Learning from GES DISC's MLS and OMI Data Users: Metrics Matter
Loading...
Links to Files
Permanent Link
Author/Creator ORCID
Date
2019-08-27
Department
Program
Citation of Original Publication
Wei, Jennifer; Shen, Suhung; Shie, Chung-Lin; Johnson, James; Lei, Guang-Dih; Owens, Elaine; Alcott, Gary; Meyer, David J.; Learning from GES DISC's MLS and OMI Data Users: Metrics Matter; NASA Technical Reports Server; https://ntrs.nasa.gov/search.jsp?R=20190030911;
Rights
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Public Domain Mark 1.0
This is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This is a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Abstract
It has been over 15 years since Aura research satellite launched in 2004 to observe the Earth's ozone layer, air quality, and climate from four different instruments - the High Resolution Dynamics Limb Sounder (HIRDLS), the Microwave Limb Sounder (MLS), the Ozone Monitoring Instrument (OMI), and the Tropospheric Emission Spectrometer (TES). Observations from the Aura mission have established a concrete understanding of the changing chemistry of our atmosphere.The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the official archive and distribution center for the HIRDLS, MLS, and OMI instruments. This presentation will report metrics of data usage and services on these instruments. Key to GES DISC's mission to provide better data support is gaining a better understanding of our users' needs and behaviors as they discover, access and utilize these data. We will summarize the users' needs from these instruments based on user inquiry information collected over the Aura mission lifetime and present findings from this ensemble metrics.