Articles | Volume 17, issue 16
https://doi.org/10.5194/amt-17-4843-2024
© Author(s) 2024. 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-17-4843-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Aug 2024
Research article |
| 23 Aug 2024
| 23 Aug 2024
Partition between supercooled liquid droplets and ice crystals in mixed-phase clouds based on airborne in situ observations
Flor Vanessa Maciel
Department of Meteorology and Climate Science, San Jose State University, San Jose, CA 95192, USA
currently at: Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA
Department of Meteorology and Climate Science, San Jose State University, San Jose, CA 95192, USA
Ching An Yang
Department of Meteorology and Climate Science, San Jose State University, San Jose, CA 95192, USA
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Flor Vanessa Maciel, Minghui Diao, and Ryan Patnaude
Atmos. Chem. Phys., 23, 1103–1129, https://doi.org/10.5194/acp-23-1103-2023, https://doi.org/10.5194/acp-23-1103-2023, 2023
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Aerosol indirect effects on cirrus clouds are investigated during cirrus evolution, using global-scale in situ observations and climate model simulations. As cirrus evolves, the mechanisms to form ice crystals also change with time. Both small and large aerosols are found to affect cirrus properties. Southern Hemisphere cirrus appears to be more sensitive to additional aerosols. The climate model underestimates ice crystal mass, likely due to biases of relative humidity and vertical velocity.
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.
Ryan Patnaude, Minghui Diao, Xiaohong Liu, and Suqian Chu
Atmos. Chem. Phys., 21, 1835–1859, https://doi.org/10.5194/acp-21-1835-2021, https://doi.org/10.5194/acp-21-1835-2021, 2021
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A comprehensive, in situ observation dataset of cirrus clouds was developed based on seven field campaigns, ranging from 87° N–75° S. The observations were compared with a global climate model. Several key factors for cirrus cloud formation were examined, including thermodynamics, dynamics, aerosol indirect effects and geographical locations. Model biases include lower ice mass concentrations, smaller ice crystals and weaker aerosol indirect effects.
Chenglai Wu, Xiaohong Liu, Minghui Diao, Kai Zhang, Andrew Gettelman, Zheng Lu, Joyce E. Penner, and Zhaohui Lin
Atmos. Chem. Phys., 17, 4731–4749, https://doi.org/10.5194/acp-17-4731-2017, https://doi.org/10.5194/acp-17-4731-2017, 2017
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This study utilizes a novel approach to directly compare the CAM5-simulated cloud macro- and microphysics with the collocated HIPPO observations for the period of 2009 to 2011. The model cannot capture the large spatial variabilities of observed RH, which is responsible for much of the model missing low-level warm clouds. A large portion of the RH bias results from the discrepancy in water vapor. The model underestimates the observed number concentration and ice water content.
M. Diao, M. A. Zondlo, A. J. Heymsfield, L. M. Avallone, M. E. Paige, S. P. Beaton, T. Campos, and D. C. Rogers
Atmos. Chem. Phys., 14, 2639–2656, https://doi.org/10.5194/acp-14-2639-2014, https://doi.org/10.5194/acp-14-2639-2014, 2014
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
In situ observations of supercooled liquid water clouds over Dome C, Antarctica by balloon-borne sondes
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
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.
Philippe Ricaud, Pierre Durand, Paolo Grigioni, Massimo Del Guasta, Giuseppe Camporeale, Axel Roy, Jean-Luc Attié, and John Bognar
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-8, https://doi.org/10.5194/amt-2024-8, 2024
Revised manuscript accepted for AMT
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Clouds in Antarctica are key elements affecting climate evolution. Some clouds are constituted 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.
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
Short summary
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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
Cited articles
American Meteorological Society: Generating cell Glossary of Meteorology, http://glossary.ametsoc.org/wiki/generating_cell (last access: 10 August 2024), 2024.
Atlas, R., Mohrmann, J., Finlon, J., Lu, J., Hsiao, I., Wood, R., and Diao, M.: The University of Washington Ice–Liquid Discriminator (UWILD) improves single-particle phase classifications of hydrometeors within Southern Ocean clouds using machine learning, Atmos. Meas. Tech., 14, 7079–7101, https://doi.org/10.5194/amt-14-7079-2021, 2021.
Avramov, A., Ackerman, A. S., Fridlind, A. M., Van Diedenhoven, B., Botta, G., Aydin, K., Verlinde, J., Korolev, A. V., Strapp, J. W., McFarquhar, G. M., Jackson, R., Brooks, S. D., Glen, A., and Wolde, M.: Toward ice formation closure in Arctic mixed-phase boundary layer clouds during ISDAC, J. Geophys. Res.-Atmos., 116, D00T08, https://doi.org/10.1029/2011JD015910, 2011.
Barone, T., Diao, M., Shi, Y., Zhao, X., Liu, X., and Silber, I.: Impacts of synoptic‐scale dynamics on clouds and radiation in high southern latitudes, J. Geophys. Res.-Atmos., 129, e2023JD040329, https://doi.org/10.1029/2023JD040329, 2024.
Baumgardner, D., Abel, S. J., Axisa, D., Cotton, R., Crosier, J., Field, P., Gurganus, C., Heymsfield, A., Korolev, A., Krämer, M., Lawson, P., McFarquhar, G., Ulanowski, Z., and Um, J.: Cloud Ice Properties: In Situ Measurement Challenges, Meteor. Mon., 58, 9.1–9.23, https://doi.org/10.1175/AMSMONOGRAPHS-D-16-0011.1, 2017.
Bühl, J., Seifert, P., Engelmann, R., and Ansmann, A.: Impact of vertical air motions on ice formation rate in mixed-phase cloud layers, npj Clim. Atmos. Sci., 2, 36, https://doi.org/10.1038/s41612-019-0092-6, 2019.
D'Alessandro, J. J., Diao, M., Wu, C., Liu, X., Jensen, J. B., and Stephens, B. B.: Cloud phase and relative humidity distributions over the Southern Ocean in austral summer based on in situ observations and CAM5 simulations, J. Climate, 32, 2781–2805, https://doi.org/10.1175/JCLI-D-18-0232.1, 2019.
D'Alessandro, J. J., McFarquhar, G. M., Wu, W., Stith, J. L., Jensen, J. B., and Rauber, R. M.: Characterizing the Occurrence and Spatial Heterogeneity of Liquid, Ice, and Mixed Phase Low-Level Clouds Over the Southern Ocean Using in Situ Observations Acquired During SOCRATES, J. Geophys. Res.-Atmos., 126, 1–18, https://doi.org/10.1029/2020JD034482, 2021.
D'Alessandro, J. J., McFarquhar, G. M., Stith, J. L., Diao, M., DeMott, P. J., McCluskey, C. S., Hill, T. C. J., Roberts, G. C., and Sanchez, K. J.: An evaluation of phase, aerosol-cloud interactions and microphysical properties of single- and multi-layer clouds over the Southern Ocean using in situ observations from SOCRATES, J. Geophys. Res.-Atmos., 128, e2023JD038610, https://doi.org/10.1029/2023JD038610, 2023.
DeMott, P. J., Prenni, A. J., Liu, X., Kreidenweis, S. M., Petters, M. D., Twohy, C. H., Richardson, M. S., Eidhammer, T., and Rogers, D. C.: Predicting global atmospheric ice nuclei distributions and their impacts on climate, P. Natl. Acad. Sci. USA, 107, 11217–11222, https://doi.org/10.1073/pnas.0910818107, 2010.
Desai, N., Diao, M., Shi, Y., Liu, X., and Silber, I.: Ship-based observations and climate model simulations of cloud phase over the Southern Ocean, J. Geophys. Res.-Atmos., 128, e2023JD038581, https://doi.org/10.1029/2023JD038581, 2023.
Diao, M.: VCSEL 1 Hz Water Vapor Data, Version 2.0, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.26023/KFSD-Y8DQ-YC0D, 2021.
Diao, M., Jensen, J. B., Pan, L. L., Homeyer, C. R., Honomichl, S., Bresch, J. F., and Bansemer, A.: Distributions of ice supersaturation and ice crystals from airborne observations in relation to upper tropospheric dynamical boundaries, J. Geophys. Res.-Atmos., 120, 5101–5121, https://doi.org/10.1002/2015JD023139, 2015.
Fan, J., Ovtchinnikov, M., Comstock, J. M., McFarlane, S. A., and Khain, A.: Ice formation in Arctic mixed-phase clouds: Insights from a 3-D cloud-resolving model with size-resolved aerosol and cloud microphysics, J. Geophys. Res., 114, D04205, https://doi.org/10.1029/2008JD010782, 2009.
Fan, J., Ghan, S., Ovchinnikov, M., Liu, X., Rasch, P. J., and Korolev, A.: Representation of Arctic mixed-phase clouds and the Wegener-Bergeron-Findeisen process in climate models: Perspectives from a cloud-resolving study, J. Geophys. Res.-Atmos., 116, D00T07, https://doi.org/10.1029/2010JD015375, 2011.
Field, P. R., Hogan, R. J., Brown, P. R. A., Illingworth, A. J., Choularton, T. W., Kaye, P. H., Hirst, E., and Greenaway, R.: Simultaneous radar and aircraft observations of mixed-phase cloud at the 100 m scale, Q. J. Roy. Meteor. Soc., 130, 1877–1904, https://doi.org/10.1256/QJ.03.102, 2004.
Field, P. R., Hill, A. A., Furtado, K., and Korolev, A.: Mixed-phase clouds in a turbulent environment. Part 2: Analytic treatment, Q. J. Roy. Meteor. Soc., 140, 870–880, https://doi.org/10.1002/QJ.2175, 2014.
Hill, A. A., Field, P. R., Furtado, K., Korolev, A., and Shipway, B. J.: Mixed-phase clouds in a turbulent environment. Part 1: Large-eddy simulation experiments, Q. J. Roy. Meteor. Soc., 140, 855–869, https://doi.org/10.1002/QJ.2177, 2014.
Hobbs, P. V. and Rangno, A. L.: Ice Particle Concentrations in Clouds, J. Atmos. Sci., 42, 2523–2549, https://doi.org/10.1175/1520-0469(1985)042<2523:IPCIC>2.0.CO;2, 1985.
Hogan, R. J., Field, P. R., Illingworth, A. J., Cotton, R. J., and Choularton, T. W.: Properties of embedded convection in warm-frontal mixed-phase cloud from aircraft and polarimetric radar, Q. J. Roy. Meteor. Soc., 128, 451–476, https://doi.org/10.1256/003590002321042054, 2002.
Huang, Y., Chubb, T., Baumgardner, D., DeHoog, M., Siems, S. T., and Manton, M. J.: Evidence for secondary ice production in Southern Ocean open cellular convection, Q. J. Roy. Meteor. Soc., 143, 1685–1703, https://doi.org/10.1002/qj.3041, 2017.
Hyder, P., Edwards, J. M., Allan, R. P., Hewitt, H. T., Bracegirdle, T. J., Gregory, J. M., Wood, R. A., Meijers, A. J. S., Mulcahy, J., Field, P., Furtado, K., Bodas-Salcedo, A., Williams, K. D., Copsey, D., Josey, S. A., Liu, C., Roberts, C. D., Sanchez, C., Ridley, J., Thorpe, L., Hardiman, S. C., Mayer, M., Berry, D. I., and Belcher, S. E.: Critical Southern Ocean climate model biases traced to atmospheric model cloud errors, Nat. Commun., 9, 3625, https://doi.org/10.1038/s41467-018-05634-2, 2018.
Jackson, R. C., McFarquhar, G. M., Korolev, A. V, Earle, M. E., Liu, P. S. K., Lawson, R. P., Brooks, S., Wolde, M., Laskin, A., and Freer, M.: The dependence of ice microphysics on aerosol concentration in arctic mixed-phase stratus clouds during ISDAC and M-PACE, J. Geophys. Res.-Atmos., 117, 1–20, https://doi.org/10.1029/2012JD017668, 2012.
Järvinen, E., McCluskey, C. S., Waitz, F., Schnaiter, M., Bansemer, A., Bardeen, C. G., Gettelman, A., Heymsfield, A., Stith, J. L., Wu, W., D'Alessandro, J. J., McFarquhar, G. M., Diao, M., Finlon, J. A., Hill, T. C. J., Levin, E. J. T., Moore, K. A., and DeMott, P. J.: Evidence for Secondary Ice Production in Southern Ocean Maritime Boundary Layer Clouds, J. Geophys. Res.-Atmos., 127, 1–31, https://doi.org/10.1029/2021JD036411, 2022.
Korolev, A. V.: About Definition of Liquid, Mixed and Ice Clouds, FAA Workshop on Mixed-Phase and Glaciated Icing Conditions, Atlantic City, NJ, 2–3 December 1998, Federal Aviation Authority, Atlantic City, NJ, USA, 325–326, 1998.
Korolev, A. V. and Field, P. R.: The Effect of Dynamics on Mixed-Phase Clouds: Theoretical Considerations, J. Atmos. Sci., 65, 66–86, https://doi.org/10.1175/2007JAS2355.1, 2008.
Korolev, A. V. and Isaac, G. A.: Relative Humidity in Liquid, Mixed-Phase, and Ice Clouds, J. Atmos. Sci., 63, 2865–2880, https://doi.org/10.1175/JAS3784.1, 2006.
Korolev, A. V. and Mazin, I. P.: Supersaturation of water vapor in clouds, J. Atmos. Sci., 60, 2957–2974, https://doi.org/10.1175/1520-0469(2003)060<2957:SOWVIC>2.0.CO;2, 2003.
Korolev, A. V. and Milbrandt, J.: How Are Mixed-Phase Clouds Mixed?, Geophys. Res. Lett., 49, 1–7, https://doi.org/10.1029/2022GL099578, 2022.
Korolev, A. V., Isaac, G. A., Cober, S. G., Strapp, J. W., and Hallett, J.: Microphysical characterization of mixed-phase clouds, Q. J. Roy. Meteor. Soc., 129, 39–65, https://doi.org/10.1256/qj.01.204, 2003.
Korolev, A. V., Emery, E. F., Strapp, J. W., Cober, S. G., and Isaac, G. A.: Quantification of the effects of shattering on airborne ice particle measurements, J. Atmos. Ocean. Tech., 30, 2527–2553, https://doi.org/10.1175/JTECH-D-13-00115.1, 2013.
Korolev, A. V., McFarquhar, G., Field, P. R., Franklin, C., Lawson, P., Wang, Z., Williams, E., Abel, S. J., Axisa, D., Borrmann, S., Crosier, J., Fugal, J., Krämer, M., Lohmann, U., Schlenczek, O., Schnaiter, M., and Wendisch, M.: Mixed-Phase Clouds: Progress and Challenges, Meteor. Mon., 58, 5.1–5.50, https://doi.org/10.1175/AMSMONOGRAPHS-D-17-0001.1, 2017.
Matus, A. V and L'Ecuyer, T. S.: The role of cloud phase in Earth's radiation budget, J. Geophys. Res.-Atmos., 122, 2559–2578, https://doi.org/10.1002/2016JD025951, 2017.
McCluskey, C. S., Hill, T. C. J., Humphries, R. S., Rauker, A. M., Moreau, S., Strutton, P. G., Chambers, S. D., Williams, A. G., McRobert, I., Ward, J., Keywood, M. D., Harnwell, J., Ponsonby, W., Loh, Z. M., Krummel, P. B., Protat, A., Kreidenweis, S. M., and DeMott, P. J.: Observations of Ice Nucleating Particles Over Southern Ocean Waters, Geophys. Res. Lett., 45, 11989–11997, https://doi.org/10.1029/2018GL079981, 2018.
McFarquhar, G. M., Bretherton, C. S., Marchand, R., Protat, A., DeMott, P. J., Alexander, S. P., Roberts, G. C., Twohy, C. H., Toohey, D., Siems, S., Huang, Y., Wood, R., Rauber, R. M., Lasher-Trapp, S., Jensen, J., Stith, J. L., Mace, J., Um, J., Järvinen, E., Schnaiter, M., Gettelman, A., Sanchez, K. J., McCluskey, C. S., Russell, L. M., McCoy, I. L., Atlas, R. L., Bardeen, C. G., Moore, K. A., Hill, T. C. J., Humphries, R. S., Keywood, M. D., Ristovski, Z., Cravigan, L., Schofield, R., Fairall, C., Mallet, M. D., Kreidenweis, S. M., Rainwater, B., D'Alessandro, J., Wang, Y., Wu, W., Saliba, G., Levin, E. J. T., Ding, S., Lang, F., Truong, S. C. H., Wolff, C., Haggerty, J., Harvey, M. J., Klekociuk, A. R., and McDonald, A.: Observations of Clouds, Aerosols, Precipitation, and Surface Radiation over the Southern Ocean: An Overview of CAPRICORN, MARCUS, MICRE, and SOCRATES, B. Am. Meteorol. Soc., 102, E894–E928, https://doi.org/10.1175/BAMS-D-20-0132.1, 2021.
Mohrmann, J., Finlon, J., Atlas, R., Lu, J., Hsiao, I., and Wood, R.: University of Washington Ice-Liquid Discriminator single particle phase classifications and 1 Hz particle size distributions/heterogeneity estimate, Version 1.0, UCAR/NCAR – Earth Observing Laboratory [data set], https://doi.org/10.26023/PA5W-4DRX-W50A, 2021.
Morrison, H., de Boer, G., Feingold, G., Harrington, J., Shupe, M. D., and Sulia, K.: Resilience of persistent Arctic mixed-phase clouds, Nat. Geosci., 5, 11–17, https://doi.org/10.1038/ngeo1332, 2012.
Moss, S. J. and Johnson, D. W.: Aircraft measurements to validate and improve numerical model parametrisations of ice to water ratios in clouds, Atmos. Res., 34, 1–25, https://doi.org/10.1016/0169-8095(94)90078-7, 1994.
Murphy, D. M. and Koop, T.: Review of the vapour pressures of ice and supercooled water for atmospheric applications, Q. J. Roy. Meteor. Soc., 131, 1539–1565, https://doi.org/10.1256/qj.04.94, 2005.
Norgren, M. S., de Boer, G., and Shupe, M. D.: Observed aerosol suppression of cloud ice in low-level Arctic mixed-phase clouds, Atmos. Chem. Phys., 18, 13345–13361, https://doi.org/10.5194/acp-18-13345-2018, 2018.
Pinto, J. O.: Autumnal mixed-phase cloudy boundary layers in the arctic, J. Atmos. Sci., 55, 2016–2038, https://doi.org/10.1175/1520-0469(1998)055<2016:AMPCBL>2.0.CO;2, 1998.
Qiu, S., Xi, B., and Dong, X.: Influence of Wind Direction on Thermodynamic Properties and Arctic Mixed-Phase Clouds in Autumn at Utqiaġvik, Alaska, J. Geophys. Res.-Atmos., 123, 9589–9603, https://doi.org/10.1029/2018JD028631, 2018.
Rauber, R. M. and Tokay, A.: An Explanation for the Existence of Supercooled Water at the Top of Cold Clouds, J. Atmos. Sci., 48, 1005–1023, https://doi.org/10.1175/1520-0469(1991)048<1005:AEFTEO>2.0.CO;2, 1991.
Romatschke, U. and Vivekanandan, J.: Cloud and precipitation particle identification using cloud radar and lidar measurements: Retrieval technique and validation, Earth and Space Science, 9, e2022EA002299, https://doi.org/10.1029/2022EA002299, 2022.
Shupe, M. D., Kollias, P., Persson, P. O. G., and McFarquhar, G. M.: Vertical Motions in Arctic Mixed-Phase Stratiform Clouds, J. Atmos. Sci., 65, 1304–1322, https://doi.org/10.1175/2007JAS2479.1, 2008.
Smith, A. J., Larson, V. E., Niu, J., Kankiewicz, J. A., and Carey, L. D.: Processes that generate and deplete liquid water and snow in thin midlevel mixed-phase clouds, J. Geophys. Res.-Atmos., 114, 1–18, https://doi.org/10.1029/2008JD011531, 2009.
Storelvmo, T.: Aerosol Effects on Climate via Mixed-Phase and Ice Clouds, Annu. Rev. Earth Pl. Sc., 45, 199–222, https://doi.org/10.1146/annurev-earth-060115-012240, 2017.
Tan, I. and Storelvmo, T.: Sensitivity Study on the Influence of Cloud Microphysical Parameters on Mixed-Phase Cloud Thermodynamic Phase Partitioning in CAM5, J. Atmos. Sci., 73, 709–728, https://doi.org/10.1175/JAS-D-15-0152.1, 2016.
Tan, I., Storelvmo, T., and Zelinka, M. D.: Observational constraints on mixed-phase clouds imply higher climate sensitivity, Science, 352, 224–227, https://doi.org/10.1126/science.aad5300, 2016.
UCAR/NCAR - Earth Observing Laboratory: SOCRATES: Low Rate (LRT - 1 sps) Navigation, State Parameter, and Microphysics Flight-Level Data, Version 1.4, UCAR/NCAR - Earth Observing Laboratory [data set], https://doi.org/10.5065/D6M32TM9, 2022.
Wallace, J. M. and Hobbs, P. V.: Atmospheric Science: An Introductory Survey, Elsevier Inc., New York, USA, https://doi.org/10.1016/C2009-0-00034-8, 1977.
Wang, D., Yang, C. A., and Diao, M.: Validation of satellite-based cloud phase distributions using global-scale in situ airborne observations, Earth and Space Science, 11, e2023EA003355, https://doi.org/10.1029/2023EA003355, 2024.
Wang, Y., McFarquhar, G. M., Rauber, R. M., Zhao, C., Wu, W., Finlon, J. A., Stechman, D. M., Stith, J., Jensen, J. B., Schnaiter, M., Järvinen, E., Waitz, F., Vivekanandan, J., Dixon, M., Rainwater, B., and Toohey, D. W.: Microphysical Properties of Generating Cells Over the Southern Ocean: Results From SOCRATES, J. Geophys. Res.-Atmos., 125, 1–23, https://doi.org/10.1029/2019JD032237, 2020.
Wang, Z., French, J., Vali, G., Wechsler, P., Haimov, S., Rodi, A., Deng, M., Leon, D., Snider, J., Peng, L., and Pazmany, A. L.: Single Aircraft Integration of Remote Sensing and In Situ Sampling for the Study of Cloud Microphysics and Dynamics, B. Am. Meteorol. Soc., 93, 653–668, https://doi.org/10.1175/BAMS-D-11-00044.1, 2012.
Westbrook, C. D. and Illingworth, A. J.: Evidence that ice forms primarily in supercooled liquid clouds at temperatures
Wood, R. and Field, P. R.: The Distribution of Cloud Horizontal Sizes, J. Climate, 24, 4800–4816, https://doi.org/10.1175/2011JCLI4056.1, 2011.
Wu, W. and McFarquhar, G. M.: On the impacts of different definitions of maximum dimension for nonspherical particles recorded by 2D imaging probes, J. Atmos. Ocean. Tech., 33, 1057–1072, https://doi.org/10.1175/JTECH-D-15-0177.1, 2016.
Yang, C. A., Diao, M., Gettelman, A., Zhang, K., Sun, J., McFarquhar, G., and Wu, W.: Ice and Supercooled Liquid Water Distributions Over the Southern Ocean Based on In Situ Observations and Climate Model Simulations, J. Geophys. Res.-Atmos., 126, e2021JD036045, https://doi.org/10.1029/2021JD036045, 2021.
Yip, J., Diao, M., Silber, I., and Gettelman, A.: Evaluation of the CAM6 Climate Model Using Cloud Observations at McMurdo Station, Antarctica, J. Geophys. Res.-Atmos., 126, 1–21, https://doi.org/10.1029/2021JD034653, 2021.
Zaremba, T. J., Rauber, R. M., McFarquhar, G. M., Hayman, M., Finlon, J. A., and Stechman, D. M.: Phase Characterization of Cold Sector Southern Ocean Cloud Tops: Results From SOCRATES, J. Geophys. Res.-Atmos., 125, e2020JD033673, https://doi.org/10.1029/2020JD033673, 2020.
Zhang, M., Liu, X., Diao, M., D'Alessandro, J. J., Wang, Y., Wu, C., Zhang, D., Wang, Z., and Xie, S.: Impacts of Representing Heterogeneous Distribution of Cloud Liquid and Ice on Phase Partitioning of Arctic Mixed-Phase Clouds with NCAR CAM5, J. Geophys. Res.-Atmos., 124, 13071–13090, https://doi.org/10.1029/2019JD030502, 2019.
Zhao, X. and Liu, X.: Global Importance of Secondary Ice Production, Geophys. Res. Lett., 48, 1–11, https://doi.org/10.1029/2021GL092581, 2021.
Zhao, X., Liu, X., Burrows, S., DeMott, P. J., Diao, M., McFarquhar, G. M., Patade, S., Phillips, V., Roberts, G. C., Sanchez, K. J., Shi, Y., and Zhang, M.: Important ice processes are missed by the Community Earth System Model in Southern Ocean mixed-phase clouds: Bridging SOCRATES observations to model developments, J. Geophys. Res.-Atmos., 128, e2022JD037513, https://doi.org/10.1029/2022JD037513, 2023.
Short summary
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.
The partition between supercooled liquid water and ice crystals in mixed-phase clouds is...
