Articles | Volume 17, issue 12
https://doi.org/10.5194/amt-17-3765-2024
© 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-17-3765-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Jun 2024
Research article |
| 25 Jun 2024
| 25 Jun 2024
Innovative cloud quantification: deep learning classification and finite-sector clustering for ground-based all-sky imaging
Jingxuan Luo
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yubing Pan
Institute of Urban Meteorology, China Meteorological Administration (CMA), Beijing 100089, China
Debin Su
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Jinhua Zhong
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Lingxiao Wu
Key Laboratory for Cosmic Rays of the Ministry of Education, Tibet University, Lhasa 850000, China
Wei Zhao
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Xiaoru Hu
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Zhengchao Qi
College of Atmospheric Sounding, Chengdu University of Information Technology, Chengdu 610225, China
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Daren Lu
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Yinan Wang
CORRESPONDING AUTHOR
Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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Zhiheng Liao, Meng Gao, Jinqiang Zhang, Jiaren Sun, Jiannong Quan, Xingcan Jia, Yubing Pan, and Shaojia Fan
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This study presents the first systematic assessment of observationally constrained UTLS O3 variability over the Northeast Asia region in the framework of upper-level circulation pattern classification. The results indicate that lower-stratospheric O3 exhibits a far stronger sensitivity to upper-level circulation patterns when compared with upper-tropospheric O3. The progression of the East Asian Trough plays a critical role in determining the location and intensity of O3 enhancements.
Ze Chen, Yufang Tian, Yinan Wang, Yongheng Bi, Xue Wu, Juan Huo, Linjun Pan, Yong Wang, and Daren Lü
Atmos. Meas. Tech., 15, 4785–4800, https://doi.org/10.5194/amt-15-4785-2022, https://doi.org/10.5194/amt-15-4785-2022, 2022
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Small-scale turbulence plays a vital role in the vertical exchange of heat, momentum and mass in the atmosphere. There are currently three models that can use spectrum width data of MST radar to calculate turbulence parameters. However, few studies have explored the applicability of the three calculation models. We compared and analysed the turbulence parameters calculated by three models. These results can provide a reference for the selection of models for calculating turbulence parameters.
Lu Yao, Yi Liu, Dongxu Yang, Zhaonan Cai, Jing Wang, Chao Lin, Naimeng Lu, Daren Lyu, Longfei Tian, Maohua Wang, Zengshan Yin, Yuquan Zheng, and Sisi Wang
Atmos. Meas. Tech., 15, 2125–2137, https://doi.org/10.5194/amt-15-2125-2022, https://doi.org/10.5194/amt-15-2125-2022, 2022
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A physics-based SIF retrieval algorithm, IAPCAS/SIF, is introduced and applied to OCO-2 and TanSat measurements. The strong linear relationship between OCO-2 SIF retrieved by IAPCAS/SIF and the official product indicates the algorithm's reliability. The good consistency in the spatiotemporal patterns and magnitude of the OCO-2 and TanSat SIF products suggests that the combinative usage of multi-satellite products has potential and that such work would contribute to further research.
Juan Huo, Yufang Tian, Xue Wu, Congzheng Han, Bo Liu, Yongheng Bi, Shu Duan, and Daren Lyu
Atmos. Chem. Phys., 20, 14377–14392, https://doi.org/10.5194/acp-20-14377-2020, https://doi.org/10.5194/acp-20-14377-2020, 2020
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A detailed analysis of ice cloud physical properties is presented based on 4 years of surface Ka-band radar measurements in Beijing, where the summer oceanic monsoon from the ocean and winter continental monsoon prevail alternately. More than 6000 ice cloud clusters were studied to investigate their physical properties, such as height, horizontal extent, temperature dependence and origination type, which can serve as a reference for parameterization and characterization in global climate models.
Juan Huo, Daren Lu, Shu Duan, Yongheng Bi, and Bo Liu
Atmos. Meas. Tech., 13, 1–11, https://doi.org/10.5194/amt-13-1-2020, https://doi.org/10.5194/amt-13-1-2020, 2020
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Cloud top height (CTH) is one of the important cloud parameters providing information about the vertical structure of cloud water content. To better understand the accuracy of CTH derived from passive satellite data, 2 years of ground-based Ka-band radar measurements are compared with CTH inferred from Terra/Aqua MODIS and Himawari AHI. It is found that MODIS and AHI underestimate CTH relative to radar by −1.10 km. Both MODIS and AHI CTH retrieval accuracy depend strongly on cloud depth.
Liang Ran, Zhaoze Deng, Xiaobin Xu, Peng Yan, Weili Lin, Ying Wang, Ping Tian, Pucai Wang, Weilin Pan, and Daren Lu
Atmos. Chem. Phys., 16, 10441–10454, https://doi.org/10.5194/acp-16-10441-2016, https://doi.org/10.5194/acp-16-10441-2016, 2016
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Vertical profiles of black carbon within 1 km above the ground were measured using a micro-aethalometer attached to a tethered balloon during the VOGA field campaign in summer 2014 at a semirural site in the North China Plain. The diurnal cycle of black carbon vertical distributions following the development of the mixing layer was analyzed for a selected dataset of 67 profiles.
Related subject area
Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
An analysis of cloud microphysical features over United Arab Emirates using multiple data sources
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
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
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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.
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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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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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Yunpei Chu, Huiying Zhang, Xia Li, and Jan Henneberger
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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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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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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
Cited articles
Alonso-Montesinos, J.: Real-Time Automatic Cloud Detection Using a Low-Cost Sky Camera, Remote Sens.-Basel, 12, 1382, https://doi.org/10.3390/rs12091382, 2020.
Changhui, Y., Yuan, Y., Minjing, M., and Menglu, Z.: CLOUD DETECTION METHOD BASED ON FEATURE EXTRACTION IN REMOTE SENSING IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W1, 173–177, https://doi.org/10.5194/isprsarchives-XL-2-W1-173-2013, 2013.
Chen, B., Xu, X. D., Yang, S., and Zhao, T. L.: Climatological perspectives of air transport from atmospheric boundary layer to tropopause layer over Asian monsoon regions during boreal summer inferred from Lagrangian approach, Atmos. Chem. Phys., 12, 5827–5839, https://doi.org/10.5194/acp-12-5827-2012, 2012.
Chi, Y., Zhao, C., Yang, Y., Zhao, X., and Yang, J.: Global characteristics of cloud macro-physical properties from active satellite remote sensing, Atmos. Res., 302, 107316, https://doi.org/10.1016/j.atmosres.2024.107316, 2024.
Dev, S., Lee, Y. H., and Winkler, S.: Color-Based Segmentation of Sky/Cloud Images From Ground-Based Cameras, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.-Basel, 10, 231–242, https://doi.org/10.1109/JSTARS.2016.2558474, 2017.
Dinc, S., Russell, R., and Parra, L. A. C.: Cloud Region Segmentation from All Sky Images using Double K-Means Clustering, 2022 IEEE International Symposium on Multimedia (ISM), Italy, 5–7 December 2022, IEEE, https://doi.org/10.1109/ISM55400.2022.00058, 2022.
Fabel, Y., Nouri, B., Wilbert, S., Blum, N., Triebel, R., Hasenbalg, M., Kuhn, P., Zarzalejo, L. F., and Pitz-Paal, R.: Applying self-supervised learning for semantic cloud segmentation of all-sky images, Atmos. Meas. Tech., 15, 797–809, https://doi.org/10.5194/amt-15-797-2022, 2022.
Gouveia, D. A., Barja, B., Barbosa, H. M. J., Seifert, P., Baars, H., Pauliquevis, T., and Artaxo, P.: Optical and geometrical properties of cirrus clouds in Amazonia derived from 1 year of ground-based lidar measurements, Atmos. Chem. Phys., 17, 3619–3636, https://doi.org/10.5194/acp-17-3619-2017, 2017.
Guo, B., Zhang, F., Li, W., and Zhao, Z.: Cloud Classification by Machine Learning for Geostationary Radiation Imager, IEEE T. Geosci. Remote, 62, 1–14, https://doi.org/10.1109/tgrs.2024.3353373, 2024.
Gyasi, E. K. and Swarnalatha, P.: Cloud-MobiNet: An Abridged Mobile-Net Convolutional Neural Network Model for Ground-Based Cloud Classification, Atmosphere., 14, 280, https://doi.org/10.3390/atmos14020280, 2023.
He, L. L., Ouyang, D. T., Wang, M., Bai, H. T., Yang, Q. L., Liu, Y. Q., and Jiang, Y.: A Method of Identifying Thunderstorm Clouds in Satellite Cloud Image Based on Clustering, CMC-Comput. Mater. Con., 57, 549–570, https://doi.org/10.32604/cmc.2018.03840, 2018.
Hensel, S., Marinov, M. B., Koch, M., and Arnaudov, D.: Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation, Energies, 14, 6156, https://doi.org/10.3390/en14196156, 2021.
Hutchison, K. D., Iisager, B. D., Dipu, S., Jiang, X. Y., Quaas, J., and Markwardt, R.: A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products-A WRF Case Study, Atmosphere, 10, 521, https://doi.org/10.3390/atmos10090521, 2019.
Irbah, A., Delanoë, J., van Zadelhoff, G.-J., Donovan, D. P., Kollias, P., Puigdomènech Treserras, B., Mason, S., Hogan, R. J., and Tatarevic, A.: The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products, Atmos. Meas. Tech., 16, 2795–2820, https://doi.org/10.5194/amt-16-2795-2023, 2023.
Jafariserajehlou, S., Mei, L., Vountas, M., Rozanov, V., Burrows, J. P., and Hollmann, R.: A cloud identification algorithm over the Arctic for use with AATSR–SLSTR measurements, Atmos. Meas. Tech., 12, 1059–1076, https://doi.org/10.5194/amt-12-1059-2019, 2019.
Kaiming, H., Jian, S., and Xiaoou, T.: Single image haze removal using dark channel prior, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, 20–25 June 2009, IEEE, https://doi.org/10.1109/CVPR.2009.5206515, 2009.
Krauz, L., Janout, P., Blazek, M., and Páta, P.: Assessing Cloud Segmentation in the Chromacity Diagram of All-Sky Images, Remote Sens.-Basel, 12, 1902, https://doi.org/10.3390/rs12111902, 2020.
Krüger, O., Marks, R., and Grassl, H.: Influence of pollution on cloud reflectance, J. Geophys. Res.-Atmos., 109, D24210, https://doi.org/10.1029/2004JD004625, 2004.
Li, P., Zheng, J. S., Li, P. Y., Long, H. W., Li, M., and Gao, L. H.: Tomato Maturity Detection and Counting Model Based on MHSA-YOLOv8, Sensors, 23, 6701, https://doi.org/10.3390/s23156701, 2023.
Li, Q., Zhang, Z., Lu, W., Yang, J., Ma, Y., and Yao, W.: From pixels to patches: a cloud classification method based on a bag of micro-structures, Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, 2016.
Li, W. W., Zhang, F., Lin, H., Chen, X. R., Li, J., and Han, W.: Cloud Detection and Classification Algorithms for Himawari-8 Imager Measurements Based on Deep Learning, IEEE T. Geosci. Remote, 60, 4107117, https://doi.org/10.1109/TGRS.2022.3153129, 2022.
Li, X., Qiu, B., Cao, G., Wu, C., and Zhang, L.: A Novel Method for Ground-Based Cloud Image Classification Using Transformer, Remote Sens.-Basel, 14, 3978, https://doi.org/10.3390/rs14163978, 2022.
Li, Z. W., Shen, H. F., Li, H. F., Xia, G. S., Gamba, P., and Zhang, L. P.: Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery, Remote Sens. Environ., 191, 342–358, https://doi.org/10.1016/j.rse.2017.01.026, 2017.
Ma, N., Sun, L., Zhou, C. H., and He, Y. W.: Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network, Remote Sens.-Basel, 13, 3319, https://doi.org/10.3390/rs13163319, 2021.
Marsing, A., Meerkötter, R., Heller, R., Kaufmann, S., Jurkat-Witschas, T., Krämer, M., Rolf, C., and Voigt, C.: Investigating the radiative effect of Arctic cirrus measured in situ during the winter 2015–2016, Atmos. Chem. Phys., 23, 587–609, https://doi.org/10.5194/acp-23-587-2023, 2023.
Matsunobu, L. M., Pedro, H. T. C., and Coimbra, C. F. M.: Cloud detection using convolutional neural networks on remote sensing images, Sol. Energy, 230, 1020–1032, https://doi.org/10.1016/j.solener.2021.10.065, 2021.
Monier, M., Wobrock, W., Gayet, J. F., and Flossmann, A.: Development of a detailed microphysics cirrus model tracking aerosol particles' histories for interpretation of the recent INCA campaign, J. Atmos. Sci., 63, 504–525, https://doi.org/10.1175/JAS3656.1, 2006.
Nakajima, T. Y., Tsuchiya, T., Ishida, H., Matsui, T. N., and Shimoda, H.: Cloud detection performance of spaceborne visible-to-infrared multispectral imagers, Appl. Optics, 50, 2601–2616, https://doi.org/10.1364/AO.50.002601, 2011.
Raghuraman, S. P., Paynter, D., and Ramaswamy, V.: Quantifying the Drivers of the Clear Sky Greenhouse Effect, 2000–2016, J. Geophys. Res.-Atmos., 124, 11354–11371, https://doi.org/10.1029/2019JD031017, 2019.
Riihimaki, L. D., Li, X. Y., Hou, Z. S., and Berg, L. K.: Improving prediction of surface solar irradiance variability by integrating observed cloud characteristics and machine learning, Sol. Energy, 225, 275–285, https://doi.org/10.1016/j.solener.2021.07.047, 2021.
Rocha, P. A. C. and Santos, V. O.: Global horizontal and direct normal solar irradiance modeling by the machine learning methods XGBoost and deep neural networks with CNN-LSTM layers: a case study using the GOES-16 satellite imagery, Int. J. Energy Environ, 13, 1271–1286, https://doi.org/10.1007/s40095-022-00493-6, 2022.
Rumi, E., Kerr, D., Sandford, A., Coupland, J., and Brettle, M.: Field trial of an automated ground-based infrared cloud classification system, Meteorol. Appl., 22, 779–788, https://doi.org/10.1002/met.1523, 2015.
Shi, X. and Liu, X.: Effect of cloud-scale vertical velocity on the contribution of homogeneous nucleation to cirrus formation and radiative forcing, Geophys. Res. Lett., 43, 6588–6595, https://doi.org/10.1002/2016GL069531, 2016.
van de Poll, H. M., Grubb, H., and Astin, I.: Sampling uncertainty properties of cloud fraction estimates from random transect observations, J. Geophys. Res.-Atmos., 111, D22218, https://doi.org/10.1029/2006JD007189, 2006.
Voigt, A., Albern, N., Ceppi, P., Grise, K., Li, Y., and Medeiros, B.: Clouds, radiation, and atmospheric circulation in the present-day climate and under climate change, WIREs Clim. Change, 12, e694, https://doi.org/10.1002/wcc.694, 2021.
Wang, G., Chen, Y. F., An, P., Hong, H. Y., Hu, J. H., and Huang, T. E.: UAV-YOLOv8: A Small-Object-Detection Model Based on Improved YOLOv8 for UAV Aerial Photography Scenarios, Sensors, 23, 7190, https://doi.org/10.3390/s23167190, 2023.
Werner, F., Siebert, H., Pilewskie, P., Schmeissner, T., Shaw, R. A., and Wendisch, M.: New airborne retrieval approach for trade wind cumulus properties under overlying cirrus, J. Geophys. Res.-Atmos., 118, 3634–3649, https://doi.org/10.1002/jgrd.50334, 2013.
Wu, L. X., Chen, T. L., Ciren, N., Wang, D., Meng, H. M., Li, M., Zhao, W., Luo, J. X., Hu, X. R., Jia, S. J., Liao, L., Pan, Y. B., and Wang, Y. A.: Development of a Machine Learning Forecast Model for Global Horizontal Irradiation Adapted to Tibet Based on Visible All-Sky Imaging, Remote Sens.-Basel, 15, 2340, https://doi.org/10.3390/rs15092340, 2023.
Wu, Z. P., Liu, S., Zhao, D. L., Yang, L., Xu, Z. X., Yang, Z. P., Liu, D. T., Liu, T., Ding, Y., Zhou, W., He, H., Huang, M. Y., Li, R. J., and Ding, D. P.: Optimized Intelligent Algorithm for Classifying Cloud Particles Recorded by a Cloud Particle Imager, J. Atmos. Ocean. Tech., 38, 1377–1393, https://doi.org/10.1175/JTECH-D-21-0004.1, 2021.
Xiao, B. J., Nguyen, M., and Yan, W. Q.: Fruit ripeness identification using YOLOv8 model, Multimed. Tools Appl., 83, 28039–28056, https://doi.org/10.1007/s11042-023-16570-9, 2023.
Yang, Y. K., Di Girolamo, L., and Mazzoni, D.: Selection of the automated thresholding algorithm for the Multi-angle Imaging SpectroRadiometer Radiometric Camera-by-Camera Cloud Mask over land, Remote Sens. Environ., 107, 159–171, https://doi.org/10.1016/j.rse.2006.05.020, 2007.
Yu, J. C., Li, Y. C., Zheng, X. X., Zhong, Y. F., and He, P.: An Effective Cloud Detection Method for Gaofen-5 Images via Deep Learning, Remote Sens.-Basel, 12, 2106, https://doi.org/10.3390/rs12132106, 2020.
Zhang, J., Liu, P., Zhang, F., and Song, Q.: CloudNet: Ground-Based Cloud Classification With Deep Convolutional Neural Network, Geophys. Res. Lett., 45, 8665–8672, https://doi.org/10.1029/2018gl077787, 2018.
Zhao, C., Yang, Y., Chi, Y., Sun, Y., Zhao, X., Letu, H., and Xia, Y.: Recent progress in cloud physics and associated radiative effects in China from 2016 to 2022, Atmos. Res., 293, 106899, https://doi.org/10.1016/j.atmosres.2023.106899, 2023.
Zhu, W., Chen, T., Hou, B., Bian, C., Yu, A., Chen, L., Tang, M., and Zhu, Y.: Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network, Appl. Sci.-Basel, 12, 1570, https://doi.org/10.3390/app12031570, 2022.
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.
Accurate cloud quantification is critical for climate research. We developed a novel computer...
