Matsuda, K., and Takahashi, K.: Lagrangian tracking simulation of droplet growth in turbulence – Turbulence enhancement of autoconversion rate, J. Atmos. Sci., 72, 2591–2607, 2015.
Ornstein, L. and Zernike, F.: Accidental deviations of density and opalescence at the critical point of a single substance, KNAW Proc., 17, 793–806, 1914.
O'Shea, S., Choularton, T., Lloyd, G., Crosier, J., Bower, K., Gallagher, M., Abel, S., Cotton, R., Brown, P., Fugal, J., Schlenczek, O., Borrmann, S., and Pickering, J.: Airborne observations of the microphysical structure of two contrasting cirrus clouds, J. Geophys. Res.-Atmos., 121, 13510–13536, 2016.
Pinsky, M. and Khain, A.: Fine structure of cloud droplet concentration as seen from the Fast-FSSP measurements. Part I: Method of analysis and preliminary results, J. Appl. Meteorol., 40, 1515–1537, 2001.
Reade, W. and Collins, L.: Effect of preferential concentration on turbulent collision rates, Phys. Fluids, 12, 2530–2540, 2000.
Ripley, B.: The second-order analysis of stationary point processes, J. Appl. Probab., 13, 255–266, 1976.
Ripley, B.: Modelling spatial paterns (with discussion), J. Roy. Stat. Soc., B39, 172–212, 1977.
Ripley, B.: Edge effects in spatial stochastic processes, in: Statistics in Theory and Practice: Essays in Honour of Bertil Matérn, edited by: Ranneby, B., 242–262, 1982.
Salazar, J., Jong, J. D., Cao, L., Woodward, C., Meng, H., and Collins, L.: Experimental and numerical inverstigation of inertial particle clustering in isotropic turbulence, J. Fluid Mech., 600, 245–256, 2008.
Saw, E.-W., Shaw, R., Ayyalasomayajula, S., Chuang, P., and Gylfason, A.: Inertial particle clustering of particles in high-Reynolds-number turbulence, Phys. Rev. Lett., 100, 214501, https://doi.org/10.1103/PhysRevLett.100.214501, 2008.
Saw, E.-W., Salazar, J., Collins, L., and Shaw, R.: Spatial clustering of polydisperse inertial particles in turbulence: I. Comparing simulation with theory, New J. Phys., 14, 105030, https://doi.org/10.1088/1367-2630/14/10/105030, 2012a.
Saw, E.-W., Shaw, R., Salazar, J., and Collins, L.: Spatial clustering of polydisperse inertial particles in turbulence: II. Comparing simulation with experiment, New J. Phys., 14, 105031, https://doi.org/10.1088/1367-2630/14/10/105031, 2012b.
Schabenberger, O. and Goway, C.: Statistical Methods for Spatial Data Analysis, Chapman and Hall/CRC, Boca Raton, 504 pp., 2005.
Schlenczek, O., Fugal, J., Lloyd, G., Bower, K., Choularton, T., Flynn, M., Crosier, J., and Borrmann, S.: Microphysical properties of ice crystal precipitation and surface-generated ice crystals in a high alpine environment in Switzerland, J. Appl. Meteorol. Climatol., 56, 433–453, 2017.
Shaw, R.: Particle-turbulence interactions in atmospheric clouds, Annu. Rev. Fluid Mech., 35, 183–227, https://doi.org/10.1146/annurev.fluid.35.101101.161125, 2003.
Shaw, R., Reade, W., Collins, L., and Verlinde, J.: Preferential concentration of cloud droplets by turbulence: Effects on the early evolution of cumulus cloud droplet spectra, J. Atmos. Sci., 55, 1965–1976, 1998.
Shaw, R., Kostinski, A., and Larsen, M.: Towards quantifying droplet clustering in clouds, Q. J. Roy. Meteorol. Soc., 128, 1043–1057, https://doi.org/10.1256/003590002320373193, 2002.
Siebert, H., Gerashchenko, S., Gylfason, A., Lehmann, K., Collins, L., Shaw, R., and Warhaft, Z.: Towards understanding the role of turbulence on droplets in clouds: In situ and laboratory measurements, Atmos. Res., 97, 426–437, 2010.
Siebert, H., Shaw, R. A., Ditas, J., Schmeissner, T., Malinowski, S. P., Bodenschatz, E., and Xu, H.: High-resolution measurement of cloud microphysics and turbulence at a mountaintop station, Atmos. Meas. Tech., 8, 3219–3228, https://doi.org/10.5194/amt-8-3219-2015, 2015.
Small, J. and Chuang, P.: New observations of precipitation initiation in warm cumulus clouds, J. Atmos. Sci., 65, 2972–2982, 2008.
Spuler, S. and Fugal, J.: Design of an in-line, digital holographic imaging system for airborne measurement of clouds, Appl. Optics, 50, 1405–1412, 2011.
Srivastava, R.: Growth of cloud drops by condensation: A criticism of currently-accepted theory and a new approach, J. Atmos. Sci., 46, 869–887, 1989.
Stoyan, D., Kendall, W., and Mecke, J.: Stochastic Geometry and its Applications, Wiley, Chichister, England, 436 pp., 1995.
Uhlig, E.-M., Borrmann, S., and Jaenicke, R.: Holographic in-situ measurements of the spatial droplet distribution in stratiform clouds, Tellus, 50B, 377–387, 1998.
Wang, L., Wexler, A., and Zhou, Y.: Statistical mechanical description and modeling of trubulent collision of inertial particles, J. Fluid Mech., 415, 117–153, 2000.
Xue, Y., Wang, L.-P., and Grabowski, W.: Growth of cloud droplets by turbulent collision-coalescence, J. Atmos. Sci., 65, 331–356, 2008.
Yang, W., Kostinski, A., and Shaw, R.: Depth-of-focus reduction for digital in-line holography of particle fields, Opt. Lett., 30, 1303–1305, 2005.
Zaichik, L. and Alipchenkov, V.: Statistical models for predicting pair dispersion and particle clustering in isotropic turbulence and their applications, New J. Phys., 11, 103018, https://doi.org/10.1088/1367-2630/11/10/103018, 2009.
Short summary
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.
A statistical tool frequently utilized to measure scale-dependent departures from perfect...
