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Description of Comparison of NAAPS and NOAA/NESDIS AOD

NAAPS Optical Depth
Today's image       Loop of last 5 days images
 
Under Construction, Last Updated 4/4/2000

NAAPS simulations of atmospheric aerosols are validated by comparing the simulated aerosol optical depth (AOD; upper-left image) with those retrieved daily by NOAA/NESDIS from AVHRR data (top row, right image). The calculation of the NAAPS AOD is discussed here.

The comparison is made on a pixel-by-pixel basis with the pixels being 1 degree square for both NAAPS and the NESDIS AOD. The NESDIS AOD is available over most of the globe on a daily basis but there are areas where no AOD is retrieved, due to cloud, land, sunglint, and night. Comparison is made only at the available pixels and only when the NESDIS AOD pixel value is within 12 hours of the time of the NAAPS simulation. These valid data are shown in the center image of the top row. The comparison is made as scatter plots with NESDIS AOD on the abscissa and NAAPS AOD on the ordinate. When either the NAAPS or NESDIS AOD values exceed the limits of axes, both are scaled by 0.25 and then the point is plotted as a larger red asterisk.

To identify regional biases, the comparison is made on a number of subdomains, denoted by the red boxes in the plot of the NAAPS AOD (upper right). These are chosen to enclose aerosol types:

Domains used after 1 April, 2000
REGION Abbreviation Predominant Aerosol Type
Northwest Pacific nwpac Dust, anthropogenic
West Tropical Atlantic. wtatl Dust
Sahara sahara Dust, smoke
Central Asia casia Dust, anthropogenic
Northeast Pacific nepac Anthropogenic, dust
South America, SW Tropical Atlantic soamer Smoke, dust
Africa africa Dust, Smoke
Indian Ocean, India, Australia, Indonesia indaus Anthropogenic, Dust, Smoke
Other. other All types
Globe globe All types


Domains used previous to 1 April, 2000

REGION Abbreviation Predominant Aerosol Type
West Tropical Atlantic. wtatl Dust
East Tropical Atlantic. etatl Dust
Northwest Pacific nwpac Anthropogenic
Southwest Asian and Indian Ocean swasi Anthropogenic, Dust, Smoke
South Tropical Atlantic statl Smoke
Europe, East Atlantic europ Anthropogenic
Indonesia indon Anthropogenic, Smoke
Other. other All types
Globe globe All types


Those valid points not included in one of the subdomains are then plotted as `other' so that unidentified or unexpected patterns and biases do not go unnoticed. All valid points are plotted in the `globe' plot (lower-right). The linear coefficient of correlation is calculated and displayed in each scatter plot following the subdomain name and the number of valid pixels.

Some preliminary findings are:

NAAPS shows skill in regions dominated by dust, such as the West and East Tropical Atlantic, and parts of the Southwest Asia domains.

On some days NAAPS shows skill in regions dominated by anthropogenic aerosols, such as the Eastern U.S. and Europe; on other days it does not.

As of August 1999, NAAPS is simulating smoke in South America using the ABBA fire detection data. As of September 1999, NAAPS is simulating smoke in Africa using the 1993 ESA IONIA ATSR fire detection data. There currently is no smoke sources in Indonesia and Southwest Asia where biomass burning is frequent.

Our first efforts should be to eliminate those cases (pixels) where NESDIS AOD consistently shows aerosol while NAAPS does not, eg. the tropical smoke sources mentioned above. Equally important will be to eliminate those cases where NAAPS consistently shows aerosol but NESDIS does not.

Subsequent efforts would be to achieve a slope in the scatter plots of 1.0 by improving the source, transformation, and sinks in the model. For example, early analysis indicates NAAPS correlates well with NESDIS in the East Tropical Atlantic, but overpredicts the optical depths by a factor of two. The NAAPS source term may be too large.

The overprediction of dust optical depths in the East Tropical Atlantic could likewise be corrected using data assimilation. The NAAPS dust concentration at all levels would be scaled by an appropriate value. Again, the preferred solution is to improve the model source.

Acknowlegements 


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