GOES Advanced Baseline Imager (ABI) Color Product

Development

Don HillgerNOAA/NESDIS/StAR

hillger@cira.colostate.edudon.hillger@noaa.gov

CoRP Third Annual Science Symposium15-16 August 2006

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

GOES-R ABI vs. Current GOESGOES-R ABI Band Central Wavelength (μm) Current GOES Band*

1 (blue) 0.47

2 (red) 0.64 1

3 0.86

4 1.38

5 1.61

6 2.26

7 3.9 2

8 6.185

9 6.95 3

10 7.34

11 8.5

12 9.61

13 10.35 4

14 11.2

15 12.3 5

16 13.3 6

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

GOES-R ABI Bands and Bandwidths

GOES-R ABI Band

Central Wavelength (μm)

Wavelength Range (μm)

Spatial Resolution (km) @ nadir

1 (blue) 0.47 0.45 - 0.49 1

2 (red) 0.64 0.59 - 0.69 0.5

3 0.86 0.846 - 0.885 1

4 1.38 1.371 - 1.386 2

5 1.61 1.58 - 1.64 1

6 2.26 2.225 - 2.275

2

7 3.9 3.80 - 4.00

8 6.185 5.77 - 6.6

9 6.95 6.75 - 7.15

10 7.34 7.24 - 7.44

11 8.5 8.3 - 8.7

12 9.61 9.42 - 9.8

13 10.35 10.1 - 10.6

14 11.2 10.8 - 11.6

15 12.3 11.8 - 12.8

16 13.3 13.0 - 13.6

Comparison of GOES-R ABI with MODIS bands

GOES-R ABI MODIS

Band Number Wavelength (μm) Band Number Wavelength (μm)

1 (blue) 0.47 3 (blue) 0.47

2 (red) 0.64 1 (red) 0.64

3 0.865 2 0.86

4 1.378 26 1.38

5 1.61 6 1.64

6 2.25 7 2.13

7 3.90 22 3.96

8 6.19 No Equivalent No Equivalent

9 6.95 27 6.7

10 7.34 28 7.3

11 8.5 29 8.55

12 9.61 30 9.7

13 10.35 No Equivalent No Equivalent

14 11.2 31 11.0

15 12.3 32 12.0

16 13.3 33 13.3

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.

First fog/stratus case

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.

3-color Product Name

Red Component

Green Component

Blue Component

Image Example

MSG “natural” 3-color product

1.6 μm 0.86 μm 0.6 μm Second from last slide

MSG “day snow-fog” product

0.8 μm* 1.6 μm* 3.9 μm (solar/reflected part only)*

Not shown

Modified 3-color fog/stratus product

0.6 μm albedo**

1.6 μm albedo**

3.9 μm (“shortwave”) albedo**

Last slide

Second fog/stratus case

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

First blowing dust case

Summary of 3-color image combinations for blowing dust detection/discrimination

3-color Product Name

Red Component

Green Component

Blue Component

Image Example

Rosenfeld 3-color “dust” product

12.0 – 10.8 μm* 10.8 – 8.7 μm* 10.8 μm* First color slide

3-color blowing dust product

PCI-2

(of 3 PCIs)

PCI-3

(of 3 PCIs)

PCI-1

(of 3 PCIs)Middle color slide

Modified 3-color blowing dust product

PCI-2

(of 4 PCIs)

PCI-4

(of 4 PCIs)

PCI-3

(of 4 PCIs)Last color slide

* Special stretching/enhancements are applied to each difference or band image before combining.

Second blowing dust case

SummaryImportant 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 variancesImportant factors in color selection:

– Bright colors for feature of interest, neutral background color

– Strongly contrasting colors for discriminating different image (cloud and surface) features