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MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning AlgorithmsTo monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.
Document ID
20120003753
Document Type
Presentation
Authors
Albayrak, Arif
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Wei, Jennifer
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Petrenko, Maksym
(NASA Headquarters Washington, DC United States)
Lary, David
(Texas Univ. Dallas, TX, United States)
Leptoukh, Gregory
(NASA Goddard Space Flight Center Greenbelt, MD, United States)
Date Acquired
August 25, 2013
Publication Date
December 5, 2011
Subject Category
Computer Systems
Report/Patent Number
GSFC.CPR.5806.2011
Meeting Information
American Geophysical Union 2011 Fall Meeting(San Francisco, CA)
Funding Number(s)
CONTRACT_GRANT: NNG06EB68C
Distribution Limits
Public
Copyright
Public Use Permitted.

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