Consistent Long-Term Aerosol Data Records Over Land and Ocean from SeaWiFS
One of the key components of NASA's Earth Science Research Strategy to better understand the complex nature of Earth's climate is the determination of the global radiation balance. Comprehensive regional-to-global climate models (R/GCM) are playing an ever-greater role in addressing this issue. Because of their important role in modifying the radiative energy balance, the characteristics of aerosols, especially near their sources and sinks, are essential parameters to the R/GCMs. Many EOS-era instruments (e.g., SeaWiFS, MODIS, MISR, etc.) are designed to provide such information with a high degree of fidelity.
With the advent of this new generation of satellite sensors in the 1990s, a well-calibrated, accurate long-term aerosol dataset from satellite measurements is now becoming possible. Such a dataset is a critical component in performing the quantitative evaluations of aerosol variability on a global basis that are needed to form a consensus among the scientific community and policy makers regarding the role of aerosols in modifying the global radiative budget due to anthropogenic activities. However, the creation of such a dataset has been a difficult task because of the limited lifetime of satellite sensors as well as the calibration issues between different satellite platforms. Furthermore, different sensors take different algorithmic approaches to retrieve aerosol properties. As a result, separating the changes due to algorithm differences and calibration effects from those due to actual climate trends can be challenging as one tries to stitch together the various satellite measurements acquired to date.
To alleviate this problem, we propose to produce long-term global aerosol records over both land and ocean by applying a consistent algorithm called, Deep Blue, to measurements from SeaWiFS. This new Deep Blue algorithm has recently been successfully integrated into the MODIS operational stream as part of PGE04. The resulting products include aerosol optical thickness, Ångström exponent, and dust absorption over the whole globe. MODIS operational retrieval algorithms previously were not able to provide aerosol properties over bright-reflecting surfaces. Our Deep Blue algorithm utilizes blue-wavelength measurements from instruments such as SeaWiFS and MODIS to infer the properties of aerosols, where the surface reflectance over land in the blue part of the spectrum is much lower than for longer wavelength channels. Deep Blue therefore eliminates the resulting gaps in MODIS aerosol products by performing retrievals over such bright-reflecting surfaces.
As a result, this proposed work will produce well-calibrated coherent long-term aerosol data records for the first time using measurements from the state-of-the-art satellite sensors such as SeaWiFS and MODIS for use in climate related studies.
Christina Hsu - PI, NASA/GSFC
Distributed by GES DISC
Page Last Updated: May 2, 2019 at 11:45 AM EDT