Daily monitoring algorithms to detect geostationary imager visible radiance...

Doelling, D. R., C. Haney, R. Bhatt, B. Scarino, and A. Gopalan (2023), Daily monitoring algorithms to detect geostationary imager visible radiance anomalies, Terms of Use, doi:10.1117/1.JRS.16.014502.

The NASA Clouds and the Earth’s Radiant Energy System (CERES) project provides observed flux and cloud products for the climate science community. Geostationary satellite (GEO) imager measured clouds and broadband derived fluxes are incorporated in the CERES SYN1deg product to provide regional diurnal information in between Sun-synchronous Terra and Aqua CERES measurements. The recently launched GEO imagers with onboard calibration systems have active calibration teams that incrementally update the calibration in order to mitigate calibration drifts. However, short-term L1B radiance anomalies and calibration adjustment discontinuities may still exist in the record. To avoid any GEO cloud and flux artifacts in the CERES SYN1deg product, these calibration events must be addressed while scaling the GEO imagers to the Aqua-moderate resolution imaging spectroradiometer (MODIS) calibration reference. All-sky tropical ocean ray-matching (ATO-RM) and deep convective cloud invariant target (DCC-IT)-based monitoring algorithms are presented to detect calibration-driven daily anomalies in the GOES-16 Advanced Baseline Imager L1B visible (0.65 μm) radiance measurements. Sufficient daily ATO-RM sampling was obtained both by ray-matching GOES-16 with multiple MODIS and visible-infrared imaging radiometer suite imagers as well as by increasing the grid resolution. Optimized angular matching and outlier filtering were most effective in reducing the ATO-RM daily gain algorithm noise. The DCC-IT daily calibration algorithm utilized a larger domain and included more GOES-16 scan times. The DCC-IT daily gain uncertainty was reduced by normalizing the DCC regional reflectance on a regional, seasonal, and diurnal basis. The combination of ATO-RM and DCC-IT daily monitoring algorithms is shown to detect, with a high degree of confidence, daily GOES-16 L1B calibration-driven radiance anomalies >2.4%, while keeping false positives at a minimum.

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Research Program: 
Radiation Science Program (RSP)
Climate Variability and Change Program