In this study, the regional Weather Research and Forecasting model (WRF)-based quantitative precipitation forecasts (QPFs) are conducted for an extreme Meiyu rainfall event over East Asia in 2020. The data of water vapor channels 9 and 10 from the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Fengyun-4A (FY-4A) satellite are assimilated through the Gridpoint Statistical Interpolation (GSI) system. It shows that a reasonable amount of assimilated AGRI data can produce reasonable water vapor increments, compared to the too sparse or dense assimilated AGRI observations. In addition, the critical success indexes (CSIs) of the precipitation forecasts within 72 h are obviously improved. The enhanced variational bias correction (VarBC) scheme is applied to remove the air-mass and scan-angle biases, and the mean observation-minus-background (O − B) values before and after the VarBC of channel 9 are −1.185 and 0.02 K, respectively, and those of channel 10 are −0.559 and −0.01 K, respectively. Assimilating the upper-level channel 9 data of AGRI (EXP_WV9) lead to a neutral-to-positive effect on QPFs, compared to the control run (CTL), which is based on the assimilation of Advanced Microwave Sounding Unit-A (AMSU-A) data. In particular, the CSIs from 42 to 72 h are significantly improved. However, the assimilation of the AGRI channel 10 (EXP_WV10) shows a neutral-to-negative effect on QPFs in this study, probably due to the complicated surface situations. This study confirms the feasibility of assimilating the water vapor channel data of FY4A AGRI in the GSI system and highlights the importance of assimilating AGRI channel 9 data to improve the QPFs of the Meiyu rainfall event.

In this study, the Fengyun-3D (FY-3D) clear-sky microwave temperature sounder-2 (MWTS-2) radiances were directly assimilated in the regional mesoscale Weather Research and Forecasting (WRF) model using the Gridpoint Statistical Interpolation (GSI) data assimilation system. The assimilation experiments were conducted to compare the track errors of typhoon Lekima from uses of the Advanced Microwave Sounding Unit-A (AMSU-A) radiances (EXP_AD) with those from FY-3D MWTS-2 upper-air sounding data at channels 5–7 (EXP_AMD). The clear-sky mean bias-corrected observation-minus-background (O-B) values of FY-3D MWTS-2 channels 5, 6, and 7 are 0.27, 0.10 and 0.57 K, respectively, which are smaller than those without bias corrections. Compared with the control experiment, which was the forecast of the WRF model without use of satellite data, the assimilation of satellite radiances can improve the forecast performance and reduce the mean track error by 8.7% (~18.4 km) and 30% (~58.6 km) beyond 36 h through the EXP_AD and EXP_AMD, respectively. The direction of simulated steering flow changed from southwest in the EXP_AD to southeast in the EXP_AMD, which can be pivotal to forecasting the landfall of typhoon Lekima (2019) three days in advance. Assimilation of MWTS-2 upper-troposphere channels 5–7 has great potential to improve the track forecasts for typhoon Lekima.

In this paper, the Advanced Weather Research Forecast model (WRF-ARW) is used to investigate the potential impacts of assimilating the FengYun-4A (FY-4A) Geostationary Interferometric Infrared Sounder (GIIRS) long-wave temperature sounding channel data on the prediction of Typhoon In-Fa (2021). In addition, a series of data assimilation experiments are conducted to demonstrate the added value of the FY-4A/GIIRS data assimilation for typhoon forecasts. It is shown that the higher spectral resolution and broader coverage of GIIRS radiance data can positively impact the model analysis and forecasts with larger temperature and moisture increments at the initial time of simulations, thus producing a better simulation for the typhoon’s warm core aloft, vortex wind structure, and spiral rainfall band. Moreover, the assimilation of the GIIRS data can also lead to better storm steering flows and, consequently, better typhoon track forecasts. Overall, the assimilation of FY-4A/GIIRS temperature sounding channel data shows some added values to improve the track and storm structure forecasts of Typhoon In-Fa.

This study uses the Weather Research and Forecasting (WRF) Model to investigate the performance of hail parameterizations of the WRF double-moment 7-class (WDM7), aerosol-aware Thompson (AAT), and National Taiwan University triple-moment (NTU3M) bulk microphysics schemes (BMSs) on a real case of a hailstorm initiated in Shandong Province, China. The maximum hail size is particularly evaluated because it is crucial to hail severity prediction, along with areal coverage and intensity of the 24-h solid precipitation during the simulations. Compared with the radar-derived maximum hail size, the objective analysis shows that the NTU3M scheme has the best score in the forecast skill of hail-fall coverage and size, while two BMSs with single-moment rimed ice species overestimate hail diameters aloft but underpredict the coverage at the surface. A deeper investigation suggests that the derived size tendencies from the three BMSs are comparable to the benchmark solutions from the detailed hailstone growth and melting models. The NTU3M scheme displays the most consistent size tendency of the maximum diameter with the benchmark solution in the growth processes. The behaviors of melted diameter by parameterizations are highly related to the treatments of number concentration, which are consistent with the predicted hail severity and coverage. Finally, the sensitivity study shows that increasing the model resolution does not improve the forecast of the maximum hail size, given the biases in the hail mass budget equations and the parameterization of particle size distribution, with single-moment rimed ice species of the AAT scheme. Significance Statement: Improving hail-forecasting skill, including the size, severity, and the spatial and temporal coverage of hail fall, has become an important subject for numerical weather prediction models as the model resolution increases. The objective of this study is to investigate the fundamental differences in hail parameterizations of three bulk microphysics schemes that lead to differences in the prediction of severe hail events and the spatial coverage of hail fall, hopefully providing insights into hail prediction with a regional numerical weather prediction model in the future.

Coarse-grained results from a large-eddy simulation (LES) using the Weather Research and Forecasting (WRF) Model were compared in this study with the WRF simulations at a typical convection-permitting horizontal grid spacing of 3 km for an idealized case of deep moist convection. The purpose of this comparison is to identify major differences at the subgrid process level between two widely used deep convection parameterization schemes in the WRF Model. It is shown that there are considerable differences in subgrid process representations between the two schemes due to different parameterization formulations and underlying assumptions. The two schemes not only differ in trigger function, subgrid cloud model, and closure assumptions but also disagree with the coarse-grained LES results in terms of vertical mass flux profiles. Thus, it is difficult to discern which scheme is more advantageous over the other at the subgrid process level. The conclusions from this study highlight the importance of establishing benchmarks using observations and LES to develop and evaluate convection parameterization schemes suitable for models at convection-permitting resolution.

In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall.