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GOES-16, the first in a series of new generation Geostationary Operational Environmental Satellite (GOES), carries an Advanced Baseline Imager (ABI) with six Visible and Near Infrared (VNIR) channels that are calibrated periodically with an onboard solar diffuser. After its launch in 2016, we found seasonal changes in the time series of some VNIR channel gains that cannot be explained by instrument degradation nor measurement errors. This was thought to be related to the implementation of the different solar calibration algorithm version and the impacts of satellite instrument angles condition. To characterize and quantify this seasonal variation and measurement bias, we firstly reprocessed all GOES-16 solar calibration events since January 15, 2017 with the same calibration algorithm and look-up-table (LUT), which excluded the impact of algorithm difference in implementation. Secondly, to correct for the impact of “imperfect” solar calibration timing (elevation angle not equal to zero) before Dec 12, 2017, we used the extra solar calibration events in 2018 with the same elevation angle as the ones in 2017 to estimate the degradation rate in the first year, and then corrected this for the previous events. After removal of the bias due to non-zero elevation angle, we then derived the relationship between seasonal variation of corrected gains with the instrument beta angle. Our result shows that there is an asymmetrical pattern between beta angle and the gains, especially band 1. This dependence of the gains on beta angle was likely related to Bidirectional Reflectance Distribution Function (BRDF) factor in solar calibration. We finally derive the bias correction coefficient and then applied to correct such a dependence. Our results show clearly that it improves the assessment the GOES-16 VNIR instrument degradation and calibration repeatability. We will present these and other details at the conference.

The Advanced Baseline Imager (ABI) is the primary instrument onboard NOAA’s current Geostationary Operational Environmental Satellites (GOES), GOES-16 (GOES East) and GOES-17 (GOES West). These 16-band instruments are collecting imagery critical to the National Weather Service for accurate weather nowcasting and forecasting over the Earth’s Western Hemisphere. While GOES-16 operates as designed, the partial failure of the GOES-17 ABI cooling system leads to a set of different operational configurations that optimizes the instrument performance under the circumstances. Since GOES-17 ABI became operational in February 2019, several major Ground System (GS) updates have been successfully implemented to improve the ABI radiance quality of the solar reflective and infrared (IR) bands. The impacts of the GS updates on radiance and image quality were intensively validated and carefully monitored using different methods. This study is to report the radiometric calibration performance after each major update. The update of the scan mode in April 2019 reduces the calibration difference between the swaths within the timeline for the GOES-17 IR bands. The predictive calibration (pCal) algorithm implemented in July 2019 significantly improves the calibration accuracy for the GOES-17 IR data when they are available during the period of unstable focal plane module (FPM) temperature, and thus greatly reduces the calibration error at night. After several solar calibration updates to both ABIs from April to June 2019, the overall difference is less than 5% for all the solar reflective bands as compared to the corresponding channels of S-NPP Visible Infrared Imaging Radiometer Suite (VIIRS). Details will be reported in the meeting.

Deep convective clouds (DCCs) are used as vicarious calibration targets because of their stability, brightness, and relatively small angular and spectral variation. DCCs at visible channels usually have relatively sharp histogram distributions with high reflectance. However, as a calibration target, there remains relatively large uncertainty in identifying uniform cold cloud top of DCCs at near infrared (NIR) channels. The main objective of this study is to reduce DCC reflectance uncertainty at NIR channels for GOES-R Advanced Baseline Imager (ABI). The DCC pixels information from Visible Infrared Imaging Radiometer Suite (VIIRS) Level1B and Level2 product are used as proxy data and analyzed to determine where convective cloud core locates and to characterize the internal structure of DCCs. This work uses DCC calibration technique suggested by Doelling et al. (2004) to get DCC pixels from VIIRS Level1B data and the target region is over GOES-R ABI check-out spatial domain (20°N-20°S and 109.5°W-69.5°W). Identification of DCC pixels from VIIRS Level2 product is followed by International Satellite Cloud Climatology Project cloud classification based on cloud optical depth and cloud top pressure (Rossow and Schiffer 1999). The overlapped Level1B and Level2 DCC pixels show different patterns of cloud top properties between the visible channels and NIR channels. The preliminary results show that the patterns of DCC properties are well correlated with DCC reflectance and their relationship with DCC reflectance are differentiated between the visible and NIR channel. Among the DCC microphysical parameters, optical thickness seems to be most important variable to characterize the overshooting part of DCCs. This work provides useful guidance toward finding core and anvil part of DCCs and thus helps to reduce the DCC NIR reflectance uncertainty from Level1B data. We will use these results to validate the algorithm with the collocated DCC area from AHI and VIIRS data.