This study applies the Advanced Radiative Transfer Modeling System (ARMS), which was developed to accelerate the uses of Fengyun satellite data in weather, climate, and environmental applications in China, to characterize the biases of seven infrared (IR) bands of the Advanced Geosynchronous Radiation Imager (AGRI) onboard the Chinese geostationary meteorological satellite, Fengyun–4A. The AGRI data are quality controlled to eliminate the observations affected by clouds and contaminated by stray lights during the mid–night from 1600 to 1800 UTC during spring and autumn. The mean biases, computed from AGRI IR observations and ARMS simulations from the National Center for Environmental Prediction (NCEP) Final analysis data (FNL) as input, are within −0.7–1.1 K (0.12–0.75 K) for all seven IR bands over the oceans (land) under clear–sky conditions. The biases show seasonal variation in spatial distributions at bands 11–13, as well as a strong dependence on scene temperatures at bands 8–14 and on satellite zenith angles at absorption bands 9, 10, and 14. The discrepancies between biases estimated using FNL and the European Center for Medium–Range Weather Forecasts Reanalysis–5 (ERA5) are also discussed. The biases from water vapor absorption bands 9 and 10, estimated using ERA5 over ocean, are smaller than those from FNL. Such discrepancies arise from the fact that the FNL data are colder (wetter) than the ERA5 in the middle troposphere (upper–troposphere).

The FY-3E satellite was successfully launched on 5 July 2021 and carries on board the Microwave Temperature Sounder-III (MWTS-III). In this study, the biases of MWTS-III data with respect to simulations are analyzed according to the instrument field of view and location latitude over the Pacific region. The cloud liquid water path (CLWP) over oceans is retrieved from two new window channels at 23.8 and 31.4 GHz and is used for detecting the clouds-affected microwave sounding data. The absolute bias between the observed and simulated brightness temperature (O–B) under the clear sky point is, in general, less than 2.0 K, depending on the MWTS-III channel. The standard deviations of O-B in most channels are less 1.0 K, but they are 1–1.5 K in channels 1–4 and 17. The average and the standard deviation of O−B from the channels 1–10 shows an obvious symmetrical variation with FOV. The evaluation results all indicate good prospects for the assimilation application of FY-3E microwave sounding data.

Accurate information on the thermal structures of tropical cyclones (TCs) is essential for monitoring and forecasting their intensity and location. In this study, a scene-dependent 1-D variation (SD1DVAR) algorithm is developed to retrieve atmospheric temperature and moisture profiles under all-weather conditions. In SD1DVAR, the background and observation error matrix varies according to the scattering intensity. Especially, the observation error matrix increases in precipitating atmospheres due to a larger uncertainty in the forward operator. With the data from the Advanced Technology Microwave Sounder (ATMS) onboard Suomi National Polar-orbiting Partnership (NPP) satellite, SD1DVAR can retrieve better thermal structures in the storm life cycle than NOAA Microwave Integrated Retrieval System (MIRS). Comparing with the aircraft dropsonde observations, the temperature and humidity errors from SD1DVAR are about 3 K and 20%, respectively, whereas those from MIRS are around 4 K–5 K and 30%, respectively. SD1DVAR is also applied for Microwave Temperature Sounder (MWTS) and Microwave Humidity Sounder (MWHS) onboard FengYun-3D (FY-3D) satellite. The MWTS and MWHS data sets are first combined into a single Comprehensive MicroWave Suite (CMWS) data stream and then used to retrieve the hurricane thermal structures. It is shown that the hurricane structure from CMWS is very similar to that from ATMS. However, due to the availability of 118-GHz measurements from the CMWS, the hurricane temperature vertical structure is better resolved, and the humidity error is also reduced by about 5%.

The microwave humidity sounder (MWHS) onboard the Fengyun-3D satellite is providing the data for profiling atmospheric temperature and moisture and has become an important data source for improving the weather forecasts. In this article, three data sources are utilized for assessing the MWHS on-orbit performance, including Global Navigation Satellite System Occultation Sounder (GNOS), ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis (ERA)-Interim reanalysis, and Advanced Technology Microwave Sounder (ATMS) data. GNOS-retrieved atmospheric profiles and the reanalysis data are used as inputs to the community radiative transfer model (CRTM) for simulating the MWHS brightness temperatures at the top of the atmosphere in July 2018 for characterizing the instrument performance. Since ATMS is a well-calibrated microwave sounding instrument onboard both Suomi NPP and NOAA-20 satellites, its measurements are also collocated with MWHS data for a consensus analysis using the simultaneous nadir overpasses (SNOs) method. In comparing GNOS simulations, MWHS upper air temperature sounding channels (3-6) have relatively larger biases (less than 2.5 K) than the water vapor sounding channels. However, the standard deviation of the difference between observations and simulations (O-B) is larger for water vapor sounding channels. For ERA simulations, MWHS sounding channels exhibit negative biases similar to GNOS results but the standard deviation of O-B at the water vapor channels is much smaller. When compared with ATMS water vapor channels, MWHS biases are mostly negative and agree with those from ERA simulation. Thus, the large uncertainty in simulating MWHS water vapor sounding channels from GNOS could result from the poor input water vapor profiles and high water vapor variability in the lower troposphere.

Land surface microwave emissivity is crucial to the accurate retrieval of surface and atmospheric parameters and the assimilation of microwave data into numerical models over land. The microwave radiation imager (MWRI) sensors aboard on Chinese FengYun-3 (FY-3) series satellites provide valuable measurements for the derivation of global microwave physical parameters. In this study, an approximated microwave radiation transfer equation was used to estimate land surface emissivity from MWRI by using brightness temperature observations along with corresponding land and atmospheric properties obtained from ERA-Interim reanalysis data. Surface microwave emissivity at the 10.65, 18.7, 23.8, 36.5, and 89 GHz vertical and horizontal polarizations was derived. Then, the global spatial distribution and spectrum characteristics of emissivity over different land cover types were investigated. The seasonal variations of emissivity for different surface properties were presented. Furthermore, the error source was also discussed in our emissivity derivation. The results showed that the estimated emissivity was able to capture the major large-scale features and contains a wealth of information regarding soil moisture and vegetation density. The emissivity increased with the increase in frequency. The smaller surface roughness and increased scattering effect may result in low emissivity. Desert regions showed high emissivity microwave polarization difference index (MPDI) values, which suggested the high contrast between vertical and horizontal microwave signals in this region. The emissivity of the deciduous needleleaf forest in summer was almost the greatest among different land cover types. There was a sharp decrease in the emissivity at 89 GHz in the winter, possibly due to the influence of deciduous leaves and snowfall. The land surface temperature, the radio-frequency interference, and the high-frequency channel under cloudy conditions may be the main error sources in this retrieval. This work showed the potential capabilities of providing continuous and comprehensive global surface microwave emissivity from FY-3 series satellites for a better understanding of its spatiotemporal variability and underlying processes.

This study conducts a one-month parallel experiment to compare the results of the operational Shanghai Typhoon Model with (WV123) and without (CTL) the assimilation of clear-sky brightness temperature from three Fengyun-4B (FY-4B) Advanced Geostationary Radiation Imager (AGRI) water-vapor (WV) channels. The results demonstrate a notable improvement (∼3%) in the moisture fields by the WV123 experiment at all atmospheric levels in the forecasts of 24–120 hours when compared to those of the CTL experiment. Besides, results show an improvement in the correlation coefficient of the 500-hPa temperature field from WV123 experiment than CTL experiment after 24 hours. For Typhoon Doksuri (2023), the WV123 experiment shows a reduction of 19.4% in the mean track errors after 24-hour and an improvement of approximately 10% in the forecasted intensities with five different initial forecasting times. In the cases of Typhoons Mawar, Khanun, and Kaihui in 2023, track errors also exhibit some degree of amelioration in forecast experiments at different initial forecasting times. Moreover, the WV123 experiment can improve the forecast accuracy for precipitation exceeding 250 mm in terms of both spatial distribution and intensity. A statistical study over one-month period indicates that the WV123 experiment shows a relatively neutral impact on the precipitation forecasts of 0–48 hours. However, for the forecast lead time of 48–120 hours, there is a considerable improvement in terms of the threat scores of predicting the precipitation exceeding 50 mm, suggesting the positive effects of assimilating clear-sky and future all-sky FY-4B AGRI WV radiances.