Articles | Volume 13, issue 1
https://doi.org/10.5194/amt-13-1-2020
© Author(s) 2020. 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-13-1-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Jan 2020
Research article |
| 02 Jan 2020
| 02 Jan 2020
Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Daren Lu
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Shu Duan
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Yongheng Bi
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Bo Liu
Key Laboratory for Atmosphere and Global Environment Observation, Chinese
Academy of Sciences, Bejing, 100029, China
Related authors
Congcong Qiao, Song Liu, Juan Huo, Xihan Mu, Ping Wang, Shengjie Jia, Xuehua Fan, and Minzheng Duan
Atmos. Meas. Tech., 16, 1539–1549, https://doi.org/10.5194/amt-16-1539-2023, https://doi.org/10.5194/amt-16-1539-2023, 2023
Short summary
Short summary
We established a spectral-fitting method to derive precipitable water vapor (PWV) and aerosol optical depth based on a strict radiative transfer theory by the spectral measurements of direct sun from EKO MS711 and MS712 spectroradiometers. The retrievals were compared with that of the colocated CE-318 photometer; the results showed a high degree of consistency. In the PWV inversion, a strong water vapor absorption band around 1370 nm is introduced to retrieve PWV in a relatively dry atmosphere.
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
Short summary
Short summary
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.
Siming Zheng, Juan Huo, Wenbing Cai, Yinhui Zhang, Peng Li, Gaoyuan Zhang, Baofeng Ji, Jiafeng Zhou, and Congzheng Han
Atmos. Meas. Tech., 15, 1675–1687, https://doi.org/10.5194/amt-15-1675-2022, https://doi.org/10.5194/amt-15-1675-2022, 2022
Short summary
Short summary
We demonstrated the processing of millimeter wave link data with a time resolution of 1 min for 60 dry periods from August 2020 to July 2021. We have proposed a new method for extracting water vapor attenuation values and applied this method to data processing within this year. We found that the water vapor density value obtained from the millimeter wave link is highly correlated with the actual measurement value of the weather station.
Yinan Wang, Hongxia Cui, Wei Zhao, Yongheng Bi, Tianqi Wang, and Daren Lu
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-552, https://doi.org/10.5194/essd-2026-552, 2026
Preprint under review for ESSD
Short summary
Short summary
We present the first openly archived multi-year, multi-wavelength Raman-lidar aerosol dataset from the clean interior Tibetan Plateau (Yangbajing, 4284 m; 2021–2024): quality-controlled profiles of backscatter, extinction, depolarisation and water vapour, with uncertainties and flags. The pristine site makes aerosol largely detection-limited. It gives a ground-truth reference for spaceborne-lidar evaluation, reanalysis and model assessment, and dust and radiative studies over the Third Pole.
Yinan Wang, Yubing Pan, and Daren Lyu
EGUsphere, https://doi.org/10.5194/egusphere-2026-3233, https://doi.org/10.5194/egusphere-2026-3233, 2026
Short summary
Short summary
Satellite columns of nitrogen dioxide show where and when NOₓ is emitted, but converting them into emissions has needed heavy computer models. Over the polluted North China Plain, we let a neural network infer emissions straight from the satellite data, with built-in physics of how the gas disperses and decays. It recovers day-by-day emissions whose pattern comes from the observations, not existing inventories, offering a fast, transferable way to monitor emissions from space.
Linjun Pan, Yinan Wang, and Yongheng Bi
Atmos. Meas. Tech., 18, 6361–6370, https://doi.org/10.5194/amt-18-6361-2025, https://doi.org/10.5194/amt-18-6361-2025, 2025
Short summary
Short summary
Ground-based infrared radiometers provide low-cost cloud monitoring. Existing methods require supporting data from other instruments – making them unusable in remote regions like the Tibetan Plateau. Our innovation enables fully independent cloud identification using only the radiometer's data. This self-contained solution eliminates dependencies on external equipment or historical datasets, providing a practical cloud monitoring method for isolated sites where conventional approaches fail.
Danyang Wang, Wenying He, Yongheng Bi, Xiangao Xia, and Hongbin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-4233, https://doi.org/10.5194/egusphere-2025-4233, 2025
Preprint archived
Short summary
Short summary
Snow forms in two ways: by droplets freezing onto snowflakes (riming) or by snowflakes sticking together (aggregation). These processes often overlap and are hard to tell apart with standard radar. We developed a new radar method using three wavelengths to track how signals change with height, allowing us to distinguish riming and aggregation. It captures subtle changes that older methods miss, providing a clearer picture of how snow forms and leading to more accurate snowfall forecasts.
Jingxuan Luo, Yubing Pan, Debin Su, Jinhua Zhong, Lingxiao Wu, Wei Zhao, Xiaoru Hu, Zhengchao Qi, Daren Lu, and Yinan Wang
Atmos. Meas. Tech., 17, 3765–3781, https://doi.org/10.5194/amt-17-3765-2024, https://doi.org/10.5194/amt-17-3765-2024, 2024
Short summary
Short summary
Accurate cloud quantification is critical for climate research. We developed a novel computer vision framework using deep neural networks and clustering algorithms for cloud classification and segmentation from ground-based all-sky images. After a full year of observational training, our model achieves over 95 % accuracy on four cloud types. The framework enhances quantitative analysis to support climate research by providing reliable cloud data.
Congcong Qiao, Song Liu, Juan Huo, Xihan Mu, Ping Wang, Shengjie Jia, Xuehua Fan, and Minzheng Duan
Atmos. Meas. Tech., 16, 1539–1549, https://doi.org/10.5194/amt-16-1539-2023, https://doi.org/10.5194/amt-16-1539-2023, 2023
Short summary
Short summary
We established a spectral-fitting method to derive precipitable water vapor (PWV) and aerosol optical depth based on a strict radiative transfer theory by the spectral measurements of direct sun from EKO MS711 and MS712 spectroradiometers. The retrievals were compared with that of the colocated CE-318 photometer; the results showed a high degree of consistency. In the PWV inversion, a strong water vapor absorption band around 1370 nm is introduced to retrieve PWV in a relatively dry atmosphere.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Siming Zheng, Juan Huo, Wenbing Cai, Yinhui Zhang, Peng Li, Gaoyuan Zhang, Baofeng Ji, Jiafeng Zhou, and Congzheng Han
Atmos. Meas. Tech., 15, 1675–1687, https://doi.org/10.5194/amt-15-1675-2022, https://doi.org/10.5194/amt-15-1675-2022, 2022
Short summary
Short summary
We demonstrated the processing of millimeter wave link data with a time resolution of 1 min for 60 dry periods from August 2020 to July 2021. We have proposed a new method for extracting water vapor attenuation values and applied this method to data processing within this year. We found that the water vapor density value obtained from the millimeter wave link is highly correlated with the actual measurement value of the weather station.
Cited articles
Ackerman, S. A., Strabala, K. I., Menzel, W. P., Frey, R. A., Moeller, C.
C., and Gumley, L. E.: Discriminating clear sky from clouds with MODIS, J.
Geophys. Res., 103, 32141–32157, 1998.
Arakawa, A.: The cumulus parameterization problem: Past, present, and
future, J. Climate, 17, 2493–2525, 2004.
Atlas, D.: The estimation of cloud parameters by radar, J. Meteor., 11,
309–317, 1954.
Baum, B. A., Menzel, W. P., Frey, R. A., Tobin, D. C., Holz, R. E.,
Ackerman, S. A., Heidinger, A. K., and Yang, P.: MODIS cloud-top property
refinements for Collection 6, J. Appl. Meteor., 51, 1145–1163, 2012.
Bessho, K., Date, K., Hayashi, M., Ikeda, A., Imai, T., Inoue, H., Kumagai,
Y., Miyakawa, T., Murata, H., Ohno, T., Okuyama, A., Oyama, R., Sasaki, Y.,
Shimazu, Y., Shimoji, K., Sumida, Y., Suzuki, M., Taniguchi, H., Tsuchiyama,
H., Uesawa, D., Yokota, H., and Yoshida, R.: An introduction to
Himawari-8/9 – Japan's new-generation geostationary meteorological
satellites, J. Meteorol. Soc. Jpn., 94,
151–183, 2016.
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster, P., Kerminen, V., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh, S., Sherwood, S., Stevens, B., and Zhang, X. Y.: Clouds and Aerosols, in: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G., Tignor, M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P.: Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 571–657, 2013.
Cess, R. D., Zhang, M. H., Zhou, Y., Jing, X., and Dvortsov, V.: Absorption
of solar radiation by clouds: Interpretations of satellite, surface, and
aircraft measurements, J. Geophys. Res.-Atmos., 101, 23299–23309, 1996.
Chang, F. L., Minnis, P., Ayers, J. K., McGill, M. J., Palikonda, R.,
Spangenberg, D. A., Smith Jr., W. L., and Yost, C. R.: Evaluation of
satellite – upper troposphere cloud top height retrievals in multilayer
cloud conditions during TC4, J. Geophys. Res., 115, https://doi.org/10.1029/2009JD013305, 2010.
Dong, X., Minnis, P., Xi, B., Sun-Mack, S., and Chen, Y.: Comparison of
CERES – stratus cloud properties with ground – measurements at the
DOE ARM Southern Great Plains site, J. Geophys. Res.-Atmos., 113,
https://doi.org/10.1029/2007JD008438, 2008.
Eyre J. R.: A fast radiative transfer model for satellite sounding systems. ECMWF Technical Memoranda, available at: https://www.ecmwf.int/node/9329, 176, https://doi.org/10.21957/xsg8d92y3, 1991.
Görsdorf, U., Lehmann, V., Bauer-Pfundstein, M., Peters, G., Vavriv, D.,
Vinogradov, V., and Volkov, V.: A 35 GHz polarimetric Doppler radar for
long-term observations of cloud parameters – Description of system and data
processing, J. Atmos. Ocean. Tech., 32, 675–690, 2015.
Håkansson, N., Adok, C., Thoss, A., Scheirer, R., and Hörnquist, S.: Neural network cloud top pressure and height for MODIS, Atmos. Meas. Tech., 11, 3177–3196, https://doi.org/10.5194/amt-11-3177-2018, 2018.
Ham, S. H., Sohn, B. J., Yang, P., and Baum, B. A.: Assessment of the
quality of MODIS cloud products from radiance simulations, J. Appl. Meteor.,
48, 1591–1612, 2009.
Hamada, A. and Nishi, N.: Development of a cloud-top height estimation
method by geostationary satellite split-window measurements trained with
CloudSat data, J. Appl. Meteor., 49, 2035–2049, 2010.
Hamann, U., Walther, A., Baum, B., Bennartz, R., Bugliaro, L., Derrien, M., Francis, P. N., Heidinger, A., Joro, S., Kniffka, A., Le Gléau, H., Lockhoff, M., Lutz, H.-J., Meirink, J. F., Minnis, P., Palikonda, R., Roebeling, R., Thoss, A., Platnick, S., Watts, P., and Wind, G.: Remote sensing of cloud top pressure/height from SEVIRI: analysis of ten current retrieval algorithms, Atmos. Meas. Tech., 7, 2839–2867, https://doi.org/10.5194/amt-7-2839-2014, 2014.
Huo, J. and Lu, D.: Cloud determination of all-sky images under
low-visibility conditions, J. Atmos. Ocean. Tech., 26, 2172–2181,
2009.
Huo, J., Bi, Y., Lü, D., and Duan, S.: Cloud Classification and
Distribution of Cloud Types in Beijing Using Ka-Band Radar Data, Adv. Atmos.
Sci., 36, 793–803, 2019.
King, M., Tsay, S., Platnick, S., Wang, M., and Liou, K.: Cloud Retrieval:
Algorithms for MODIS: Optical thickness, effective particle radius, and
thermodynamic phase, Algorithm Theoretical Basis Document,
ATBD-MOD-05, 83 pp., 1998.
Kouki, M., Hiroshi, S., Ryo, Y., and Toshiharu, I.: Algorithm theoretical
basis document for cloud top height product, Meteorolgical Satellite Center
Technical Note,
https://www.data.jma.go.jp/mscweb/technotes/msctechrep61-63.pdf (last access: 19 June 2019), 2016.
Liou, K. N.: Radiation and cloud rrocesses in the atmosphere:
theory, observation, and modeling, Oxford University Press, 487 pp, 1992.
Marchand, R.: Trends in ISCCP, MISR, and MODIS cloud-top-height and
optical-depth histograms, J. Geophys. Res.-Atmos., 118, 1941–1949, 2013.
Marchand, R., Ackerman, T., Smyth, M., and Rossow, W.: A review of cloud top
height and optical depth histograms from MISR, ISCCP, and MODIS, J. Geophys.
Res.-Atmos., 115, https://doi.org/10.1029/2009JD013422, 2010.
Menzel, W., Frey, R., Zhang, H., Wylie, D., Moeller, C., Holz, R., Maddux,
B., Baum, B., Strabala, K., and Gumley, L.: MODIS global cloud-top pressure
and amount estimation: algorithm description and results, J. Appl. Meteorol., 47, 1175–1198, 2008.
Mouri, K., Izumi, T., Suzue, H., and Yoshida, R.: Algorithm Theoritical
Basis Document of cloud type/phase product, Meteorological Satellite Center,
Technical Note, 61, 19–31, 2016.
Naud, C., Muller, J. P., and Clothiaux, E. E.: Comparison of cloud top
heights derived from MISR stereo and MODIS CO2, Geophys. Res.
Lett., 29, 421–424, 2002.
Nieman, S. J., Schmetz, J., and Menzel, W. P.: A comparison of several
techniques to assign heights to cloud tracers, J. Appl. Meteorol., 32,
1559–1568, 1993.
Pavolonis, M. J. and Heidinger, A. K.: Daytime cloud overlap detection from
AVHRR and VIIRS, J. Appl. Meteorol., 43, 762–778, 2004.
Pincus, R., Platnick, S., Ackerman, S. A., Hemler, R. S., and Patrick
Hofmann, R. J.: Reconciling simulated and observed views of clouds: MODIS,
ISCCP, and the limits of instrument simulators, J. Climate, 25, 4699–4720,
2012.
Remer, L. A., Kleidman, R. G., Levy, R. C., Kaufman, Y. J., Tanré, D.,
Mattoo, S., Martins, J. V., Ichoku, C., Koren, I., Yu, H., and Holben, B. N.:
Global aerosol climatology from the MODIS satellite sensors, J. Geophys.
Res.-Atmos., 113, https://doi.org/10.1029/2007JD009661, 2008.
Ramanathan, V. L. R. D., Cess, R. D., Harrison, E. F., Minnis, P.,
Barkstrom, B. R., Ahmad, E., and Hartmann, D.: Cloud-radiative forcing and
climate: Results from the Earth Radiation Budget Experiment, Science,
243, 57–63, 1989.
Rodell, M. and Houser, P. R.: Updating a land surface model with
MODIS-derived snow cover, J. Hydrometeorol., 5, 1064–1075, 2004.
Roskovensky, J. K. and Liou, K. N.: Simultaneous determination of aerosol
and thin cirrus optical depths over oceans from MODIS data: Some case
studies, J. Atmos. Sci., 63, 2307–2323, 2006.
Rossow, W. B. and Schiffer, R. A.: Advances in understanding clouds from
ISCCP,
Bound.-Lay. Meteorol., 80, 2261–2288, 1999.
Schmetz, J., Holmlund, K., Hoffman, J., and Strauss, B.: Operational cloud
motion winds from Meteosat infrared images, J. Appl. Meteorol.,
32, 1207–1225, 1993.
Schiffer, R. A. and Rossow, W. B.: The International Satellite Cloud
Climatology Project (ISCCP): The first project of the world climate research
programme, Bound.-Lay. Meteorol., 64, 779–784, 1983.
Smith, W. L. and Platt, C. M. R.: Comparison of satellite-deduced cloud
heights with indications from radiosonde and ground-based laser
measurements, J. Appl. Meteorol., 17, 1796–1802, 1978.
Stephens, G. L. and Kummerow, C. D.: The remote sensing of clouds and
precipitation from space: A review, J. Atmos. Sci., 64, 3742–3765,
2007.
Stubenrauch, C. J., Del Genio, A. D., and Rossow, W. B.: Implementation of
subgrid cloud vertical structure inside a GCM and its effect on the
radiation budget, J. Climate, 10, 273–287, 1997.
Wang, T., Luo, J., Liang, J., Wang, B., Tian, W., and Chen, X.: Comparisons
of AGRI/FY-4A cloud fraction and cloud top rressure with MODIS/Terra
measurements over East Asia, J. Meteorol. Res., 33, 705–719, 2019.
Weisz, E., Li, J., Menzel, W. P., Heidinger, A. K., Kahn, B. H., and Liu, C.
Y.: Comparison of AIRS, MODIS, CloudSat and CALIPSO cloud top height
retrievals, Geophys. Res. Lett., 34, https://doi.org/10.1029/2007GL030676, 2007.
Wetherald, R. T. and Manabe, S.: Cloud feedback processes in a general
circulation model, J. Atmos. Sci., 45, 1397–1416, 1988.
Xi, B., Dong, X., Minnis, P., and Sun-Mack, S.: Comparison of marine
boundary layer cloud properties from CERES – Edition 4 and DOE ARM AMF
measurements at the Azores, J. Geophys. Res.-Atmos., 119, 9509–9529,
https://doi.org/10.1002/2014JD021813, 2014.
Xiao, P., Huo, J., and Bi, Y.: Ground-based Ka- band cloud radar data
quality control. Journal of Chengdu University of Information Technology,
Journal of Chengdu University of Information Technology, 33, 129–136,
https://doi.org/110.16836/j.cnki.jcuit.12018.16802.16005, 2018.
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/310.1016/j.atmosenv.2019.1001.1012, 2019.
Short summary
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.
Cloud top height (CTH) is one of the important cloud parameters providing information about the...
