Vol. 37, issue 02, article # 4

Abstract:

To improve the accuracy of weather and climate forecasts, a deeper understanding of atmospheric processes and phenomena, which are determined, among other things, by high-level clouds (HLCs), is required. The experimental results on polarization laser sensing of high-level clouds are presented. The data of systematic (from December 2009 to present) lidar measurements performed with the high-altitude matrix polarization lidar developed at the Tomsk State University are combined. Optical (backscattering phase matrix, optical depth, and scattering ratio) and geometric (lower and upper boundary altitudes and vertical thickness) characteristics of clouds are determined from the lidar measurements. The dataset is supplemented with corresponding vertical profiles of meteorological quantities (temperature, relative and specific humidity, and wind direction and speed) obtained from radiosonde observations and ERA5 reanalysis. The frequency of lidar detection of HLCs and those of them which are characterized by the preferred horizontal orientation of non-spherical ice particles is estimated. The results were combined into a database and used to create a software product based on neural networks to retrieve the dependencies between the atmospheric meteorological parameters and HLC optical characteristics. The database can be used for various training options in solving problems of atmospheric optics including independent ones.

Keywords:

high-level clouds, oriented ice crystal, polarization lidar, backscattering phase matrix, radiosonde observations, ERA5 reanalysis, database, artificial neural network, simple multi-layer perceptron, random forest method, primary component analysis

References:

1. Radiatsionnye svoistva peristykh oblakov / pod red. E.M. Feigel'sona. M.: Nauka, 1989. 223 p.
2. Shanks J.G., Lynch D.K. Specular scattering in cirrus clouds // Proc. SPIE. 1995. V. 2578. P. 227–238.
3. Noel V., Chepfer H. A global view of horizontally oriented crystals in ice clouds from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) // J. Geophys. Res. 2010. V. 115. P. D00H23.
4. Balin Yu.S., Kaul' B.V., Kokhanenko G.P. Nablyudenie zerkal'no otrazhayushchikh chastits i sloev v kristallicheskikh oblakakh // Optika atmosf. i okeana. 2011. V. 24, N 4. P. 293–299.
5. Morozov A.M., Galileiskii V.P., Elizarov A.I., Kokarev D.V. Nablyudenie zerkal'nogo otrazheniya osveshchennoi podstilayushchei poverkhnosti oblachnym sloem iz ledyanykh plastinok // Optika atmosf. i okeana. 2017. V. 30, N 1. P. 88–92.
6. Kaul' B.V. Optiko-lokatsionnyi metod polyarizatsionnykh issledovanii anizotropnykh aerozol'nykh sred: dis. ... dokt. fiz.-mat. nauk. Tomsk, 2004. 219 p.
7. Samokhvalov I.V., Kaul' B.V., Nasonov S.V., Zhivotenyuk I.V., Bryukhanov I.D. Matritsa obratnogo rasseyaniya sveta zerkal'no otrazhayushchikh sloev oblakov verkhnego yarusa, obrazovannykh kristallicheskimi chastitsami, preimushchestvenno orientirovannymi v gorizontal'noi ploskosti // Optika atmosf. i okeana. 2012. V. 25, N 5. P. 403–411.
8. Rozenberg G.V. Vektor-parametr // Uspekhi fiz. nauk. 1955. V. 56. P. 79–110.
9. Kuchinskaia O., Bryukhanov I., Penzin M., Ni E., Doroshkevich A., Kostyukhin V., Samokhvalov I., Pustovalov K., Bordulev I., Bryukhanova V., Stykon A., Kirillov N., Zhivotenyuk I. ERA5 reanalysis for the data interpretation on polarization laser sensing of high-level clouds // Remote Sens. 2023. V. 15, N 1. P. 109.
10. Bryukhanov I.D. Opticheskie svoistva oblakov verkhnego yarusa estestvennogo i antropogennogo proiskhozhdeniya, soderzhashchikh orientirovannye kristally l'da, po dannym polyarizatsionnogo lazernogo zondirovaniya: dis. ... kand. fiz.-mat. nauk. Tomsk, 2022. 138 p.
11. Nasonov S.V. Opticheskie kharakteristiki oblakov verkhnego yarusa i ikh svyaz' s meteorologicheskimi parametrami atmosfery: dis. ... kand. fiz.-mat. nauk. Tomsk, 2015. 114 p.
12. Ni E.V., Bryukhanov I.D., Kuchinskaya O.I., Penzin M.S., Zhivotenyuk I.V., Doroshkevich A.A., Kirillov N.S., Samokhvalov I.V. Sravnitel'nyi analiz matrits obratnogo rasseyaniya sveta kristallicheskikh oblakov, poluchennykh s parallel'nym i posledovatel'nym nakopleniem lidarnykh signalov // Optika atmosf. i okeana. Fizika atmosfery: Materialy XXIX Mezhdunar. simpoz. Tomsk: Izd-vo IOA SO RAN, 2023. P. B-416–B-420.
13. University of Wyoming. URL: http://weather.uwyo.edu (last access: 20.06.2023).
14. Climate Data Store. URL: https://cds.climate.copernicus.eu (last access: 20.06.2023).
15. ECMWF. URL: https://www.ecmwf.int (last access: 20.06.2023).
16. Gordov E.P., Bogomolov V.Yu., Genina E.Yu., Shul'gina T.M. Analiz regional'nykh klimaticheskikh protsessov Sibiri: podkhod, dannye i nekotorye rezul'taty // Vestn. NGU. Ser.: Informatsionnye tekhnologii. 2011. V. 9, N 1. P. 56–66.
17. Han Y., Yang Q., Nana L., Zhang K., Qing C., Li X., Wu X., Luo T. Analysis of wind-speed profiles and optical turbulence above Gaomeigu and the Tibetan Plateau using ERA5 data // Mon. Not. R. Astron. Soc. 2021. V. 501, N 4. P. 4692–4702.
18. Mayer J., Mayer M., Haimberger L. Consistency and homogeneity of atmospheric energy, moisture, and mass budgets in ERA5 // J. Clim. 2021. V. 34, N 10. P. 3955–3974.
19. Zhu J., Xie A., Qin X., Wang Y., Xu B., Wang Y. An assessment of ERA5 reanalysis for Antarctic near-surface air temperature // Atmosphere. 2021. V. 12, N 2. P. 217.
20. Griffiths R., Osborn J., Farley O., Butterley T., Townson M.J., Wilson R. Demonstrating 24-hour continuous vertical monitoring of atmospheric optical turbulence // Opt. Exp. 2023. V. 31, N 4. P. 6730–6740.
21. Um J., McFarquhar G.M., Hong Y.P., Lee S.-S., Jung C.H., Lawson R.P., Mo Q. Dimensions and aspect ratios of natural ice crystals // Atmos. Chem. Phys. 2015. V. 15, N 7. P. 3933–3956.
22. Shchukin G.G., Itkin M.I., Karavaev D.M., Chichkova E.F. Validatsiya sputnikovykh dannykh temperaturno-vlazhnostnogo zondirovaniya atmosfery dlya Severo-Zapadnogo regiona RF // II Vserossiiskie Armandovskie chteniya [Elektronnyi resurs]: Radiofizicheskie metody v distantsionnom zondirovanii sred / Materialy V Vserossiiskoi nauchnoi konferentsii (Murom, 26–28 june 2012 year). Murom: Izd.-poligraf. tsentr MI VlGU, 2012. P. 147–152.
23. Kaul B.V., Samokhvalov I.V., Volkov S.N. Investigating particle orientation in cirrus clouds by measuring backscattering phase matrices with lidar // Appl. Opt. 2004. V. 43, N. 36. P. 6620–6628.
24. Machine Learning: A Probabilistic Perspective / K.P. Murphy. The MIT Press, 2012. 29 p.
25. Bishop C. M. Pattern Recognition and Machine Learning. New York: Springer, 2006. 778 p.