End-to-End Design, Development and Testing of GOES-R Level 1 and 2 Algorithms

GOES-R is the next generation of the National Oceanic and Atmospheric Administration's (NOAA) Geostationary Operational Environmental Satellite (GOES) System, and it represents a new technological era in operational geostationary environmental satellite systems. GOES-R will provide advanced products, based on government-supplied algorithms, which describe the state of the atmosphere, land, and oceans over the Western Hemisphere. The Harris GOES-R Core Ground Segment (GS) Team will provide the ground processing software and infrastructure needed to produce and distribute these data products. As part of this effort, new or updated Level 1b and Level 2+ algorithms will be deployed in the GOES-R Product Generation (PG) Element. In this work, we describe the general approach currently being employed to migrate these Level 1b (L1b) and Level 2+ (L2+) GOES-R PG algorithms from government-provided scientific descriptions to their implementation as integrated software, and provide an overview of how Product Generation software works with the other elements of the Ground Segment to produce Level 1/Level 2+ end-products.

In general, GOES-R L1b algorithms ingest reformatted raw sensor data and ancillary information to produce geo-located GOES-R L1b data, and GOES-R L2+ algorithms ingest L1b data and other ancillary/auxiliary/intermediate information to produce L2+ products such as aerosol optical depth, rainfall rate, derived motion winds, and snow cover. In this presentation we provide an overview of the Algorithm development life cycle, the common Product Generation software architecture, and the common test strategies used to verify/validate the scientific implementation. This work will highlight the Software Integration and Test phase of the software life-cycle and the suite of automated test/analysis tools developed to insure the implemented algorithms meet desired reproducibility. As part of this discussion we will summarize the results of our algorithm testing to date, and provide illustrated examples from our ongoing algorithm implementation.