Two flight models of the Advanced Baseline Imager (ABI) are in-orbit on the GOES-16 and GOES-17 geostationary satellites, with two more planned to be launched on GOES-T (2021) and GOES-U (2024). The ABI is the primary Earthviewing weather imaging instrument on the GOES-R Series, producing Level 1b (L1b) radiances and Cloud and Moisture Imagery (CMI) data products. The ABI L1b product is the source for all the ABI Level 2+ (L2+) products, including CMI, which makes the maturity process for these two products important. CMI is the only key performance parameter (KPP) of the GOES-R Series mission and thus CMI takes precedence over other ABI L2+ products. As the only KPP, CMI follows the same maturity schedule as the ABI L1b product. For the ABI L1b and CMI data products to be declared operational, they must pass through a series of calibration and validation tests and analyses, with the peerreviewed results showing that the instruments and products have achieved each level of maturity consistent with mission success. This paper describes the assessment process, the definitions of the product validation maturity levels, and an overview of the product performance for each instrument at each validation level. Additionally, this paper will describe planned programmatic changes aimed at streamlining the maturity process for the upcoming GOES-T and GOES-U satellites.
As part of NOAA’s Big Data Project, near real time GOES-16 and GOES-17 Advanced Baseline Imager (ABI) Level 1b (L1b) and Level 2 (L2), Geostationary Lightning Mapper (GLM) L2, and Solar Ultraviolet Imager (SUVI) L1b data are being provided through cloud-computing platforms such as Google Cloud Platform and Amazon Web Services. This partnership allows data users to access and analyze large amounts of GOES-R Series data without needing to download the data files or use much of their personal computing resources for analysis. Another benefit of cloud data access is the ability to create value-added products and tools from existing GOES-R Series data for downstream users who are more interested in having easily accessible end products for decision making rather than in performing research analyses. This paper will first describe how a user can access GOES-R Series data from a cloud platform service, and will then illustrate the application of a value-added tool using those data.