Retrospective retrieval of high-level cloud boundary altitudes

Bryukhanov I.D., Kuchinskaia O.I., Pustovalov K.N., Penzin M.S., Akimov I.M., Romanov D.A., Ni E.V., Doroshkevich A.A., Zhivotenyuk I.V.

For Russian citation (Opticheskii Zhurnal):

Брюханов И.Д., Кучинская О.И., Пустовалов К.Н., Пензин М.С., Акимов И.М., Романов Д.А., Ни Е.В., Дорошкевич А.А., Животенюк И.В. Ретроспективное восстановление информации о высотах границ облаков верхнего яруса // Оптический журнал. 2026. Т. 93. № 10. С. 12–21. http://doi.org/10.17586/1023-5086-2026-93-10-12-21

Bryukhanov I.D., Kuchinskaia O.I., Pustovalov K.N., Penzin M.S., Akimov I.M., Romanov D.A., Ni E.V., Doroshkevich A.A., Zhivotenyuk I.V. Retrospective retrieval of high-level cloud boundary altitudes [in Russian] // Opticheskii Zhurnal. 2026. V. 93. № 10. P. 12–21. http://doi.org/10.17586/1023-5086-2026-93-10-12-21

For citation (Journal of Optical Technology):

Abstract:

Scope of research is the possibility of retrospective retrieval of information on the altitudes of high-level cloud boundaries. The purpose of the work is the software and hardware tools for retrieving the altitudes of lower and upper boundaries of high-level clouds based on the data on polarization laser sensing of the atmosphere and vertical profiles of meteorological variables from various sources. Method. A joint analysis of the altitudes of high-level clouds formation based on the lidar sensing data, meteorological conditions at corresponding altitudes according to the ERA5 and MERRA-2 reanalysis, and the results of the MODIS satellite spectroradiometer measurements. Main results. Results of applying a machine learning model for retrieving the information about high-level cloud formation altitudes using the vertical profiles of air temperature and its relative and absolute humidity are presented. The characteristic features of changes in air humidity parameters near the boundaries of high-level clouds are obtained. Practical significance. It is shown that the vertical profile of air humidity is the most significant factor for determining the boundaries of high-level clouds, and the effect of temperature is manifested indirectly through relative humidity. The present work notes the need to take into account additional atmospheric characteristics, such as the parameters of the frontal zones, in order to increase the accuracy of predicting the boundaries of high-level clouds using machine learning methods.

Keywords:

high-level clouds, polarization lidar, backscattering phase matrix, anomalous backscattering, radiosonde observations, machine learning methods, ERA5, MERRA-2, MODIS

Acknowledgements:

this research was funded by the Russian Science Foundation, Grant № 24-72-10127

OCIS codes: 010.1290, 010.3640

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