A comparison of satellite-retrieved and simulated cloud coverage in the Baltic Sea area as part of the BALTIMOS project

Abstract

A cloud-detection algorithm for METEOSAT first generation data has been developed. The algorithm utilizes solely infrared data from the METEOSAT thermal infrared window channel at around 11.5 μm. The developed algorithm estimates an assumed clear-sky brightness temperature from time series analysis on pixel bases. Land-/sea-depending dynamic thresholds are then utilized discriminating the infrared images in cloudy, undecided, and cloud free pixels. The cloud-detection algorithm has been validated against synoptic observations. The developed cloud-detection scheme has been applied to 10 years (1992–2001) of METEOSAT data, extracting cloud coverage statistics for the Baltic Sea catchment area. These have been compared to corresponding cloud coverage statistics derived from the BALTIMOS coupled model system. Building overall averaged values of the cloud coverage in the period from 1999 to 2001 gives results with very good agreement between simulation and observation: the total METEOSAT-derived cloud coverage amounts to 0.65 compared to 0.63 for BALTIMOS. In contrast, large discrepancies in the phase of the diurnal cycle of cloud coverage have been observed. A significant trend in total cloud amount was observed neither from the model nor from the satellite.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

References

  1. Ackerman S et al (1998) Discriminating clear sky from clouds with MODIS. J Geophys Res-Atmos 103(D24):32141–32157

    Article  Google Scholar 

  2. Ackerman S et al. (2002) Discriminating clear sky from cloud (algorithm theoretical basis documents) NASA Goddard space flight center, ATBD-MOD-06, Version 4 (10.01.2002) http://modis.gsfc.nasa.gov/data/atbd/atbd_mod06.pdf.

  3. Deeter MN, Evans KF (1998) A hybrid Eddington single scattering radiative transfer model for computing radiances from thermally emitting atmospheres. J Quant Spectrosc Radiat Transfer 60(4):635–648

    Article  Google Scholar 

  4. Hahn CJ, Warren SG, London J (1995) The effect of moonlight on observation of cloud cover at night, and application to cloud climatology. J Climate 8(5):1429–1446

    Article  Google Scholar 

  5. Houghton JT et al (2001) Climate Change 2001—the scientific basis, contribution of working Group I to the third assessment report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, p 944

    Google Scholar 

  6. Jacob D (2001) A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteorol Atmos Phys 77:61–73

    Article  Google Scholar 

  7. Jacob D, Van den Hurk BJJM, Andrae U, Elgered G, Fortelius C, Graham LP, Jackson SD, Karstens U, Köpken Chr, Lindau R, Podzun R, Rockel B, Rubel F, Sass BH, Smith RNB, Yang X (2001) A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period. Meteorol Atmos Phys 77:19–43

    Article  Google Scholar 

  8. Joseph JH, Wiscombe WJ, Weinman JA (1976) Delta-Eddington approximation for radiative flux-transfer. J Atmos Sci 33(12):2452–2459

    Article  Google Scholar 

  9. Kriebel KT et al (2003) The cloud analysis tool APOLLO: improvements and validations. Int J Remote Sens 24(12):2389–2408

    Article  Google Scholar 

  10. Lorenz P, Jacob D (2009) BALTIMOS—a fully coupled modeling system for the Baltic Sea and its drainage basin. Theoretical and Applied Climatology, this issue (submitted 2006)

  11. McClatchey RA et al. (1972) Optical properties of the atmosphere (third edition), Air Force Cambridge Research Laboratories, Report AFCRL-72-0497

  12. Mohr T (1971) Comparison of satellite and soil observations—cloud behavior (cloud amount greater than 4/8) in European North Atlantic Region, April 1, 1966, to March 31, 1967. Meteorol Rundsch 24(4):112–120

    Google Scholar 

  13. Rathke C, Fischer J (2000) Retrieval of cloud microphysical properties from thermal infrared observations by a fast iterative radiance fitting method. J Atmos Ocean Technol 17(11):1509–1524

    Article  Google Scholar 

  14. Reuter M (2005) Identification of cloudy and clear sky areas in MSG SEVIRI images by analyzing spectral and temporal information (Ph.D. thesis), http://www.diss.fuberlin.de/2005/194/,07/2005

  15. Reuter M, Thomas W, Albert P, Lockhoff M, Weber R, Karlsson K-G, Fischer J (2009) The CM-SAF and FUB cloud detection schemes for SEVIRI: validation with synoptic data and initial comparison with MODIS and CALIPSO. J Appl Meteorol Climatol 48(2):301–316

    Article  Google Scholar 

  16. Roeckner E, Arpe K, Bengtsson L, Christoph M, Claussen M, Dümenil L, Esch M, Schlese U, Schulzweida U (1996) The atmospheric general circulation model ECHAM4: model description and simulation of present-day climate. Max-Planck-Institut für Meteorologie, Report No. 218

  17. Rossow WB (1989) Measuring cloud properties from space: a review. J Climate 2(3):201–213

    Article  Google Scholar 

  18. Rossow WB, Garder LC (1993a) Cloud detection using satellite measurements of infrared and visible radiances for ISCCP. J Climate 6(12):2341–2369

    Article  Google Scholar 

  19. Rossow WB, Garder LC (1993b) Validation of ISCCP cloud detections. J Climate 6(12):2370–2393

    Article  Google Scholar 

  20. Rossow WB, Walker AW, Garder LC (1993) Comparison of ISCCP and other cloud amounts. J Climate 6(12):2394–2418

    Article  Google Scholar 

  21. Saunders RW, Kriebel KT (1988) An improved method for detecting clear sky and cloudy radiances from AVHRR data. Int J Remote Sens 9(1):123–150

    Article  Google Scholar 

  22. Schwartz B, Govett M (1992) A hydrostatically consistent North American radiosonde data base at the forecast systems laboratory, 1946–present (NOAA technical memorandum). NOAA, Forecast Systems Laboratory, Boulder

    Google Scholar 

  23. Walther A, Bennartz R (2009) The diurnal cycle of non-frontal precipitation in the Baltic area from observations and models. Theoretical and Applied Climatology, this issue (submitted 2006)

  24. Wilks, D. S., 1995: Statistical methods in the atmospheric sciences. Academic, San Diego, ISBN 0-12-751965-3, pp. 248-250

  25. Woodcock F (1976) Evaluation of yes–no forecasts for scientific and administrative purposes. Mon Weather Rev 104(10):1209–1214

    Article  Google Scholar 

Download references

Acknowledgments

This work was funded by the BALTIMOS project which is a contribution to the BMBF research program DEKLIM-FKZ: 01 LD 0027.

We thank Dr. E. Reimer for providing the dataset of synop observations and Philip Lorenz for providing the BALTIMOS data.

We thank the reviewers for their constructive revisions and comments.

Author information

Affiliations

Authors

Corresponding author

Correspondence to M. Reuter.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Reuter, M., Fischer, J. 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 (2014). https://doi.org/10.1007/s00704-009-0208-8

Download citation

Keywords

  • Cloud Coverage
  • Brightness Temperature
  • Diurnal Cycle
  • International Satellite Cloud Climatology Project
  • Total Cloud Coverage