Assimilating GOES-16 ABI All-Sky Brightness Temperature Into the HAFS Dual-Resolution Self-Consistent EnVar DA System: Methods for Observation Error Estimation and Impact on Hurricane Laura (2020)

This study investigates the impact of assimilating GOES-16 all-sky Advanced Baseline Imager (ABI) brightness temperature observations using a newly developed, continuously self-cycled, dual-resolution, 3DEnVar data assimilation system within the Hurricane Analysis and Forecast System. Focusing on the pre-rapid intensification period of Hurricane Laura, the results demonstrated that assimilating ABI observations without proper observation error treatment can be neutral or even detrimental. However, using a symmetric cloud impact approach to adaptively estimate observation errors enhances the Gaussianity of the Observation-Minus-Background Probability Distribution Functions, and significantly improves the analysis and predictions of Hurricane Laura. The improvements in the track forecasts can be attributed to better environmental analyses due to more effective use of clear sky observations, while the improved intensity forecasts stem from improved inner-core dynamic and thermodynamic structures, achieved through the more effective use of cloudy-sky observations.