A Bias Correction Scheme with the Symmetric Cloud Proxy Variable and Its Influence on Assimilating All-Sky GOES-16 Brightness Temperatures

Abstract

Abstract All-sky assimilation of brightness temperatures (BTs) from GOES-16 infrared water vapor channels is challenging, primarily because these channels are sensitive to cloud ice that causes large nonlinear errors in the forecast and forward models. Thus, bias correction (BC) for all-sky assimilation of GOES-16 BTs is vital. This study examines the impacts of different BC schemes, especially for a scheme with a quartic polynomial of cloud predictors (the ASRBC4 scheme), on the analysis and WRF Model forecasts of tropical cyclones when assimilating the all-sky GOES-16 channel-8 BTs using the NCEP GSI-based 3D ensemble–variational hybrid data assimilation (DA) system with variational BC (VarBC). Long-term statistics are performed during the NASA Convective Processes Experiment field campaign (2017). Results demonstrate that the ASRBC4 scheme effectively reduces the average of all-sky scaled observation-minus-backgrounds (OmBs) in a cloudy sky and alleviates their nonlinear conditional biases with respect to the symmetric cloud proxy variable, in contrast to the BC schemes without the cloud predictor or with a first-order cloud predictor. In addition, adopting the ASRBC4 scheme in DA decreases the positive temperature increments at 200 hPa and the accompanying midlevel cyclonic wind increments in the analysis of Tropical Storm (TS) Cindy (2017). Applying the ASRBC4 scheme also leads to better storm-track predictions for TS Cindy (2017) and Hurricane Laura (2022), compared to experiments with other BC schemes. Overall, this study highlights the importance of reducing nonlinear biases of OmBs in a cloudy sky for successful all-sky assimilation of BTs from GOES-16 infrared water vapor channels.