Comparison of Assimilating Clear and All-Sky GOES-16 ABI Radiance in a Convective-Scale Model to Improve the Forecast of an MCS Event

Abstract

Geostationary infrared observations provide frequent, spatially continuous information on rapidly evolving convective systems. This study assimilates clear-sky and all-sky Channel 10 brightness temperatures (BTs) from GOES-R Advanced Baseline Imager (ABI). The impacts on the analysis and short-range forecast of a continental meso-scale convective system (MCS) are compared. The implementation is achieved in a convection-permitting model using a 5 min cycling ensemble Kalman filter (EnKF) framework. A quality control procedure is developed to enable the effective assimilation of clear-sky ABI observations. Results show that clear-sky assimilation partially removes spurious background clouds and adjusts temperature and moisture in the near-storm environment. All-sky assimilation substantially improves the analyzed cloud field, produces stronger in-storm thermodynamic and dynamical increments, as well as directly constrains hydrometeor variables. In addition, assimilating all-sky observations reduces errors of analyzed surface temperature and humidity by over 25%. Forecast verification further indicates that all-sky assimilation provides more persistent forecast improvements in terms of BT and reflectivity quantities than clear-sky assimilation does. Both clear-sky and all-sky assimilations improve the spatial coverage and intensity of accumulated precipitation, but assimilating all-sky observations produces substantially larger FSS gains (0.163–0.380 versus 0.02–0.13).