Testing Variational Bias Correction of Satellite Radiance Data in the ACCESS-C: Australian Convective-Scale NWP System

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 23; p. 9504
• Main Authors: Samrat, Nahidul Hoque, Smith, Fiona, Lee, Jin, Smith, Andrew
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 05-12-2022; MDPI
• Subjects: Australia; Bias; Cities; Climate; Data assimilation; Domains; Instrument errors; Meteorological satellites; Meteorology; Methods; Numerical prediction; Numerical weather forecasting; numerical weather prediction; Radiance; Radiative transfer; regional models; satellite radiance; Weather; Weather forecasting; Working groups; Australia; France; Japan; satellite radiance; regional models; data assimilation; numerical weather prediction; bias
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22239504

Radiance observations are typically affected by biases that come mainly from instrument error (scanning or calibration) and inaccuracies of the radiative transfer model. These biases need to be removed for successful assimilation, so a bias correction scheme is crucial in the Numerical Weather Prediction (NWP) system. Today, most NWP centres, including the Bureau of Meteorology (hereafter, "the Bureau"), correct the biases through variational bias correction (VarBC) schemes, which were originally developed for global models. However, there are difficulties in estimating the biases in a limited-area model (LAM) domain. As a result, the Bureau's regional NWP system, ACCESS-C (Australian Community Climate and Earth System Simulator-City), uses variational bias coefficients obtained directly from its global NWP system ACCESS-G (Global). This study investigates independent radiance bias correction in the data assimilation system for ACCESS-C. We assessed the impact of using independent bias correction for the LAM compared with the operational bias coefficients derived in ACCESS-G between February and April 2020. The results from our experiment show no significant difference between the control and test, suggesting a neutral impact on the forecast. Our findings point out that the VarBC-LAM strategy should be further explored with different settings of predictors and adaptivity for a more extended period and over additional domains.