Articles | Volume 19, issue 20
https://doi.org/10.5194/amt-19-6757-2026
© Author(s) 2026. 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-19-6757-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
27 Oct 2026
Research article |
| 27 Oct 2026
| 27 Oct 2026
A self-supervised and distillation-guided framework for robust cloud-layer identification in multi-channel Raman lidar networks
Weijie Zou
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Tingxin Sun
Hangzhou Institute of Advanced Studies, Zhejiang Normal University, Hangzhou, China
Zhichao Bu
Meteorological Observation Center, China Meteorological Administration, Beijing, China
Yaru Dai
Meteorological Observation Center, China Meteorological Administration, Beijing, China
Haofei Wang
National Satellite Meteorological Center, China Meteorological Administration, Beijing, China
Longlong Wang
Hangzhou Institute of Advanced Studies, Zhejiang Normal University, Hangzhou, China
National Field Observation and Research Station (Hubei Wuhan) for Atmospheric Remote Sensing, Wuhan, China
School of Earth and Space Science and Technology, Wuhan University, Wuhan, China
Shuangliang Li
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Hubei Luojia Laboratory, Wuhan University, Wuhan, China
Jiarui Chen
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Xuan Wang
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Siwei Li
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
Hubei Luojia Laboratory, Wuhan University, Wuhan, China
Detlef Müller
CORRESPONDING AUTHOR
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, China
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Preprint withdrawn
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Cited articles
Alizadeh-Choobari, O. and Gharaylou, M.: Aerosol impacts on radiative and microphysical properties of clouds and precipitation formation, Atmos. Res., 185, 53–64, https://doi.org/10.1016/j.atmosres.2016.10.021, 2017.
Ansmann, A., Riebesell, M., and Weitkamp, C.: Measurement of atmospheric aerosol extinction profiles with a Raman lidar, Opt. Lett., 15, 746–748, https://doi.org/10.1364/OL.15.000746, 1990.
Baars, H., Kanitz, T., Engelmann, R., Althausen, D., Heese, B., Komppula, M., Preißler, J., Tesche, M., Ansmann, A., Wandinger, U., Lim, J.-H., Ahn, J. Y., Stachlewska, I. S., Amiridis, V., Marinou, E., Seifert, P., Hofer, J., Skupin, A., Schneider, F., Bohlmann, S., Foth, A., Bley, S., Pfüller, A., Giannakaki, E., Lihavainen, H., Viisanen, Y., Hooda, R. K., Pereira, S. N., Bortoli, D., Wagner, F., Mattis, I., Janicka, L., Markowicz, K. M., Achtert, P., Artaxo, P., Pauliquevis, T., Souza, R. A. F., Sharma, V. P., van Zyl, P. G., Beukes, J. P., Sun, J., Rohwer, E. G., Deng, R., Mamouri, R.-E., and Zamorano, F.: An overview of the first decade of PollyNET: an emerging network of automated Raman-polarization lidars for continuous aerosol profiling, Atmos. Chem. Phys., 16, 5111–5137, https://doi.org/10.5194/acp-16-5111-2016, 2016.
Baars, H., Seifert, P., Engelmann, R., and Wandinger, U.: Target categorization of aerosol and clouds by continuous multiwavelength-polarization lidar measurements, Atmos. Meas. Tech., 10, 3175–3201, https://doi.org/10.5194/amt-10-3175-2017, 2017.
Campbell, J. R., Hlavka, D. L., Welton, E. J., Flynn, C. J., Turner, D. D., Spinhirne, J. D., Scott, V. S., and Hwang, I. H.: Full-time, eye-safe cloud and aerosol lidar observation at Atmospheric Radiation Measurement Program sites: Instruments and data processing, J. Atmos. Ocean. Technol., 19, 431–442, https://doi.org/10.1175/1520-0426(2002)019<0431:FTESCA>2.0.CO;2, 2002.
Chisari, A. B., Ortis, A., Guarnera, L., Patatu, W. C., Giandolfo, R. A., Spampinato, E., Battiato, S., and Giuffrida, M. V.: On the cloud detection from backscattered images generated from a lidar-based ceilometer: Current state and opportunities, in: 2024 IEEE International Conference on Image Processing (ICIP), Abu Dhabi, United Arab Emirates, 27–30 October 2024, 144–150, https://doi.org/10.1109/ICIP51287.2024.10647352, 2024.
Chisari, A. B., Guarnera, L., Ortis, A., Patatu, W. C., Battiato, S., and Giuffrida, M. V.: Benchmarking computer vision architectures for cloud detection from lidar ceilometer backscatter data, Vis. Comput., 41, 9441–9458, https://doi.org/10.1007/s00371-025-03960-3, 2025.
Clothiaux, E. E., Mace, G. G., Ackerman, T. P., Kane, T. J., Spinhirne, J. D., and Scott, V. S.: An automated algorithm for detection of hydrometeor returns in micropulse lidar data, J. Atmos. Ocean. Technol., 15, 1035–1042, https://doi.org/10.1175/1520-0426(1998)015<1035:AAAFDO>2.0.CO;2, 1998.
Cordeiro, L. P., Gouveia, D. A., and Barbosa, H. M. J.: Diurnal cycle and seasonality of cirrus clouds over the Amazon from a seven-year ground-based lidar record, J. Geophys. Res.-Atmos., 130, e2025JD044592, https://doi.org/10.1029/2025JD044592, 2025.
Cromwell, E. and Flynn, D.: Lidar cloud detection with fully convolutional networks, in: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA, 7–11 January 2019, 619–627, https://doi.org/10.1109/WACV.2019.00071, 2019.
Fang, J., Huang, K., Cheng, W., Zhang, Z., Cao, R., Cheng, F., and Yi, F.: Seasonal variation and vertical characteristic of cirrus clouds at a midlatitude monsoon site from Ka-band cloud radar observation, Atmos. Res., 322, 108118, https://doi.org/10.1016/j.atmosres.2025.108118, 2025.
Fernald, F. G.: Analysis of atmospheric lidar observations: Some comments, Appl. Opt., 23, 652–653, https://doi.org/10.1364/AO.23.000652, 1984.
Floutsi, A. A., Baars, H., Engelmann, R., Althausen, D., Ansmann, A., Bohlmann, S., Heese, B., Hofer, J., Kanitz, T., Haarig, M., Ohneiser, K., Radenz, M., Seifert, P., Skupin, A., Yin, Z., Abdullaev, S. F., Komppula, M., Filioglou, M., Giannakaki, E., Stachlewska, I. S., Janicka, L., Bortoli, D., Marinou, E., Amiridis, V., Gialitaki, A., Mamouri, R.-E., Barja, B., and Wandinger, U.: DeLiAn – a growing collection of depolarization ratio, lidar ratio and Ångström exponent for different aerosol types and mixtures from ground-based lidar observations, Atmos. Meas. Tech., 16, 2353–2379, https://doi.org/10.5194/amt-16-2353-2023, 2023.
Fu, C., Yang, Z., Ji, C., Fu, T., Tao, F., and Zheng, J.: Aerosol identification based on Attention-Unet neural network, Chin. J. Lasers, 52, 1010001, https://doi.org/10.3788/CJL241202, 2025.
Hartmann, D. L., Ockert-Bell, M. E., and Michelsen, M. L.: The effect of cloud type on Earth's energy balance: Global analysis, J. Climate, 5, 1281–1304, https://doi.org/10.1175/1520-0442(1992)005<1281:TEOCTO>2.0.CO;2, 1992.
He, K., Chen, X., Xie, S., Li, Y., Dollár, P., and Girshick, R.: Masked autoencoders are scalable vision learners, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, 18–24 June 2022, 15979–15988, https://doi.org/10.1109/CVPR52688.2022.01553, 2022.
He, Y., Jing, D., Yin, Z., Ohneiser, K., and Yi, F.: Long-term (2010–2021) lidar observations of stratospheric aerosols in Wuhan, China, Atmos. Chem. Phys., 24, 11431–11450, https://doi.org/10.5194/acp-24-11431-2024, 2024.
Illingworth, A. J., Hogan, R. J., O'Connor, E. J., Bouniol, D., Brooks, M. E., Delanoë, J., Donovan, D. P., Eastment, J. D., Gaussiat, N., Goddard, J. W. F., Haeffelin, M., Klein Baltink, H., Krasnov, O. A., Pelon, J., Piriou, J.-M., Protat, A., Russchenberg, H. W. J., Seifert, A., Tompkins, A. M., van Zadelhoff, G.-J., Vinit, F., Willén, U., Wilson, D. R., and Wrench, C. L.: Cloudnet: Continuous evaluation of cloud profiles in seven operational models using ground-based observations, B. Am. Meteor. Soc., 88, 883–898, https://doi.org/10.1175/BAMS-88-6-883, 2007.
Jiang, Y., Yao, H., Tao, S., and Liang, J.: Gated skip-connection network with adaptive upsampling for retinal vessel segmentation, Sensors, 21, 6177, https://doi.org/10.3390/s21186177, 2021.
Jing, D., He, Y., Yin, Z., Huang, K., Liu, F., and Yi, F.: Evolution of tropospheric aerosols over central China during 2010–2024 as observed by lidar, Atmos. Chem. Phys., 25, 17047–17067, https://doi.org/10.5194/acp-25-17047-2025, 2025.
Kalesse-Los, H., Schimmel, W., Luke, E., and Seifert, P.: Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network, Atmos. Meas. Tech., 15, 279–295, https://doi.org/10.5194/amt-15-279-2022, 2022.
Kanamitsu, M.: Description of the NMC Global Data Assimilation and Forecast System, Weather Forecast., 4, 335–342, https://doi.org/10.1175/1520-0434(1989)004<0335:DOTNGD>2.0.CO;2, 1989.
Klett, J. D.: Stable analytical inversion solution for processing lidar returns, Appl. Optics, 20, 211–220, https://doi.org/10.1364/AO.20.000211, 1981.
Kuhn, H. W.: The Hungarian method for the assignment problem, Nav. Res. Logist. Q., 2, 83–97, https://doi.org/10.1002/nav.3800020109, 1955.
Loshchilov, I. and Hutter, F.: Decoupled weight decay regularization, in: 7th International Conference on Learning Representations (ICLR), New Orleans, LA, USA, 6–9 May 2019, https://openreview.net/forum?id=Bkg6RiCqY7 (last access: 30 September 2026), 2019.
Mona, L., Liu, Z., Müller, D., Omar, A., Papayannis, A., Pappalardo, G., Sugimoto, N., and Vaughan, M.: Lidar measurements for desert dust characterization: An overview, Adv. Meteorol., 2012, 356265, https://doi.org/10.1155/2012/356265, 2012.
Mylonaki, M., Giannakaki, E., Papayannis, A., Papanikolaou, C.-A., Komppula, M., Nicolae, D., Papagiannopoulos, N., Amodeo, A., Baars, H., and Soupiona, O.: Aerosol type classification analysis using EARLINET multiwavelength and depolarization lidar observations, Atmos. Chem. Phys., 21, 2211–2227, https://doi.org/10.5194/acp-21-2211-2021, 2021.
Noel, V., Chepfer, H., Chiriaco, M., and Yorks, J.: The diurnal cycle of cloud profiles over land and ocean between 51° S and 51° N, seen by the CATS spaceborne lidar from the International Space Station, Atmos. Chem. Phys., 18, 9457–9473, https://doi.org/10.5194/acp-18-9457-2018, 2018.
Oktay, O., Schlemper, J., Le Folgoc, L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N. Y., Kainz, B., Glocker, B., and Rueckert, D.: Attention U-Net: Learning where to look for the pancreas, arXiv [preprint], https://doi.org/10.48550/arXiv.1804.03999, 11 April 2018.
Pal, S. R., Steinbrecht, W., and Carswell, A. I.: Automated method for lidar determination of cloud-base height and vertical extent, Appl. Optics, 31, 1488–1494, https://doi.org/10.1364/AO.31.001488, 1992.
Peng, D., Bian, J., Li, D., Bai, Z., Vömel, H., Wienhold, F. G., Brunamonti, S., Luo, B., Zhang, J., Li, Q., and Peter, T.: In situ balloon-borne observations of the vertical structure of relative humidity in Asian summer monsoon cirrus clouds, Geophys. Res. Lett., 53, e2025GL118090, https://doi.org/10.1029/2025GL118090, 2026.
Pruppacher, H. R. and Klett, J. D.: Microphysics of Clouds and Precipitation, 2nd rev. and enl. ed., Kluwer Academic Publishers, Dordrecht, the Netherlands, 954 pp., ISBN 978-0-7923-4211-3, 1997.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional networks for biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Lecture Notes in Computer Science, 9351, edited by: Navab, N., Hornegger, J., Wells, W. M., and Frangi, A. F., Springer, Cham, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from ISCCP, B. Am. Meteor. Soc., 80, 2261–2287, https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999.
Sassen, K.: The polarization lidar technique for cloud research: A review and current assessment, B. Am. Meteor. Soc., 72, 1848–1866, https://doi.org/10.1175/1520-0477(1991)072<1848:TPLTFC>2.0.CO;2, 1991.
Sassen, K., Wang, Z., and Liu, D.: Global distribution of cirrus clouds from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements, J. Geophys. Res.-Atmos., 113, D00A12, https://doi.org/10.1029/2008JD009972, 2008.
Shao, N., Wang, Q., Bu, Z., Yin, Z., Dai, Y., Chen, Y., and Wang, X.: China Aerosol Raman Lidar Network (CARLNET) – Part I: Water vapor Raman channel calibration and quality control, Remote Sens., 17, 414, https://doi.org/10.3390/rs17030414, 2025.
Thorsen, T. J., Fu, Q., Comstock, J. M., Sivaraman, C., Vaughan, M. A., Winker, D. M., and Turner, D. D.: Macrophysical properties of tropical cirrus clouds from the CALIPSO satellite and from ground-based micropulse and Raman lidars, J. Geophys. Res.-Atmos., 118, 9209–9220, https://doi.org/10.1002/jgrd.50691, 2013.
Thorsen, T. J., Fu, Q., Newsom, R. K., Turner, D. D., and Comstock, J. M.: Automated retrieval of cloud and aerosol properties from the ARM Raman lidar. Part I: Feature detection, J. Atmos. Ocean. Tech., 32, 1977–1998, https://doi.org/10.1175/JTECH-D-14-00150.1, 2015.
Vainiger, A., Shubi, O., Schechner, Y. Y., Yin, Z., Baars, H., Heese, B., and Althausen, D.: Supervised learning calibration of an atmospheric lidar, in: IGARSS 2022–2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 17–22 July 2022, 7317–7320, https://doi.org/10.1109/IGARSS46834.2022.9883078, 2022.
Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders, in: Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, 5–9 July 2008, 1096–1103, https://doi.org/10.1145/1390156.1390294, 2008.
Voudouri, K. A., Giannakaki, E., Komppula, M., and Balis, D.: Variability in cirrus cloud properties using a PollyXT Raman lidar over high and tropical latitudes, Atmos. Chem. Phys., 20, 4427–4444, https://doi.org/10.5194/acp-20-4427-2020, 2020.
Wang, M., Su, J., Peng, N., Xu, Y., and Ge, J.: Diurnal cycle of cirrus cloud and its associated radiative effects at the SACOL site, Atmos. Res., 265, 105887, https://doi.org/10.1016/j.atmosres.2021.105887, 2022.
Wang, Y., Zhao, C., Dong, Z., Li, Z., Hu, S., Chen, T., Tao, F., and Wang, Y.: Improved retrieval of cloud base heights from ceilometer using a non-standard instrument method, Atmos. Res., 202, 148–155, https://doi.org/10.1016/j.atmosres.2017.11.021, 2018.
Wang, Z. and Sassen, K.: Cloud type and macrophysical property retrieval using multiple remote sensors, J. Appl. Meteorol., 40, 1665–1682, https://doi.org/10.1175/1520-0450(2001)040<1665:CTAMPR>2.0.CO;2, 2001.
Wijnands, J. S., Apituley, A., Gouveia, D. A., and Noteboom, J. W.: Deep-Pathfinder: a boundary layer height detection algorithm based on image segmentation, Atmos. Meas. Tech., 17, 3029–3045, https://doi.org/10.5194/amt-17-3029-2024, 2024.
Xie, Q., Luong, M.-T., Hovy, E., and Le, Q. V.: Self-training with noisy student improves ImageNet classification, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), online, 14–19 June 2020, 10684–10695, https://doi.org/10.1109/CVPR42600.2020.01070, 2020.
Yin, Z., Baars, H., Seifert, P., and Engelmann, R.: Automatic lidar calibration and processing program for multiwavelength Raman polarization lidar, EPJ Web Conf., 237, 08007, https://doi.org/10.1051/epjconf/202023708007, 2020.
Zhang, J., Li, J., Xia, X., Chen, H., and Ling, C.: Cloud properties under different synoptic circulations: Comparison of radiosonde and ground-based active remote sensing measurements, Atmosphere, 7, 154, https://doi.org/10.3390/atmos7120154, 2016.
Zhao, C., Xie, S., Klein, S. A., Protat, A., Shupe, M. D., McFarlane, S. A., Comstock, J. M., Delanoë, J., Deng, M., Dunn, M., Hogan, R. J., Huang, D., Jensen, M. P., Mace, G. G., McCoy, R., O'Connor, E. J., Turner, D. D., and Wang, Z.: Toward understanding of differences in current cloud retrievals of ARM ground-based measurements, J. Geophys. Res.-Atmos., 117, D10206, https://doi.org/10.1029/2011JD016792, 2012.
Zhao, C., Wang, Y., Wang, Q., Li, Z., Wang, Z., and Liu, D.: A new cloud and aerosol layer detection method based on micropulse lidar measurements, J. Geophys. Res.-Atmos., 119, 6788–6802, https://doi.org/10.1002/2014JD021760, 2014.
Zhou, Q., Zhang, Y., Li, B., Li, L., Feng, J., Jia, S., Lv, S., Tao, F., and Guo, J.: Cloud-base and cloud-top heights determined from a ground-based cloud radar in Beijing, China, Atmos. Environ., 201, 381–390, https://doi.org/10.1016/j.atmosenv.2019.01.012, 2019.
Zou, W., Yin, Z., Dai, Y., Chen, Y., Bu, Z., Li, S., He, Y., Hu, X., Müller, D., Lu, T., Dong, X., and Wang, X.: Robust lidar-radar composite cloud boundary detection method with rainfall pixels removal, IEEE T. Geosci. Remote, 62, 1–16, https://doi.org/10.1109/TGRS.2024.3476127, 2024.
Short summary
Consistent cloud detection across lidar instruments and sites remains challenging. We develop a transferable artificial intelligence method using multi-wavelength lidar signals to improve cloud boundary detection with limited expert labels. Tests at independent sites and comparisons with weather balloon observations demonstrate its reliability. It will be deployed in the China Aerosol Raman Lidar Network (CARLNET) to support reliable automated cloud and aerosol processing.
Consistent cloud detection across lidar instruments and sites remains challenging. We develop a...
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