M. Cervino

Precipitation estimations from geostationary orbit and prospects for METEOSAT Second Generation

For over two decades operational rainfall estimations from geostationary satellites have represented an ambitious aspiration of scientists and an identified need of operational meteorologists. A wide variety of infrared and combined visible and infrared methods have been proposed for the identification of suitable relationships between satellite-observed cloud top radiative features and rainfall at the ground. Microwave-based retrievals, however, correlate rainfall and internal cloud microphysical features more successfully. The most significant limitation, however, is the indirect character of the retrieval that correlates microphysical and dynamical cloud characteristics with rain amounts at ground level. METEOSAT Second Generation signals a new era for geostationary satellites with its new 12 channel imager SEVIRI and 15 minute full-disk image repeat cycle. SEVIRI is expected to contribute significantly to a better characterisation of clouds and atmospheric stability by means of improved infrared calibration, radiometric performances, imaging frequency and multispectral image analysis. The significant increase of multispectral cloud observations is expected to provide new data for the improvement of rainfall estimations from geostationary orbit. The anticipated progress from enhanced imaging frequency and multispectral data for the definition of new techniques is discussed. Considerations for operational applications, chiefly for nowcasting, are also provided as they are the main goal of the satellite. Future developments and synergies with other geostationary and polar orbiting instruments, passive and active, are finally considered as the ultimate strategy for more accurate instantaneous rainfall estimations at all latitudes. Copyright

CLOUD FRACTION WITHIN GOME FOOTPRINT USING A REFINED CLOUD CLEARING ALGORITHM

Abstract The Global Ozone Monitoring Experiment (GOME) flies on-board the ERS2 satellite since 1995 and its main mission is the retrieval of total ozone at the nominal ground resolution of 320×40 km 2 . Cloud detection and characterization, an interesting result in itself, are needed to analyse spectral data prior to the retrieval of columnar ozone as well as other atmospheric constituents. The Cloud Clearing Algorithm (CCA) available in literature was developed based on a simple thresholding method: cloud detection is obtained within the Polarisation Measurements Devices (PMDs) ground pixel (20×40 km 2 , one-sixteenth of the GOME spatial resolution) using thresholds that depend primarily on surface type and reflection, and solar zenith angles. A refinement of the CCA is presented. Thresholds over the ocean have been computed by comparing PMD detection results with the ERS2 Along Track Scanning Radiometer 2 (ATSR2) cloud masks, being ATSR2 measurements coincident in time and space with GOME ones. Refined CCA performances have been compared with a totally independent cloud classification algorithm that uses visible-infrared, high resolution full disk METEOSAT images. Case studies are presented, and differences between the two methods are discussed on the PMD and spectral GOME ground pixel sizes.

Cloud detection with GOME: a refinement of the cloud clearing algorithm using ATSR-2 images

The Global Ozone Monitoring Experiment (GOME), flown on-board the ERS2 satellite since 1995, has the main mission of retrieving total ozone at the nominal ground resolution of 320/spl times/40 km/sup 2/. The retrieval of different trace gases and aerosol can also be attempted. Cloud detection and characterization, an interesting result in itself, are needed to analyse spectral data prior the retrieval of columnar ozone as well as other atmospheric constituents. The Polarisation Measurement Devices (PMDs) allow for detection of radiation leaving the Earth-atmosphere system at three spectral broad channels, from 300 to 800 nm. The Cloud Clearing Algorithm (CCA) was developed based on a simple thresholding method: cloud detection is obtained within the PMD ground pixel (20/spl times/40 km/sup 2/, one-sixteenth of GOMEs spatial resolution) using thresholds; that depend primarily on surface type and reflection, and solar zenith angles. A refinement of the CCA is presented hereafter. Thresholds over the ocean have been computed by comparing PMD detection performances with the Along Track Scanning Radiometer 2 (ATSR2) cloud masks. ATSR2 masks are available on a 2/spl times/2 km/sup 2/ spatial resolution. Note that GOME and ATSR2 do fly on-board the same spacecraft, thus producing simultaneous nadir images with very reliable colocation. Refined CCA performances have been compared with a totally independent cloud classification algorithm that uses visible-infrared, high resolution full disk METEOSAT images. Case studies are presented, and differences between the two methods are discussed at PMD and spectral GOME ground pixel sizes.

GASP: GOME Aerosol Spectral Processor

GASP is the GOME Aerosol Spectral Processor developed in order to generate the aerosol optical thickness (AOT) product starting from the spectra measured by GOME instrument. The GOME instrument, on board the ERS-2 satellite, has been designed in order to collect radiation over the entire wavelength region from 240 to 790 nm, in which several atmospheric species and also aerosols and clouds can be observed. In this paper the scheme and the functionalities of GASP are described. GASP provides the aerosol optical thickness at the reference wavelength of 550 nm and the aerosol class for cloud free pixels. The retrieval scheme, based on the maximum likelihood principle, uses as forward model GOMESIM, suitably tuned in order to simulate a MODTRAN like behavior. The aerosol classification is made choosing the minimum among the least squares residuals computed for different aerosol classes that can be distinguished in the troposphere. In order to show the results obtained by GASP a first set of real data has been processed and analyzed.

The aerosol optical thickness retrieval from GOME spectra

A prototype processor for the aerosol optical thickness retrieval and aerosol classification starting from GOME data has been developed. The aerosol classification is made choosing the minimum among the least squares residuals computed for different aerosol classes. For each pixel the output of processor gives the aerosol optical thickness, the aerosol classification, a relative retrieval residual and a flag that indicates if the pixel is cloudy. The results of some different GOME real data sets are shown.