GRATDAT: A novel approach to monitoring and processing radiometric data from GOES-R ABI

The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellites (GOES)-R Series will feature increased spatial, spectral and temporal coverage over the current N-Series Imagers. With this added capability comes an order of magnitude increase in the number of detectors capturing data. Trending the performance of these detectors, along with the radiometric parameters derived from on-orbit calibration maneuvers, is a critical capability to ensure product quality. A new tool being developed, the GOES-R ABI Trending and Data Analysis Toolkit (GRATDAT), will accomplish this trending through the use of its novel Adaptive Trending and Limit-Monitoring Algorithm (ATLMA). Residing within the GOES-R Ground Segment, GRATDAT will have access to all ABI radiometric parameters in near-real time, along with the entire mission life store of parameters. Since many parameters will exhibit a diurnal behavior they can be expressed as a Fourier expansion. The ATLMA is trained nightly on data from the previous 48-72 hours using an iterative, least-square fitting to compute the coefficients of the Fourier expansion. The expansion is then used to predict the values of each parameter for the next 24 hours. As new data are received by GRATDAT in near-real time the parameters are compared to their predicted values. Thresholds are set as a user-defined integer number of standard deviations, as computed by the least-square fitting. These thresholds are considered to be adaptive as they change throughout the day, and allow for tighter bounds about the expected values. If any data point falls beyond the threshold it is flagged as an outlier for further investigation. This process allows thousands of detectors and their parameters to be trended throughout operations with minimal user intervention.

A second component of GRATDAT is the ability to replicate a portion of the Level-0 (L0) to Level-1b (L1b) processing of ABI data performed within the Ground Segment in an offline environment. GRATDAT can decompress raw L0 data to the L1a stage representing detector counts, and optionally apply a calibration to reach the L1alpha stage of detector-level radiances. Analyzing data processed to these intermediate stages can provide critical insight into any anomalous behaviors seen in the data before they are irreversibly processed to full L1b products.

Results from testing the ATLMA on proxy data processed during GOES-R Ground Segment rehearsals will be presented, along with an illustration of an outlier that would not be flagged by static thresholds. In addition a brief example of L0 data conversion will be presented to highlight the utility of intermediate products.