AbstractRed–green–blue (RGB) composites are increasingly used by operational forecasters to interpret vast amounts of satellite imagery by reducing several bands into a single, easily understood product which identifies important atmospheric features with unique colors. Limb effects, a result of an increase in optical pathlength of the absorbing atmosphere between the satellite and Earth as viewing zenith angle increases, adversely affects RGB composite interpretation by causing anomalous reductions in brightness temperature, thus changing the color interpretation of the RGB composites. In a previous paper, Elmer et al. demonstrated a limb correction technique that effectively removes limb effects from polar-orbiting infrared channels in both clear and cloudy regions using latitudinally and seasonally varying correction coefficients. This study applies the Elmer et al. limb correction to Air Mass RGB composites derived from geostationary sensors and compares the limb-corrected geostationary imagery to limb-corrected polar-orbiter imagery and satellite-derived atmospheric profiles. A statistical comparison in overlapping regions shows that the limb correction reduces the absolute mean brightness temperature difference from 4–12 K to 0–2 K for all infrared bands, demonstrating that the Elmer et al. limb correction algorithm is a robust method of removing limb effects from infrared imagery derived from both geostationary and polar-orbiting sensors. The limb-corrected RGB composites derived from geostationary sensors present several advantages, including the improved depiction of atmospheric features and enabling wider use of imagery from overlapping geostationary coverage regions where viewing zenith angles are large for both geostationary sensors.
A real-time gesture recognition system using near-infrared imagery
