Assimilating GOES-16 All-Sky ABI Radiances with the HAFS Dual-Resolution EnVar Data Assimilaton System for Hurricane Predictions

Hurricanes spend the majority of their lifespan over the open ocean, making the optimal utilization of all-sky radiance observations from satellites essential in improving its numerical prediction. Despite this, the assimilation of all-sky radiances in convection-allowing hurricane prediction is still in its early stages, with limited studies exploring the assimilation of all-sky Advanced Baseline Imager (ABI) observations onboard GOES-16 for hurricane predictions, even after the launch of the first GOES-R series of the next-generation geostationary weather satellites in 2016.

To address this issue, we made efforts toward optimizing the assimilation of the all-sky ABI radiance data assimilation using the state-of-the-art Hurricane Analysis and Forecast System (HAFS), the next-generation hurricane modeling and data assimilation system. Specifically, we implemented a method to adaptively estimate the ABI radiance observation errors and a method to correct the innovation biases in HAFS for all-sky ABI assimilation. We then investigate the impact of all-sky ABI assimilation on the storm inner-core structure evolution and intensity prediction.

Multiple experiments have been conducted with Hurricane Laura (2020) prior to its rapid intensification onset, and the results demonstrate the positive impacts of assimilating all-sky ABI radiances in HAFS and the value of bias corrections and adaptive observation errors. These findings will be presented at the conference, along with in-depth diagnostics.