Articles | Volume 15, issue 16
https://doi.org/10.5194/amt-15-4989-2022
© Author(s) 2022. 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-15-4989-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Aug 2022
Research article |
| 30 Aug 2022
Detection and analysis of cloud boundary in Xi'an, China, employing 35 GHz cloud radar aided by 1064 nm lidar
Yun Yuan
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Huige Di
CORRESPONDING AUTHOR
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Yuanyuan Liu
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Tao Yang
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Qimeng Li
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Qing Yan
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Wenhui Xin
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Shichun Li
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
Dengxin Hua
CORRESPONDING AUTHOR
School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology, Xi'an 710048, China
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It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
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It is necessary to correctly evaluate the amount of cloud water resources in an area. Currently, there is a lack of effective observation methods for atmospheric column condensate evaluation. We propose a method for atmospheric column condensate by combining millimetre cloud radar, lidar and microwave radiometers. The method can realise determination of atmospheric column condensate. The variation of cloud before precipitation is considered, and the atmospheric column is deduced and obtained.
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Revised manuscript not accepted
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According to the absorption spectrum characteristics of oxygen A-band, a comprehensive budget is made in connection with various errors. The main purpose is to select a group of detection wavelengths with excellent performance and small error to match the evaluated radar system model, so as to provide a reference idea for the actual establishment of the experimental system in the future.
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Subject: Clouds | Technique: In Situ Measurement | Topic: Data Processing and Information Retrieval
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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
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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.
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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Bu-Yo Kim, Joo Wan Cha, and Ki-Ho Chang
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Jun Inoue, Yutaka Tobo, Kazutoshi Sato, Fumikazu Taketani, and Marion Maturilli
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Ilona Ylivinkka, Santeri Kaupinmäki, Meri Virman, Maija Peltola, Ditte Taipale, Tuukka Petäjä, Veli-Matti Kerminen, Markku Kulmala, and Ekaterina Ezhova
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Georgios Touloupas, Annika Lauber, Jan Henneberger, Alexander Beck, and Aurélien Lucchi
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Carola Barrientos Velasco, Hartwig Deneke, Hannes Griesche, Patric Seifert, Ronny Engelmann, and Andreas Macke
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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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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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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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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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Hsu-Yung Cheng and Chih-Lung Lin
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A cloud detection method for all-sky images is proposed. Obtaining improved cloud detection results is helpful for cloud classification, tracking and solar irradiance prediction. The features are extracted from local image patches with different sizes to include local structure and multi-resolution information. The cloud models are learned through the training process. We have shown that taking advantages of multiple classifiers and various patch sizes is able to increase the detection accuracy.
Qingyong Li, Zhen Zhang, Weitao Lu, Jun Yang, Ying Ma, and Wen Yao
Atmos. Meas. Tech., 9, 753–764, https://doi.org/10.5194/amt-9-753-2016, https://doi.org/10.5194/amt-9-753-2016, 2016
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This paper proposes a new cloud classification method, named bag of micro-structures (BoMS), for whole-sky imagers. BoMS treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. BoMS identifies five different sky conditions: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness (often appearing in all-sky images but seldom addressed in the literature). The performance of BoMS overperforms those of traditional methods.
Som Sharma, Rajesh Vaishnav, Munn V. Shukla, Prashant Kumar, Prateek Kumar, Pradeep K. Thapliyal, Shyam Lal, and Yashwant B. Acharya
Atmos. Meas. Tech., 9, 711–719, https://doi.org/10.5194/amt-9-711-2016, https://doi.org/10.5194/amt-9-711-2016, 2016
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Cloud base height observations from Ceilometer CL31 were extensively studied during May 2013 to January 2015 over Ahmedabad (23.03°N, 72.54°E), India. Results indicate that the ceilometer is an excellent instrument to precisely detect low- and mid-level clouds, and that the MODIS satellite provides accurate retrieval of high-level clouds over this region.
S. Gagné, L. P. MacDonald, W. R. Leaitch, and J. R. Pierce
Atmos. Meas. Tech., 9, 619–630, https://doi.org/10.5194/amt-9-619-2016, https://doi.org/10.5194/amt-9-619-2016, 2016
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Measurements of clouds with an aircraft are essential to understand how clouds form and how they affect the Earth's climate. These measurements are used in climate models to help predict how our climate might develop in the next century. Aircraft measurements are, however, difficult for modellers to interpret because the way they were acquired and analyzed varies from one team of scientists to the next. We present a software platform for scientists to share and compare their analysis tools.
H.-Y. Cheng and C.-C. Yu
Atmos. Meas. Tech., 8, 1173–1182, https://doi.org/10.5194/amt-8-1173-2015, https://doi.org/10.5194/amt-8-1173-2015, 2015
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This work performs cloud classification on all-sky images. To deal with mixed cloud types, we propose performing block-based classification. The proposed method combines local texture features with classical statistical texture features. The experimental results have shown that applying the combined feature results in higher classification accuracy. It is also validated that using block-based classification outperforms classification on the entire images.
A. Korolev and P. R. Field
Atmos. Meas. Tech., 8, 761–777, https://doi.org/10.5194/amt-8-761-2015, https://doi.org/10.5194/amt-8-761-2015, 2015
M. L. Krüger, S. Mertes, T. Klimach, Y. F. Cheng, H. Su, J. Schneider, M. O. Andreae, U. Pöschl, and D. Rose
Atmos. Meas. Tech., 7, 2615–2629, https://doi.org/10.5194/amt-7-2615-2014, https://doi.org/10.5194/amt-7-2615-2014, 2014
M. I. A. Berghof, G. P. Frank, S. Sjogren, and B. G. Martinsson
Atmos. Meas. Tech., 7, 877–886, https://doi.org/10.5194/amt-7-877-2014, https://doi.org/10.5194/amt-7-877-2014, 2014
A. Schwarzenboeck, G. Mioche, A. Armetta, A. Herber, and J.-F. Gayet
Atmos. Meas. Tech., 2, 779–788, https://doi.org/10.5194/amt-2-779-2009, https://doi.org/10.5194/amt-2-779-2009, 2009
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Short summary
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.
We put forward a new algorithm for joint observation of the cloud boundary by lidar and Ka-band...
