Assimilation of GOES-16 ABI All-sky Radiance Observations in RRFS using EnVar: Methodology, System Development, and Impacts for a Severe Convective Event

The Advanced Baseline Imager (ABI) aboard the GOES-16 satellite provides high-resolution observations of cloud structures that could be highly beneficial for convective-scale DA. However, only clear-air radiance observations are typically assimilated at operational centers due to a variety of problems associated with cloudy radiance data. As such, many questions remain about how to best assimilate all-sky radiance data, especially when using hybrid DA systems such as EnVar wherein a nonlinear observation operator can lead to cost function gradient imbalance and slow minimization.

In this presentation, we present new methods for assimilating all-sky radiance observations in EnVar using the novel Rapid Refresh Forecasting System (RRFS) that utilizes the Finite-Volume Cubed-Sphere (FV3) model. We first further developed the EnVar solver by directly including brightness temperature (T_b) as a state variable. This modification improves the balance of the cost function gradient and speeds up minimization. Including T_b as a state variable also improves the model fit to observations and increases forecast skill compared to utilizing a standard state vector configuration. We also evaluate the impact of assimilating ABI all-sky radiances in RRFS for a severe convective event in the central Great Plains. Assimilating these radiance observations results in better spin-up of a tornadic supercell and enhanced suppression of spurious convection. These benefits are shown to continue into the forecast period, especially for localized convective events.