Direct assimilation of ground-based microwave radiometer observations with machine learning bias correction based on developments of RTTOV-gb v1.0 and WRFDA v4.5

Abstract. The application of ground-based microwave radiometers (MWRs), which provide high-quality and continuous vertical atmospheric observations, has traditionally focused on the indirect assimilation of retrieved profiles. This study advanced this application by developing a direct assimilation capability for MWR radiance observations within the Weather Research and Forecasting model data assimilation (WRFDA) system, along with a bias correction scheme based on random forest technique. The proposed bias correction scheme effectively reduced the observation-minus-background (O−B) biases and standard deviations by 0.83 K (97.1 %) and 1.63 K (64.6 %), respectively. A series of ten-day-long experiments demonstrated that assimilating MWR radiances improves both the initial conditions and the forecasts, with additional benefits from higher assimilation frequencies. In the initial conditions, hourly assimilation significantly enhanced low-level temperature and humidity fields, reducing the root-mean-square-error (RMSE) for temperature and water vapor mixing ratio by 6.32 % below 1 km and 1.98 % below 5 km. These improvements extended to forecasts, where 2 m temperature and humidity showed sustained benefits for over 12 hours, and precipitation forecasts exhibited notable gains, particularly for higher intensity events. The time-averaged Fractions Skill Score (FSS) for 3 h accumulated precipitation within the 24 h forecasts increased by 0.04–0.11 (10.2–58.1 %) for thresholds of 6–15 mm.