A comparison of satellite-retrieved and simulated cloud coverage in the Baltic Sea area as part of the BALTIMOS project
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.
- 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.
- 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 944Google Scholar
- 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 77:19–43CrossRefGoogle Scholar
- 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)Google Scholar
- McClatchey RA et al. (1972) Optical properties of the atmosphere (third edition), Air Force Cambridge Research Laboratories, Report AFCRL-72-0497Google Scholar
- 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):112–120Google Scholar
- 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
- 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. 218Google Scholar
- 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, BoulderGoogle Scholar
- 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)Google Scholar
- Wilks, D. S., 1995: Statistical methods in the atmospheric sciences. Academic, San Diego, ISBN 0-12-751965-3, pp. 248-250Google Scholar