This study explores the direct assimilation of Level-1 observations from the Hyperspectral Infrared Atmospheric Sounder (HIRAS-Ⅱ) aboard the Fengyun-3E (FY-3E) satellite into a global real-time atmospheric analysis system. A dedicated operational processing chain for HIRAS-Ⅱ data has been developed within the Gridpoint Statistical Interpolation (GSI) assimilation framework, incorporating data preprocessing, quality control, bias correction, cloud detection and variational assimilation. By combining the CrIS-FSR channel selection method with Jacobian sensitivity analysis of HIRAS-Ⅱ, a subset of 28 temperature-sensitive channels is identified from the long-wave infrared band. Assimilation experiments demonstrate that this channel subset significantly improves temperature assimilation and effectively suppresses errors in humidity. This approach contributes to better temperature analyses in the mid-to-upper troposphere while preserving positive effects in the lower troposphere. A maximum reduction of 2% in the Root Mean Square Error (RMSE) of temperature analyses is observed over the northern Hemisphere. This study marks the first successful operational assimilation of FY-3E/HIRAS-Ⅱ data into the global real-time atmospheric analysis system, providing reliable technical support for the operational assimilation of Fengyun satellite hyperspectral infrared data.

To address the lack of the assimilation capability for Fengyun satellite microwave imager observations in the CMA-RA v1.5 development system and to enhance the application value of domestic satellite observations, this study processes L1 observations from the Fengyun-3D (FY-3D) MicroWave Radiation Imager (MWRI), constructs an assimilation module based on the Gridpoint Statistical Interpolation (GSI) system, and performs a one-month batch experiment and evaluation using the Hybrid four-dimensional ensemble-variational (Hybrid-4DEnVar) assimilation method. The results show that the adopted quality control and bias correction schemes are reasonable and reliable, which can effectively screen high-quality clear-sky over-ocean observations and correct systematic biases. Assimilation of MWRI observations improves the quality of specific humidity analysis, i.e., the Root Mean Square Error (RMSE) of 800 hPa specific humidity analysis is reduced by up to 1.05% (passing the significance test), and the dry bias near 800 hPa in the tropics and the wet bias below 700 hPa in the southern Hemisphere are corrected. The 0—72 h specific humidity forecasts show significant improvements in the lower and middle troposphere (950—600 hPa), with particularly notable improvements occurring near 800 hPa during the first 24 h of forecast (the maximum error reduction reaches 8.74 mg/kg). The RMSEs of 3 h, 6 h, and 9 h forecasts are reduced by up to 1.4%, and the anomaly correlation coefficients of 700 hPa specific humidity forecasts are increased by 0.001—0.01. The forecasts of wind and temperature fields exhibit a neutral-to-positive effect, and the geopotential height forecast is improved at certain time steps of medium-range forecasts. The core contribution of FY-3D MWRI data assimilation focuses on the improvement of humidity analysis and forecasts, while its overall impact on other meteorological elements is relatively neutral.

Atmospheric reanalysis is a process that utilizes numerical weather prediction (NWP) models and data assimilation systems to integrate historical meteorological observations, thereby reconstructing datasets that represent past atmospheric states. This paper presents the China Meteorological Administration’s (CMA) latest global atmospheric reanalysis product, CRA1.5, which employs the Global Spectral Model (GSM) and the Gridpoint Statistical Interpolation (GSI) Data Assimilation system. CRA1.5 features a model spectral resolution of T_L1534 (equivalent to approximately 13 km in horizontal grid spacing), a product time interval of 1 h, a model top at 0.27 hPa, and a hybrid-4DEnVar scheme along with an Ensemble Kalman Filter for data assimilation. The temporal coverage of CRA1.5 extends from 1979 to near real-time. During the reanalysis production, we assimilated a substantial volume of reprocessed satellite data and extensive conventional observations. These observations include dense networks of conventional observations within China (comprising approximately 120 sounding stations and 2400 meteorological stations) as well as satellite observations from China’s Fengyun, Haiyang, and Yunyao satellite series. Compared to CRA-40, the root-mean-square error (RMSE) of geopotential height at 500 hPa is reduced by 22.46%, the RMSE of temperature at 200 hPa is reduced by 21.7%, and the RMSE of zonal wind at 850 hPa is reduced by 14.41%. CRA1.5 outperforms ERA5 in 100-m wind speed over China (RMSE: 3.21–3.36 m s⁻¹ vs. 3.46–3.59 m s⁻¹) and achieves comparable accuracy to ERA5 in atmospheric precipitable water (RMSE: 3.10 mm vs. 3.13 mm). CRA1.5’s 2-m temperature trend aligns closely with HadCRUT5, capturing global warming accurately. With higher spatiotemporal resolution (0.1° × 0.1°, hourly) and advanced assimilation, CRA1.5 provides a critical dataset for climate monitoring, NWP, and AI-based meteorological research. CRA1.5 has been implemented for real-time operation at the CMA. Updates are available up to 6 h after the analysis time, with a final update provided at a 3-day lag.