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Seze, G.

Publications and source records attributed to Seze, G..

Sampling and Representativeness for a Spaceborne Wind Lidar

The capability of an Earth-orbiting lidar to produce a reliable windfield sampling with an error of 1-3 ms(exp -1) and that is relevant to numerical forecasting and climate studies is discussed. The spatial and temporal resolutions range from 100 km to 1000 km and 3 hr to 1 month respectively. In this respect cloud obstruction is of great concern, as it can prevent the lidar probing whole parts of the atmosphere, for both short and long periods of time. A worldwide analysis of cloudiness from either visual observations made from the ground or else from the Stratospheric Aerosol and Gas Experiment (SAGE) instrument, has shown that the average cloud cover is 60 percent. This analysis is only valid at large scales (time and space) compatible with the currently operating satellites. However, a lidar footprint is only about a hundred meters or less, and it was observed from ground based lidars that, even when the cloudiness is 100 percent, some lidar shots get through. A complete analysis of the probability of probing through clouds by a spaceborne lidar is presently beyond our capacities. Hence, no small scale cloudiness data set is yet available representing all kinds of meteorological situations at all latitudes.

Lieutaud, F.

Clear line of sight (CLOS) statistics within cloudy regions and optimal sampling strategies for space-based lidars

Simulated cloud/hole fields as well as Landsat imagery are used in a computer model to evaluate several proposed sampling patterns and shot management schemes for pulsed space-based Doppler lidars. Emphasis is placed on two proposed sampling strategies - one obtained from a conically scanned single telescope and the other from four fixed telescopes that are sequentially used by one laser. The question of whether there are any sampling patterns that maximize the number of resolution areas with vertical soundings to the PBL is addressed.

Emmitt, G. D.

Time-cumulated visible and infrared histograms used as descriptor of cloud cover

To study the statistical behavior of clouds for different climate regimes, the spatial and temporal stability of VIS-IR bidimensional histograms is tested. Also, the effect of data sampling and averaging on the histogram shapes is considered; in particular the sampling strategy used by the International Satellite Cloud Climatology Project is tested.

Seze, G.

Automatic classification of clouds on Meteosat imagery - Application to high-level clouds

A statistical classification method based on clustering on three-dimensional histograms is applied to the three channels of the Meteosat imagery. The results of this classification are studied for different cloud cover cases over tropical regions. For high-level cloud classes, it is shown that the bidimensional IR-water vapor histogram allows one to deduce the cloud top temperature even for semi-transparent clouds.

Desbois, M.