Diurnal variation of cloud cover over the Baltic Sea

• Autorzy: Paszkuta Marcin , Zapadka Tomasz , Krężel Adam

Identyfikatory

DOI

10.1016/j.oceano.2021.12.005

• Języki publikacji: EN

Abstrakty

EN

Instantaneous cloud cover over the Baltic Sea, estimated from satellite information, may differ by as much as several dozen percent between the day and night. This difference may result from both weather conditions and different algorithms used for the day and night. The diurnal differences in cloudiness measured by proprietary and operational systems were analysed as part of research on marine environmental assessment and monitoring. An optimised algorithm for 2017 was presented and supplemented with information from radiation modelling. The study showed that, in general, the average values of daily changes in cloud cover over the sea depend on the season, which generally corresponds to the length of the day and contrasts with the amount of cloudiness. The results were compared with available online data that met the night and day detection criteria, the climate model, and the climate index. The averaged analysis of seasonal changes showed that similar values of the satellite estimates are higher than those obtained from the climate model and the lidar estimation. The satellite estimates from SatBaltic showed the lowest uncertainty. The diurnal cycle was confirmed by all analysed systems. These results may indicate common physical mechanisms and a methodological reason for the uncertainty of satellite-based data. The results clearly showed the existing diurnal difference in the amount of cloud cover over the Baltic Sea and indicated that this difference is not always explained by the physical properties of the atmosphere. The probable causes for these uncertainties were identified and diagnosed.

Słowa kluczowe

EN

clouds; Baltic Sea; environmental assessment

• Wydawca: Polish Academy of Sciences, Institute of Oceanology ; Elsevier
• Czasopismo: Oceanologia
• Rocznik: 2022
• Tom: No. 64 (2)
• Strony: 299--311
• Opis fizyczny: Bibliogr. 45 poz., map., rys., tab., wykr.

Twórcy

autor

Paszkuta Marcin

• Institute of Oceanography, University of Gdańsk, Gdynia, Poland

• https://orcid.org/0000-0002-8506-0648

autor

Zapadka Tomasz

• Institute of Physics, Pomeranian University in Słupsk, Poland

• https://orcid.org/0000-0002-9149-9403

autor

Krężel Adam

• Institute of Oceanography, University of Gdańsk, Gdynia, Poland

• https://orcid.org/0000-0002-7930-3009

Bibliografia

• 1. Anthis, A.I., Cracknell, A.P., 1999. Use of satellite images for fog detection (AVHRR) and forecast of fog dissipation (METEOSAT) over lowland Thessalia, Hellas. Int. J. Remote Sens. 20, 1107-1124. https://doi.org/10.1080/014311699212876
• 2. Banks, A., Melin, F., 2015. An Assessment of Cloud Masking Schemes for Satellite Ocean Colour Data of Marine Optical Extremes. Int. J. Remote Sens. 36, 797-821. https://doi.org/10.1080/01431161.2014.1001085
• 3. Bennouna, Y.S., Curier, L., de Leeuw, G., Piazzola, J., Roebeling, R., de Valk, P., 2010. An automated day-time cloud detection technique applied to MSG-SEVIRI data over Western Europe. Int. J. Remote Sens. 31, 6073-6093. https://doi.org/10.1080/01431160903376399
• 4. Bergman, J.W., Salby, M.L., 1996. Diurnal variations of cloud cover and their relationship to climatological conditions. J. Climate 9 (11), 2802-2820. https://doi.org/10.1175/1520-0442(1996)009〈2802:DVOCCA〉2.0.CO;2
• 5. Bergman, J.W., Salby, M.L., 1997. The role of cloud diurnal variations in the time-mean energy budget. J. Climate 10 (5), 1114-1124. https://doi.org/10.1175/1520-0442(1997)010〈1114:TROCDV〉2.0.CO;2
• 6. Chepfer, H., Brogniez, H., Noel, V., 2019. Diurnal variations of cloud and relative humidity profiles across the tropics. Sci. Rep-UK 9, 16045. https://doi.org/10.1038/s41598-019-52437-6
• 7. Chepfer, H., Cesana, G., Winker, D., Getzewich, B., Vaughan, M., Liu, Z., 2013. Comparison of two different cloud climatologies derived from CALIOP attenuated backscattered measurements (Level 1): the CALIPSO-ST and the CALIPSO-GOCCP. J.Atmos. Ocean. Tech. 30, 725-744. https://doi.org/10.1175/JTECH- D- 12- 00057.1
• 8. Chepfer, H., Bony, S., Winker, D.M., Chiriaco, M., Dufresne, J.-L., Seze, G., 2008. Use of CALIPSO lidar observations to evaluate the cloudiness simulated by a climate model. Geophys. Res. Lett. 35, 1-6. https://doi.org/10.1029/2008GL034207
• 9. Cracknell, A.P., 1997. The Advanced Very High Resolution Radiometer AVHRR. Taylor & Francis, London, 1-556.
• 10. EUMETSAT, 01 17, 2007. Radiometric Calibration of MSG SEVIRI Level 1.5 Image Data in Equivalent Spectral Blackbody Radiance, Doc. No.: EUM/OPS-MSG/TEN/03/0064, 26. https://www.eumetsat.int
• 11. EUMETSAT, 10 25, 2012. Conversion from radiances to reflectances for SEVIRI warm channels. Description of the conversion from radiance to reflectance for the SEVIRI reflective bands (VIS06,
• VIS08, NIR16 and HRV), 6. http://www.eumetsat.int
• 12. Finkensieper, S., Stengel, M., Selbach, N., Hollmann, R., Werscheck, M., Meirink, F., 2018. J. ICDR SEVIRI Clouds — based on CLAAS-2 methods, Satellite Application Facility on Climate Monitoring. https://wui.cmsaf.euGomis,
• 13. D., Ruiz, S., Sotillo, M.G., Álvarez-Fanjul, E., Terradas, J., 2008. Low frequency Mediterranean sea level variability: The contribution of atmospheric pressure and wind, Global Planet. Change 63, 215-229. https://doi.org/10.1016/j.gloplacha.2008.06.005
• 14. Hurrell, J.W., 1995. Decadal Trends in the North Atlantic Oscillation. Science 269, 676-679. https://doi.org/10.1126/science.269.5224.676
• 15. Jakobson, E., Keernik, H., Luhamaa, A., Ohvril, H., 2014. Diurnal variability of water vapour in the Baltic Sea region according to NCEP-CFSR and BaltAn65+ reanalyses. Oceanologia 56 (2), 191-204. https://doi.org/10.5697/oc.56-2.191
• 16. Jedlovec, G., 2009. Automated detection of clouds in satellite imagery. Adv. Geosci. Remote Sens. 303-316. https://doi.org/10.5772/8326
• 17. Jędrasik, J., 2019. Modelowanie retrospektywne i prognozowanie hydrodynamiki Morza Bałtyckiego, Gdańsk, UG, 190 pp.
• 18. Jędrasik, J., Kowalewski, M., 2019. Mean annual and seasonal circulation patterns and long-term variability of currents in the Baltic Sea. J. Marine Syst. 193, 1-26. https://doi.org/10.1016/j.jmarsys.2018.12.011
• 19. Kaczmarek, S., Dera, J., 1998. Radiation flux balance of the sea-atmosphere system over the southern Baltic Sea. Oceanologia 40, 277-306.
• 20. Kowalewska-Kalkowska, H., Kowalewski, M., 2019. Combining Satellite Imagery and Numerical Modelling to Study the Occurrence of Warm Upwellings in the Southern Baltic Sea in Winter. Remote Sens.-Basel. 11 (24), 2982. https://doi.org/10.3390/rs11242982
• 21. Kowalewski, M., 1997. A three-dimensional, hydrodynamic model of the Gulf of Gdańsk. Oceanol. Stud. 26 (4), 77-98.
• 22. Krężel, A., Kozłowski, Ł., Paszkuta, M., 2008. A simple model of light transmission through the atmosphere over the Baltic Sea utilising satellite data. Oceanologia 50 (2), 125-146.
• 23. Krężel, A., Paszkuta, M., 2011. Automatic Detection of Cloud Cover over the Baltic Sea. J. Atmos. Ocean. Tech. 28, 1117-1128. https://doi.org/10.1175/JTECH- D- 10- 05017.1
• 24. Kriebel, K.T., Gesell, G., Kästner, M., Mannstein, H., 2003. The cloud analysis tool APOLLO: improvements and validations. Int. J. Remote Sens. 24 (12), 2389-2408. https://doi.org/10.1080/01431160210163065
• 25. Kriebel, K.T., Saunders, R.W., Gesell, G., 1989. Optical properties of clouds derived from fully cloudy AVHRR pixels. Beiträge zur Physik der Atmosphäre 62, 165-171.
• 26. Kryvobok, O., Senesi, S., Morel, C., 2005. Using Meteosat second generation high resolution visible data for the improvement of the rapid developping thunderstorm product. World Weather Research, Programme Symposium on Nowcasting and Very Short Range Forecas. Toulouse, France.
• 27. Latos, B., Lefort, T., Flatau, M.K., Flatau, P.J., Permana, D.S., Baranowski, D.B., Paski, J.A.I., Makmur, E., Sulystyo, E., Peyrillé, P.,Feng, Z., Matthews, A.J., Schmidt, J.M., 2021. Equatorial Waves Triggering Extreme Rainfall and Floods in Southwest Sulawesi, Indonesia. Mon. Weather Rev. 149 (5), 1381-1401. https://doi.org/10.1175/MWR- D- 20- 0262.1
• 28. Lehmann, A., Krauss, W., Hinrichsen, H.-H., 2002. Effects of remote and local atmospheric forcing on circulation and upwelling in the Baltic Sea. Tellus A 54 (3), 299-316. https://doi.org/10.3402/tellusa.v54i3.12138
• 29. Li, X., Zheng, H., Han, C., Wang, H., Dong, K., Jing, Y., Zheng, W.,2020. Cloud Detection of SuperView-1 Remote Sensing Images Based on Genetic Reinforcement Learning. Remote Sens.-Basel.12 (19), 190. https://doi.org/10.3390/rs12193190
• 30. Mahajan, S., Fataniya, B., 2020. Cloud detection methodologies: variants and development—a review. Complex & Intelligent Systems 6, 251-261. https://doi.org/10.1007/s40747-019-00128-0
• 31. Masuda, K., Takashima, T., Takayama, Y., 1988. Emissivity of pure and sea waters for the model sea surface in the infrared window regions. Remote Sens. Environ. 24 (2), 313-329. https://doi.org/10.1016/0034-4257(88)90032-6
• 32. Paszkuta, M., Zapadka, T., Kr ̨e ̇zel, A., 2019. Assessment of cloudiness for use in environmental marine research. Int. J. Remote Sens. 40 (24), 9439-9459. https://doi.org/10.1080/01431161.2019.1633697
• 33. Platnick, S., Meyer, K., King, M.D., Wind, G., Amarasinghe, N.,Marchant, B., Arnold, G.T., Zhang, Z., Hubanks, P.A., Holz, R.E.,Yang, P., Ridgway, W.L., Riedi, J., 2017. The MODIS cloud optical and microphysical products: Collection 6 updates and examples from Terra and Aqua. IEEE T. Geosci. Remote 55, 502-525. https://doi.org/10.1109/TGRS.2016.2610522
• 34. Post, P., Aun, M., 2020. Changes in satellite-based cloud parameters in the Baltic Sea region during spring and summer (1982-2015). Adv. Sci. Res. 17, 219-225. https://doi.org/10.5194/asr-17-219-2020
• 35. Reuter, M., Fischer, J., 2014. A comparison of satellite-retrieved and simulated cloud coverage in the Baltic Sea area as part of the BALTIMOS project. Theor. Appl. Climatol. 118, 695-706. https://doi.org/10.1007/s00704-009-0208-8
• 36. Rozwadowska, A., 2004. Optical thickness of stratiform clouds over the Baltic inferred from on-board irradiance measurements. Atmos. Res. 72, 129-147. https://doi.org/10.1016/j.atmosres.2004.03.012
• 37. Ruiz, S.D., Gomis, M.S., Josey, S., 2008. Characterization of surface heat fluxes in the Mediterranean Sea from a 44-year high-resolution atmospheric data set, Global Planet. Change 63 (2—3), 258-274. https://doi.org/10.1016/j.gloplacha.2007.12.002
• 38. Saunders, R.W., Kriebel, K.T., 1988. An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int. J. Remote Sens. 9, 123-150. https://doi.org/10.1080/01431168808954841
• 39. Wang, M., He, G., Zhang, Z., Wang, G., Wang, Z., Yin, R., Cui, S.,Wu, Z., Cao, Xi., 2019. A radiance-based split-window algorithm for land surface temperature retrieval: Theory and application to MODIS data. Int. J. Appl. Earth Obs. 76, 204-217. https://doi.org/10.1016/j.jag.2018.11.015
• 40. Winker, D.M., Vaughan, M.A., Omar, A., Hu, Y., Powell, K.A., 2009. Overview of the CALIPSO mission and CALIOP dataprocessing algorithms. J. Atmos. Ocean. Tech. 26, 2310-2323. https://doi.org/10.1175/2009JTECHA1281.1
• 41. Woźniak, B., Bradtke, K., Darecki, M., Dera, J., Dudzińska-Nowak, J., Dzierzbicka-Głowacka, L., Ficek, D., Furmańczyk, K., Kowalewski, M., Krężel, A., Majchrowski, R., Ostrowska, M., Paszkuta, M., Stoń-Egiert, J., Stramska, M., Zapadka, T., 2011a. SatBaltic — a Baltic environmental satellite remote sensing system- an ongoing project in Poland. Part 1: Assumptions, scope and operating range. Oceanologia 53 (4), 897-924. https://doi.org/10.5697/oc.53-4.897
• 42. Woźniak, B., Bradtke, K., Darecki, M., Dera, J., Dudzińska-Nowak, J., Dzierzbicka-Głowacka, L., Ficek, D., Furmańczyk, K., Kowalewski, M., Krężel, A., Majchrowski, R.,
• 43. Ostrowska, M., Paszkuta, M., Stoń-Egiert, J., Stramska, M., Zapadka, T., 2011b. SatBaltic — a Baltic environmental satellite remote sensing system — an ongoing project in Poland. Part 2: Practical applicability and preliminary results. Oceanologia 53 (4), 925-958. https://doi.org/10.5697/oc.53-4.925
• 44. Zapadka, T., Kręźel, A., Paszkuta, M., Darecki, M., 2015. Daily radiation budget of the Baltic sea surface from satellite data. Pol. Marit. Res. 22 (3), 50-56. https://doi.org/10.1515/pomr-2015-0056
• 45. Zapadka, T., Ostrowska, M., Stoltmann, D., Krężel, A., 2020. A satellite system for monitoring the radiation budget at the Baltic Sea surface. Remote Sens. Environ. 240, 11683. https://doi.org/10.1016/j.rse.2020.111683

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).