Cloud detection using meteosat imagery and numerical weather prediction model data

The cloud detection algorithm of the Royal Netherlands Meteorological Insti tute (KNMI) Meteosat Cloud Detection and Characterization KNMI (Metclock) s cheme is introduced. The algorithm analyzes the Meteosat infrared and visua l channel measurements over an area from about 25 degrees W to 25 degrees E and from 35 degrees to 70 degrees N, encompassing Europe and a small part of northern Africa. The scheme utilizes surface temperatures from a numeric al weather prediction model. Synoptic observations are used to adjust the m odel surface temperatures to represent satellite brightness temperatures fo r cloud-free conditions. The measured reflected sunlight is analyzed using a minimum reflectivity atlas. Comparison of cloud detection results with sy noptic observations of cloud cover at about 800 synoptic stations over land and 50 over sea were made on a 3-h basis for 1997. In total, two million s ynaptic observations were used to evaluate the detection method. Of the rep orted cloud cover, Metclock detected 89% during daytime and 73% during nigh ttime over land and 86% during daytime and 80% during nighttime over sea. T he fraction of pixels labeled as cloud free in reported cloud-free conditio ns was 92% for daytime and 90% for nighttime over land and 94% during dayti me and 90% during nighttime over sea. The largest contribution to the cloud detection capability is the threshold comparison of the satellite brightne ss temperatures with the adjusted model surface temperatures. The cloud det ection method is used for the initialization of a short-term cloud predicti on model and testing of cloud parameterizations of atmospheric models that will be used as an aid to meteorologists in analyzing Meteosat data.