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During the conference on 'passive remote sensing of clouds and the atmosphere III' in 1995, a comparison of cloud amounts derived from two retrieval schemes, APOLLO and CHAPS, was presented. It showed good results over ocean but not so good ones over land surfaces. Reasons for the discrepancy have been suggested. Meanwhile, a modified version of the APOLLO scheme has been developed and compared to a one year data set of synop data. They agree quite well. This modified version of APOLLO is used for a new comparison with CHAPS. Using the same data set, the agreement is very god. The remaining difference is probably due to insufficient cloud detection with CHAPS.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

The present understanding of moist atmospheric processes and the role of clouds in the hydrologic cycle shows severe gaps of knowledge. Water vapor plays an essential part in atmospheric dynamics. For example, the release of large amounts of latent heat, due to the condensation in convective clouds, plays an important role in the general circulation. Knowledge of the distribution of clouds and its transport is essential to understand atmospheric dynamics. Clouds can have a positive as well as a negative contribution to the greenhouse effect. A cloud cover climatology in a 15 km grid resolution has been retrieved by means of the APOLLO algorithm using the 5 calibrated AVHRR channels. The monthly means of total cloud cover are about 15 percent too high compared to conventional data, the standard deviation is +/- 12 percent. The high resolution cloud cover maps show topometeorological features like 'Fohn' on single days but not in monthly means, because these events are too rare. But increased cloud cover in the luff regions are detected in monthly means as well as some cloud sparse regions like Lake Garda, Ticino or the Swiss Rhone valley. The different annual cycles of cloud cover show the different climatic regions, which are temperate, Alpine, and Mediterranean climate. This is indicated, for example, by the remarkably smaller cloud cover in the Alpine region in winter as compared to the northern and southern forelands.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Cloud detection from satellite data usually relies on the assignment of reflectance and radiation temperature of a pixel to a cloud or a cloud free surface according to preset thresholds. It also exploits the contrast between adjacent pixels. It will be shown that the so called split window information like that provided by the `Advanced Very High Resolution Radiometer' (AVHRR) onboard the NOAA satellites is a powerful tool to reduce ambiguity in cloud detection. The reflected part of the medium infrared spectral information is used for phase detection. Double view information like that of the `Along Track Scanning Radiometer' (ATSR) on ERS-1 may be of substantial help in cloud identification, but a careful georeferencing is required. An algorithm package called `AVHRR Processing scheme Over cLouds, Land and Ocean' (APOLLO) which can also process ATSR data, is used to derive cloud masks from AVHRR and ATSR data for day and night satellite overpasses. Several products can be obtained from the image files together with the cloud mask. From the cloud free pixels surface products can be derived. Cloud cover is derived from all pixels while cloud properties like the liquid water path are derived from fully cloudy pixels only. Examples are shown which are derived from a regional cloud climatology.

Clouds play an important role in the hydrological cycle and they are a climatic component. It is therefore necessary to known the distribution of clouds. A high-spatial resolution climatology of monthly mean cloud cover has been produced from the daily afternoon passages of the NOAA satellites since September 1991. The APOLLO (AVHRR Processing scheme Over cLouds, Land and Ocean) algorithm has been applied to AVHRR data to obtain a pixel by pixel cloud mask for an area of 560 X 560 km2 over the Alps and their forelands. Cloud cover is calculated for subareas of 14 X 15 km2 for the following classifications: all clouds, thick clouds, thin clouds, thick clouds with low, medium or high tops. This regional cloud climatology reveals topometeorological features like an increased cloud cover in the luff regions at the edges of the Alps. A strong horizontal inhomogeneity on a regional scale is typical in summertime. The seasonal changes of cloud cover are greater in the south than in the north of the Alps due to different climate types. Although the APOLLO algorithm still needs improvement it has proved to be a powerful tool for cloud detection and classification.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.