AeroStat: An Online Platform for the Statistical Intercomparison of Aerosols
The largest uncertainty in computer models of the Earth's future climate is the magnitude of the primary and secondary aerosol climate feedbacks. In order to understand the contribution of changing tropospheric aerosol burdens on climate and constrain the associated uncertainties, it is imperative to quantify the current global distribution of aerosols.
However, differences between near-coincident satellite observations of aerosol parameters are often larger than the respective reported uncertainties. The scientific community is currently concentrating on identifying, understanding and resolving specific differences between tropospheric aerosol observations. One cannot address these issues without a fairly comprehensive environment in which to perform the data intercomparisons. Such a platform must provide not only the data itself, but also ready access to correlative information on data quality and data lineage (provenance).
We propose to create and implement AeroStat, an online environment for the direct statistical intercomparison of global aerosol parameters in which the provenance and data quality can be readily accessed by scientists. AeroStat builds upon the framework of an existing NASA online data visualization and analysis tool, Aerosol Giovanni (http://giovanni.gsfc.nasa.gov/), and leverages other ongoing projects (TRL 6-9) to provide the most cost-effective means of comparing satellite, ground-based and model aerosol data.
AeroStat will provide a creative, collaborative research environment where users can seamlessly share AeroStat workflow execution, algorithms, best practices, known errors and other pertinent information with the science community so other users can reproduce their results AeroStat’s collaborative environment will allow users to share the full details of individual case studies by registering them on a web-accessible Wiki.
Christopher S. Lynnes - PI, NASA's Goddard Space Flight Center
Last Updated: Nov 15, 2017 at 2:35 PM EST