IN31A-3711:
Tactical Approaches for Making a Successful Satellite Passive Microwave ESDR

Wednesday, 17 December 2014
Molly Hardman1,2, Mary J. Brodzik1,2, Jacob Gotberg3, David G Long3 and Aaron C Paget3, (1)University of Colorado at Boulder, Boulder, CO, United States, (2)National Snow and Ice Data Center, Boulder, CO, United States, (3)Brigham Young University, Provo, UT, United States
Abstract:
Our NASA MEaSUREs project is producing a new, enhanced resolution gridded Earth System Data Record for the entire satellite passive microwave (SMMR, SSM/I-SSMIS and AMSR-E) time series. Our project goals are twofold: to produce a well-documented, consistently processed, high-quality historical record at higher spatial resolutions than have previously been available, and to transition the production software to the NSIDC DAAC for ongoing processing after our project completion. In support of these goals, our distributed team at BYU and NSIDC faces project coordination challenges to produce a high-quality data set that our user community will accept as a replacement for the currently available historical versions of these data. We work closely with our DAAC liaison on format specifications, data and metadata plans, and project progress. In order for the user community to understand and support our project, we have solicited a team of Early Adopters who are reviewing and evaluating a prototype version of the data. Early Adopter feedback will be critical input to our final data content and format decisions. For algorithm transparency and accountability, we have released an Algorithm Theoretical Basis Document (ATBD) and detailed supporting technical documentation, with rationale for all algorithm implementation decisions. For distributed team management, we are using collaborative tools for software revision control and issue tracking. For reliably transitioning a research-quality image reconstruction software system to production-quality software suitable for use at the DAAC, we have adopted continuous integration methods for running automated regression testing. Our presentation will summarize both
advantages and challenges of each of these tactics in ensuring production of a successful ESDR and an enduring production software system.