Impacts of Assimilating GOES‐16 ABI Channels 9 and 10 Clear Air and Cloudy Radiance Observations With Additive Inflation and Adaptive Observation Error in GSI‐EnKF for a Case of Rapidly Evolving Severe Supercells

Abstract

The Advanced Baseline Imager (ABI) instrument on the geostationary National Oceanographic and Atmospheric Administration (NOAA) GOES-16 satellite provides continuous coverage over the United States with unprecedentedly high spatial (about 2 km) and temporal (about every 5 min) coverage. The ABI infrared (IR) observations are expected to be particularly well suited to address the challenges of predicting the early stages of convection initiation (CI) and the rapid evolution of convective-scale systems (Schrӧttle et al., 2021). Such systems typically evolve on time scales of minutes and space scales of kilometers. These space and time scales are consistent with the ABI resolution and are much finer than existing in situ observation networks.

Online Nonlinear Bias Correction in Ensemble Kalman Filter to Assimilate GOES‐R All‐Sky Radiances for the Analysis and Prediction of Rapidly Developing Supercells

“…The vertical localization scale of 1.1 was used for both observations, based on the sensitivity tests. For the purpose of vertical localization, the height of ABI radiance observation is determined based on the location of the peak of weighting function (e.g., Campbell et al., 2010; Johnson et al., 2021). Covariance inflation is performed using Relaxation To Prior Spread (RTPS, Whitaker & Hamill, 2012) approach with a factor of 0.95.…”

“…This study focuses on the analysis and shortterm prediction of the two long-lived supercells. This event was also used by Johnson et al (2021) in the context of studying the impact of channel 9 and 10 ABI radiance assimilation with adaptive observation error and additive inflation. Studies have shown (e.g., Burghardt et al, 2014;Cintineo & Stensrud, 2013;Kain et al, 2013;Zhang, Minamide, & Clothiaux, 2016) the challenges for an accurate prediction of the convection initiation and early development of the storm.…”

“…Most of the above OSSEs focus on extra-tropical cyclone systems. Most recently, several studies were conducted to assimilate real ABI radiance with radar observations for rapidly developing thunderstorm and supercell events (e.g., Johnson et al, 2021;Zhang et al, 2019Zhang et al, , 2018 at convection permitting resolution. These studies found that for convective scale data assimilation (DA) systems, the frequent assimilation of all-sky BT along with radar observations results in better representation of storms in the analysis and improved forecast lead time than radar only experiments.…”

“…Although early studies have suggested the importance of correcting the bias of the radiance during DA, limited efforts were made to explore the radiance bias correction for convective scale all-sky infrared radiance DA. Most studies assimilating real all-sky infrared radiance for convective scales either perform no bias correction (e.g., Zhang et al, 2018Zhang et al, , 2019 or use a simple offline approach where domain-wide averaged innovation was used to approximate the bias (e.g., Johnson et al, 2021). Otkin and Pothast (2019) implemented an offline approach where a nonlinear function was used to fit innovations.…”

“…Harnisch et al (2016) andJohnson et al (2021). The pixels are then categorized as (a) Cloud_Cloud where both observed and model simulated BT are identified as cloudy; (b) Cloud_Clear where cloudy ABI observation and clear-sky model simulated BT are identified; (c) Clear_cloud which has clear-sky observation over cloudy model simulated BT and (d) Clear_clear which refers to clear-sky observations over clear-sky model simulated BT.…”