Assimilating all-sky infrared radiances from GOES-16 ABI using an ensemble Kalman filter for convection-allowing severe thunderstorms prediction

Abstract

This article presents the first practice of assimilating real-world all-sky GOES-16 ABI infrared brightness temperature (BT) observations using an ensemble-based data assimilation system coupled with the Weather Research and Forecasting (WRF) Model at a convection-allowing (1 km) horizontal resolution, focusing on the tornadic thunderstorm event across Wyoming and Nebraska on 12 June 2017. It is found that spurious clouds created before observed convection initiation are rapidly removed, and the analysis and forecasts of thunderstorms are significantly improved, when all-sky BT observations are assimilated with the adaptive observation error inflation (AOEI) and adaptive background error inflation (ABEI) techniques. Better forecasts of the timing and location of convection initiation can be achieved after only 30 min of assimilating BT observations; both deterministic and probabilistic WRF forecasts of midlevel mesocyclones and low-level vortices, started from the final analysis with 100 min of BT assimilation, closely coincide with the tornado reports. These improvements result not only from the effective suppression of spurious clouds, but also from the better estimations of hydrometeors owing to the frequent assimilation of all-sky BT observations that yield a more accurate analysis of the storm. Results show that BT observations generally have a greater impact on ice particles than liquid water species, which might provide guidance on how to better constrain modeled clouds using these spaceborne observations.