GeoColor: A Blending Technique for Satellite Imagery

Abstract

Value-added imagery is a useful means of communicating multispectral environmental satellite radiometer data to the human analyst. The most effective techniques strike a balance between science and art. The science side requires engineering physical algorithms capable of distilling the complex scene into a reduced set of key parameters. The artistic side involves design and construction of visually intuitive displays that maximize information content within the product image. The utility of such imagery to human analysts depends on the extent to which parameters or features of interest are conveyed unambiguously. Here, we detail and demonstrate a dynamic blended imagery technique, based on spatially variant transparency factors whose values are tied to algorithmically isolated parameters. The technique enables seamless display of multivariate information, and is applicable to any imaging system based on red–green–blue composites. We illustrate this technique in the context of GeoColor—an application of the Geostationary Operational Environmental Satellite R (GOES-R) series Advanced Baseline Imager (ABI) supporting operational forecasting and used widely in public communication of weather information.

Satellite Imagery and Products of the 16–17 February 2020 Saharan Air Layer Dust Event over the Eastern Atlantic: Impacts of Water Vapor on Dust Detection and Morphology

“…Superimposed on GOES-16 GeoColor imagery (Miller et al, 2016(Miller et al, , 2020 valid at 1800 UTC on 16 February 2020 ( Fig. 1), was an inverted trough (Schlueter et al, 2019) at 700 hPa, indicated by a the geopotential heights and a black dashed contour, and was positioned over the eastern Atlantic.…”

Combined Dust Detection Algorithm for Asian Dust Events Over East Asia Using GK2A/AMI: a Case Study in October 2019

“…The BT near 10.5 μm can change depending on the surface temperature and atmospheric conditions and we used the 90 % upper limit and 10 % lower limit as the MAX and MIN. For dust-enhanced false color imagery, we modulated the BI with the DD parameter and a weighting factor in each color gun (R, G, and B component) to emphasize the pixels with higher DD parameters (Miller et al 2017(Miller et al , 2020. In this study, each color component is represented as follows:…”

Satellite imagery and products of the 16–17 February 2020 Saharan Air Layer dust event over the eastern Atlantic: impacts of water vapor on dust detection and morphology

“…Sounding-1 illustrates the dry atmosphere that is characteristic of the northern dust region compared to an atmosphere that is more moist and is characteristic of the southern dust region. (Rubin et al, 2017) of aerosol distribution; the European Centre for Medium-Range Weather Forecasts (ECMWF), which utilizes a 4D-Var data assimilation algorithm to update aerosol and atmospheric states in their Integrated Forecast System (IFS) (Morcrette et al, 2008;Benedetti et al, 2009); and the Japan Meteorological Agency (JMA), which runs the Model of Aerosol Species in the Global Atmosphere (MASINGAR; Tanaka and Chiba, 2005), as well as a 2D-Var data assimilation method to provide operational aerosol and dust forecasts and analyses (Yumimoto et al, 2018). In addition, the NASA Global Modeling and Assimilation Office (GMAO) also assimilates aerosols in their Goddard Earth Observing System Version 5 (GEOS-5; Randles et al, 2017).…”