Abstract
Abstract. A methodology for representing much of the physical information content of the METEOSAT Second Generation (MSG) geostationary satellite using red-green-blue (RGB) composites of the computed physical values of the picture elements is presented. The physical values are the solar reflectance in the solar channels and brightness temperature in the thermal channels. The main RGB compositions are (1) "Day Natural Colors", presenting vegetation in green, bare surface in brown, sea surface in black, water clouds as white, ice as magenta; (2) "Day Microphysical", presenting cloud microstructure using the solar reflectance component of the 3.9 μm, visible and thermal IR channels; (3) "Night Microphysical", also presenting clouds microstructure using the brightness temperature differences between 10.8 and 3.9 μm; (4) "Day and Night", using only thermal channels for presenting surface and cloud properties, desert dust and volcanic emissions; (5) "Air Mass", presenting mid and upper tropospheric features using thermal water vapor and ozone channels. The scientific basis for these rendering schemes is provided, with examples for the applications. The expanding use of these rendering schemes requires their proper documentation and setting as standards, which is the main objective of this publication.
Highlights
Satellite observations play a key role in understanding cloudaerosol climate effects
With the rich spectral information of the SEVIRI instrument on the METEOSAT Second Generation (MSG) satellite, we review and update the new capabilities of the Rosenfeld Lensky Technique (RLT) qualitative approach
In the “Convective Storms” color scheme (Fig. 9) the brightness temperature difference (BTD) between 6.2 and 7.3 μm channels (BTD6.2−7.3) is regulating the red, which is modulated by the mid level moisture
Summary
Satellite observations play a key role in understanding cloudaerosol climate effects. (AVHRR) data on the polar orbiting NOAA satellites This technique uses both qualitative and quantitative approaches to understanding precipitation forming processes. The evolution of cloud top effective radius (re) with height (or cloud top temperature, T ), observed by the satellite at a given time (snapshot) for a cloud ensemble over an area, is similar to the T −re time evolution of a given cloud at one location This is the ergodicity assumption, which means exchangeability between the time and space domains. The rich spectral information of the SEVIRI instrument was used to expand both the qualitative and the quantitative parts of the RLT. We gave this tool the name CAPSAT: Clouds-Aerosols-Precipitation Satellite Analysis Tool. In a subsequent publication we will do the same for the quantitative approach
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