Irkutsk, Russian Federation
employee
Irkutsk, Russian Federation
Irkutsk, Russian Federation
Irkutsk, Russian Federation
The paper addresses the issue of how much cloud cover data obtained using satellite and model-interpolation techniques are suitable for monitoring the transparency of the atmosphere and determining conditions for airglow observations at a local geophysical observatory. For this purpose, we compared the temporal dynamics of cloud cover from ECMWF’s ERA5 reanalysis and NOAA satellites with the night atmosphere transparency according to a digital camera. We considered the dynamics of the addressed parameters at the Geophysical Observatory of the Institute of Solar-Terrestrial Physics, located in the Baikal Natural Territory near the village of Tory (Republic of Buryatia, Russia), during December 2020. The comparative analysis showed a generally good agreement between cloud cover data from ECMWF’s ERA5 climate reanalysis and those observed with the camera. Disadvantages are the lack of information on rapid variations in cloud cover in the reanalysis and positive and negative delays in the dynamics of cloud fields that last about two hours. Due to irregular satellite data, large time gaps between passes and difficulties in estimating cloud cover at night, we could not come to reliable conclusions concerning the applicability of satellite data.
cloud cover, atmospheric transparency, ECMWF’s ERA5 reanalysis, satellite observations
1. Ahlgrimm M., Forbes R. Improving the representation of low clouds and drizzle in the ECMWF model based on ARM observations from the Azores. Monthly Weather Review. 2014, vol. 142, iss. 2, pp. 668-685. DOI:https://doi.org/10.1175/MWR-D-13-00153.1.
2. Darchia Sh.P. Ob astronomicheskom climate SSSR [On the astronomical climate of the USSR]. Moscow, Nauka Publ., 1985, 175 p. (In Russian).
3. Forbes R.M., Ahlgrimm M. On the representation of high-latitude boundary-layer mixed-phase cloud in the ECMWF global model. Monthly Weather Review. 2014, vol. 142, iss. 9, pp. 3425-3445. DOI:https://doi.org/10.1175/MWR-D-13-00325.1.
4. Forbes R.M., Tompkins A.M. An improved representation of cloud and precipitation. ECMWF Newsletter. 2011, vol. 129, pp. 13-18. DOI:https://doi.org/10.21957/nfgulzhe.
5. Forbes R.M., Tompkins A.M., Untch A. A new prognostic bulk microphysics scheme for the IFS. ECMWF Technical Memorandum No. 649. 2011, 28 p. DOI:https://doi.org/10.21957/bf6vjvxk.
6. Hersbach H., Bell B., Berrisford P., Hirahara S., Horányi A., Muñoz-Sabater J., Nicolas J., Peubey C., Radu R., Schepers D., et al. The ERA5 Global Reanalysis. QJRMS. 2020, vol. 146, iss. 730, pp. 1999-2049. DOI:https://doi.org/10.1002/qj.3803.
7. Kazakovtsev A.F., Kolin’ko V.I. Method for estimating cloudiness of night atmosphere and a night cloud sensorfor implementation thereof. Patent RU 2678950 C1. 2019.
8. Kokarev D.V., Galileiskii V.P., Morozov A.M., Elizarov A.I. Device for observing the optical state of the sky within the visible hemisphere. Patent RU 191582 U1. 2019.
9. Lei Y., Letu H., Shang H., Shi J. Cloud cover over the Tibetan Plateau and eastern China: a comparison of ERA5 and ERA-Interim with satellite observations. Climate Dynamics. 2020, vol. 54, pp. 2941-2957. DOI:https://doi.org/10.1007/s00382-020-05149-x.
10. Mikhalev A.V., Podlesny S.V., Stoeva P.V Night airglow in RGB mode. Solar-Terrestrial Physics. 2016, vol. 2, iss. 3, pp. 106-114. DOI:https://doi.org/10.12737/22289.
11. Qinglong You, Yang Jiao, Houbo Lin, Jinzhong Min, Shichang Kang, Guoyu Ren, Xianhong Meng. Comparison of NCEP/NCAR and ERA-40 total cloud cover with surface observations over the Tibetan Plateau. International Journal of Climatology. 2014, vol. 34, iss. 8, pp. 2529-2537. DOI:https://doi.org/10.1002/joc.3852.
12. Shikhovtsev A.Yu., Kovadlo P.G., Kiselev A.V. Astroclimatic statistics at the Sayan Solar Observatory. Solar-Terrestrial Physics. 2020, vol. 6, iss 1. pp. 102-107. DOI:https://doi.org/10.12737/stp-61202012.
13. Stowe L., Davis P., McClain E. Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer. J. Atmos. Ocean. Technol. 1999, vol. 16, pp. 656-681. DOI:https://doi.org/10.1175/1520-0426(1999)016<0656:SBAIEO>2.0.CO;2.
14. Tiedtke M. A comprehensive mass flux scheme for cumulus parameterization in large-scale models. MonthlyWeather Review. 1989, vol. 117, iss. 8, pp. 1779-1800. DOI:https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF> 2.0.CO;2.
15. Tiedtke M. Representation of clouds in large-scale models. Monthly Weather Review. 1993, vol. 121, iss. 11, pp. 3040-3061. DOI:https://doi.org/10.1175/1520-0493(1993)121<3040:ROCILS>2.0.CO;2.
16. Zagainova Yu.S., Karavayev Yu.S. Estimation of the state of cloudiness on an 8-point scale using the histogram method from images in the visible range obtained from the full sky camera. Solar-Terrestrial Physics (Solnechno-Zemnaya Fizika). 2013, vol. 23, pp. 120-128. (In Russian).
17. Zdor S.E., Kolin’ko V.I. Night cloud sensor. Patent RU 2436133 C2. 2011.
18. URL: https://www.ecmwf.int (accessed May 30, 2022).
19. URL: https://www.scanex.ru (accessed May 30, 2022).
20. URL: http://ckp-rf.ru/ckp/3056 (data obrascheniya 30 maya 2022 g.).
