The Impact of Dynamic Observation Error and Hydrometeor Control Variables on GK-2A AMI All-Sky Radiance Assimilation

Assimilation of all-sky radiance (ASR) observations informs atmospheric states and cloud distributions; however, it does not always lead to improved analyses or forecasts. In particular, directly updating hydrometeor fields introduces substantial uncertainty into ASR assimilation. This study examines the impact of dynamic observation errors on analyses and precipitation forecasts under different hydrometeor control variable (HCV) configurations. Observation errors are prescribed using a fifth-order polynomial model as a function of a cloud impact parameter, allowing spatiotemporally varying (i.e., scene-dependent) errors that adapt to cloud conditions. Results indicate that dynamic observation errors generally improve cloud analyses and associated thermodynamic fields. By contrast, constant errors tend to overweight ASR observations in heavily cloud-affected regions, thereby degrading analysis quality. The advantages of dynamic errors are more pronounced when solid-phase hydrometeors are included in the HCV, as these strongly influence brightness temperature (BT) analysis and the representation of convective cloud tops. Among all experiments, those combining dynamic errors with direct updates of solid-phase hydrometeors produce the most realistic BT and reflectivity analyses, as well as the greatest improvements in precipitation forecasts. These results underscore the importance of cloud-dependent observation error modeling in ASR assimilation, particularly when solid-phase HCVs are employed.