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GOES Advanced Baseline Imager (ABI) Color Product Development

GOES Advanced Baseline Imager (ABI) Color Product Development. Don Hillger NOAA/NESDIS/StAR hillger@cira.colostate.edu don.hillger@noaa.gov CoRP Third Annual Science Symposium 15-16 August 2006. GOES ABI advances. Improved resolutions: Spatial (0.5 km visible, 2 km IR)

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GOES Advanced Baseline Imager (ABI) Color Product Development

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  1. GOES Advanced Baseline Imager (ABI) Color Product Development Don Hillger NOAA/NESDIS/StAR hillger@cira.colostate.edu don.hillger@noaa.gov CoRP Third Annual Science Symposium 15-16 August 2006

  2. GOES ABI advances • Improved resolutions: • Spatial (0.5 km visible, 2 km IR) • Temporal (5 min full-disk, 30 s rapid scan) • Spectral (16 vs. 5 bands) • Radiometric (lower noise) • Also improved navigation/registration • Leading to new and improved image products

  3. GOES-R ABI vs. Current GOES * Current GOES contains 5 of the 6 listed bands, GOES-8/11 with band-5 and GOES-12/13/etc. with band-6.

  4. GOES-R ABI Bands and Bandwidths

  5. Comparison of GOES-R ABI with MODIS bands

  6. Product example: New Daytime Fog/Stratus Product • Start with current products used for fog/stratus detection • Shortwave Albedo “Fog” product • Apply MSG “natural” 3-color product idea • Apply/re-apply to new ABI bands and adjust bands as needed to improve discrimination of features in the product.

  7. First fog/stratus case

  8. Summary of 3-color image combinations for fog/stratus detection/discrimination * The counts in these bands/images are non-linearly gamma-adjusted (to a power of 1/1.7) before combining (Gaertner 2005). ** Albedo is the solar-zenith-angle corrected reflectance.

  9. Second fog/stratus case

  10. Product example: New Blowing Dust Product • Start with current products used for detecting blowing dust • Longwave difference product (IR vs. visible bands are generally better for dust, also then works day and night) • Rosenfeld* 3-color dust product • Apply Principal Component Image (PCI) transformation (as a pseudo-enhancement) to same bands as Rosenfeld, plus. • Apply/re-apply to new ABI bands, and adjust bands as needed to improve the discrimination of features in the product. * Daniel Rosenfeld, Hebrew University, Jerusalem

  11. First blowing dust case

  12. Summary of 3-color image combinations for blowing dust detection/discrimination * Special stretching/enhancements are applied to each difference or band image before combining.

  13. Second blowing dust case

  14. Summary Important factors in band selection: • Start with bands available with GOES-R ABI • Use spectral regions important for the feature of interest • Prefer window/lower atmospheric bands • Leverage existing products as foundation for improvement • Use Principal Component Image (PCI) analysis, if needed to extract explained variances Important factors in color selection: • Bright colors for feature of interest, neutral background color • Strongly contrasting colors for discriminating different image (cloud and surface) features

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