The application of ground-based microwave radiometers (GMWRs), 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 GMWR radiance observations within the Weather Research and Forecasting Data Assimilation (WRFDA) system, along with a bias correction scheme based on the 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 10 d experiments demonstrated that assimilating GMWR 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 by 6.32 % below 1 km and for water vapor mixing ratio by 1.98 % below 5 km. These improvements extended to forecasts, where 2 m temperature and humidity showed sustained benefits for over 12 h, and precipitation forecasts exhibited improvements to a certain extent. The time-averaged Fractions Skill Score (FSS) for 3 h accumulated precipitation within the 24 h forecasts increased by 0.02–0.04 (3.9 %–10.2 %) for thresholds of 3–6 mm.

All-sky radiance assimilation can increase the utilization of satellite observations in cloudy regions and improve typhoon forecasts. This study focuses on the newly launched FengYun-3F satellite equipped with the Microwave Humidity Sounder II (MWHS-II) and develops an all-sky assimilation capability for its radiance data. A series of assimilation experiments were conducted to evaluate their impacts on the forecast of Typhoon Yagi (2024), demonstrating that all-sky assimilation leads to reductions in track error (23.14%) and improvements in precipitation forecasts (Equitable Threat Score increase of 16.92%) compared to clear-sky assimilation. Furthermore, a detailed comparison of assimilation experiments shows that using only the 183 GHz humidity channels yields limited improvement in tropospheric humidity, whereas assimilating the 118 GHz temperature channels significantly enhances temperature and wind forecasts. Combined assimilation of both frequency bands synergistically maintains accurate track and intensity predictions while further improving precipitation prediction. These findings demonstrate the value of multichannel all-sky assimilation and inform future satellite data assimilation strategies.

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.

Different from the aircraft TC reconnaissance flight missions before, a tropical cyclone (TC) field campaign project with a Doppler radar equipped on an airship that could hang over on the top of a TC (about 20 km) has been recently carried out in China. To understand the impact of airship-borne radar radial wind observations in TC forecasting, this work conducted quick observation simulation system experiments (QuickOSSE) by assimilating simulated airship-borne Doppler radar radial winds with an Ensemble Kalman Filter (EnKF) algorithm. The results show that airship-borne radial winds assimilation reproduces the forecasted track and minimum sea level pressure of the nature run. The forecast of dynamic and thermodynamic TC structures, such as tangential wind, secondary circulation, and warm core, are also improved. In addition, two determining factors, the radar depression angle (D-ang) and the distance from the airship to the TC center (DIS), are found to primarily affect the forecast of the TC track and intensity, respectively. Benefiting from a larger horizontal coverage, observations under a smaller D-ang improved the track more significantly. Meanwhile, the intensity forecast error with a DIS around the radius of the maximum wind is the smallest among several sensitive experiments, which may because the peak-velocity winds representing the TC’s intensity could be observed by radar. The results are expected to help establish an observational strategy for upcoming airship flight missions in practice.

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.

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.