Targeted observations for Typhoon Maria (2018) using the Geostationary Interferometric Infrared Sounder (GIIRS) at a temporal resolution of 15 min provide unprecedented information for impact studies of high‐temporal‐resolution from geostationary hyperspectral IR sounders. This study investigates the impacts of different temporal resolutions for GIIRS assimilation on Maria analyses and forecasts. Results reveal that assimilating higher resolution data captures more detailed temporal and spatial structures and stronger warm anomalies. Additionally, the track forecast for Maria from assimilating higher temporal resolution GIIRS radiances is better than those from assimilating radiances with lower temporal resolution, and both are better than the control experiment. The accuracy of the different temporal resolutions GIIRS experiments (from 3 h to 15 min) is improved (from 18% to 43%), and the intensity forecast errors are also reduced (from 12% to 18%). Meanwhile, high‐temporal GIIRS assimilation also improves the coastal precipitation forecasts during typhoon landfall.

The theory of classical variational assimilation assumes that biases are unbiased Gaussian. This article investigates the bias characteristic estimate and the bias correction of Geostationary Interferometric Infrared Sounder (GIIRS) on board the FY‐4A satellite. Quality‐control procedures for GIIRS long‐wave temperature channels brightness temperature include cloud detection based on the Advanced Geosynchronous Radiation Imager (AGRI) and outlier removal. The mean biases for most channels are within ±2 K after quality control except for the contaminated channels. Statistical evaluation of the first‐guess departures of GIIRS observations in GRAPES (Global/Regional Assimilation and Prediction Enhanced System) global 4D‐Var reveal that biases for the long‐wave temperature channels depend on fields of view (FOVs) and latitudinal distribution. Additionally, the diurnal variation of biases is obvious only for the upper tropospheric channels, and the biases for high tropospheric channels are smaller than the biases for low tropospheric channels. Finally, off‐line bias correction that was used in this study accounts for the field‐of‐regard (FOR) dependence and the diurnal variation bias characteristics of GIIRS. After bias correction, the results show that biases of long‐wave temperature sounding channels are reduced to ±0.02 K, and the standard deviations are less than 1 K except for the contaminated channels. The probability density function of the differences between observations and simulations for some common assimilation channels is closer to the unbiased Gaussian distribution.

A record-breaking extreme rainstorm occurred in Henan Province of China on 20 July 2021. To investigate the impacts of the Geostationary Interferometric Infrared Sounder (GIIRS) data assimilation on model analysis and forecasts of this rainfall event, the high temporal resolution GIIRS water vapor (WV) channel data were assimilated in the high-resolution CMA-MESO (Mesoscale Weather Numerical Forecast System of China Meteorological Administration) in this study. The results showed that the GIIRS WV radiance assimilation could improve the model WV analysis, which in turn adjusted the distributions of hydrometeors (radar composite reflectivities) and wind field, and finally improved the precipitation forecast. Additionally, although barely any GIIRS observations were assimilated over the cloudy area, the precipitation forecast errors of “21·7” extreme rainstorm events could be reduced by improving the structure of atmospheric circulations through the assimilation of neighboring data around Henan, especially over the upstream region. With the GIIRS WV data assimilation, the location error of maximum 24-h accumulated precipitation forecasts decreased from 128.48 km to 28.97 km (improved by 77.45%) for the cold start at 0000 UTC (Universal Time Coordinated) on 19 July 2021, and it was also reduced by about 60.52% for the warm start experiment at 0600 UTC on 19 July 2021. In addition, the GIIRS assimilation experiment showed an extraordinarily heavy rainfall area (above 250 mm/24 h) around Zhengzhou station, which did not appear in the control experiment, and was closer to the observed extreme precipitation. This study demonstrates the potential value of geostationary hyperspectral infrared sounders data assimilation in extreme weather early warning and forecasting.

Extreme torrential rainfall events are low‐probability events. A “China‐Record Extremely Heavy Rainfall” (CREHR) event with rain rate of 201.9 mm/hr occurred on 20 July in Zhengzhou in North China. Using high‐density meteorological observations, ERA5 reanalysis data, remote sensing data from China's FY‐4A satellite, and numerical simulation, we revealed that sufficient warm–humid airflows were continuously transported to the Zhengzhou area via multiple pathways that were mainly modulated by the large‐scale western Pacific subtropical high to the north along with the anomalies of the westerly belt and a meso‐scale binary typhoon system. At the local scale, under the combined action of the vertical circulation that caused low‐level convergence and high‐level divergence, and the rapid uplift of water vapor related to the blocking by mountainous terrain, this CREHR event was eventually triggered. Particularly, our present work shows the roles of both typhoon Cempaka and meso‐scale convective systems to south of Henan province can not be ignored, producing a strong super‐thick water vapor transport layer. The existence of the binary typhoon raises the level of uncertainty involved in the water vapor transport of this CREHR event.

In this study, a novel method for retrieving atmospheric temperature profiles with tree-structured Parzen estimator (TPE) and multilayer perceptron (MLP) algorithms was proposed, using FY-4A/GIIRS (Geosynchronous Interferometric Infrared Sounder) and ERA5 data. Firstly, by adding solar altitude angle, satellite zenith angle, 2m temperature, and surface temperature to the input layer of MLP, there is an improvement in retrieval accuracy. Secondly, TPE is effective in optimizing the hyper-parameters of MLP, and a set of optimized hyper-parameters is obtained through iterative optimization. Thirdly, comparing the retrieved temperature profiles with ERA5 data, we found that retrieval accuracy is influenced by detector, signal-to-noise ratio, terrain, solar altitude angle, satellite zenith angle, and the horizontal temperature gradient. The mean biases of the two adjacent detectors show significant differences, and the retrieval accuracy of the center detectors is greater than that of the north and south sides. The retrieval accuracy is relatively poor in areas with high terrain and large satellite zenith angle. There is a monthly variation in the retrieval accuracy due to the horizontal temperature gradient and signal-to-noise ratio and a significant diurnal variation due to solar altitude angle and signal-to-noise ratio. Compared to in situ sounding data, the mean biases vary from −0.56 K to 0.60 K, and the standard deviations vary from 1.26 K to 2.17 K. The analysis of factors influencing retrieval accuracy provides important insights into improving the ability to retrieve atmospheric temperatures from geostationary hyperspectral IR sounder observations for near real-time (NRT) applications.

Hyperspectral infrared sounders on board geostationary satellite, represented by the Geostationary Interferometric Infrared Sounder (GIIRS), can provide unprecedented and valuable observations with high-temporal-resolution for disaster prevention, mitigation, and meteorological support services. FY-4B GIIRS has carried out targeted sounding observations with a temporal resolution of 15 minutes for two super typhoons in 2024 (Typhoon Gaemi and Typhoon Yagi). In this study, an operational parallel experiment system was established to assimilate the FY-4B GIIRS targeted radiances in real time, and the diagnosis and analysis of typhoon forecast were carried out after the typhoon dissipated. The results indicate that assimilating FY-4B GIIRS can improve the track forecast of the two super typhoons in real-time operational environment, and the improvement is more significant after 60 hour forecast. The average track forecast was increased by 22.5% in Typhoon Gaemi and by 6.3% in Typhoon Yagi, although the impact on typhoon intensity forecast was not significant. Additionally, the difference in the number of clear sky points assimilated in the two case experiments shows the importance of cloud sky assimilation in the future. This study reveals the potential and value of FY-4B GIIRS quantitative assimilation application to improve numerical weather prediction skills, especially highimpact weather events.