Improving Convection Initiation Forecasts by Assimilating High-Resolution GOES-16 ABI Infrared Water Vapor and Cloud Sensitive Radiances Using GSI-Based EnKF/EnVar

Convective Initiation (CI) is a unique challenge for NWP due to the initially small space and time scales that it occurs on, and strong sensitivity to small differences in environmental moisture at both low levels and aloft. Severe weather outbreaks characterized by rapid intensification of the first storms commonly occur in the U.S. Great Plains. In such cases, better predictions of CI of the 1^st storms is a key limiting factor for increasing tornado and severe weather warning lead times and better protecting lives and property.

The high space and time resolution of GOES-16 ABI radiances are hypothesized to enable better CI forecasts, and an accompanying increased lead time for warning that can not be obtained by assimilating radar reflectivity after storms have already developed. In this study, a case of rapidly intensifying tornadic and non-tornadic supercells from 18 May 2017 is used to test this hypothesis.

The GSI based ensemble data assimilation system is first extended to assimilate both the clear and cloudy radiances of GOES-16. The ABI mid-level water vapor channel radiances, in addition to NEXRAD radar reflectivity observations, are assimilated into the WRF model with 3 km grid spacing and a 10 minute cycling interval. DA diagnostics and forecast verification are compared to a similar experiment that only assimilates the radar observations to reveal the impact of assimilating the GOES-16 all sky radiances. Initial results have found assimilating the clear air radiance improved the CI forecast. Impact of assimilating cloudy radiances, the QC, and bias correction methods will be discussed also during the presentation.