Impact of Adaptive Observation Error Estimate on Direct Assimilation of GOES-16 ABI All-Sky Radiances within HAFS Self-Cycled Dual-Resolution EnVar DA System

The assimilation of all-sky radiance observations from the Advanced Baseline Imager (ABI) aboard the GOES-16 satellite has the potential to address the forecasting challenges of the tropical cyclone (TC) because of its high spatial and temporal resolution and coverage over the open ocean. Effective assimilation of ABI all-sky radiances requires an accurate estimation of observation errors. The representativeness error portion of the observation error due to model and observation mismatch may vary along the TC lifecycle (e.g., genesis, rapid intensification, and mature stages). Therefore, this study develops a method to estimate observation errors adaptively depending on the TC lifecycle. In the ABI-enhanced Hurricane Analysis and Forecast System (HAFS) self-cycled dual-resolution three-dimensional ensemble–variational (3DEnVar) data assimilation system, observation error statistics adaptive to cloud features are estimated with samples over a running time window from previous cycles, resulting in the spatially and temporally varied observation error estimation. Experiments to physically understand the impact of the adaptive observation error estimate for Hurricane Laura (2020) will be presented at the conference.