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.

A new advanced microwave humidity sounder FY-3D MWHS2 radiance has been assimilated under the clear-sky conditions by implementing its data assimilation interface. The case of the tropical storm Ampil in 2018 is selected to address the effectiveness of the new-built module in the initialization and forecast of typhoons. Apart from the experiment assimilating both the Global Telecommunications System (GTS) data and the FY-3D MWHS2 radiance data, an experiment with only GTS data is also conducted for comparison. The results show that the bias correction of this humidity sounder is effective, and the analysis field after assimilating its radiance data matches well with the observation. The increment of specific humidity below the middle layers is evident after the assimilation of the radiance data. Besides, the geopotential height increment and the specific humidity increment at 500 hPa and 850 hPa, respectively, are favorable, resulting in more accurate rain belt distribution and a higher fraction skill score (FSS). In the deterministic forecast, the track error of the FY-3D MWHS2 experiment is consistently less than 90 km.

In this study, the impact of assimilating MWHS2 radiance data under different background conditions on the analyses and deterministic prediction of the Super Typhoon Muifa case, which hit China in 2022, was explored. The fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data and the Global Forecast System (GFS) analysis data from the National Centers for Environmental Prediction (NCEP) were used as the background fields. To assimilate the Microwave Humidity Sounder II (MWHS2) radiance data into the numerical simulation experiments, the Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system were employed. The results show that after the data assimilation, the standard deviation and root-mean-square error of the analysis significantly decrease relative to the observation, indicating the effectiveness of the assimilation process with both background fields. In the MWHS_GFS experiment, a subtropical high-pressure deviation to the east is observed around the typhoon, resulting in its northeast movement. In the differential field of the MWHS_ERA experiment, negative sea-level pressure differences around the typhoon are observed, which increases its intensity. In the deterministic predictions, assimilating the FY3D MWHS2 radiance data reduces the typhoon track error in the MWHS_GFS experiment and the typhoon intensity error in the MWHS_ERA experiment. In addition, it is found that the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for Tovs (RTTOV) model show similar performance in assimilating MWHS2 radiance data for this typhoon case. It seems that the data assimilation experiment with the CRTM significantly reduces the typhoon track error than the experiment with the RTTOV model does, while the intensity error of both experiments is rather comparable.

Based on the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR) data assimilation system, this study investigates the effects of assimilation of radar reflectivity and radial velocity under different momentum control variables on the forecast of Southwest China Vortex precipitation. It is shown that the U−V control variable strengthens the wind speed and vorticity to be better matching the observation, while using ψ−χ as the control variable will produce too large increments which are unphysical. The root mean square errors (RMSE) of radar radial velocity are around 2.4 m/s in the experiment using ψ−χ control variables, while the RMSE are below 2 m/s in the experiment with U−V control variables. The composite reflectivity from the analysis of the U−V control variables matches better with the observation than that from the analysis of the ψ−χ control variables, i.e., the forecast rain band location under U−V control variables is more accurate. ψ−χ control variable enhances the cold high-pressure system in near surface, while the U−V control variable is not significant. The water vapor flux convergence in the lower layers of the ψ−χ control variable is overestimated leading excessive precipitation in the forecast. The Equitable Threat Score (ETS) of the U−V control variable is about 0.1 higher than ψ−χ control variable. In summary, the U−V control variable is superior to the ψ−χ control variable in terms of analysis and forecasting about Southwest China Vortex precipitation.

In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity.

Operational and research applications generally use the consensus approach for forecasting the track and intensity of tropical cyclones (TCs) due to the spatial displacement of the TC location and structure in ensemble member forecasts. This approach simply averages the location and intensity information for TCs in individual ensemble members, which is distinct from the traditional pointwise arithmetic mean (AM) method for ensemble forecast fields. The consensus approach, despite having improved skills relative to the AM in predicting the TC intensity, cannot provide forecasts of the TC spatial structure. We introduced a unified TC ensemble mean forecast based on the feature-oriented mean (FM) method to overcome the inconsistency between the AM and consensus forecasts. FM spatially aligns the TC-related features in each ensemble field to their geographical mean positions before the amplitude of their features is averaged. We select 219 TC forecast samples during the summer of 2017 for an overall evaluation of the FM performance. The results show that the TC track consensus forecasts can differ from AM track forecasts by hundreds of kilometers at long lead times. AM also gives a systematic and statistically significant underestimation of the TC intensity compared with the consensus forecast. By contrast, FM has a very similar TC track and intensity forecast skill to the consensus approach. FM can also provide the corresponding ensemble mean forecasts of the TC spatial structure that are significantly more accurate than AM for the low- and upper-level circulation in TCs. The FM method has the potential to serve as a valuable unified ensemble mean approach for the TC prediction.