Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 1, pp. 122-134

Using CALIOP data for multilayer cloud base height estimation from MODIS imagery based on fuzzy logic methods

A.V. Skorokhodov 1 
1 V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, Russia
Accepted: 16.01.2024
DOI: 10.21046/2070-7401-2024-21-1-122-134
We present an algorithm for base height estimation of separate levels in multilayer clouds from passive satellite sensors based on fuzzy logic methods. The procedure for retrieving the cloud-base height is considered as a special case of solving the classification problem. The classes are narrow value ranges of target parameter. The classification features are cloud parameters recovered from passive satellite sensors. One object can belong to several classes at the same time, but with different degrees of membership according to fuzzy set theory. This feature provides the ability to estimate the base height for multiple cloud levels at once. The classifier is trained based on synchronous data from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) and MODIS (Moderate Resolution Imaging Spectroradiometer) obtained in summer over the territory of Western Siberia in the period 2013–2018. Whereas cloud-base height estimation is performed using only data from passive satellite sensors. Two fuzzy self-organizing methods (Fuzzy C-means and Gustafson – Kessel) are considered. It has been found that the second approach is more efficient and provides a bias of the retrieved values of the base height for clouds with an optical thickness less than 10 compared to the reference ones of –0.5 km at a standard deviation of 1.5 km for the overlying cloud layer and –0.1 at 2.1 km for the underlying one.
Keywords: CALIOP, cloud base height, fuzzy logic methods, MODIS, multilayer clouds, neural network, satellite data
Full text

References:

  1. Astafurov V. G., Skorokhodov A. V., Multi-layer cloud classification from MODIS data using neural network technology and fuzzy logic approach, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2015, Vol. 12, No. 6, pp. 162–173 (in Russian).
  2. Bespalov D. P., Devyatkin A. M., Dovgalyuk Yu. A., Kondratyuk V. I., Kuleshov Yu. V., Svetlova T. P., Suvorov S. S., Timofeev V. I., Atlas oblakov (Cloud Atlas), Saint Petersburg: Publ. House of D’ART, 2011, 248 p.
  3. Boreisho A. S., Kim A. A., Konyaev M. A. et al., Modern lidar systems for atmosphere remote sensing, Fotonika, 2019, Vol. 13, No. 7, pp. 648–657 (in Russian), DOI: 10.22184/1992-7296.FRos.2019.13.7.648.657.
  4. Dashko N. A., Kurs lektsii po sinopticheskoi meteorologii (Course of lectures on synoptic meteorology), Vladivostok: Publ. House of DVGU, 2005, 523 p (in Russian).
  5. Kod dlya operativnoi peredachi dannykh prizemnykh meteorologicheskikh nablyudenii s seti stantsii Rosgidrometa (KN-01 SYNOP) (Code for live data transfer surface meteorological observations from the network of Roshydromet stations (KN-01 SYNOP)), Fakhrutdinov N. P. (ed.), Moscow, 2013, 79 p. (in Russian).
  6. Kkhyong N. V., Evaluation of the influence of meteorology on the propagation of radio waves in X-bands, Trudy Moskovskogo fiziko-tekhnicheskogo instituta, 2020, Vol. 12, No. 3, pp. 94–103 (in Russian).
  7. Oblaka i oblachnaya atmosfera: spravochnik (Clouds and cloudy atmosphere), Mazin I. P., Khrgian A.Kh. (eds.), Leningrad: Gidrometeoizdat, 1989, 647 p. (in Russian).
  8. Osovskii S., Neironnye seti dlya obrabotki informatsii (Neural networks for information processing), Moscow: Finansy i statistika, 2002, 344 p. (in Russian).
  9. Skorokhodov A. V., Kuryanovich K. V., Using CALIOP data to estimate the cloud base height on MODIS images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 43–56 (in Russian), DOI: 10.21046/2070-7401-2022-19-2-43-56.
  10. Tolmacheva N. I., Kryuchkova A. D., Metody i sredstva meteorologicheskikh izmerenii: uchebnoe posobie (Methods and instruments of meteorological measurements), Perm: Publ. House of PGNIU, 2013, 253 p. (in Russian).
  11. Shakina N. P., Dinamika atmosfernykh frontov (Dynamics of weather fronts), Leningrad: Gidrometeoizdat, 1985, 264 p. (in Russian).
  12. Bessho K., Date K., Hayashi M. et al., An introduction to Himawari-8/9 — Japan’s new-generation geostationary meteorological satellites, J. Meteorological Society of Japan, Ser. II, 2016, Vol. 94, No. 2. pp. 151–183, DOI: 10.2151/jmsj.2016-009.
  13. Chang F. L., Li Z., A near-global climatology of single-layer and overlapped clouds and their optical properties retrieved from Terra/MODIS data using a new algorithm, J. Climate, 2005, Vol. 18, No. 22, pp. 4752–4771, DOI: 10.1175/JCLI3553.1.
  14. Chen S., Cheng C., Zhang X. et al., Construction of nighttime cloud layer height and classification of cloud types, Remote Sensing, 2020, Vol. 12, Article 668, DOI: 10.3390/rs12040668.
  15. Dunn J. C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybernetics, 1973, Vol. 3, No. 3, pp. 32–57, DOI: 10.1080/01969727308546046.
  16. Forsythe J. M., Vonder Haar T. H., Reinke D. L., Cloud base height estimates using a combination of meteorological satellite imagery and surface reports, J. Applied Meteorology, 2000, Vol. 39, pp. 2336–2347, DOI: 10.1175/1520-0450(2000)039<2336:CBHEUA>2.0.CO;2.
  17. Gebremariam S., Li S., Weldegaber M., Observed correlation between aerosol and cloud base height for low clouds at Baltimore and New York, United States, Atmosphere, 2018, Vol. 9, No. 4, Article 143, DOI: 10.3390/atmos9040143.
  18. Gustafson D. E., Kessel W. C., Fuzzy clustering with a fuzzy covariance matrix, Proc. IEEE Conf. Decision and Control including the 17 th Symp. Adaptive Processes, 1979, pp. 761–766, DOI: 10.1109/CDC.1978.268028.
  19. Hutchison K. D., The retrieval of cloud base heights from MODIS and three-dimensional cloud fields from NASA’s EOS Aqua mission, Intern. J. Remote Sensing, 2002, Vol. 23, pp. 5249–5265, DOI: 10.1080/01431160110117391.
  20. Mecikalski J. R., Feltz W. F., Murray J. J. et al., Aviation applications for satellite-based observations of cloud properties, convection initiation, in-flight icing, turbulence, and volcanic ash, Bull. American Meteorogical Society, 2007, Vol. 88, pp. 1589–1607, DOI: 10.1175/BAMS-88-10-1589.
  21. Minnis P., Sun-Mack S., Smith W. L. J. et al., Advances in neural network detection and retrieval of multilayer clouds for CERES using multispectral satellite data, Proc. SPIE, 2019, Vol. 11152, DOI: 10.1117/12.2532931.
  22. Minnis P., Sun-Mack S., Chen Y. et al., CERES MODIS cloud product retrievals for Edition 4 — Part I: Algorithm changes, IEEE Trans. Geoscience and Remote Sensing, 2021, Vol. 59, pp. 2744–2780, DOI: 10.1109/TGRS.2020.3008866.
  23. Mülmenstädt J., Sourdeval O., Henderson D. S., et al., Using CALIOP to estimate cloud-field base height and its uncertainty: the Cloud Base Altitude Spatial Extrapolator (CBASE) algorithm and dataset, Earth System Science Data, 2018, Vol. 10, pp. 2279–2293, DOI: 10.5194/essd-10-2279-2018.
  24. Noh Y.-J., Forsythe J. M., Miller S. D. et al., Cloud-base height estimation from VIIRS. Part II: A statistical algorithm based on A-Train satellite data, J. Atmospheric and Oceanic Technology, 2017, Vol. 34, pp. 585–598, DOI: 10.1175/JTECH-D-16-0110.1.
  25. Noh Y.-J., Haynes J. M., Miller S. D. et al., A framework for satellite-based 3D cloud data: An overview of the VIIRS cloud base height retrieval and user engagement for aviation applications, Remote Sensing, 2022, Vol. 14, Article 5524, DOI: 10.3390/rs14215524.
  26. Platnick S. K., Meyer G., King M. D. et al., The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua, IEEE Trans. Geoscience and Remote Sensing, 2017, Vol. 55, pp. 502–525, DOI: 10.1109/TGRS.2016.2610522.
  27. Skorokhodov A. V., Pustovalov K. N., Khatyutkina E. V., Astafurov V. G., Cloud-base height retrieval from MODIS satellite data based on self-organizing neural networks, Atmospheric and Oceanic Optics, 2023, Vol. 36, No. 6, pp. 723–734, DOI: 10.1134/S1024856023060209.
  28. Tan Z., Huo J., Ma S. et al., Estimating cloud base height from Himawari-8 based on a random forest algorithm, Intern. J. Remote Sensing, 2021, Vol. 42, No. 7, pp. 2485–2501, DOI: 10.1080/01431161.2020.1854891.
  29. Tan Z., Ma S., Wang X. et al., Estimating layered cloud cover from geostationary satellite radiometric measurements: a novel method and its application, Remote Sensing, 2022, Vol. 14, Article 5693, DOI: 10.3390/rs14225693.
  30. Teng S., Liu C., Zhang Z. et al., Retrieval of ice‐over‐water cloud microphysical and optical properties using passive radiometers, Geophysical Research Letters, 2020, Vol. 47, No. 16, Article e2020GL088941, DOI: 10.1029/2020GL088941.
  31. Wilheit T. T., Hutchison K. D., Retrieval of cloud base heights from passive microwave and cloud top temperature data, IEEE Trans. Geoscience and Remote Sensing, 2000, Vol. 38, pp. 1253–1259, DOI: 10.1109/36.843017.
  32. Winker D. M., Vaughan M. A., Omar A. et al., Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, J. Atmospheric and Oceanic Technology, 2009, Vol. 26, pp. 2310–2323, DOI: 10.1175/2009JTECHA1281.1.
  33. Zadeh L. A., Fuzzy sets, Information and Control, 1965, Vol. 8, No. 3, pp. 338–353, DOI: 0.1016/S0019-9958(65)90241-X.