This study assessed the impact of assimilating the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) observations on the Super Typhoon “In-Fa” event based on the Weather Research and Forecasting Data Assimilation (WRFDA) system of the three-dimensional variational data assimilation (3DVAR) method. It was found that the two water vapor channels 9–10 from the full-disk AGRI datasets yield relatively stable results in terms of the track forecast of In-Fa. A new cloud-detection method using a Particle Filter (PF) was firstly employed to remove the cloud-affected observations by identifying the channel’s weighting function. Compared to the other cloud-detection schemes based on the AGRI “Cloud_Binary_Mask” (CLM) products, the PF method is conducive to reducing the track error of typhoon prediction after improving the utilization of observations under clear-sky conditions. Furthermore, the proposed cycling assimilation scheme has a potential positive effect on the intensity forecast of In-Fa. It seems that assimilating the FY-4A AGRI radiance data improves the predictability of Typhoon In-Fa by adjusting the atmospheric environment.

The module for assimilating radiance data of the Microwave Humidity Sounder-2 (MWHS-2) onboard the Feng Yun 3D (FY-3D) satellite is built in the Weather Research and Forecasting (WRF) model data assimilation (WRFDA) system. The CONV, 3DVar, and EnVar experiments are conducted to investigate the impact of assimilating the new humidity sounder based on Typhoon Ampil (2018). Both the 3DVar and EnVar experiments assimilate FY-3D MWHS-2 radiance data on top of the conventional data, while the CONV experiment only applies conventional data. In the EnVar experiment, notable geopotential height increment is observed around the typhoon, leading the typhoon to move northeast. In addition, the moisture field is improved to some extent. Finally, from the analysis of the dynamic field of the typhoon, it can be found that the EnVar experiment can adjust the dynamic structure of the typhoon. Furthermore, the assimilation of FY-3D MWHS-2 radiance data reduces the forecast error of the typhoon track and intensity. Additionally, the precipitation skill is improved in terms of rainfall pattern and the verification score. This improvement in the precipitation may be closely related to the features of the circulation structure concerning the evolution of the typhoon. The improved prediction of the position and intensity of rainbands in the FY-3D MWHS-2 radiance data assimilation experiment corresponds to a better prediction of typhoon structure.

The background error covariance is an important component in data assimilation systems, which largely dominates the error correlations between different analysis variables. This study aimed to assess the impact of the determination of the background error length and variance scale factors on the track and intensity forecast of hurricane systems for radar radial velocity (Vr) data assimilation. To test the sensitivity of the background error length scale and variance scale, multiple data assimilation analyses were performed using a 3D variational data assimilation method with a parameter‐sweeping strategy for the case of Hurricane Ike (2008) by varying the decorrelation length scale (LEN_SCALING) and the variance (VAR_SCALING) of the background error step by step. It is shown that, in radar Vr data assimilation, generally smaller variance scale and length scale factors tend to improve the prediction of the hurricane track. Setting both VAR_SCALING and LEN_SCALING equivalent to 0.4 provides a better track than other combinations. In particular, the impact of the variance scale factor on the track forecasts is larger than that of the length scale factor. There is no direct impact of tuning variance scales and length scales on the forecast intensity for the first few forecast hours. Tuning length scale factors less than 0.4 provides smaller forecast errors of minimum sea level pressure and maximum surface wind with increasing forecast lead time.

Bias correction is a key prerequisite for radiance data assimilation. Directly assimilating the radiance observations generally involves large systematic biases affecting the numerical prediction accuracy. In this study, a nonlinear bias correction scheme with Random Forest (RF) technology is firstly proposed based on the Fengyun-4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) channels 9–10 observations in the Weather Research and Forecasting Data Assimilation (WRFDA) system. Two different settings of the predictors are additionally designed and evaluated based on the performance of the RF model. It seems that an apparent scene temperature-dependent bias could be effectively resolved by the RF scheme when applying the RF method with newly added predictors. Results suggest that the proposed nonlinear scheme of RF performs better than the linear scheme does in terms of reducing the systematic biases. A more idealized error distribution of observation minus background (OMB) is found in the RF-based experiments that measure the nonlinear relationship between the OMB biases and the predictors when using the Gaussian distribution as the reference. Furthermore, the RF scheme shows a consistent improvement in bias correction with the potential to ameliorate the atmospheric variables of analyses.

The variational data assimilation (DA) method seeks the optimal analyses by minimizing a cost function with respect to control variables (CVs). CVs are extended in this study to include hydrometeor mixing ratios related variables besides the widely used sets of CVs (momentum fields, surface pressure, temperature, and pseudo-relative humidity). The impacts of the extra CVs are investigated in terms of hydrometeor mixing ratios to the assimilation of radar radial velocity (Vr) and reflectivity (RF) for the analysis and prediction of Typhoon Chanthu (2010). It is found that the background error statistics of the extended CVs from the National Meteorological Center (NMC) method is reliable. The track forecast is improved significantly by including hydrometeor mixing ratios as CVs to assimilate radar Vr and RF. The DA experiments using the hydrometer CVs show much improved intensity analysis and forecast. It also improves the precipitation forecast skills to some extent. The positive impact is significant using a direct RF assimilation scheme, when Vr and RF data are applied together. It suggests that when we applying an indirect RF assimilation scheme, the fitting of more hydrometers in the cost function will tend to cause a slight degradation for other variables such as the wind and temperature.

The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables.