Adaptation of OCA Algorithm for GK2A/AMI: Sensor-Specific Look-Up Table Regeneration

The Optimal Cloud Analysis (OCA) algorithm developed by EUMETSAT retrieves cloud optical thickness (COT), cloud effective radius (CRE), and cloud-top pressure (CTP) at pixel level using optimal estimation techniques with pre-computed Look-Up Tables (LUTs). Current operational CTP products from GK2A tend to underestimate cloud-top height when validated against CALIPSO, with larger errors observed in lower atmospheric layers and ice cloud regions. To address these limitations, we adapt the OCA algorithm for the Advanced Meteorological Imager (AMI) aboard the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since OCA was originally designed for the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard Meteosat Second Generation, direct application is limited due to differences in spectral response functions (SRFs) between sensors. In this study, we regenerate sensor-specific LUTs for 11 channels covering visible to thermal infrared wavelengths (VI006, VI008, NR016, SW038, WV063, WV073, IR087, IR096, IR105, IR112, IR133) using the libRadtran radiative transfer model. Radiative transfer calculations employ the DISORT solver with Mie scattering for liquid water clouds and parameterized ice crystal optical properties, covering comprehensive ranges of cloud optical thickness, effective radius, and viewing geometries. A total of eight radiative parameters (Rbd, Rd, Rfd, Tb, Td, Tfbd, Tfd, Em) for both solar and thermal channels are recalculated with GK2A/AMI SRFs. Comparison between original EUMETSAT LUTs and newly generated GK2A LUTs reveals systematic differences across all channels, demonstrating the necessity of sensor-specific LUT adaptation rather than direct algorithm porting. The adapted algorithm will be validated against CALIPSO products over the East Asian region.

Acknowledgements: This work was funded by the Korea Meteorological Administration Research and Development Program under Grant (RS-2025-02221093).