V. G. Astafurov, A. V. Skorokhodov, and K. V. Kurya-novich, “Variability of parameters of single-layer cloud fields over Western Siberia in summer for the period from 2001 to 2019 according to MODIS data,” Atmos. Ocean. Opt. 36 (4), 329–336 (2023).

Google Scholar 

V. G. Astafurov, A. V. Skorokhodov, and K. V. Kurya-novich, “Variability of parameters of single-layer cloud fields over Western Siberia in winter in 2001-2019 according to MODIS data,” Atmos. Ocean. Opt. 37 (2), 211–219 (2024).

Google Scholar 

V. P. Gorbatenko, I. I. Ippolitov, and N. V. Podnebesnykh, “Atmospheric circulation over Western Siberia in 1976–2004,” Russ. Meteorol. Hydrol. 32 (5), 301–306 (2007).

Google Scholar 

E. L. Tunaev, V. P. Gorbatenko, and N. V. Podnebesnykh, “Distinctive features of cyclogenesis over the territory of Western Siberia during 1976–2015,” Trudy Gidrometeorologicheskogo Nauchno-Issledovatel’skogo Tsentra Rossiiskoi Federatsii, No. 364, 81–92 (2017).

Google Scholar 

E. V. Kharyutkina, S. V. Loginov, E. I. Usova, and Yu. V. Martynova, “Tendencies in changes of climate extremality in Western Siberia at the end of the XX century and the beginning of the XXI century,” Fundam. Prikl. Klimatol. 2, 45–65 (2019). https://doi.org/10.21513/2410-8758-2019-2-45-65

Article  Google Scholar 

Land Surface Temperature Anomaly. 2000–2024. https://earthobservatory.nasa.gov/global-maps/MOD_LSTAD_M. Cited November 18, 2024.

X. Jing, H. Zhang, J. Peng, J. Li, and H. W. Barker, “Cloud overlapping parameter obtained from CloudSat/CALIPSO dataset and its application in AGCM with McICA scheme,” Atmos. Res. 170, 52–65 (2016). doi 0169-8095https://doi.org/10.1016/j.atmosres.2015.11.007

E. Johansson, A. Devasthale, A. M. L. Ekman, M. Tjernstrom, and T. L’Ecuyer, “How does cloud overlap affect the radiative heating in the tropical upper troposphere/lower stratosphere?,” Geophys. Rev. Lett. 46, 5623–5631 (2019). https://doi.org/10.1029/019GL082602

Article  ADS  Google Scholar 

H. Luo, J. Quaas, and Y. Han, “Examining cloud vertical structure and radiative effects from satellite retrievals and evaluation of CMIP6 scenarios,” Atmos. Chem. Phys. 23 (14), 8169–8186 (2023). https://doi.org/10.5194/acp-23-8169-2023

Article  ADS  Google Scholar 

L. Oreopoulos, N. Cho, and D. Lee, “New insights about cloud vertical structure from CloudSat and CALIPSO observations,” J. Geophys. Res.: Atmos. 122, 9280–9300 (2017). https://doi.org/10.1002/2017JD026629

Article  ADS  Google Scholar 

Interim Guidelines for the Use of DMRL-S Doppler Meteorological Radar Data in Synoptic Practice, Ed. by Yu.B. Pavlyukov, N.I. Serebryannik, S.G. Belikov, (Roshydromet, Moscow, 2017) [in Russian]

Google Scholar 

G. P. Kokhanenko, Y. S. Balin, M. G. Klemasheva, S. V. Nasonov, M. M. Novoselov, I. E. Penner, and S. V. Samoilova, “Scanning polarization lidar LOSA-M3: Opportunity for research of crystalline particle orientation in the clouds of upper layers,” Atmos. Meas. Tech. 13 (3), 1113–1127 (2020). https://doi.org/10.5194/amt-13-1113-2020

Article  Google Scholar 

I. V. Chernykh and O. A. Aldukhov, “Estimating the number of cloud layers through radiosonde data from Russian aerological stations for 1964–2014,” Russ. Meteorol. Hydrol. 43 (3), 152–160 (2018).

Google Scholar 

I. V. Chernykh and O. A. Aldukhov, “Long-term estimates of the number of cloud layers from radiosonde data for 1964–2017 in different latitudinal zones,” Russ. Meteorol. Hydrol. 45 (4), 227–238 (2020).

Google Scholar 

A. Arking and J. D. Childs, “Retrieval of cloud cover parameters from multispectral satellite images,” J. Appl. Meteorol. Climatol. 24 (4), 322–333 (1985). https://doi.org/10.1175/1520-0450(1985)024<0322:ROCCPF>2.0.CO;2

Article  ADS  Google Scholar 

M. Min, J. Li, F. Wang, Z. Liu, and W. P. Menzel, “Retrieval of cloud top properties from advanced geostationary satellite imager measurements based on machine learning algorithms,” Remote Sens. Environ. 239 (2020). https://doi.org/10.1016/j.rse.2019.111616

C. Y. Liu, C. H. Chiu, P. H. Lin, and M. Min, “Comparison of cloud-top property retrievals from advanced Himawari imager, MODIS, CloudSat/CPR, CALIPSO/CALIOP and radiosonde,” J. Geophys. Res.: Atmos. 125 (15) (2020). https://doi.org/10.1029/2020JD032683

N. Zakhvatkina, V. Smirnov, and I. Bychkova, “Satellite SAR data-based sea ice classification: An overview,” Geosci. 9 (4) (2019). https://doi.org/10.3390/geosciences9040152

S. Tanelli, S. Durden, E. Im, K. Pak, D. G. Reinke, P. Partain, J. Haynes, and R. Marchand, “CloudSat’s cloud profiling radar after two years in orbit: Performance, calibration, and processing,” IEEE Trans. Geosci. Remote Sens. 46 (11), 3560–3573 (2008). https://doi.org/10.1109/TGRS.2008.2002030

Article  ADS  Google Scholar 

D. M. Winker, M. A. Vaughan, A. Omar, Y. Hu, and K. A. Powell, “Overview of the CALIPSO mission and CALIOP data processing algorithms,” J. Atmos. Ocean. Technol. 26, 2310–2323 (2009). https://doi.org/10.1175/2009JTECHA1281.1

Article  ADS  Google Scholar 

C. J. Stubenrauch, S. Cros, A. Guignard, and N. Lamquin, “A 6-year global cloud climatology from the atmospheric InfraRed Sounder AIRS and a statistical analysis in synergy with CALIPSO and CloudSat,” Atmos. Chem. Phys. 10, 7197–7214 (2010). https://doi.org/10.5194/acp-10-7197-2010

Article  ADS  Google Scholar 

W. B. Rossow and Y. Zhang, “Evaluation of a statistical model of cloud vertical structure using combined CloudSat and CALIPSO cloud layer profiles,” J. Clim. 23 (24), 6641–6653 (2010). https://doi.org/10.1175/2010JCLI3734.1

B. Koffi, M. Schulz, F.-M. Breon, J. Griesfeller, D. Winker, Y. Balkanski, S. Bauer, T. Berntsen, M. Chin, W. D. Collins, F. Dentener, T. Diehl, R. Easter, S. Ghan, P. Ginoux, S. Gong, L. W. Horowitz, T. Iversen, A. Kirkevag, D. Koch, M. Krol, G. Myhre, Ph. Stier, and T. Takemura, “Application of the CALIOP layer product to evaluate the vertical distribution of aerosols estimated by global models: AeroCom phase I results,” J. Geophys. Res. 117 (2012).https://doi.org/10.1029/2011JD016858

J. Li, J. Huang, K. Stamnes, T. Wang, Q. Lv, and H. Jin, “A global survey of cloud overlap based on CALIPSO and CloudSat measurements,” Atmos. Chem. Phys. 15 (1), 519–536 (2015). https://doi.org/10.5194/acp-15-519-2015

Article  ADS  Google Scholar 

Q. Li and S. Groß, “Satellite observations of seasonality and long-term trends in cirrus cloud properties over Europe: Investigation of possible aviation impacts,” Atmos. Chem. Phys. 22, 15963–15980 (2022). https://doi.org/10.5194/acp-22-15963-2022

Article  ADS  Google Scholar 

Q. Song, Z. Zhang, H. Yu, P. Ginoux, and J. Shen, “Global dust optical depth climatology derived from CALIOP and MODIS aerosol retrievals on decadal timescales: Regional and interannual variability,” Atmos. Chem. Phys. 21, 13 369–13 395 (2021). https://doi.org/10.5194/acp-21-13369-2021

Article  Google Scholar 

B. M. Portella and H. M. J. Barbosa, “Climatology and trends of cirrus geometrical and optical properties in the Amazon Region from 7-yr of CALIPSO observations,” Atmos. Res. 299 (2024). https://doi.org/10.1016/j.atmosres.2023.107167

A. V. Skorokhodov, K. N. Pustovalov, E. V. Kharyutkina, and V. G. Astafurov, “Cloud-base height retrieval from MODIS satellite data based on self-organizing neural networks,” Atmos. Ocean. Opt. 36 (6), 723–734 (2023).

Google Scholar 

Z. Wang, CloudSat 2B-CLDCLASS-LIDAR Product Process Description and Interface Control Document (Colorado State University, Colorado, 2019).

Google Scholar 

Handbook on Clouds and Cloudy Atmosphere, Ed. by I.P. Mazin and A.Kh. Khrgian (Gidrometeoizdat, Gidrometeoizdat, 1989) [in Russian].

Google Scholar 

V. G. Astafurov, A. V. Skorokhodov, and K. V. Kur’yanovich, “Summer statistical models of cloud parameters over Western Siberia according to MODIS data,” Russ. Meteorol. Hydrol. 46 (11), 735–746 (2021).

Google Scholar 

T. Wehr, T. Kubota, G. Tzeremes, K. Wallace, H. Nakatsuka, Y. Ohno, R. Koopman, S. Rusli, M. Kikuchi, M. Eisinger, T. Tanaka, M. Taga, P. Deghaye, E. Tomita, and D. Bernaerts, “The EarthCARE mission—science and system overview,” Atmos. Meas. Tech. 16 (15), 3581–3608 (2023). https://doi.org/10.5194/amt-16-3581-2023

Article  Google Scholar 

M. M. Loranty, D. A. Heather, H. Kropp, A. C. Talucci, and E. E. Webb, “Siberian ecosystems as drivers of cryospheric climate feedbacks in the terrestrial Arctic,” Front. Clim. 3 (2021). https://doi.org/10.3389/fclim.2021.730943

Y. V. Martynova, N. N. Voropay, and A. A. Matyukhina, “Variability of temporal characteristics of snow cover in Siberia on ground-based data,” Environ. Dyn. Glob. Clim. Change 14 (3), 181–197 (2023). https://doi.org/10.18822/edgcc625771

Article  Google Scholar 

M. A. Vaughan, D. M. Winker, and D. A. Powell, CALIOP Algorithm Theoretical Basis Document Part 2. Feature Detection and Layer Properties Algorithms (SAIC, Hampton, 2005).

Google Scholar 

W. B. Rossow, F. Mosher, E. Kinsella, A. Arking, M. Desbois, E. Harrison, P. Minnis, E. Ruprecht, G. Seze, C. Simmer, and E. Smith, “ISCCP cloud algorithm intercomparison,” J. Appl. Meteorol. Climatol. 24 (9), 877–903 (1985). https://doi.org/10.1175/1520-0450(1985)024<0887-:ICAI>2.0.CO;2

Article  ADS  Google Scholar 

Report on the Climate Features in the Russian Federation for 2012 (Rosgidromet, Moscow, 2013) [in Russian].

Report on the Climate Features in the Russian Federation for 2016 (Rosgidromet, Moscow, 2017) [in Russian].

Report on the Climate Features in the Russian Federation for 2020 (Rosgidromet, Moscow, 2021) [in Russian].

Report on the Climate Features in the Russian Federation for 2022 (Rosgidromet, Moscow, 2023) [in Russian].

Report on the Climate Features in the Russian Federation for 2023 (Rosgidromet, Moscow, 2024) [in Russian].